Postponement Strategies in the Supply Chain

Postponement Strategies in the Supply Chain

– How do the reasons underlying demand uncertainty affect the

choice of an appropriate postponement strategy?

Final Master’s Thesis

Miriam Bartels

University of Maastricht Faculty of Economics and Business Master of Science in International Business Supply Chain Management Miriam Bartels Student ID: i342793 Final Thesis Supervisor: Prof. Dr. Martin Wetzels Maastricht, 20th of august 2010

I

Preface

In the first year of my Bachelor studies I read a case study on the postponement concept

implemented at the textile company called Benetton. Back then this concept already intrigued

me and it was in regard to my Master Thesis that I finally took the opportunity to explore this

concept in more detail. I feel privileged that my topic suggestion was supported by my

supervisor Prof. Dr. Martin Wetzels, whom I would like to thank for guiding me through the

complete journey of this Master Thesis.

Furthermore, I would like to thank everyone that supported my research and without whom

this research could not have taken place. In the first place, I would like to thank the three

interview partners who contributed to this thesis by providing me with valuable practical

insights and interesting discussions in reference to real life examples. In addition, I would like

to thank all survey participants for taking the time to fill in the questionnaire. Your

contribution enabled me to conclude my thesis by means of studying empirical data, which is

one of the first attempts in the history of postponement choice theory. Finally, I would like to

thank the Dutch Logistics Institute that supported me in spreading the invitation to answer my

survey. Many thanks! Without you all, this thesis could not have been accomplished!

Finally, I would also like to thank my family and friends whose encouragement, especially in

periods in which my thesis progress did not go smoothly, was a tremendous help. In particular,

I would like to thank my boyfriend Peter for his patience and support. Especially during the

last months, when I had already started with my first job 800 km away, it certainly was a

challenging situation.

Finally, I would like to conclude this preface and wish you a pleasant reading experience.

Hopefully this thesis gives you interesting and valuable insights and inspires you in your

future academic or business efforts!

Miriam Bartels

II

Abstract

Coping with demand uncertainties is one of the greatest challenges that companies are facing

in an ever-changing world. One instrument that supports flexibility in this unstable

environment is postponement. By postponing certain activities of the supply chain to a later

stage, flexibility can be achieved, deferring decision making until more knowledge has been

acquired. A variety of postponement strategies is defined in current literature, among which

the most widely used are price postponement, logistics postponement, production

postponement, purchasing postponement and product development postponement. With such

a broad range of postponement strategies available, the question arises of how to choose the

most adequate postponement strategy for a certain company facing industry specific demand

uncertainties. This is exactly what this thesis investigates. Presently, available literature points

out several postponement indicators, including the level of uncertainty. The research at hand

however takes it one step further by exploring how different reasons of uncertainty actually

influence postponement choice.

For this purpose, a research model connecting reasons of uncertainty and different

postponement strategies has been developed, adapted and verified by means of three

interviews with senior supply chain professionals. Subsequently, the adapted research model

has been tested by conducting an online survey among professionals in a variety of industries.

Finally, the survey data has been analysed by means of PLS Path Modelling and cross-

validated with the help of Canonical Correlation and PLS Regression.

This research concludes that different reasons for demand uncertainty require the application

of different situation-specific postponement strategies. Several hypotheses were confirmed

and additional significant factors identified. The findings are summarized by means of a

postponement decision matrix that combines demand uncertainty reasons with organizational

aims. This tool can lead managers through the analysis and decision making process for

postponement strategies.

III

Table of Content

Preface ........................................................................................................................................I

Abstract .................................................................................................................................... II

1. Introduction ...................................................................................................................... 1

1.1. Research Motivation .................................................................................................. 1

1.2. Research Question...................................................................................................... 2

1.3. Outline........................................................................................................................ 3

2. Literature Review............................................................................................................. 4

2.1. The Concept of Postponement.................................................................................... 4

2.2. Postponement Strategies ............................................................................................ 6 2.2.1. Price Postponement ....................................................................................................... 8 2.2.2. Logistics Postponement................................................................................................. 8 2.2.3. Production Postponement.............................................................................................. 9 2.2.4. Purchasing Postponement ........................................................................................... 11 2.2.5. Product Development Postponement .......................................................................... 12

2.3. Existing Strategy Selection Techniques.................................................................... 13

2.4. Conclusion................................................................................................................ 17

3. Conceptual Research Model.......................................................................................... 18

3.1. Demand Uncertainty ................................................................................................ 18 3.1.1. Factors influencing Demand Uncertainty.................................................................... 19

3.2. The Research Model................................................................................................. 25

3.3. Conclusion................................................................................................................ 27

4. Methodology ................................................................................................................... 28

4.1. Preliminary field interviews ..................................................................................... 28 4.1.1. Interview results .......................................................................................................... 29 4.1.2. Adapted research model .............................................................................................. 32

4.2. Online Survey ........................................................................................................... 34 4.2.1. Questionnaire Development ........................................................................................ 34 4.2.2. Execution of Survey .................................................................................................... 35

4.3. Analytical Tools........................................................................................................ 36

4.4. Construct Validation ................................................................................................ 39

4.5. Conclusion................................................................................................................ 40

5. Findings ........................................................................................................................... 41

5.1. General Sample Characteristics .............................................................................. 41

5.2. The Research Model................................................................................................. 44

IV

5.2.1. Link 1: Uncertainty – Postponement Strategies .......................................................... 47 5.2.2. Link 2 and 3: Postponement Aims and Performance Impact ...................................... 47

5.3. Cross-validation with other statistical approaches ................................................. 48

5.4. Conclusion................................................................................................................ 51

6. Discussion........................................................................................................................ 52

6.1. Postponement Strategies .......................................................................................... 52 6.1.1. Price Postponement ..................................................................................................... 52 6.1.2. Logistics Postponement............................................................................................... 54 6.1.3. Production Postponement............................................................................................ 55 6.1.4. Purchasing Postponement ........................................................................................... 57 6.1.5. Product Development Postponement .......................................................................... 59

6.2. The Decision Matrix................................................................................................. 61

6.3. Conclusion................................................................................................................ 64

7. Conclusion and Future Research.................................................................................. 65

7.1. Problem Statement ................................................................................................... 65

7.2. Theoretical contributions ......................................................................................... 66

7.3. Managerial contributions......................................................................................... 67

7.4. Limitations and future research ............................................................................... 67

Appendix ................................................................................................................................. 70

Appendix I: Unstructured questionnaire for interviews....................................................... 70

Appendix II: Online questionnaire ....................................................................................... 71

Appendix III: Variance Inflation Factors (VIF)................................................................... 78

Appendix IV: Sample Characteristics .................................................................................. 79

Appendix V: Canonical Correlation Analysis ...................................................................... 80

Appendix VI: PLS Regression .............................................................................................. 82

References ............................................................................................................................... 84

V

List of Figures

Figure 1: Postponement strategies and the positioning of the CODP (Yang et al. 2007).......... 6

Figure 2: Postponement-speculation strategy continua (Pagh and Cooper 1998) ................... 14

Figure 3: Postponement-speculation strategy profile analysis (Pagh and Cooper 1998)......... 14

Figure 4: Supply and demand uncertainty matrix (Lee 2002) ................................................. 15

Figure 5: The initial conceptual research model ...................................................................... 26

Figure 6: The CODP and reasons of demand uncertainty (adapted from Yang et al. 2007) ... 27

Figure 7: Research plan............................................................................................................ 28

Figure 8: Adapted overview of research construct................................................................... 33

Figure 9: Adapted research model ........................................................................................... 34

Figure 10: Number of respondents per postponement strategy................................................ 41

Figure 11: Importance/extent of application within respondents’ companies ......................... 42

Figure 12: Distribution of survey respondents across industries ............................................. 43

Figure 13: PLS PM Model for Price Postponement................................................................. 44

Figure 14: Research model adapted for the findings of this study........................................... 60

Figure 15: Postponement strategy decision matrix .................................................................. 61

Figure 16: Example of matrix evaluation................................................................................. 63

List of Tables Table 1: Demand uncertainty factors ....................................................................................... 20

Table 2: Overview of uncertainty factors mentioned in interviews ......................................... 31

Table 3: PLS PM results per postponement strategy ............................................................... 46

Table 4: Overview of cross-validation ..................................................................................... 50

1. Chapter: Introduction

1

1. Introduction

“When one admits that nothing is certain one must, I think, also admit that some things are

much more nearly certain than others.”

Bertrand Russell, "Am I An Atheist Or An Agnostic?", 1947

British author, mathematician, & philosopher (1872 - 1970)

Uncertainties are amongst the biggest enemies of companies’ supply chains, gaining ever

more importance in the presence of an increasingly changing environment. Reacting to

uncertainties presents a great challenge and thus tools have to be found allowing companies to

become more flexible when reacting to these instabilities. Postponement strategies are one

option that companies can opt for to reduce the negative effects in light of demand uncertainty.

By postponing certain activities of the supply chain to a later stage, that is deferring decision

making until more knowledge has been acquired, flexibility can be achieved. A famous

example of a practically applied postponement strategy is the company Benetton that

postpones the dying of their garments to just prior to delivery, enabling a faster reaction to

changing customer demand (Dapiran 1992).

1.1. Research Motivation

The business economics motivation stems from companies’ continuous strive for cost

reduction, while improving or maintaining a satisfactory level of customer service. Especially

in the current economic situation, it is extremely important for companies to minimize costs

and maintain stable product prices viewed as reasonable by market demand. This can be

achieved by maintaining, for example, low inventories, high customer service and good

product quality. This thesis will explore what postponement strategies can achieve by

enabling more company-wide flexibility. The academic motivation departs from the fact that

not much research has been done yet on the topic of postponement strategies. It is only

recently that the postponement strategy phenomenon has gained more attention in literature.

Even though academic research mainly focuses on the operational effects of postponement

strategies, the decision making process for postponement itself is only slightly touched upon.

Furthermore, most studies with regard to postponement strategies are based on case studies

and only little empirical research has been conducted. This thesis focuses on providing


2

support for a company’s postponement decision making in light of demand uncertainties, by

concluding from empirical research results.

The postponement concept was already established by Alderson (1950). However, as Boone

et al. (2007) point out, only lately there is an increasing research interest in this field and still

many challenges remain unaddressed. Although postponement concepts can be very simple,

gaps in understanding lead to a slow rate of adoption. The following research question is

therefore developed to fill a gap in the literature and to foster implementation initiatives by

creating a deeper understanding of the applicability of postponement strategies.

1.2. Research Question

One major gap in research conducted thus far is to explain the linkage that exists between

postponement strategies and the reasons underlying demand uncertainty. More specifically its

meaning for the determination of appropriate postponement strategies is yet to be researched

(Boone, Craighead et al. 2007). This thesis tries to answer just this, whereby the following

problem statement has been developed:

“How do the reasons underlying demand uncertainty affect the choice of an appropriate

postponement strategy?”

The relationship between postponement strategies and uncertainty is that postponement is

used to create flexibility in order to better react to demand uncertainties that a company is

confronted with. Yang et al. (2004) classified postponement strategies according to the degree

of uncertainty and product modularity. In contrast to this, the problem statement in this thesis

does not focus on the degree of uncertainty but rather on the type of uncertainty that can be

linked to different types of postponement strategies. In order to answer the problem statement

mentioned above a series of sub-questions will need to be investigated being:

- How is postponement defined?

- Which postponement strategies do exist and what is their specific purpose?

- How is demand uncertainty defined?

- Which are the most important reasons of demand uncertainty?

- Which postponement strategy reacts to which demand uncertainty dimension?


3

- Which postponement strategy has what operational aim?

- What is the impact of postponement strategies on company performance?

Hence, the objective of this research is to analyze the interrelationship between the

dimensions of demand uncertainty and the different types of postponement strategies. This

aims to lead to three important contributions: (1) better understanding of postponement

strategies and their application, (2) a theoretical relationship construct that extends existing

literature and (3) a decision tool with guidelines that will support managers to select

appropriate postponement strategies.

1.3. Outline

To analyze the research question, a conceptual model will be developed and tested by means

of interviews and an online survey. As a foundation for this research, Chapter 2 presents the

literature review with respect to postponement strategies. Subsequently, Chapter 3 highlights

various reasons of demand uncertainty and provides a research construct by establishing links

between demand uncertainties and specific postponement strategies. Chapter 4 elaborates on

the underlying research methodology used. It includes the interview results, the consequential

adaptations to the research model, the questionnaire development process as well as data

analysis tools and the associated construct validation. Thereafter, Chapter 5 presents the

findings of the empirical study, followed by a detailed discussion in Chapter 6. Finally,

Chapter 7 offers a conclusion to this research including the answer to the research question,

contributions to theory and practise, limitations and future research topics.

2. Chapter: Literature Review

4

2. Literature Review

Prior to linking postponement strategies to sources of demand uncertainty, the concept of

postponement and its application needs to be understood. This chapter therefore explains the

evolution of the concept of postponement. Subsequently, the concept of postponement is

defined and various postponement strategies are discussed and illustrated. Finally, existing

postponement strategy selection techniques are explored.

2.1. The Concept of Postponement

The concept of postponement was first introduced by Alderson (1950), who focused on the

postponement of product differentiation “to the latest possible point in time” (Alderson 1950),

that is until an actual customer order is received. Bucklin (1965) further explains

postponement as a trade-off between the inventory cost of the seller and the opportunity cost

of the buyer. Thereby demand uncertainty, which results in additional inventory cost of the

seller due to required safety stocks, is related to the opportunity cost in terms of purchasing of

the buyer. In theory, storage of finished goods is replaced by storage of product components

and hence slightly increases lead time, due to the necessary finalization of production after a

customer order is received. Bucklin designates this idea as postponement-speculation,

meaning that “a speculative inventory will build up at each point in a distribution channel,

whenever its costs are less than the net savings to both buyer and seller from postponement”

(Bucklin 1965, p.28). Accordingly, postponement would only be favourable, if transportation

costs are less than inventory cost plus opportunity cost, taking the factor of time into account.

This is further supported in the article written by Matthews and Syed (2004). Concluding on

the result of the APICS Membership Survey (2003), they state that 60% of the companies

regard inventory cost reduction as well as increased customer satisfaction as top benefit from

postponement.

Even though it was Alderson that introduced the concept of postponement in 1950, it was

only in the late 80s that researchers devoted more attention to this theory (Yang, Burns et al.

2004). Yang et al. (2005, p.992) summarize the reasons for this growing importance of

postponement strategies that can be found in literature as follows:

• “Product life cycles are increasingly shortening

• Product proliferations continue to expand


5

• Technological developments are continuing at an ever-increasing pace

• Customers are more and more sophisticated

• Success is increasingly driven not so much by cost or quality, but by speed

• Mass customization and agility have been receiving increasing interest from both

researchers and practitioners

• With the ever-expanding presence of information and communication systems the

potential afforded by e-commerce is wide reaching

• Companies are increasingly focused on improving overall supply chain

management”

Most of these factors are confirmed by Matthews and Syed (2004), who elaborate on the

results of the APICS Membership Survey 2003. In this survey, it is interesting to note that

73% of the respondents perceive the “increased difficulty in forecasting consumer demand” as

the most important catalyst driving postponement. This result shows that it may be especially

useful to search for postponement selection techniques when referring to customer demand

uncertainty and its reasons.

The market is constantly changing and innovations and technological advancements speed up

introductions of new products, leading to demand variability. Lee (2002) points out that in the

presence of high demand unpredictability, responsive supply chains should be created that

enable flexible reaction to this uncertainty. Organic organizational structures that are

characterised by responsiveness and flexibility are beneficial in uncertain environments (e.g.

(Duncan 1972). Postponement is therefore a means to support the creation of an organic

system to achieve, for instance, overall lead time reduction or product development

acceleration. This can “shorten the forecasting horizon and lower risk of error” (Bowersox,

Stank et al. 1999, p.562). In extreme cases of demand uncertainty, Lee (2002, p.116) even

proposes to “make use of the concept of postponement to pursue aggressive build-to-order

strategies”.

In summary, there is a consensus that postponement helps companies to respond effectively to

uncertainty by creating organisational flexibility. In order to reach this goal a number of

different postponement strategies can be employed. The following section first discusses

postponement strategies in general, followed by an overview of the most widely-used

strategies.


6

2.2. Postponement Strategies

In recent literature postponement is generally defined as a concept, whereby activities in the

supply chain are delayed until demand is realized, that is until customer orders are received

(van Hoek 2001). This point, called the customer-order decoupling point (CODP),

distinguishes supply chain activities between forecast-driven activities located upstream of the

CODP and order-driven activities located downstream of the CODP (Yang, Burns et al. 2004).

Yang et al. (2007) show that the different postponement strategies determine the location of

the CODP. The dashed line in Figure 1 illustrates this change in the CODP location. The type

of postponement strategy determines how far upstream in the supply chain the decoupling

point is moved or corresponding supply chain activities delayed (Yang, Yang et al. 2007). In

case of product development postponement, the CODP is located prior to the design phase,

whereas in case of logistics postponement the CODP is located just prior to distribution.

Production postponement is naturally based on the make-to-order principle, whereas logistics

postponement is naturally based on the make-to-stock principle as distribution follows upon

customer order. In the latter case, stock is managed by means of forecast and delivered upon

customer order.

Figure 1: Postponement strategies and the positioning of the CODP (Yang et al. 2007)

The first column in Figure 1 could be explained by logistics or price postponement. As

logistics postponement, stock may be decentralized and thereby partly distributed, where

stock levels and therefore production are determined by forecasts. Price postponement on the

other hand would really focus on the moment after final distribution to customers, and


7

accordingly set the price at this late moment. This would however not require make-to-

forecast, that is make-to-stock. More detailed explanations of the postponement strategies will

be provided in following subsections.

The aim of postponement and the relocation of the CODP can be viewed from two

perspectives: the information flow perspective and the material flow perspective (Yang and

Burns 2003). The information flow perspective emphasizes the importance of information

availability throughout supply chain processes. Postponement delays certain activities in order

to benefit from additionally acquired market insights, due to extra information on product

demand as time passes. Yang and Burns (2003) underline that not all information is required

at the same time, if the supply chain tasks are sequenced (postponed or prolonged)

accordingly. The material flow perspective on the other hand stresses that inventory has to be

managed, which can be done by adequately locating the CODP. Thereby, inventory levels and

costs can be reduced, taking into account that inventory cost of materials is lower than of

finished products. However, the trade-off between inventory and lead time is not to be

disregarded. This is due to the fact that, if not finished but semi-finished goods are stored,

lead time may increase as the finishing process still needs to be finalized. Summarizing,

shifting the CODP upstream, meaning postponing supply chain activities downstream,

increases the knowledge of customer specifications at the point of production (Olhager 2003),

whilst reducing inventory cost.

Following the discussion above, postponement can now be defined as the action of delaying

certain activities of the supply chain downstream of the customer-order decoupling point in

order to achieve more flexibility and responsiveness with regard to demand uncertainty. In the

following paragraphs five strategies will be elaborated upon in more detail, including price

postponement, logistics postponement, production postponement, purchasing postponement

and product development postponement. The latter four strategies are derived from the

research of Yang et al. (2007). Price postponement was added in order to cover an additional

factor that influences demand and is therewith of special value for this research. These

aforementioned five strategies cover the entire range of the supply chain and enable the

researcher to focus on a limited set. Other postponement strategies, such as the postponement

of hiring new people, are not considered in this research as they are too focused and would

render this study inexplicit.


8

2.2.1. Price Postponement

Price Postponement means to “defer the pricing decision until demand uncertainty is

resolved” (Van Mieghem and Dada 1999, p.1632). The price is thus not set before but after

production, resulting in a “make-to-stock strategy with an ex-post price flexibility” (Van

Mieghem and Dada 1999, p.1632). The price can then be set according to acquire demand

information, which was not available before production. This strategy may have a great

influence on total revenue, due to the fact that products are likely to be sold at the highest

price the customer is willing to pay.

According to Granot and Yin (2008), price postponement always occurs, when the customer

still has the option to negotiate the price. Car manufacturers, for instance, postpone the pricing

decision by negotiating at the moment of sale with a customer (Van Mieghem and Dada 1999).

Moreover, as van Mieghem and Dada (1999) point out, a company can also postpone the

pricing decision to influence the market-clearing price, the price at which supply is equal to

demand. In the farming industry, capacity is set during harvest. The price can thus be

increased, by bringing only part of the harvest to market. One challenge is however, that even

though price postponement may be beneficial for the buyer, profit sharing may prove to be

complicated and not favourable for an ongoing business relationship due to extensive

negotiation (Granot and Yin 2008). A sophisticated form of price postponement is yield

management, which is a price setting technique originally introduced by airlines (Biller,

Muriel et al. 2006). Here, price is adjusted as soon as new demand information is available.

Nowadays, this technique is also employed in other businesses, such as car rentals, fashion

companies or hotels, in other words in the service sector. Price postponement however should

be applied carefully because price uncertainty may alienate customers (Biller, Muriel et al.

2006).

2.2.2. Logistics Postponement

The principle of logistics postponement is best described by the use of direct distribution of

finished products from a central location (Pagh and Cooper 1998). This strategy is clearly

based on place and time postponement. Place postponement in regard to logistics

postponement means maintaining finished inventory at a central location, before it is

delivered to the final customer. Time postponement generally delays the forward movement

of inventory (van Hoek 2001). As Bowersox et al. (1999, p.563) point out it “provides

inventory-positioning flexibility by alleviating the need for forward deployment of inventory”.


9

Logistics postponement thus enables shipping of “exact product quantities from a central

location to satisfy specific customer requirements” (Bowersox et al. 1999, p.562). Pagh and

Cooper (1998) argue that this strategy increases on-time deliveries, shortens lead times,

improves reliability, reduces inventory cost, achieves constant transportation cost, and enables

a faster introduction of new products. However, van Hoek (2001, p.161) predicates that

logistics postponement may result in a significant increase in transportation cost and is

therefore only “relevant when products are more sensitive to inventory than to transport cost”.

The company Amazon.com, an online catalogue firm, benefits tremendously from applying

logistics postponement. Most products are stored at a central warehouse, from where

distribution to the end-customer is initiated. The customer order is the trigger for a delivery

release. Up-to-date information is thereby used to prompt and guide final shipments. In order

to increase the ability to handle varying volume and timing of orders, Amazon.com is

working closely together with logistics service providers and vendors (Yang, Yang et al.

2007). This close cooperation also minimizes transportation costs. Hewlett-Packard Company

also implemented logistics postponement, even though it is used in a slightly different way.

By using the concept of “merge-in-transit”, Hewlett-Packard Company avoids unnecessary

transportation of finalized products to a central location by merging components of a product

at the closest merge centres or consolidation points before delivery to the customer (Lee and

Whang 2001). Transportation distances are thereby reduced and costs avoided. Finally,

another option of logistics postponement is to implement a “rolling warehouse”. Companies

such as the Orient Overseas Container Line are using this concept, whereby product

destinations are only defined close to or at the customer location, taking the latest available

customer order information into account (Lee and Whang 2001). Thus, final delivery

specifications can be postponed to the point in time, when the order is close to the customer.

2.2.3. Production Postponement

Production postponement, also referred to as manufacturing postponement, form

postponement or delayed differentiation, focuses on postponing differentiation of the base

product. It involves production processes such as assembly, packaging, labelling and

manufacturing (Bowersox, Stank et al. 1999). This results in quicker responsiveness and

lower finished goods inventories. If it is crucial to be close to the customer, meaning that the

ability of quick average response throughout the product line is important, production

postponement is beneficial (Pagh and Cooper 1998). As Van Hoek et al. (2001) point out,


10

production postponement is also based on time and place postponement. Inventory of

components is kept at a central production facility (place postponement), used for product

finalization upon customer order and then shipped to the customer or a distribution centre

(time postponement).

To optimally support this strategy, the product should consist of a base product or generic

modules that can be differentiated into a variety of derivatives at a later stage (Yang, Burns et

al. 2004). Derivatives are to be understood as product as well as packaging options. Product

variety can thus be achieved with a small amount of generic base products (Lee and Tang

1997). Since forecasts are more accurate at the component level than at the finished product

level, demand forecasts are less prone to error (Yang, Burns et al. 2004). Furthermore, as

Pagh & Cooper (1998) point out, this product structure will also achieve a substantial

inventory reduction, because different products are based on the same base component.

However, it should not be neglected that customer order processing may increase in

complexity thanks to a late initiation of the differentiation process, and that costs associated

with coordination may increase (Pagh and Cooper 1998). Similarly, postponement will only

be appropriate, if no specialized manufacturing capabilities or highly restrictive economies of

scale are required (Pagh and Cooper 1998). Yang et al. (2004, p.1058) therefore conclude that

a “balance between the potential sacrifice that customers make and the company’s ability to

produce individualized products within an acceptable time and cost frame” has to be found.

The most famous example of this oldest form of postponement is the Benetton Group, a

textile company. Especially in the textile industry short average lead times are crucial due to

short life-cycles. Fashion collections change every few months to accommodate for seasonal

clothing needs, new market trends and changing customer preferences. Particularly, when

stores are reordering certain garments of one collection, it poses a great challenge for a textile

company to deliver these garments before the collection is replaced by a new one. It was at

this point that Benetton realized the benefit that production postponement could offer. They

decided to produce their garments in the base colour to stock and to delay the dying process of

garments to the point in time, when a customer order is received. Lead times were thereby

reduced from four to two weeks, which enables Benetton to satisfy reorders of retailers before

the new collection was introduced (Heskett and Signorelli 1984). Additionally a reduction in

inventory was achieved, because garments only had to be stocked in the base colour and not

in all different derivatives (Dapiran 1992). Dell Computers is another example of a company

using production postponement. With a clear implementation of a make-to-order strategy,


11

products are assembled according to customer specifications. The customer-order decoupling

point is thereby moved to prior to assembly. This enables Dell to maintain a vast scale of

mass customization with an estimated 100 million computer configurations (Kumar and Craig

2007).

2.2.4. Purchasing Postponement

The purchasing postponement strategy suspends the purchase of certain inputs for as long as

possible. Yang et al. (2004) mention that the aim of purchasing postponement is to delay

costly ownership of inventory especially in situations of quick obsolescence, such as

“expensive and fragile materials that come in many sizes and shapes” (Yang, Burns et al.

2004, p.1055). This is achieved by time and/or place postponement. Materials are stored at the

supplier’s facility until an order is placed (time postponement) or until the agreed upon

delivery date (place postponement).

Furthermore, base components that are usually characterized by relatively stable demand can

be ordered in response to a longer forecasting horizon. However, surge components such as

referred to above, that feature a higher degree of demand uncertainty, can be delayed to the

moment in time, when more precise demand information is available (Yang, Burns et al.

2004). Benetton, for example, operates with 90% standard components that are purchased in

advance and 10% surge components that are purchased shortly before usage (Dapiran 1992).

Another reason for engaging in this type of postponement is to benefit from declining

component prices and therewith reduced material costs. On the other hand, however, the shift

of inventory ownership has to be taken into account (Yang, Burns et al. 2004). Thus, it is

crucial to be engaged in a high level of cooperation with suppliers, as for example strategic

partnerships, in order to assure timely deliveries (Yang, Yang et al. 2007). Yang et al. (2004)

underline, that B2B e-markets that are increasingly utilized throughout the last decade foster

the implementation of purchasing postponing by improved information sharing and

forecasting capabilities between involved parties. Vendor managed inventory (VMI) is the

extreme variant of purchasing postponement. By means of real time information sharing,

inventory is replenished by the vendor as needed.

Purchasing postponement in form of VMI can be illustrated by revisiting the example of Dell

Computers. Dell Computers established close relationships with its suppliers in order to

arrange an automatic replenishment of its inventory. By taking ownership of component parts

as late as possible, inventories can be radically reduced and risk of obsolescence decreased. In


12

order to support the stability of this system, Dell is working closely together with its suppliers

and supports them in various situations. In this way, Dell reduces inventory costs and at the

same time assures availability of components; a crucial aspect when engaging in a make-to-

order strategy (Kumar and Craig 2007). Other examples of purchasing postponement include

supermarkets that also frequently engage in VMI or companies in the food industry that are

subject to strongly fluctuating component prices.

2.2.5. Product Development Postponement

“In the early phases of a product development project, detailed information on the product

attributes may not be available” (Yang, Burns et al. 2005, p.993), for example due to

uncertainty of customer preferences and new technology. Therefore product development

postponement delays the design of critical components to a moment, when better information

is obtainable (Yang, Burns et al. 2004), enabling product design improvements or production

costs reductions. Information is the driver of this strategy (Yang, Burns et al. 2004). In

quickly changing markets it can even be extended to: make a little – sell a little – adapt a little

– make a little – sell a little and so forth, in order to adapt to quickly changing market

information and needs (Mullins and Sutherland 1998). Product development postponement

can entail delaying the internal development processes or the external development process in

cooperation with suppliers. In both cases close cooperation with suppliers is essential in order

to assure the supply of appropriate parts or materials.

Toyota is engaged in product development postponement, frequently in cooperation with their

suppliers. Being a Japanese automaker, Toyota is characterised by very close relationships,

called Keiretsus, which are based on close cooperation and characterised by joint learning and

prospering (Liker and Choi 2004). Information on market and technological developments is

continually compiled and in turn enters the product development process. This permits the

postponement of product specifications and development to points in time, when better

information is available regarding customer preferences or new technologies (Yang, Yang et

al. 2007). Equally, cooperative product development together with suppliers is executed.

Thereby, product development postponement results in an incremental development process,

where specifying and structuring design tasks is essential (Yang, Burns et al. 2004).

After exploring these five types of postponement strategies, the question arises of how to

select the appropriate strategy for a company. The following section will investigate existing


13

strategy selection techniques and will show that the link between postponement strategies and

the source of demand uncertainty is not considered yet, in currently published literature, as a

means for postponement strategy choice.

2.3. Existing Strategy Selection Techniques

When implementing postponement, meaning when repositioning the decoupling point further

upstream, it is important to take the nature of demand into account. There are ‘base’ and

‘surge’ elements in a company’s supply chain. Base elements are characterized by low

demand uncertainty where production can be based on long-term forecasts. On the other hand,

however, as Yang et al. (2004) point out, surge elements are subject to postponement.

Because of their high demand uncertainty, they “have to be delayed until further information

on market demand is available” (Yang, Burns et al. 2004, p.1056). When the presence of

surge elements is observed, an adequate postponement strategy will have to be selected.

Existing literature offers first anchor points for postponement strategy selection, which will be

discussed throughout the following paragraphs.

Pagh et al. (1998) suggest that companies have to define a speculation-postponement strategy

along the manufacturing and logistics continuum, illustrated by figure 2. Their idea is based

on the concept of the two extremes: ‘speculation’ and ‘postponement’. Speculation indicates

that all actions are based on forecasts by means of make-to-inventory and/or decentralization

of inventory. Postponement, on the other hand, bases actions on gathered information by

means of make-to-order and/or centralized inventory with direct shipments. Logistics

postponement and manufacturing postponement are hybrid strategies. In case of logistics

postponement it is assumed that decisions with regard to logistics are based on market

information (direct shipment), whereas decisions with regard to manufacturing are managed

purely on forecasts (make-to-inventory). On the other hand, manufacturing postponement is

assumed to manage logistics according to forecasts (decentralized inventories), whereas

manufacturing is organized according to market information (make-to-order). Therefore both

postponement strategies include a speculative as well as postponement dimension. This seems

contradictory to the idea of Yang et al. (2004), mentioned earlier in this chapter, who state

that downstream of the customer-order-decoupling point, activities are based on customer-

order information, whereas upstream activities are driven by forecasts. However, Pagh et al.


14

(1998) argue that in the case of manufacturing postponement, full anticipatory logistics is

applied, and thereby planned prior to manufacturing activities according to forecast.

Figure 2: Postponement-speculation strategy continua (Pagh and Cooper 1998)

Each of the four strategies determined by Pagh et al. (1998) exhibits different characteristics

or determinants. The characteristics determined by Pagh et al. (1998) can be observed in their

example of a profile analysis shown in Figure 3.

Figure 3: Postponement-speculation strategy profile analysis (Pagh and Cooper 1998)


15

By specifying the level of these determinants, as indicated by the dots in Figure 3, a

postponement/speculation strategy profile is created for the company in question that will

indicate which of the four strategies appears most adequate. Note that the level of demand

uncertainty is one of the determinants. Other characteristics possibly indicate a reason

underlying this uncertainty, for instance a mature life-cycle stage. However, the profile

analysis does not give a clear view on the source of demand uncertainty.

In contrast to Pagh et al. (1998), Lee (2002) does not regard such a wide spectrum including

product, organizational and market characteristics for strategy classifications. He focuses on

the level of demand and supply uncertainty as frameworks to classify four supply chain types,

which in turn link to postponement opportunities. Low demand uncertainty is associated with

functional products and high demand uncertainty with innovative products; whereas low

supply uncertainty is associated with stable processes and high supply uncertainty with

evolving processes. Figure 4 shows the classification of supply chain types along these

dimensions.

Figure 4: Supply and demand uncertainty matrix (Lee 2002)

For the purpose of this research, especially responsive supply chains are of interest. In the

presence of innovative products with high demand uncertainty and the existence of stable

processes resulting in low supply uncertainty, responsiveness can be achieved by means of

postponement strategies. This is in line with the examples given in previous sections. Agile

supply chains may also apply postponement strategies, as they focus on responsiveness

downstream of the decoupling point to deal with demand uncertainty; however, additionally

risk-hedging is applied upstream of the decoupling point to cope with supply uncertainty. This

illustrates the concept of positioning the decoupling point explained earlier, however in


16

combination with actions taken to oppose supply uncertainty. Since this paper only focuses on

demand uncertainty, only responsive supply chains are subject to this research. Whereas Lee

(2002) only classifies supply chain types and creates a link to postponement, Boone et al.

(2007) make a first attempt to classify different postponement strategies according to the

types of supply chains. However, this attempt shows that almost all postponement types were

appropriate for responsive and agile supply chains. Thus, additional differentiating parameters

for postponement classification are needed.

Yang et al. (2004) takes in addition to the degree of demand uncertainty, the degree of

modularity as an additional factor into account. According to their framework, purchasing

postponement and product development postponement are applied in situations of high

uncertainty, whereas logistics postponement and production postponement are used in case of

low uncertainty. Additionally, product development postponement and production

postponement are more applicable for products with a high level of modularity. This

framework proves to be a simple generalization which may indicate a starting point for the

strategy selection process. However, in the opinion of the author of this thesis, production

postponement may even result to be valuable in case of low product modularity, that is, if the

final product is bulky or expensive. Additionally, production postponement can also be

especially effective in the presence of high demand uncertainty, alleviating the company from

the extremely high amount of safety stock needed. Thus, this framework gives a general

initial indication, but not a complete insight into the selection of postponement strategies.

It becomes obvious that all classification attempts published in current literature take demand

uncertainty into account, as this is the main driver for implementing postponement. Demand

uncertainty is an important issue throughout the supply chain, not only within processes close

to the customer. Yang et al. (2007) pointed out that postponement strategies are useful

throughout the whole network or modular organizational firm. Furthermore the authors

illustrate the locations of postponement applications within the supply chain. Product

development postponement and purchasing postponement is thus applied upstream of the

manufacturer and production and logistics postponement downstream. Identifying the location

of the supply chain, which is affected the most by an identified source of demand uncertainty,

will hence give a first indication of an appropriate postponement strategy.


17

2.4. Conclusion

Summarizing, this second chapter provided an overview of current postponement theories that

can be found in currently published literature. Firstly, postponement was defined as the action

of delaying certain activities of the supply chain downstream of the customer-order-

decoupling point in order to achieve more flexibility and responsiveness in light of demand

uncertainty. Secondly, five widely applied postponement strategies that cover the whole range

of the supply chain were discussed in more detail, including price postponement, logistics

postponement, production postponement, purchasing postponement and product development

postponement. Finally, existing postponement strategy selection techniques were explored. In

conclusion, various selection techniques can be identified in literature. Although almost all

techniques take the level of demand uncertainty into account, none considers the source of

demand uncertainty as a determinant. The following chapter will therefore develop a

conceptual research model that will analyze the implication of sources of demand uncertainty

within the postponement strategy selection process.

3. Chapter: Conceptual Research Model

18

3. Conceptual Research Model

The literature review revealed that demand uncertainty is an important aspect regarding

postponement strategy selection. This thesis argues that not only the level but also the source

of demand uncertainty is a very influential determinant of postponement strategies. In order to

analyse this proposition a conceptual research model will be developed throughout this

chapter. First, the concept of demand uncertainty will be explained. Subsequently, various

sources of demand uncertainty will be identified and logically linked to postponement

strategies. Finally, an overview of the established construct links is presented.

3.1. Demand Uncertainty

As pointed out by Yang et al. (2005), the greatest challenge of postponement implementation

consists of how to manage the external network of the company, including the supply as well

as the demand side. Especially managing the customer side however brings difficulties,

particularly, if demand is uncertain and thus characterised by a lot of unforeseeable volume

variability. “Uncertainty is best understood as an information defect” (Spender 1993, p.16).

When incomplete demand information is available, demand cannot be predicted with certainty.

Demand is especially uncertain when high demand variability is present.

In order to adapt to demand variability Bernhardt (1977) suggests two actions:

(a) Adjust terms of sale or

(b) Adjust operations

Both suggestions aim at increasing flexibility, when facing demand uncertainty. Adjusting

terms of sale refers to creating more flexibility in contractual agreements to maintain freedom

of action to a certain extent. This is dealt with by the sales and the legal department. On the

other hand, adjusting operations refers to operational means for creating flexibility, including

postponement strategies. This relates to the purpose of this paper, namely the investigation of

the relation between demand uncertainty and postponement strategies.

Demand uncertainty is caused by a series of factors. The following section will identify and

categorize potentially significant uncertainty reasons for the postponement decision.

Subsequently, these factors will be explained successively and relations between

postponement strategies will be established. Finally, an overview of the resulting relationship


19

construct will be presented which will be subject for verification throughout the remainder of

this research.

3.1.1. Factors influencing Demand Uncertainty

Demand uncertainty can be due to short- or long-run fluctuations in demand. Short-run

fluctuations may require large amounts of safety stocks, costly production techniques or an

adaptation of terms of sales in order to prevent costly stock outs. Long-run fluctuations

require scalability of facilities or an adaptation of terms of sale. In both cases, flexibility is

desirable to alleviate the burden of demand variability (Bernhardt 1977). Flexibility refers to

operational means that accommodate greater output variability (Stigler 1939). Postponement

is a mean that focuses on short-run flexibility. Spontaneous demand variations shall be

handled by postponing the customer-order-decoupling point further upstream in the supply

chain, thereby increasing responsiveness as discussed above.

Factors influencing demand uncertainty can be either controllable or non-controllable.

Controllable factors are factors that can be controlled by the organization (Cooper and

Kleinschmidt 1987). They can be influenced by the organization with the effect that uncertain

demand variability is reduced or effectively managed. Demand uncertainty reduction could be

achieved, for instance, by adjusting the forecasting techniques. Another option is to create

more flexible organizational processes like postponement strategies. Non-controllable factors

on the other hand are mainly environmental or market-related factors that are dependent on

external factors (Cooper and Kleinschmidt 1987). They cannot be controlled by a company,

because the power over these factors lies with another external party or nature. Examples are

new regulations, available market/product information, or income changes. Non-controllable

factors will be excluded from this study, because postponement aims at gaining control over

short-run demand uncertainty.

Summarizing, controllable factors that have an effect on demand uncertainty in the short-run

can be managed by postponement strategies. Table 1 provides a preliminary overview factors

that will be included in the research model. This list will still be subject to adaptation

according to findings resulting from the field interviews that will be conducted for this

research. The factors are grouped into four broad categories, namely operative, customers,

product characteristics and market. It is a condensed list of factors taken from a variety of


20

sources as well as logical derivation. The following paragraphs will briefly elaborate on these

factors.

1. Operational

1. Price of competitive products (substitutes +

complements)

2. Price of materials/components

2. Customers

1. Changing customer preferences/tastes across product

lines

2. Changing customer preferences/tastes in regard to new

or differentiated products

3. Irregularity of purchases, fluctuations in market

demand (due to bullwhip effect, advertising, changes is

amount and product mix of competitor)

3. Product

Characteristics

1. Innovation/short life-cycles/insufficient market

information

4. Market 1. Variety of customer groups with different

characteristics

Table 1: Demand uncertainty factors

1. Operational factors

1.1 Price of competitive products (substitutes + complements)

The price of a substitute products influences demand variability. In situations, in which the

price of substitutes or complements experiences a sudden or incremental increase or decrease,

demand of the own product will vary, even if its price is stable. This is due to the fact that

customers tend to start switching to competitive products, which become more attractive or

vice versa. Whereas a positive relationship exists between the price of substitutes and demand,

there is a negative relationship between the price of complements and demand. For example,

if the price of filters of a water filter increases, the demand of water filters will decrease.

Frequent price changes of substitutes and complements therefore lead to demand uncertainty.

As pricing behaviour of competitors is usually unknown, it creates demand uncertainty.

One option is to adapt the pricing strategy by keeping price determination flexible as long as

possible, in other words postponing the pricing decision. This is only possible in a few

industries, namely where price determination is either decentralized or flexible enough to be


21

postponed centrally. When price determination is decentralized, price negotiation can occur at

the point of sale. When price determination is centralized, price displays should be controlled

centrally.

H1.1: In the presence of price fluctuations of substitutes or complements, price postponement

is applied to mitigate the inherent risk.

1.2 Price of materials/components

Similarly, demand can be influenced by the price of materials and components that are being

used within the manufacturing process of the final product. If the price fluctuation is passed

on to the customer by changing the price of the end-product, demand variability is very high.

The challenge remains in keeping prices of end-products stable in order to reduce demand

uncertainty. If the end-product price is stable, but material and component prices are

fluctuating to a great extent, which is often the case in the food industry, a negative effect on

the product margin is reported. According to the DuPont method, only a small increase of

material costs can deflate return on net assets to a great extent (Weele 2005). Therefore it is

important to stabilize material costs.

One option is to hedge against the risk of declining margins by engaging in forward auctions.

Materials are thereby purchased to be delivered at some future moment, usually about 6-8

month later. However, also the opposite namely purchasing postponement may be practised.

Hereby, as explained above, materials are purchased as late as possible, in order to benefit

form declining material prices, thereby keeping material costs as well as associated storage

costs low.

H1.2: In the presence of price fluctuations of materials and component, purchasing

postponement is applied to mitigate the inherent risk.

2. Customers

2.1 Changing customer preferences/tastes

Customers may have frequently changing tastes and preferences, especially in the fast moving

consumer goods or information technology industry. Customer preferences can vary in two

ways. Either preferences change across an existing product line, meaning that demand

variability exists across the own product line. On the other hand, preferences change in regard


22

to desired new products or product differentiations. Demand uncertainty results because these

changes in preferences are difficult to predict. Additionally, AMR Research points out that

resulting frequent product launches may further spur demand uncertainty (AMR Research

2009).

In both cases of changing customer preferences, organizational responsiveness is essential to

meet customer needs. In the first case, production postponement can alleviate demand

uncertainty, by differentiating the base product shortly before delivery. For example, if red

shirts experience a high demand within one period and blue shirts in the next but this is

difficult to predict, this situation can be approached by delayed differentiation of the basic

shirt to customer-order decoupling point. In the second case, when new products are

demanded, product development has to accelerate. Product development postponement

reaches this goal, by delaying final product differentiations in order to be able to take newly

gained customer or market information into account.

H2.1a: In the presence of changing customer preferences and tastes across product lines,

production postponement is applied to mitigate the inherent risk.

H2.1b: In the presence of changing customer preferences and tastes in regard to new or

differentiated product, product development postponement is applied to mitigate the inherent

risk.

2.2 Irregularity of purchases

Irregularity of purchases results in great fluctuations of market demand. In the first place this

irregularity can be due to the bullwhip effect. “The bullwhip effect […] refers to the

phenomenon, where orders to the supplier tend to have larger variance than sales to the buyer

(i.e., demand distortion), and the distortion propagates upstream in an amplified form (i.e.,

variance amplification)” (Lee and Padmanabhan 1997, p.145). Furthermore, advertising can

influence the irregularity of purchases. Sudden sales promotions by competitors may create a

sudden decline in sales followed by an uncertain period of sales level recovery. Imagine that

customer X uses Nivea Shampoo, then sees a promotion from Pantene Pro V, and therefore

tries Pantene Pro V Shampoo instead of Nivea. However, he does not like it and therefore

buys the Nivea Shampoo again next time. This uncertainty of product choice and their triggers

influence the variability of demand. Thus, replenishment levels at retailers may result in

irregular orders at the production side. Moreover, changes in amount and variety of the


23

product mix of competitors, influences the purchasing decision of customers (Prockl, Bauer et

al. 2004). If a new substitute is introduced into the market, customers may switch either in the

short- or long-run to this substitute. High irregularity of purchases as just described can result

in high levels of costly inventory. Finally, irregularity of purchases can be influenced by

changing availability of product information. Frequent new information about new

technologies, product tests, health insights, etc. may change the attitude of customers towards

certain products. Probiotic foods are, for instance, subject to this changing awareness. Many

tests are conducted, some approving effectiveness others doubting it, leading to changing

customer attitudes towards these products.

Thus, high finished product inventory levels should be avoided despite high demand

uncertainty in order to circumvent storage costs and costs related to the risk of obsolescence,

which is higher for finished goods than for materials. It follows that production postponement

appears to be appropriate. By delaying postponing or product differentiating, the number of

stock keeping units as well as the storage time of finished goods can be reduced, while being

able to respond to impulsive demand patterns. However, in situations of irregular purchases

where lead time is very important logistics postponement seems more adequate. The final

products would be fully assembled and then stored at a central location. From there customer

orders will be fulfilled individually achieving the shortest lead time possible as well as

inventory reduction through central storage.

H2.2a: In the presence of irregular purchases per customer, production postponement is

applied to mitigate the inherent risk.

H2.2b: In the presence of irregular purchases per customer and high importance of short lead

times, logistics postponement is applied to mitigate the inherent risk.

3. Product Characteristics

3.1 Innovation/short life-cycles/insufficient market information

Innovation is a sudden trigger for unexpected demand (Prockl, Bauer et al. 2004). The trouble

with innovation is the fact that a true innovative product is something that customers could

not imagine before. Thus, demand is difficult to predict, since it cannot be drawn from

experience data. Demand for innovative products is therefore uncertain. Post-its from 3M can

be put forward as an example. Nobody, not even their inventors could imagine these sticky

pieces of paper, but they turned out to be a great success once customers got confronted with


24

them. For new product development and introduction, especially for innovative products, it is

of utmost importance to gather as much market information as possible in order to assure that

only potentially very successful products will be introduced. Otherwise the risk of a product

flop and the associated financial loss is too substantial.

One possibility of assuring that products are developed in accordance with the latest market

information is product development postponement. Market knowledge may not be available,

when the product development process begins. It follows that, if information regarding certain

product modules is lacking at the initiation of the development process, their development

should be postponed to a later stage, when sufficient information is available.

H3.1: In the presence of insufficient information for product development, product

development postponement is applied to mitigate the inherent risk.

4. Market

4.1 Variety of customer groups with different characteristics

Some products are targeted at a variety of customer groups, each with differing demand

patterns. Paint, for instance, is used on a regular basis by professional painters, however only

occasionally by do-it-yourselfers. Paint for kids is also sold, however in different

compositions and for different purposes. It follows that the demand across the product line of

a paint producer is influenced by a variety of customer groups. Similarly, aggregated demand

from a variety of geographic markets leads to demand variability across the product line, due

to geographic differences in demand characteristics (AMR Research, 2009). The wider the

potential group of customers, the more uncertain demand behaves.

In such situations, it is important to adapt the supply chain and product offering to clear

market segmentation. Market segments may however be difficult to determine in an

unambiguous way. If this is the case, production postponement is expected to be adequate. By

differentiating base products, for example by mixing colour derivatives as late as possible,

responsiveness can be maintained and inventory levels be reduced. Regarding geographic

differences, postponement of packaging or assembly according to country characteristics will

reduce lead time because the assembly location may be located closer to the market than the

production plant. Additionally inventory reduction can be achieved, because only base

products will have to be stocked and not all derivatives.


25

H4.1: In the presence of a variety of customer groups with different characteristics,

production postponement is applied to mitigate the inherent risk.

Additional factors that are influencing demand can be found in currently published literature.

These will however be neglected for the purpose of this research, because they are rather

long-term in nature or create structured and not unstructured demand uncertainty. One of

these factors is the type of market, which has an influence on the level of demand uncertainty.

As van Weele (2005) points out, industrial markets are characterized by derived demand that

may fluctuate strongly, whereas consumer markets are characterized by autonomous demand

that is rather stable. Furthermore van Weele (2005) underlines that nowadays products create

customer value in various ways and not only by utility of the object. For instance

customization in form of special features or corresponding services may add additional value,

of which each influences demand variability.

Additionally, in case of an industrial customer, his market share may change and therefore

influence demand. Market share however is generally gained or lost over the long-term,

meaning that it is not a driver for postponement which shall achieve short term flexibility.

Last but not least, the influence of the length of the forecast horizon was considered. If

demand is forecasted a long time in advance, the forecast is more prone to uncertainty (Prockl,

Bauer et al. 2004). Contrarily, if forecasts are made based on too little historical data,

forecasts tend to be inaccurate. However, this source of uncertainty is a controllable factor

meaning that it should rather be managed by adjusting the forecasting method instead of

accepting the uncertainty and adapting operations.

3.2. The Research Model

Figure 5 summarizes the factors and relationships established in the previous section. Eight

sources of demand uncertainty are expected to influence the postponement strategy decision.

Between the demand factors and the postponement strategy one additional layer was

positioned. This layer is meant to explain shortly, why a specific postponement strategy is

appropriate in the presence of a certain demand uncertainty source. For example in hypothesis

1.1, the fluctuating price of a competitive product may cause demand fluctuations which can

be alleviated by price optimization through price postponement. In proposition 1b fluctuating


26

material prices may cause demand fluctuations which can be alleviated by taking advantage of

decreasing materials prices by means of purchasing postponement.

Purchasing Postponement

Product Development

Postponement

Production Postponement

Logistics Postponement

Price Postponement

Postponement Strategies

1.) Operative



2.) Customers

2.1 Changing customer preferences/tastes across product lines

2.2 Changes in customer preferences/tastes in regard to new or differentiated product

2.3 Irregularity of purchases (bullwhip etc.)

3.) Product Characteristics

3.1 Insufficient information for product development/Innovations

4.) Market

4.1 Various customer groups with different characteristics

Reasons for Demand Uncertainty

H1.1

H1.2

H2.1a

H2.1b

H2.2b

H2.3a

H3.1

H4.1

Benefiting from decreasing prices

New product responsiveness

(long-term)

Customer responsiveness

(short-term)

Inventory –transportation cost

trade-off

Price optimization

Characteristic


Product Development

Postponement



Price Postponement


1.) Operative



2.) Customers






4.) Market



H1.1

H1.2

H2.1a

H2.1b

H2.2b

H2.3a

H3.1

H4.1

Benefiting from decreasing prices

New product responsiveness

(long-term)

Customer responsiveness

(short-term)

Inventory –transportation cost

trade-off

Price optimization

Characteristic

Figure 5: The initial conceptual research model

These relationships can be visualized by means of the effect of demand uncertainty sources on

the customer-order decoupling point (CODP). As introduced in chapter two, Yang et al.

(2007) established a clear relationship between postponement strategies and the location of

the CODP. In Figure 6, this framework is extended by indicating the influences of demand

uncertainty reasons on the various positions of the CODP, linking them thereby to different

postponement strategies. Four examples are shown in Figure 6 illustrating hypothesised

effects. ‘Variation in product price’ influences the CODP at the point of sale and thus

demands for price postponement to alleviate uncertainty risk. ‘Changing customer preferences

and behaviour’ pushes the CODP to positions around the production process and may be

countered by logistics and production postponement. ‘Price sensitivity of materials’ is

influencing the purchasing process and thus purchasing postponement can push the CODP to

the latest moment of purchase. ‘Innovation and quick life-cycles’ requires fast responsiveness

in product development by companies and thus product development postponement pushes


27

the CODP to a later stage of product development. Hence, each reason underlying demand

uncertainty influences the supply chain at a specific point. By moving the CODP to this point

in the supply chain, that is applying a certain postponement strategy, the negative effect of

demand uncertainty may be mitigated.

Price sensitivity of

materials

Changing customer preferences and

behavior

Variation of Product price

Innovations/ quick life-cycles

Price Postponement

Dashed line = Customer order decoupling point

Price sensitivity of

materials

Changing customer preferences and

behavior

Variation of Product price

Innovations/ quick life-cycles

Price Postponement

Dashed line = Customer order decoupling point

Figure 6: The CODP and reasons of demand uncertainty (adapted from Yang et al. 2007)

3.3. Conclusion

In conclusion, this chapter developed a conceptual research model by identifying various

sources of demand uncertainty and linking them to the postponement strategies discussed in

the previous chapter. Eight potentially influential demand uncertainty sources were thereby

linked to five different postponement strategies. Thus, some sources may demand for the

same postponement strategies, namely logistics and production postponement. In essence the

proposed strategy choice model illustrates the impact of the sources of demand uncertainty on

the positioning of the CODP. The following chapter will explain the methodology for testing

the established research model.

4. Chapter: Methodology

28

4. Methodology

Subsequent to the establishment of the above research model, the methodology for model

verification needs to be determined. The research was accomplished in two phases. In the first

phase the completeness of the model was verified my conducting field interviews. The

interviews as well as the resulting model adaptations are described in the following section. In

the second phase, the adapted model was examined by means of a structured online

questionnaire. The questionnaire composition, the pre-test as well as the procedure of data

collection is discussed in section 4.2. In the third phase the tools used for the subsequent data

analysis as well as the data validation will be gathered, which is explained in section 4.3.

Figure 7 summarizes this research process.

Figure 7: Research plan

4.1. Preliminary field interviews

Before starting data collection by means of a survey, three interviews were conducted with

senior supply chain managers of multinational organisations. The aim was to verify the

completeness of the factors included in the previously established model and deepen the

researchers understanding of postponement practices. The first interview was conducted with

a Senior Director of Global Logistics of a specific business unit focusing on consumer

products of a multinational technology company. Throughout this chapter, this company will

be referred to as Company 1. The second interview was conducted with a Supply Chain

Planning Manager EMEA of a multi-technology company, focusing on consumer as well as

industrial products. In the following, this company will be referred to as Company 2. The

Planning and Design

Literature Review

Field Interviews

Online Survey

Data Analysis

6 m

on

ths

Write-up


29

third interview was conducted with an ex supply chain professional of a chemicals company,

focusing on industrial products. This company will be referred to as Company 3. In this way,

B2B as well as B2C operations in the technology as well as chemicals industry could be

covered. Furthermore, all three interview partners had more than 15 years of work

experiences in the field of supply chain management at multinational companies as well as at

least 10 years of experience in their respective industries. They were therefore able to judge, if

the postponement applications at their companies are best practise or a very specific “outlier”

applications, which was taken into account when evaluating the interview results.

An unstructured questionnaire, which is provided in Appendix I, formed the guideline for

these interviews, allowing for and fostering any related additional information. The guidelines

included questions concerning the following aspects: company specific information,

application of postponement strategies, level of and reason for demand uncertainty. The

findings of this phase and the resulting adaptation of the original research model will be

described in the following subsections.

4.1.1. Interview results

Overall, the research model could be confirmed and complemented by means of the field

interviews. All three contact persons expressed a general familiarity with the concept of

postponement and reported a medium to wide-spread application within their companies. One

company even predicated that postponement would be the direction of the future.

Postponement would mainly be realized throughout the final stages of the product life cycle,

where efficiency gains have to substitute demand increases. The interviews were covering two

main areas of interest, namely postponement application and demand uncertainty, which will

be discussed in the following paragraphs. Note that only interview findings that contribute to

the model verification and adaptation will be presented. Further aspects and in-depth

examples were discussed during the interviews. These will not be reported explicitly, because

they did not directly but indirectly contribute to the research goal by deepening the

researchers understanding of the postponement concepts, and thereby supporting the

interpretation and judgement of the results.

Postponement Application

All five postponement strategies could be discovered in at least one of the interviewed

companies. Price postponement was mentioned in relation to promotions that led to later

adaptation of the product price. It should however be kept in mind that the theoretical idea of


30

postponement delays activities to the moment after a customer order is received. In that sense

only promotions that are put into place while or after communicating with the customer

should be considered as postponement. Promotions that trigger demand relocates price

postponement until after the completion of production but prior to purchase and can thus not

be considered as true postponement.

Logistics postponement is applied in all three companies. Specific products are stored at a

central location, from where customer orders are fulfilled by means of direct deliveries. This

concept seems more popular in the chemicals industry than in the technology industry.

Production postponement on the other hand seems more popular in the technology industry.

Concepts such as make-to-order, pack-to-order, and assemble-to-order are expressed to be

used extensively by Company 1 and Company 2. Interesting is that both companies stated that

close supplier cooperation is essential for the success of these strategies, as lead time

reliability is essential for assuring the desired customer service level.

Purchasing Postponement is applied in all three companies for certain materials. These

materials are usually characterized by fluctuating prices, where the purchase on the spot

market is postponement to a moment of low prices. On the other hand, purchasing

postponement is practiced in the form of just-in-time mentality, which aims at keeping a

minimal inventory.

Product development postponement was applied in two of the interviewed companies, more

precisely in the two technology companies. Company 1 reported that Research &

Development (R&D) first focuses on the innovative product functions development and

postpones the development of the product in which the new function will be used. Company 2

expressed that the primary focus is on innovative products that assure the continuity of their

trend setter history and postpones more general product development phases.

One postponement example was mentioned by Company 2 that could not be classified clearly

into one of the five postponement strategies. It was stated that delaying the hiring of

temporary labour to the latest possible moment is postponement. This example could be

classified as purchasing postponement, because temporary labour is purchased. However, as

labour is not directly but indirectly adding value to the customer, the hiring process will not

be taken into account throughout this research. Future research should analyse postponement

in the area of HR in more detail.


31

Demand Uncertainty

All three companies reported a medium to high level of demand uncertainty. The major

reason especially in the chemicals industry was indicated to be the bullwhip effect. Lack of

information about timing and amount of upcoming orders causes demand uncertainty and

thereby costs due to, for instance, longer lead times or higher safety stocks. Company 3 stated

that the lack of trust between companies is clearly the reason of the bullwhip effect.

Information sharing, which would reduce the bullwhip effect, will only occur if both parties

trust each other.

Furthermore, various other reasons of demand uncertainty were stated. For instance, Price

changes of materials as well as competitive products achieve a partially temporary and

partially continuous shift in demand. Unforeseen weather conditions can drastically increase

or decrease demand of a product, leading to the risk of stock-outs or high inventories. The

shop around mentality of end-customers leads to irregular and spontaneous purchases that are

difficult to forecast. Changes in market share were reported to be an important factor for

demand uncertainty especially in the fast moving technology industries. Tenders that usually

lead to extensive orders may or may not create sudden production requirements.

Moreover, environmental factors such as competition, governmental regulations and

macroeconomic trends were mentioned. These however, are not relevant for this research

because postponement strategies in relation to continuous short-term demand fluctuations are

being analyzed and not abrupt extensive or long-term demand variations.

Table 2 summarizes all factors that were mentioned during the interviews.

Reasons for Demand Uncertaintymarket uncertaintiescompetitionchange in market sharemacroeconomic trendsemerging marketstax changesprice changestenderschanging regulationsshop arount mentalitybullwhip effectsupply uncertaintysupplier lead timesseasonalitylack of trust

Table 2: Overview of uncertainty factors mentioned in interviews


32

Ways of coping with demand uncertainty

Postponement strategies aim at increasing flexibility to mitigate the negative effects of

demand uncertainty. The interviewed companies were asked how they cope with the indicated

demand uncertainty. Interestingly all interviewees mentioned different business concepts

before postponement was brought up. Predominantly, the importance of precise forecasting

and honest information sharing was stated to reduce the demand uncertainty created by the

bullwhip effect. At Company 3 collaborative planning, forecasting and replenishment (CPFR)

with suppliers in combination with long-term contracts is perceived to be the solution.

Furthermore, VMI is conducted at Company 1 and Company 3. By sharing substantive sales

information, the inventory of the customer is managed by its supplier.

Still these procedures only increase the visibility of demand variability and can add to more

precise forecasts. But what are tools that foster the efficient and effective reaction of

production process to demand uncertainty? Close cooperation with suppliers was mentioned

that shortens delivery lead times. Furthermore, multi-scale production sites offer the

opportunity to produce different products in the same production site and thus increase and

decrease capacity for SKU production. A wide product mix with similar ingredients can

compensate for different uncertainties. Last but not least, postponement was mentioned as a

tool to create flexibility as explained above. This shows that postponement is not yet clearly

seen as a concept for reacting to demand uncertainty and more awareness and understanding

may foster application of postponement variants.

4.1.2. Adapted research model

The interview findings did not only deepen the understanding of postponement strategies in

practice, but also led to an adaptation of the original research model. Three reasons for

demand uncertainty that were considered to be important by the interviewees and agreed to be

missing in the model were added, namely change in market share, seasonality and weather.

Therefore, three more hypotheses are established:

Market

H4.2 In the presence of an instable market share, purchasing postponement has a positive

effect on the firm’s ability to react to demand uncertainty. (…by assuring flexibility in case

of a sudden market share decrease.)


33

H4.3 In the presence of seasonality, purchasing postponement has a positive effect on the

firm’s ability to react to demand uncertainty. (…by optimizing product availability and

reduces stock levels in case of uncertain high and low season timing.)

Other H5.1 In the presence of unforeseeable weather conditions, production postponement has a

positive effect on the firm’s ability to react to demand uncertainty. (…by reducing the

necessary amount of safety stock.)

Furthermore, it is perceived that the questionnaire should also cover the aims of the

implemented postponement strategies, as well as their impact on company performance in

order to receive a greater picture of the benefit of a postponement strategy in certain

circumstances. Figure 8 presents an overview of the adapted research construct.

DemandUncertainty


PostponementAims

Impact on Performance

DemandUncertainty


PostponementAims

Impact on Performance

Figure 8: Adapted overview of research construct

Figure 9 below shows the different construct linkages in more detail. At the beginning of the

chain the hypothesized linkages between the identified reasons underlying demand

uncertainty and the five postponement strategies are indicated by the arrows. Subsequently,

the aims or expected achievements of each postponement strategy are shown, followed by the

impact on company performance.


34


Product Development

Postponement



Price Postponement

Postponement Strategies1.) Operative



2.) Customers






4.) Market


4.2 Change in Market share

4.3 High seasonality

5.) Other

5.1 Weather


H1.1

H1.2

H2.1a

H2.1b

H2.2bH2.2a

H3.1

H4.1

H4.2

H4.3

H5.1

•Product price optimization

•Lead time reduction

•Increased customer responsiveness (short-term, operational)

•Inventory reduction

•Overall cost reduction

•Benefit from decreasing material prices

•Increased customer orientation (long-term, e.g. regarding new product development)•Increased innovativeness

Postponement Aims

Performance Impact


Product Development

Postponement



Price Postponement



Product Development

Postponement



Price Postponement




2.) Customers






4.) Market




5.) Other

5.1 Weather


H1.1

H1.2

H2.1a

H2.1b

H2.2bH2.2a

H3.1

H4.1

H4.2

H4.3

H5.1








Postponement Aims

Performance Impact

Figure 9: Adapted research model

In order to test this construct, data is collected by means of an online survey, which will be

discussed in the following subsection. Subsequently, the analytical tools for testing the

construct as well as the construct validation process will be explained.

4.2. Online Survey

The next step in the research process is the empirical validation of the research model. For

this purpose an online survey was carried out among supply chain practitioners. The

following section will outline the questionnaire development as well as the execution process.

4.2.1. Questionnaire Development

The questionnaire was designed with the intention of collecting information from companies

about their application of different postponement strategies, their aims and the reasons for

demand uncertainty that the company is facing. Prior to asking the respondent to answer a

question regarding postponement strategies, he is offered a general explanation of

postponement and its variants to ensure a common understanding. Subsequently, three

question blocks follow, covering the aspects just mentioned. Additionally, some general


35

questions about company characteristics were included. The complete questionnaire can be

found in Appendix II.

The measurement scales could not be adopted from earlier research, because very little

empirical research on postponement strategies has been conducted. Therefore, similar to

previous research, a 7-point Likert scale was initially used for most questions, however with

the aim of testing its effectiveness throughout the pilot study. A 5-point Likert scaled turned

out to be the preferred measurement scale. About ten supply chain professionals and

professionals from other business professions were asked to fill in the questionnaire and

provide feedback on wording, question difficulty, layout, length of questionnaire, sequence,

spelling, mood upon completion of the questionnaire and its reasons. Seven of the ten

participants indicated a preference for a 5-point Likert scale, without being confronted with

this idea.

4.2.2. Execution of Survey

A web-based version of a survey was chosen for data collection. In this way, also respondents

that were located out of the physical reach of the researcher could be contacted. Furthermore,

the online version is convenient for respondents, because time and location for participation

can be chosen individually, and answer transmission is not time consuming. The

disadvantages of this approach are the lack of personal contact and potential technical

problems, leading to lower response rates due to incomplete questionnaires. For the

construction of the questionnaire the software program NetQuestionnaire was used. The

questionnaire was administered in English as well as in German, depending on the

respondent’s preference.

The survey was directed to supply chain professionals that have a general understanding of

the supply chain structure of their companies. Optimally, these contact persons were Supply

Chain Managers, but also other professionals that are for example working in the field of

materials management proved to be able to give very valuable input. Potential participants

were contacted by a variety of methods, including direct e-mail, forwarding through personal

contacts, forum discussions on Xing.com and LinkedIn.com, direct contacting through

xing.com, and through cooperation with the Dutch Logistics Association “Vereniging

Logistiek Management”. In total 57 professionals completed the online survey and 53

responses remained valuable after data cleansing. The majority is active in the industry


36

sectors technology, consumer goods, fashion and chemicals. An overview of the sample

characteristics will be provided in section 5.1.

The following subsection will describe the statistical tools, as well as validation of the

research construct that is conducted by means of the questionnaire data.

4.3. Analytical Tools

Partial Least Squares Path Modelling (PLS PM) is used to analyze the research model. The

findings are then cross-validated by means of Canonical Correlation and PLS Regression.

These three statistical approaches and their relationship will be explained briefly in the

following paragraphs followed by a short elaboration on the reasoning of the final choice of

methods.

Canonical Correlation

Horst’s Generalized Canonical Correlation Analysis can investigate the relationship between

two sets of variables (Thompson 1984). Thereby, the first link of the research model, as it was

depicted in Figure 9, can be analysed, that is the relationship between the set of reasons for

demand uncertainty and the set of different postponement strategies. Canonical roots can be

identified that explain an underlying latent variable. Horst’s Generalized Canonical

Correlation Analysis is based on several assumptions. In the first place the distribution of the

data is assumed to be multivariate normal. For larger sample sizes canonical correlation

however proved to be robust against other distributions. For small sample sizes, it is important

to note, that canonical correlations may only be detected, if they are very strong (> 0.7). That

means that in case of a small sample size a low significance may be detected although the link

may actually be significant (Guinot C., Latreille et al. 2001). Furthermore, outliers should be

detected in the data set because they can seriously influence canonical correlations. Finally,

the data set should not contain any redundant variables. Due to these limitations, canonical

correlations will only be used for cross-validation purposes. Instead PLS Path Modelling can

be used to emulate canonical correlation and to avoid these hard assumptions.

PLS Path Modelling

PLS Path Modelling is a specific type of Structural Equation Modelling (SEM), which is less

subject to constraints than Horst’s generalized canonical correlation analysis. In fact,


37

canonical correlation is a special case of PLS PM, as the stationary equations of Horst’s

generalized canonical correlation can also be found in the estimation of the path model

(Tenenhaus, Vinzi et al. 2005). Recently this statistical analysis became more popular among

operations management researchers. It is used for this research, because it can be employed as

a diagnostic tool that allows the simultaneous analysis of several causal relationships of

manifest (MVs) and latent variables (LVs) (Chin 1998). PLS PM is a component-based SEM

method, in contrast to a standard covariance-based SEM approach (Wetzels, Odekerken-

Schröder et al. 2009). The aim of this method is to minimize the residual variance of

dependent variables. PLS Path Modelling is adequate for the research at hand due to its

limited assumptions. It does not assume normal distribution, requires only a small data sample

and has minimal demands on measurement scale. However, according to Hsu, Chen et al.

(2006) it should be kept in mind that a small sample size leads to a downward bias in form of

underestimation of path coefficients. Furthermore, this method can not only be used for theory

verification, but also for theory development and can easily handle formative constructs

(Wetzels, Odekerken-Schröder et al. 2009).

The model specifications can be split into an outer and an inner model. The outer model, also

called the measurement model, describes the relationship between the MVs to their LVs

(Tenenhaus, Vinzi et al. 2005). Within this research it relates for instance the reasons of

uncertainty to a certain postponement strategy, which is a link that was illustrated in Figure 9.

The inner model, also called the structural model, describes the relationship among the LVs

(Tenenhaus, Vinzi et al. 2005). Furthermore, formative indicators are chosen, in contrast to

reflective indicators, because they reveal a causal link from the indicators to the latent

variable (Tenenhaus, Vinzi et al. 2005). This is in line with the research aim of this paper,

which strives to investigate, if potential reasons for demand uncertainty trigger the use of a

specific postponement strategy. To estimate the inner and outer parameters of the model, the

software SmartPLS is used, that can conduct PLS path modelling with a centroid weighting

scheme. Estimating LVs with formative indicators and a centroid weighting scheme actually

copies the stationary conditions of Horst’s generalized canonical correlation (Guinot C.,

Latreille et al. 2001). Finally, the bootstrapping procedure is used to estimate the significance

of the PLS estimates (Chin 1998).


38

PLS Regression

PLS Regression focuses on the estimation of the outer weights of formative models. In PLS

Path Modelling the outer weights are computed by a system of multiple and simple

regressions. The problem here is that formative blocks often violate the independence

hypothesis of the classical multiple and simple regressions, which leads to increased variance

of estimation, insignificant regression coefficients and not interpretable weights. Therefore

PLS Regression was developed as a new multivariate method of linear multiple regression for

formative models and is thus also applicable for the research model of this study (Esposito

Vinzi, Russolillo et al. 2009). PLS regression can handle multicollinearity which occurs when

components are not independent. It proved to be a powerful diagnostic tool detecting those

latent dimensions underlying a block that are useful for explaining LV (Tenenhaus 2005).

Finally, PLS regression preserves the asymmetry of the model opposed to canonical

correlation. As the aim of this research is to analyse the causal relationship between

predetermined variables, PLS Path Modelling is the preferred approach. PLS Regression will

however be valuable in cross-validating the PLS PM findings for a specific model link.

Thus, there are several arguments for using PLS PM as main statistical tool for the research at

hand. In the first place, PLS PM provides the researcher with greater flexibility for the

interaction between theory and data (Chin 1998). Hypothesized theoretical relationships,

including unobservable latent variables, can be modelled and then tested for significance,

whereas canonical correlation and PLS Regression do not allow for pre-specification of

relations. Canonical correlation cannot determine statistical significance of weights and

loadings resulting in a subjective and thus arbitrary cut-off value (Bagozzi, Fornell et al.

1981). However, estimating the LVs by employing mode B and the centroid scheme in PLS

PM, recovers the stationary condition of Horst’s generalized canonical correlation (Tenenhaus,

Vinzi et al. 2005). Furthermore, chains of causal relations instead of pairs of variable groups

can be analyzed taking causal effects into account. Last but not least, due to a small sample

size and hard assumptions, canonical correlations may prove to be insignificant although in

fact a significant relation exists, thereby not unrevealing all significant links of the model

(Guinot C., Latreille et al. 2001). In conclusion, PLS PM should be used as the primary

statistical tool, and canonical correlation and PLS regression will be employed for cross-

validation purposes.


39

4.4. Construct Validation

In this section construct validation will be performed. Since the model at hand includes

formative indicators, the guidelines for formative model assessment by Petter et al. (2007)

will be followed. It follows that construct validity and reliability will be examined. It is not

necessary to check for unidimensionality, as this only needs to be examined in case of

reflective models, because formative models are by definition multidimensional.

4.4.1.1.Validity

As we are analysing a formative model, the general validity tests, such as Average Variance

Extracted (AVE) and Goodness-of-Fit (GoF) that are applied on reflective models cannot be

applied. This is due to the fact that the indicators do not have to be correlated, because they

are giving rise instead of influencing the LVs (Hulland 1999). As an alternative a Principal

Component Analysis (PCA) could be conducted (Petter, Straub et al. 2007). However, in

order to preserve content validity a PCA is not conducted. This is due to the fact that all items

will be kept in the model to assure not to omit a part of the construct. A model with causal

indicators requires a “census of indicators, not a sample” (Bollen and Lennox 1991, p.308).

Additionally, clear theoretically backed judgement lead to a list of indicators that is assumed

to be complete. This was achieved by means of literature review, interviews and forum

discussions. Hence, it is assumed that all indicators contribute significantly to scale reliability

and content validity.

4.4.1.2. Reliability

The uncertainty and aims are examined for multicollinearity by means of the variance

inflation factor (VIF). The corresponding tables are provided in Appendix III. Severe

multicollinearity is said to exist, when VIF > 10 (Kutner, Nachtsheim et al. 2004). None of

the uncertainty factors and aims proved to suffer significantly from multicollinearity (VIF <

10) and therefore none was omitted from the subsequent analysis. Note, however, that

uncertainty factors C1 and D 2 , aims B 3 and C 4 and aims A 5 and D 6 indicate moderate

collinearity.

1 Uncertainty factor C = Changing customer preferences (product line) 2 Uncertainty factor D = Changing customer preferences (new product) 3 Achievement factor B = Lead time reduction 4 Achievement factor C = Inventory reduction


40

4.5. Conclusion

The initial research model has been verified as well as rounded up by means of expert

interviews. To test the resulting adapted model, an online questionnaire was developed and

executed. Finally, 53 responses resulted to be valuable for the subsequent statistical analysis.

PLS Path Modelling is used for the main analysis and Canonical Correlation as well as PLS

Regression as cross-validation. For the latter two approaches it is important to keep their

constraints and limitations in mind, while interpreting the results. The following chapter will

present the findings of the statistical analysis.

5 Achievement factor A = Overall cost reduction 6 Achievement factor D = Product price optimization

5. Chapter: Findings

41

5. Findings

This chapter will analyse the data collected by means of the online survey. In the first place,

general findings regarding postponement strategies and sample characteristics will be

presented. Thereafter, the findings of the PLS PM analysis of the research model as well as

the cross-validation by means of Canonical Correlation as well as PLS Regression will be

presented. Finally, a conclusion to this chapter is provided.

5.1. General Sample Characteristics

The data collected by means of the online questionnaire indicates a frequent application of all

five postponement strategies among the 53 respondents. Logistics, production and purchasing

postponement proved to be applied most frequently, namely by 47, 45, and 46 respondents

respectively as can be observed in Figure 10 below.

05

101520253035404550

pric

epo

stpo

nem

ent

logi

stic

spo

stpo

nem

ent

prod

uctio

npo

stpo

nem

ent

purc

hasi

ngpo

stpo

nem

ent

prod

uct

deve

lopm

ent

post

pone

mnt

number of respondents

Figure 10: Number of respondents per postponement strategy

Furthermore, logistics, production and purchasing postponement are not only most frequently

applied but also receive higher importance and are more extensively applied (Figure 11).

Price and product development postponement show rather little or moderate importance for

the respondents.


42

0

5

10

15

20

25

not applied littleimportance

moderatelyimportant

important veryimportant

importance of postponement strategy / extent of application

number of indications

price

logistics

production

puchasing

prod.dev.

Figure 11: Importance/extent of application within respondents’ companies

Referring to the length of the history of postponement strategy application, it can be stated

that price and logistics postponement have been frequently applied for more than ten years in

various companies, whereas production and purchasing postponement are on average used for

about 5 years. Product development postponement, on the other hand, indicates to be a rather

recent phenomenon as the majority of respondents indicates an application of less than 3 years.

The corresponding Figure is provided in appendix IV.

The impact of postponement on company performance was generally rated as positive to very

positive as can be observed in appendix IV. Only in very few instances, for product

development postponement and purchasing postponement, a negative influence was indicated

by two to three respondents. In case of product development postponement this can be due to

a negative impact on financial performance, even though a positive effect on innovation may

have been achieved. In case of purchasing postponement a negative impact may be due to the

risk of price variations.

The respondents are originating from different industries as shown in Figure 12. 22.2% of the

respondents are working in the technology industry, followed by 18.5 % in consumer goods

and 16.7 % in fashion as well as chemicals. There are eight respondents that fall into the

category “other”. Among these are four companies that could be classified as technology

companies and one as fashion company. In addition, two other companies indicate to produce

furniture and agricultural products, respectively.


43

0

2

4

6

8

10

12

14

cons

umer

good

s

techn

ology

fashio

n

chem

icals

autom

otive

pharm

aceu

ticals

other

# of respondents

Figure 12: Distribution of survey respondents across industries

Moreover, 33% of the respondents indicated to operate internationally, whereas 61% operate

globally. The head quarter of 80% of the companies is located in Europe, 16% in America,

and 2 % in Asia. Only slight difference in postponement application could be detected among

European and North American companies. The p-values of the independent t-tests between

origin and logistics postponement, as shown in appendix IV, indicated that the means are

significantly different, namely significantly more important in the US compared to Europe.

This may be due to the longer distances in North America that require more frequent direct

deliveries compared to, for instance, milk runs, which are round trips by truck. However, the

findings should be interpreted with caution due to the small sample size of American firms. A

comparison to Asian companies could not be established, because the sample size was too

small. Logistics postponement also varies in importance between internationally and globally

active firms. At a 10% significant level international firms indicated a significantly lower

average importance of logistics postponement.

The relation between postponement strategies and level of uncertainty does not show stronger

postponement application in the presence of high uncertainty. The following PLS path

modelling analysis will therefore investigate, if the reason of demand uncertainty has a

decisive influence on the choice of postponement application.


44

5.2. The Research Model

The following subsections will report the findings in the sequence of links established above:

(1) PLS PM for Link 1: Uncertainty – Postponement Strategies, (2) PLS PM for Link 2 and 3

Aims/Impact, (3) Cross-validation by means of Canonical Correlation and PLS Regression.

The main focus will be on the results of PLS PM, because it proved to be the most robust

measure. For each postponement strategy, one PLS PM model was created in the software

program SmartPLS. Figure 13 shows the PLS PM model for price postponement as an

example. Each model includes four LVs, being demand uncertainty, the postponement

strategy, aim as well as the performance impact, that are in turn influenced by various

formative indicators (MVs).

Figure 13: PLS PM Model for Price Postponement

In order to understand the data structure behind the model, the construction of each latent

variable will now be explained.

The latent variable uncertainty is influenced by ten formative indicators (MVs). These are the

reasons underlying demand uncertainty. Data regarding these indicators was collected by

asking the respondents to specify the importance of each of the ten indicators in influencing

the demand uncertainty their company is facing.

The latent variable demand uncertainty is hypothesized to influence a certain postponement

strategy; in the example above this is price postponement (Figure 13). Price postponement

has one formative MV that is determined by asking the survey respondents to specify the

importance/extent of application of price postponement at their company.

Subsequently, price postponement leads to a certain aim of price postponement, which is

illustrated by the third LV. Eight formative postponement strategy specific indicators


45

influence the aim of price postponement. Each indicator represents a possible aim of

postponement strategies. Data for these indicators was collected by asking the survey

respondents to determine the importance of each of the eight possible aims, when applying

price postponement.

Finally, the aim of price postponement is modelled to have an impact on company

performance, which is represented by the fourth LV. The impact on company performance is

influenced by one formative indicator. Data regarding this indicator was collected by asking

the survey respondents to rate the impact of price postponement on company performance.

In summary, data for the indicators of the first LV (demand uncertainty) is collected by means

of general questions, whereas the data for the indicators of the other three LVs is collected by

means of postponement strategy specific questions. Finally, the PLS algorithm is run in

SmartPLS for each of the five postponement strategy models, employing the centroid

weighting scheme, as every factor should be given the same weight (Vinzi, Chin et al. 2010).

The bootstrapping procedure subsequently provides an insight into the significance of outer

weights and path coefficients by calculating t-statistics. A summary of the findings of the PLS

PM analyses are provided in table 3, including the weights, path coefficients between LVs, t-

statistics as well as p-values. The findings will briefly be explained throughout the following

subsections. A more detailed discussion is provided in chapter 6.


46

Price PostponementUncertainty weight T statistic p-value Aim weight T statistic p-value Path coefficients coefficient T statistic p-value reasuncA -0.0286 0.209 0.836 achPriceA 0.5801 1.8384 0.078 achiev. --> impact -0.1956 2.3283 0.028 reasuncB 0.2027 1.4714 0.154 achPriceB 0.3941 0.8395 0.409 price P. --> achiev. 0.3451 4.0431 0.000 reasuncC 0.2216 0.9502 0.351 achPriceC -0.6584 1.4738 0.153 uncert. --> price P. 0.5006 7.1917 0.000 reasuncD 0.2453 0.9699 0.341 achPriceD 0.8096 2.9882 0.006 reasuncE 0.0349 0.3183 0.753 achPriceE 0.5252 1.9476 0.063 reasuncF 0.9304 4.3106 0.000 achPriceF -0.098 0.5222 0.606 reasuncG -0.0366 0.3677 0.716 achPriceG -0.4612 1.8661 0.074 reasuncH -0.5791 2.5547 0.017 achPriceH 0.6008 1.8093 0.082 reasuncI -0.4026 2.0458 0.051 reasuncJ 0.1074 0.8275 0.416Logistics PostponementUncertainty weight T statistic p-value Aim weight T statistic p-value Path coefficients coefficient T statistic p-value reasuncA -0.0884 0.6799 0.503 achLogA 0.1796 0.6161 0.543 achiev. --> impact 0.3186 2.7275 0.012 reasuncB 0.4543 2.4935 0.020 achLogB 0.7529 2.8715 0.008 log. P. --> achiev. 0.368 3.4463 0.002 reasuncC 0.5875 1.9506 0.062 achLogC 0.3585 1.2531 0.222 uncert. --> log. P. 0.4979 6.2093 0.000 reasuncD -0.1043 0.4997 0.622 achLogD -0.0492 0.2152 0.831 reasuncE 0.4677 2.9732 0.006 achLogE 0.0573 0.4026 0.691 reasuncF -0.2025 1.3903 0.177 achLogF 0.298 1.2982 0.206 reasuncG 0.4373 2.7834 0.010 achLogG -0.5698 2.3398 0.028 reasuncH -0.1167 0.9133 0.370 achLogH -0.1606 1.0269 0.314 reasuncI -0.0635 0.5585 0.581 reasuncJ -0.4856 2.5177 0.019Production PostponementUncertainty weight T statistic p-value Aim weight T statistic p-value Path coefficients coefficient T statistic p-value reasuncA 0.2934 1.3947 0.175 achProdA 0.142 0.7561 0.457 achiev. --> impact 0.4383 5.9622 0.000 reasuncB -0.3168 1.9386 0.064 achProdB -0.0232 0.1269 0.900 prod P. --> achiev. 0.303 3.2706 0.003 reasuncC 0.5799 2.4229 0.023 achProdC 0.3682 1.7665 0.090 uncert. --> prod P. 0.5449 7.5044 0.000 reasuncD -0.0724 0.3611 0.721 achProdD 0.5233 2.2564 0.033 reasuncE 0.0953 0.7349 0.469 achProdE -0.5901 2.339 0.028 reasuncF -0.3339 1.8269 0.080 achProdF 0.4393 2.5593 0.017 reasuncG 0.1286 1.1134 0.276 achProdG 0.337 1.7123 0.099 reasuncH 0.4377 2.8093 0.010 achProdH -0.2446 1.489 0.149 reasuncI 0.1241 0.805 0.428 reasuncJ -0.9303 6.5523 0.000Purchasing PostponementUncertainty weight T statistic p-value Aim weight T statistic p-value Path coefficients coefficient T statistic p-value reasuncA 0.1006 0.6593 0.516 achPurchA -0.1563 0.9147 0.369 achiev. --> impact -0.1606 2.0259 0.054 reasuncB -0.4219 2.2362 0.035 achPurchB -0.4383 1.5972 0.123 purch P. --> achiev. 0.1765 2.1808 0.039 reasuncC 0.3733 1.2001 0.241 achPurchC 0.6555 3.0395 0.005 uncert. --> purch P. -0.4192 5.4058 0.000 reasuncD -0.4318 1.1568 0.258 achPurchD 0.3819 1.7152 0.099 reasuncE -0.2822 1.4894 0.149 achPurchE -0.0968 0.5051 0.618 reasuncF 0.2177 1.0907 0.286 achPurchF 1.0038 2.4103 0.024 reasuncG 0.2269 1.3184 0.199 achPurchG -0.5278 1.4743 0.153 reasuncH -0.1758 0.9494 0.352 achPurchH 0.3438 1.2617 0.219 reasuncI -0.3411 1.6839 0.105 reasuncJ 1.0563 5.7894 0.000Product Development PostponementUncertainty weight T statistic p-value Aim weight T statistic p-value Path coefficients coefficient T Statistics p-value reasuncA 0.0235 0.2257 0.823 achPrDevA 0.6398 2.0274 0.053 achiev. -> impact 0.2933 3.1323 0.004 reasuncB 0.2714 1.8281 0.079 achPrDevB 0.4112 2.0464 0.051 prod.dev. P. -> achiev. 0.2914 3.4927 0.002 reasuncC 0.3041 1.303 0.204 achPrDevC -0.3973 1.6808 0.105 uncert. -> prod.dev. P. 0.6034 10.6417 0.000 reasuncD 0.1253 0.6133 0.545 achPrDevD -0.242 0.7624 0.453 reasuncE -0.4583 3.5787 0.001 achPrDevE 0.1785 0.4419 0.662 reasuncF 0.231 1.5565 0.132 achPrDevF -0.3314 1.6419 0.113 reasuncG 0.0929 0.9826 0.335 achPrDevG -0.1615 0.469 0.643 reasuncH -0.0867 0.6723 0.508 achPrDevH 1.0124 2.564 0.017 reasuncI 0.2906 1.8372 0.078 reasuncJ 0.3343 2.0292 0.053

= positive significance expected = significant at 5% = slightly above 5%

Reasons underlying demand uncertainty Achievements/AimsA - price fluctuations (compet. Product) A- Overall cost reductionB - price fluctuations (material/component) B- Lead time reductionC - chang. customer preferences (pr. line) C- Inventory reductionD - chang. customer preferences (new prod.) D- Product price optimizationE - irregular purchases E- Benefit from decreasing material pricesF - innovation F- Increased customer responsiveness (short-term)G - change in market share G- Increased customer orientation (long-term)H - many different customer groups H- Increased innovativenessI - high seasonalityJ - weather

Table 3: PLS PM results per postponement strategy


47

5.2.1. Link 1: Uncertainty – Postponement Strategies

First of all, the results show that all postponement strategies except purchasing postponement

indicate high path coefficients between 0.5 and 0.6, indicating a strong influence of

uncertainty reasons on postponement implementation. This indirectly confirms earlier

research that corroborated that postponement strategies are applied in reaction to demand

uncertainty. Purchasing postponement is the only exception, which shows a negative path

coefficient of -0.4192. This may be due to the fact that companies want to assure supply and

thus in situations of high uncertainty will not postpone purchasing to a later point in time but

rather advance the moment of purchase.

Based on the results provided in table 3, it can be stated that five of eleven hypotheses turn

out to be significant, of which however two prove to have the opposite sign (highlighted as

well as circled p-values in table 3). Logistics postponement is indeed positively influenced by

irregular purchases. Production postponement is indeed positively influenced by changing

customer preferences and various different customer groups, however negatively influenced

by demand changes due to weather conditions. Finally, purchasing postponement turns out to

be negatively and not positively influenced by price fluctuations of materials and components.

Eight additional relations turn out to be significant. These are highlighted but not circled in

the first column of table 3.

5.2.2. Link 2 and 3: Postponement Aims and Performance Impact

The second link was established to provide a clearer picture of the value of postponement

strategies in given market dynamics, analyzing the resulting achievement and their impact on

company performance. All path coefficients between latent variables are significant. The

results are provided in the second column of table 3.

Four of ten expected links between postponement and aims could be confirmed. Price

postponement is indeed positively related to product price optimization. Logistics

postponement strives for lead time reduction. Production postponement is in fact aiming at

increased customer responsiveness in the short-term and product development postponement

results in increased innovativeness. Five additional factors were found to be significant. These

are highlighted but not circled in the second column of table 3.


48

Finally the results for the third link, namely the impact on company performance, demonstrate

that the aims of all postponement strategies except purchasing postponement have a path

coefficient on performance impact that is significant at 5%. The path coefficient for

purchasing postponement however has a p-value of only slightly above 5% and may therefore

also be considered significant. The p-values for the path coefficients are reported in column

three of table 3. Note however, that price and purchasing postponement have a negative path

coefficient of -0.1956 and -1.606 respectively. In both cases this may be due to hedging

against price risk by advancing instead of postponing activities.

5.3. Cross-validation with other statistical approaches

Tow questions arise with regard to the first link (uncertainty-postponement), which will help

to answer the problem statement. Firstly, should the latent factors determined by means of the

PLS PM analysis be taken for granted? Thus, canonical correlation analysis will be used to

determine latent factors based on the actual data. As discussed this analysis however requires

hard assumptions and cannot cope with multicollinearity. Thus, factor D is eliminated for this

analysis due high correlation with factor C and relatively high VIF of > 3.3 (Diamantopoulos

and Siguaw 2006). The second question follows from this argumentation. Can factor D be

integrated into the analysis? PLS Regression can accomplish this, as it can cope with

multicollinearity. Therefore, the following paragraphs will provide the results of Canonical

Correlation as well as PLS Regression to cross-validate the findings from the PLS PM

analysis. Cross-validation is only conducted for the first model link, as this link is the main

focus of this thesis. The corresponding tables are provided in Appendix V and VI,

respectively.

Canonical Correlation Analysis

As Canonical Correlation Analysis assumes low multicollinearity, factor “D- Changing

customer preferences/tastes in regard to new or differentiated products” was eliminated for

this test. It should be taken into consideration that the canonical weights may be

underestimated due to a small sample size and thus may reveal insignificant results. Analysis

outputs as well as explanations are provided in Appendix V.

It can be observed that merely logistics postponement, production postponement and product

development postponement show significant relations with demand uncertainty reasons. Four

of seven significant canonical loadings support significant indicators found by means of the


49

PLS PM analysis. Not all significant relations from the PLS PM analysis could be validated,

as canonical correlations may be underestimated due to the hard assumptions of canonical

correlation. Overall, only two hypotheses could be confirmed (H4.1, H4.4 (negative)),

underlining the importance of other factors than expected.

PLS Regression

As the results of the canonical correlation may be unstable due to a small sample size and

presence of multicollinearity, the results are verified by means of Partial Least Squares

Regression (PLS R). This additional analysis may also render conclusions to price

postponement and product development postponement, which could not be significantly

interpreted by means of canonical correlation. Furthermore uncertainty factor D may now be

included in the analysis, because PLS R can handle multicollinearity for formative constructs,

representing thereby an alternative to PLS Path Modelling (Tenenhaus, Vinzi et al. 2005).

Table 4 shows the significant indicators determined by PLS R. Analysis outputs as well as

explanations on PLS R are provided in appendix VI.

For all postponement strategies significant indicators were found. However, only five of the

twelve factors that are significant by PLS R support the findings from the PLS PM Analysis.

The other factors indicate that more uncertainty reasons may be important. Two hypotheses

could be confirmed. However, these results should be interpreted with caution as PLS

Regression does not draw clear links between postponement strategies and uncertainty

indicators but rather forms new latent variables with a combination of several variables.

Table 4 summarizes all findings of the link between demand uncertainty and postponement

strategies. Comparing the results of all statistical approaches, it can be concluded that they are

too a great extent confirming the results of PLS Path Modelling. The difference in significant

factors in the cross-validation analyses opposed to significant factors with PLS PM should be

judged with caution, as this discrepancy may be due to statistical constraints.


50

Canonical loadings

PLS Regression Hypothesis

Price Postponement 0.694Uncertainty weight T statistic p-value reasuncA -0.0286 0.209 0.836 H1.1 reasuncB 0.2027 1.4714 0.154 reasuncC 0.2216 0.9502 0.351 0.472 reasuncD 0.2453 0.9699 0.341 0.486 reasuncE 0.0349 0.3183 0.753 reasuncF 0.9304 4.3106 0.000 0.384 reasuncG -0.0366 0.3677 0.716 reasuncH -0.5791 2.5547 0.017 reasuncI -0.4026 2.0458 0.051 reasuncJ 0.1074 0.8275 0.416Logistics Postponement -0.687 0.581Uncertainty weight T statistic p-value reasuncA -0.0884 0.6799 0.503 reasuncB 0.4543 2.4935 0.020 reasuncC 0.5875 1.9506 0.062 reasuncD -0.1043 0.4997 0.622 reasuncE 0.4677 2.9732 0.006 H2.2b reasuncF -0.2025 1.3903 0.177 reasuncG 0.4373 2.7834 0.010 -0.572 0.493 reasuncH -0.1167 0.9133 0.370 reasuncI -0.0635 0.5585 0.581 reasuncJ -0.4856 2.5177 0.019 0.485 -0.582Production Postponement -0.687 0.597Uncertainty weight T statistic p-value reasuncA 0.2934 1.3947 0.175 reasuncB -0.3168 1.9386 0.064 reasuncC 0.5799 2.4229 0.023 H2.1a reasuncD -0.0724 0.3611 0.721 reasuncE 0.0953 0.7349 0.469 H2.2a reasuncF -0.3339 1.8269 0.080 reasuncG 0.1286 1.1134 0.276 -0.572 0.493 reasuncH 0.4377 2.8093 0.010 H4.1 reasuncI 0.1241 0.805 0.428 reasuncJ -0.9303 6.5523 0.000 0.485 -0.582 H4.4Purchasing Postponement 0.787Uncertainty weight T statistic p-value reasuncA 0.1006 0.6593 0.516 0.418 reasuncB -0.4219 2.2362 0.035 0.591 H1.2 reasuncC 0.3733 1.2001 0.241 reasuncD -0.4318 1.1568 0.258 reasuncE -0.2822 1.4894 0.149 reasuncF 0.2177 1.0907 0.286 -0.534 reasuncG 0.2269 1.3184 0.199 H4.2 reasuncH -0.1758 0.9494 0.352 reasuncI -0.3411 1.6839 0.105 H4.3 reasuncJ 1.0563 5.7894 0.000 -Product Development Postponement -0.821 0.769Uncertainty weight T statistic p-value reasuncA 0.0235 0.2257 0.823 reasuncB 0.2714 1.8281 0.079 0.472 reasuncC 0.3041 1.303 0.204 -0.692 0.486 reasuncD 0.1253 0.6133 0.545 H2.1b reasuncE -0.4583 3.5787 0.001 reasuncF 0.231 1.5565 0.132 H3.1 reasuncG 0.0929 0.9826 0.335 reasuncH -0.0867 0.6723 0.508 reasuncI 0.2906 1.8372 0.078 -0.596 reasuncJ 0.3343 2.0292 0.053 -0.697

Reasons underlying demand uncertainty = positive significance expected A - price fluctuations (compet. Product) = significant at 5% B - price fluctuations (material/component) = slightly above 5% C - chang. customer preferences (pr. line) = - signifiance D - chang. customer preferences (new prod.) = + significance E - irregular purchases

F - innovationG - change in market shareH - many different customer groupsI - high seasonalityJ - weather

PLS PM

Table 4: Overview of cross-validation


51

5.4. Conclusion

This chapter provided the findings of the empirical research which is investigating the

relationship between demand uncertainty and postponement strategies as well as the influence

on postponement aims and the impact on company performance. From PLS PM analysis it

can be concluded that only five of the eleven hypothesis regarding demand uncertainty

reasons were confirmed and seven additional relations were identified. Cross-validation by

means of canonical correlation and PLS Regression confirmed the findings from PLS PM to a

great extent. The following chapter will discuss the findings in more detail, including

discrepancies between expectations and findings. The discussion chapter will conclude with

the development of a decision matrix for postponement strategy choice.

6. Chapter: Discussion

52

6. Discussion

In the following sections each postponement strategy will be discussed by interpreting the

findings of the statistical analysis. Hereafter, the findings will be summarized by means of a

postponement decision matrix and recommendations for its application will be presented.

6.1. Postponement Strategies

The five postponement strategies will be discussed along the upwards direction of the supply

chain including all three model links.

6.1.1. Price Postponement

Uncertainties

Price postponement was expected to be positively influenced by demand uncertainty resulting

from price fluctuations of competitive products (H1.1). However, the findings in this thesis do

not confirm this hypothesis. A possible explanation for this outcome is that frequent price

adaptations, apart from promotional campaigns, do generally not occur. For this reason, price

postponement is not applied to existing products, but rather applied to new products that are

introduced into the market. The researcher however expects this factor to be significant for

service firms, which were not included in this study. By setting the price of a service, which

will take place at a fixed future time, as late as possible, revenue can be maximized. For

instance, in the airline industry, ticket prices are adapted according to demand development

throughout the time period prior to flight. Thereby, a low price can initially foster demand and

a high price can finally take advantage of the urgent need for this service. Revenue or demand

management is therefore a special case of price postponement and should be covered in future

research.

Furthermore, price postponement is found to be positively influenced by a high degree of

innovativeness within the industry at hand. In particular, final price determination in this kind

of situation turns out to be difficult. The reason is that market and competitor behaviour can

only be anticipated to a limited extent due to short life-cycles and a variety of customer

preferences. Hence, the pricing decision is postponed as much as possible to base it on the

most complete set of market information available.

On the contrary, the factor variety of different customer groups is and should be negatively

related to price postponement. Price discrimination may prove to be too complex and may


53

even be legally forbidden. Evidently, a teenager may not be charged a different price than a

senior. Due to different demand characteristics of various customer groups, a unique view of

demand can not be established, therefore not allowing price postponement to deliver benefits

when compared to prior to production price setting. However, price differentiation according

to a few very specific customer characteristics is allowed and even beneficial. This is as

mentioned above conducted by demand management that discriminates prices for example

according to early and late bookers.

Moreover, high seasonality is also negatively related to price postponement. In the presence

of high seasonality, prices rise and decrease according to season specific conditions. In

particular, the degree of demand uncertainty as well as customers’ price sensitivity is reduced

thanks to seasonality. Thus, as these price fluctuations can be forecasted relatively precisely,

price postponement is not necessary and does not lead to great benefits. The cost-benefit

analysis may well show that the costs are higher than the benefits associated with price

postponement in this case.

Aims

Moving one step forward in the causal link, the analysis in chapter 5 indicates that companies

aim to achieve product price optimization. Final price determination is postponed or is subject

to future adaptations in order to benefit from better market information leading to an

optimization of product pricing that maximizes revenue. Again not that in the service industry,

which was not subject to this study, continuous price adaptation prior to the service, referred

to as demand management, may also be seen as price postponement that maximizes revenues,

a potential additional aim.

Performance Impact

Even though the explanations stated above may sound logical, it was discovered that price

postponement actually had a negative impact on company performance. Although it was

assumed that price postponement would lead to price optimization and should have lead to

revenue maximization, the model showed that this was not the case. A possible explanation is

that price postponement could lead to unclear price communication to the customer, which

could result in confusion, irritation and annoyance on the side of the customer. Consequently,

customer satisfaction is reduced and the product buying decision could be postponed or

suspended.


54

6.1.2. Logistics Postponement

Uncertainties

Logistics Postponement was expected to be positively related to irregularity of purchases

(H2.2b). The results published in this thesis confirm this hypothesis. It was argued that in case

of short lead times being very important, direct shipments from a central distribution centre

could be beneficial in situations of irregular purchases. Nonetheless, it is important to take

into account that stocking all items at a distribution centre, while demand is highly variable,

will lead to a high level of space, handling and thus cost requirements. Obviously, customers

always prefer shorter lead times, but the actual benefits of this have to be precisely analysed

and compared to higher space and distribution costs to avoid unnecessary costly stock. In the

situation of irregular purchases, logistics postponement should therefore only be applied to

items for which short lead times are essential.

Additional uncertainty factors were also found to be significant. First of all, price fluctuations

of materials or components show to positively influence logistics postponement. It has to be

noted here that this result may well have been reached due to logistics postponement being

confused with purchasing postponement. Despite early purchases, the delivery of purchased

material may have been postponement instead of the final good. Therefore, this cannot be

considered as logistics postponement.

Thirdly, a changing market share also has a significantly positive effect on logistics

postponement. Despite this surprising outcome, a possible explanation is that in this situation,

product lines should be pooled in one distribution centre to offset the demand variability of

different products. As such, variability in stock requirements and hence warehouse space can

be balanced out. As a matter of fact, changes in market share could be translated into demand

variability across and between product lines. Consequently, the pooling effect of a distribution

centre also applies in this situation.

Finally, weather conditions are negatively related to logistics postponement. Evidently, this

result refers to products whose demand is sensitive to weather conditions. For instance, in

cold summers, sunscreen experiences a much lower demand than in warm summers. Hence,

high levels of inventory at distribution centres should be avoided, if weather conditions have a

strong influence on actual demand.

Aims

Observing the results of logistics postponement, the hypothesis with respect to lead time

reduction was confirmed. Naturally, direct deliveries from stock ensure the shortest possible


55

lead time. Furthermore, the aim of increased customer orientation in the long-run was found

to be significantly negatively related to logistics postponement. In fact, logistics

postponement rather focuses on short-run customer orientation by being flexible in quickly

fulfilling certain needs as opposed to a long-run customer orientation, where customer

preferences are taken into account with regard to new product development.

Performance Impact

The performance impact of logistics postponement is found to be significantly positive. In

particular, the higher the level of centralization of inventory, the higher are the pooling effects

and consequently the higher are also the associated cost savings. Furthermore, short lead

times will increase customer satisfaction and may thus generate more demand. Nevertheless,

it should not be neglected that logistics cost may increase significantly due to costly direct

shipments instead of transportation bundling.

6.1.3. Production Postponement

Uncertainties

Production Postponement was expected to be positively related to changing customer

preferences across product lines, irregular purchases, various different customer groups and

weather conditions. Apart from the factor irregular purchases, all factors have proven to be

significantly related to production postponement; the latter however shows the opposite sign.

Changing customer preferences across product lines (H2.1a) is indeed positively related to

production postponement. The reasoning is such that product modules or differentiations are

applied or added upon customer request and thus only the basic products need to be on stock.

Evidently, if demand varies a lot between differentiations within a product line, production

postponement can react to this flexibly with only limited amounts of stock levels.

The presence of various different customer groups (H4.1) also proves to have a significantly

positive effect on production postponement. These customer groups are characterised by

different customer characteristics and thus a segmented market. Production postponement

does not only achieve flexibility for local demand differences across product lines; but also

addresses geographic differences as well as variation between product lines. As a matter of

fact, this positive effect can be amplified by centralizing production at a distribution centre.

Hence, the benefits of logistics postponement and production postponement can be combined.

The mutual effect could be further investigated by future research.


56

Weather conditions (H5.1) are significantly negatively related to production postponement.

This is surprising as it was expected that uncertain weather conditions and the resulting

variable demand would require production postponement. Nonetheless, this negative relation

may be explained due to the fact that in this situation a demand decrease or increase requires

capacity flexibility and not production flexibility. In fact, demand in this situation is not

shifting, but increasing or decreasing, which renders production postponement as ineffective.

A second explanation may be that unstable weather conditions may influence materials and

components needed for product differentiations, which increases safety stock requirements.

As low stock levels should be maintained, while materials availability must be high,

production postponement may result to be inappropriate.

The forth hypothesis regarding irregular purchases (H2.2a) could not be confirmed. Similar

to the reasoning for the influence of weather conditions, capacity flexibility and not

production flexibility is required in this case. The reason is that irregular purchases lead to

demand swings. Consequently, capacity has to be flexible regarding output levels. Combined

with inventory methods based on pull principles, such as Kanban systems7, overproduction

can be avoided. Especially in cases of little product modularity or differentiations, production

postponement cannot cope with irregularity of purchases.

Aims

Increased customer responsiveness in the short-term is confirmed to be significant. The

reason is that customer preferences regarding product modules and differentiations can

thereby be satisfied quickly. The other two hypotheses relating inventory reduction and

overall cost reduction to production postponement were not confirmed. This is surprising;

nevertheless the reasoning could be explained as follows. A significant inventory reduction

may not be achieved, if the amount of differentiation or the modularity per product is

relatively low. The reason for this is that in such a case the benefit from merely stocking base

products instead of finished product is limited. Moreover, overall cost reduction may not be

accomplished as the production set-up needs to be flexible and their cost might offset a

potential inventory reduction.

Two further aims were found to be significant: product price optimization (positive relation)

and the benefit from decreasing material prices (negative relation). Product price optimization

7 A Kanban system is a materials management system, which “pulls” replenishment, if a certain inventory level is reached. This system is often managed by two buckets, where an empty bucket is handed over to replenishment, as soon as it is empty.


57

can be achieved by adapting the product modules to the customer that will achieve optimal

cost-benefit for the customer. The automotive industry exemplifies this observation by taking

pricing decision upon defining the modularity of a car during customer contact. In fact,

modules, price and customer satisfaction is balanced to reach the optimal product price.

Furthermore, benefiting from decreasing material prices is not an aim of production

postponement. Production postponement focuses on flexibly meeting customers’ needs and

not on increasing the cost structure of production. Decreasing material prices should instead

be taken into account throughout the purchasing process.

Performance Impact

With regard to company performance it could be confirmed that production postponement has

a positive impact. In particular, increased customer satisfaction due to increased

responsiveness as well as inventory reduction in case of high product modularity, improve

company performance.

6.1.4. Purchasing Postponement

Uncertainties

Purchasing postponement was expected to be positively related to price fluctuations of

materials, changes in market share and seasonality. Only price fluctuations of materials

(H1.2) turned out to be significant, however with a negative relationship. For example, a

harvest of wheat depends on weather conditions and determines the available quantity of

supply and sets price. Therefore, purchasing of wheat is expected to be postponed until a

forecast on the harvest can be made and therefore on the price that it can be bought for. On

the contrary, explaining the resulting negative relationship, it can be argued, that an early

purchase with direct full delivery or later delivery arrangements are submitted to hedge

against potential price fluctuations. Evidently, this constitutes rather an advancement of

purchasing instead of postponement.

Frequent changes in market share (H4.2) were assumed to lead to purchasing postponement

to assure flexibility in case of unstable market share division. This relation was not confirmed

by the findings in this thesis. One could assume that not many companies go through quick

market share shifts. In addition, the market introduction of new competitive products should

usually be managed by market studies and forecasts, good product positioning and fast

reaction by product differentiation, which renders purchasing postponement to have a minor

importance in this situation.


58

Furthermore, high seasonality (H4.3) did also not prove to be significant. As a matter of fact,

in this situation forecasts usually are relatively precise due to the fact that high season timing

is known and planning can account for seasonality in advance. Hence, the use of purchasing

postponement realizes no incremental benefits.

A factor that may render the forecast of a season as imprecise is unforeseen weather

conditions, which is another indicator found to be significant by this research. In case that the

demand for certain products is influenced by weather conditions, such as for ice cream, the

purchase of materials (ingredients) may be postponed to the forecasted season start.

Aims

The expected aims, namely overall cost reduction and benefit from decreasing materials

prices, are not found to be significant in the case of purchasing postponement. The aim of

overall cost reduction may be rather substituted by risk or loss avoidance. Traceable cost

reductions are not achieved. Moreover, the benefit from decreasing material prices seems to

be of minor importance. A possible explanation is that the benefit of purchasing

postponement rather results from shorter inventory ownership than from the benefit from

decreasing material prices. However, two other aims were found to be significant in the

executed research instead. Inventory reduction is another significant aim. Due to shorter

material ownership, inventory levels and thus costs can be decreased. One way of maintaining

low inventory levels is by managing close supplier cooperation such as by means of VMI.

Materials are thereby only ordered, when needed which is continuously controlled by the

supplier. It follows that no bulk purchases are required that cover a certain demand horizon.

Finally, increased customer orientation in the long-run proves to be significant. If purchasing

of new components during new product development is postponed, more accurate

specifications can be taken into account, when developing a new product. Similarly, if the

purchase of materials for product differentiation can be postponed, new and more economic

materials, or materials of higher quality, could be used. This can in turn lead to continuous

improvement and increased customer satisfaction.

Performance Impact

Finally, the impact of aims and thus achievements of purchasing postponement on company

performance is only significant at a significance level of slightly above 5%. The impact is

furthermore negative. It can be argued that, as mentioned before, purchasing postponement is

frequently used to mitigate risk instead of proactively reducing cost. Furthermore, purchasing


59

postponement is only indirectly influencing customer satisfaction, and thus the benefit is

difficult to measure.

6.1.5. Product Development Postponement

Demand Uncertainties

Product Development Postponement was expected to be positively related to changing

customer preference in the long term and little market information in innovative industries.

Both hypotheses were however not confirmed by the findings of this thesis. This is surprising;

however potential explanations could be as follows. Changing customer preferences may not

demand for product development postponement, because new market information gained

through postponement may already indicate another new potential product. In innovative

industries with little available market knowledge, product development of any kind should not

be postponed, due to the extreme importance of the first-mover advantage. Furthermore,

innovations are completely new products, something that a customer could not really imagine

yet. It follows that most information necessary for new innovations does not necessarily come

from the target market but rather from the ideas of developers or from other markets.

Finally, irregularity in purchases is found to be significantly negatively related to product

development postponement. This result should be interpreted with caution. Irregular

purchases can only occur, when a product already exists and an adaptation or new module is

developed. In this situation, it could be argued, that product development should not be

postponed. The reason is that adapting the product as soon as possible and thereby positioning

it better in the market may smoothen demand. On the other hand, the negative outcome can

mean that the bullwhip effect created by irregular purchases should be taken care of by using

other and potentially more effective methods.

Aims

As expected a significant aim of product development postponement is increased

innovativeness. In particular, the product development process is optimized since the project

subtasks are organized in a way that takes as much market information into account as

possible. Thereby, the quality of new product development is maximized; hence innovation is

accelerated.


60

Performance Impact

The overall performance impact of product development aims is positively significant.

Although the performance impact of product development postponement does not have a

direct impact on the bottom line of a company, it has a clear impact on demand as well as

customer satisfaction. Thereby it clearly results in a positive company performance impact.

Concluding on the discussion of the five postponement strategies, this subsection has

provided a deeper insight into the findings of the statistical analysis and offered the

opportunity to understand the relationship between uncertainty, aims, performance impact and

the respective postponement strategy in more depth. An explanation for all significant factors,

expected or unexpected, was provided. Note that uncertainty factors D and I as well as aims E,

G and H did not show any positive, significant influences. Thus, these factors are no triggers

for certain postponement strategies. In fact, for product development postponement this study

could not find any significant reason for uncertainty that positively triggers its implementation.

Summarizing, Figure 14 shows the research model adapted for the discussed findings of this

thesis. All positive effects and thus triggers are displayed. In order to make the findings

adequate for practical use, the following subsection will develop a postponement decision tool.


Product Development

Postponement



Price Postponement




2.) Customers






4.) Market




5.) Other

5.1 Weather


H2.1a

H2.2b

H4.1

H5.1








Postponement Aims

Performance Impact


Product Development

Postponement



Price Postponement



Product Development

Postponement



Price Postponement




2.) Customers






4.) Market




5.) Other

5.1 Weather


H2.1a

H2.2b

H4.1

H5.1








Postponement Aims

Performance Impact

Figure 14: Research model adapted for the findings of this study


61

6.2. The Decision Matrix

After discussing the findings of this research throughout the previous section, this subsection

summarizes the findings by means of presenting a postponement strategy decision matrix

based on the findings of this thesis as shown in Figure 15. In fact, the goal of this decision

matrix is to support managers, when analyzing and deciding on an appropriate postponement

strategy for their company. Based on the findings of this thesis, the matrix gives an indication

on which postponement strategy is and which one is not appropriate for the demand

uncertainty and postponement aim structure of the company at hand. Before being able to

evaluate the decision matrix for a specific company, the following steps have to be carefully

taken to retrieve the necessary input.

Demand Uncertainty Reasons

do not apply/ negative impact of uncertainty

Lead time reduction

Inventory reduction

Product price optimization

Benefit from decreasing

material prices

Increased customer

responsiveness (short-term)

Increased customer ori-

entation (long-term)

Increased innovativeness

Price fluctuations (material/component)



Chang. customer preferences (prod. line)



Chang.customer preferences (new prod.)

Irregular purchases

Product Development Postponement


Innovation

Price Postponement

Change in market share


Various different customer groups

Price Postponement



High seasonality

Price Postponement

Weather

Logistics/ Production

Postponement



Consider applying/ positive impact on company performance

+ Logistics Postponement


Price/Production Postponement

Price Postponement

Production/ Purchasing

Postponement


Do not apply/ negative impact on company performance

- Production Postponement


Aims/Achievements

Figure 15: Postponement strategy decision matrix

In the first place, it is important to gain a clear understanding of the reasons underlying the

demand uncertainty that the company is facing. The following questions help to gather the

necessary information:

How variable is the demand uncertainty? Which data indicates demand variability?

Why do we face demand uncertainty? What are the reasons of demand uncertainty?


62

In the second step, the aims and desired achievements, which the company is striving for in

order to effectively cope with the effects of demand uncertainty, have to be defined. The

following questions support this process:

Which difficulties/challenges do we face with regard to demand uncertainty?

How do we currently respond to demand uncertainty?

What do we want to achieve in the near future? On which aspects do we want to improve?

Subsequently, after these two aspects are clearly analysed and defined, the postponement

strategy decision matrix can be evaluated to determine, if and how postponement could

support the business goals. If the matrix indicates a certain postponement strategy to be

appropriate, the explanations regarding this type of postponement strategy throughout this

thesis can be considered to evaluate in what way this strategy could fit to the company needs.

In addition, several case studies can be found in literature, that are also included in the

references of this thesis, providing ideas on how to implement such postponement strategies.

The argumentation along the matrix evaluation can be elaborated upon by using interview

results that were acquired from the interview with Company 1. In the first place, reasons for

demand uncertainty as well as aims that shall be achieved in order to reduce the demand

uncertainty effects need to be identified. Referring to the reasons of demand uncertainty,

Company 1 indicated that the weather has a great influence on the variability of their demand.

If the weather is unexpectedly bad during the summer months, demand will decrease, in

contrast to the forecast. In order to cope with this kind of demand uncertainty, Company 1 put

forward the goals, which they want to achieve in coping with the identified demand

uncertainty. In the first place, their aim is to achieve increased customer-orientation in the

short term to be able to react to customer needs in a flexible way. Furthermore, they aim to

ensure that inventories remain minimal, even in periods, when demand is lower than expected.

On the other hand, availability needs to be guaranteed, even when demand turns out to be

higher than expected.

Subsequent to the identification of reasons for demand uncertainty and aims, the decision

matrix is evaluated. As illustrated in Figure 16, it can be observed that seasonality in

combination with the aims of inventory reduction and increased customer orientation in the

short-term indicate purchasing postponement to be the adequate postponement strategy. The

reasoning for this combination has been explained in subsection 6.1 above. Company 1 has


63

already implemented purchasing postponement in form of vendor managed inventory. In this

case, not Company 1 but the supplier is responsible for replenishing inventory as needed,

thereby reducing safety stock requirements due to shorter lead times. On the other hand, the

supplier receives accurate and timely demand data and can thereby adapt his offering in order

to satisfy Company 1’s needs in the long-term. Additionally, other ways of applying

purchasing postponement should be explored. For instance, the product range could be

analysed for potential opportunities for just-in-time supplies. Also the potential from

deepened supplier relationships should be analysed to reduce inventory. At this point of the

matrix evaluation, it is important to search for implementation ideas of the identified

postponement strategies and to examine their suitability for the specific company processes.

Finally, the matrix also indicates that production postponement may also reach one of their

aims, but in presence of the given demand uncertainty reason, production postponement

should not be applied. Also Logistics postponement is not appropriate in the presence of

changing weather conditions, as indicated in the second column. This empirical research

could unfortunately not conclude on the adequacy of price and product development

postponement in this study, as no significant results were found.

Demand Uncertainty Reasons

do not apply/ negative impact of uncertainty

Lead time reduction

Inventory reduction

Product price optimization

Benefit from decreasing

material prices

Increased customer

responsive-ness (short-term)

Increased customer ori-

entation (long-term)

Increased innovative-ness

Price fluctuations (material/component)



Chang. customer preferences (prod. line)



Chang.customer preferences (new prod.)

Irregular purchases



Innovation

Price Postponement

Change in market share


Various different customer groups

Price Postponement



High seasonality

Price Postponement

Weather

Logistics/ Production

Postponement



Consider applying/ positive impact on company performance

+ Logistics Postponement


Price/Production Postponement

Price Postponement

Production/ Purchasing

Postponement


Do not apply/negative impact on company performance

- Production Postponement


Aims/Achievements

Figure 16: Example of matrix evaluation


64

Concluding, the postponement strategy decision matrix indicates which postponement

strategy is appropriate in the company specific situation, as well as which strategy is not

appropriate. Postponement strategies that are not indicated for a certain combination of

indicators, neither positively nor negatively, should be subject to further investigation as no

significant conclusion in this respect could be drawn from this research. ^

6.3. Conclusion

This chapter discussed the research findings for each postponement strategy in detail. For all

significant relations, suggestions for interpretation were provided. The findings were

additionally summarized by means of a postponement strategy decision matrix, which links

demand uncertainty reasons and aims to postponement strategies. Guidelines for how to apply

this matrix were provided, which enable managers to evaluate postponement opportunities

within their company. Note that it is very important to evaluate and assess the outcome of the

matrix in light of company specific processes and market specific characteristics.

7. Chapter: Conclusion

65

7. Conclusion and Future Research

This final chapter will in the first place conclude this research by answering the research

question, followed by an overview of the theoretical as well as managerial contributions.

Finally, the limitations of this research are discussed and future research opportunities

highlighted.

7.1. Problem Statement

The sub-questions8 developed for answering the problem statement, can be answered as

follows. First of all, this thesis defines postponement as the action of delaying certain

activities of the supply chain downstream of the customer-order decoupling point in order to

achieve more flexibility and responsiveness to demand uncertainty. A variety of

postponement strategies exist which can focus on any activity of the supply chain. From the

literature review it resulted that the most widely discussed and applied postponement

strategies are price postponement, logistics postponement, production postponement,

purchasing postponement and product development postponement. This group of strategies

was chosen for this research, also because it covers extensive parts of a companies supply

chain. Furthermore, this thesis positions demand uncertainty into relation with the just

mentioned postponement strategies. “Uncertainty is best understood as an information defect”

(Spender 1993, p.16). When incomplete demand information is available, demand cannot be

predicted with certainty. The literature review in combination with field interviews resulted in

a list of ten reasons underlying demand uncertainty that were expected to affect the choice of

postponement strategies, and which became part of the research model.

Based on the findings and discussion of the previous chapters, the research question,

including the last three sub-questions, can be answered. The research question asked: “How

do the reasons underlying demand uncertainty affect the choice of an appropriate

postponement strategy?” It can be concluded that demand uncertainty reasons indeed have a

8 Sub-questions: - How is postponement defined?

- Which postponement strategies do exist and what is their specific purpose? - How is demand uncertainty defined? - Which are the most important reasons for demand uncertainty? - Which postponement strategy reacts to which demand uncertainty dimension? - Which postponement strategy has what operational goal? - What is the impact of postponement strategies on company performance?


66

significant effect on the choice of postponement strategies. Demand uncertainty and its

reasons are affecting a company’s supply chain and specific postponement strategies that can

be applied in these situations in order to mitigate the uncertainty risk or to improve company

performance under these conditions. Five hypotheses could be confirmed of which however

two proved to have a negative impact on the respective postponement strategy. In addition

eight other significant factors were found to be significant, four with a negative impact. These

results emphasize that demand uncertainty reasons cannot only foster postponement

implementation, but may also render a postponement strategy inappropriate. All positive

triggers are visualized by the adapted research construct in Figure 14. Furthermore, the

postponement strategy decision matrix as shown in Figure 15 shows in detail, which demand

uncertainty reasons have a significantly positive or negative impact on a certain postponement

strategy. In addition, the matrix visualizes which operational goals are achieved with which

postponement strategy. Finally, the findings of this thesis indicate a positive impact of

logistics, production and product development postponement and a negative impact of price

and purchasing postponement on company performance. The negative impact should however

not be overestimated, as price and purchasing postponement are frequently seen as a means to

ensure instead of create high profit.

7.2. Theoretical contributions

First of all, this study contributes to the understanding of postponement strategies in currently

published literature, by explaining their specific purpose and providing a discussion on

existing strategy decision tools. One aspect that is discussed several times in the literature

regarding postponement strategy choice is the link between postponement application and the

level of demand uncertainty. The research at hand extends this aspect by not only considering

the level of demand uncertainty, but also additionally analysing the reasons underlying

demand uncertainty, when investigating the question of which strategy to chose in which

specific situation. A causal chain including demand uncertainty – postponement strategy –

postponement aims – performance impact was developed and tested empirically. The causal

chain together with the resulting decision matrix results in a clear contribution to current

literature and to a deeper understanding of demand uncertainty in regard to postponement

strategy choice. Moreover, currently very little empirical research has been conduct in the

field of postponement strategies. This study conducted an empirical investigation and thereby

contributes to overcoming this gap in current literature.


67

7.3. Managerial contributions

The thesis focuses on postponement strategy choice and thus leads to several managerial

implications. In the first place, when initiating a postponement evaluation process, managers

should first analyze their environment as well as their processes. In particular, this study

encourages managers to understand the reasons and sources of demand uncertainty their

company is facing, in contrast to merely focusing on the level of demand uncertainty.

Depending on the discovered reasons underlying uncertainty, certain postponement strategies

will result to be most appropriate and whose application possibilities should be explored. First

of all, managers should follow a postponement choice process that conducts certain analysis

steps that were explained in section 6.2. The evaluation of their analysis can in turn be based

on the postponement strategy decision matrix that was developed by means of the empirical

investigation of this study. The matrix gives a clear indication of which postponement

strategy appears most appropriate within the companies demand uncertainty situation and

thereby offers managers a good starting point for in depth investigation of potential

postponement applications. The literature review of this thesis supports practitioners to

understand, what postponement strategies are and provides them with insights into specific

postponement application. Furthermore, the presentation of existing postponement decision

tools provides management the opportunity to reflect on and evaluate their own possible

postponement implementations.

7.4. Limitations and future research

This final section will shed light on the limitations of this study and the resulting future

research opportunities. The first shortcoming concerns the choice of uncertainty indicators for

this research. Due to the fact that reasons and types of demand uncertainty are only rarely

discussed in literature, the reasons underlying demand uncertainty used in this thesis could not

be drawn on an established list of factors. The final list of indicators, which was created by

means of a literature review and the conducted interviews, may not be complete. Further

research should therefore focus on verifying and extending the list of uncertainty factors that

influences postponement strategy choice.

Furthermore, some surprising results of the online survey indicate that there might well have

been one or more possible misinterpretations of questions by respondents. As a consequence,

slightly inaccurate results may have occurred. This possibly stems from the fact that even


68

though the concepts of postponement and demand uncertainty are generally applied, these

could well be named and defined differently throughout different companies and industries.

Therefore, future empirical research in the field of postponement as well as demand

uncertainty could focus on refining the questionnaire of this thesis, ensuring a clear common

understanding of stated concepts and survey questions among respondents.

Moreover, the focus of this study was not on one or two specific industries but on any non-

service firm. As not much research has been conducted in the field of postponement strategy

choice, the aim was to retrieve a broad picture that in turn offers the starting point for more

focused studies within specific industries. This high level point of view should be kept in

mind, when applying the proposed decision matrix. Furthermore, the sample size of this

research (n=53) could be considered to be relatively small, even though the study was not

limited to a certain industry. The limited amount of data may have caused some links of the

model to be incorrectly insignificant. Future research should therefore check this model by

using a larger sample and focus on specific industries to discover differences in postponement

applications across industries. This will help to improve the effectiveness of the postponement

strategy decision matrix.

Following from the argumentation above, it is important to note that the research at hand did

not include service firms into its analysis. The reason is that their business processes and thus

their postponement application may evolve quite differently. As mentioned throughout the

discussion of this thesis, demand and revenue management is increasingly gaining importance

in the service sector and can be categorized as a special kind of price postponement.

Therefore, future research could focus on the application of postponement strategies in service

firms as well as the applicability of postponement choice parameters, especially with regard to

demand management.

Furthermore, postponement strategies can differ for different company processes, depending

on the company size, main tasks and the supply chain structure. Thus, future research could

investigate what postponement strategies mean for different supply chain structures.

Furthermore, the findings discovered a common factor between logistics and production

postponement, which may also have caused certain misunderstanding of the postponement

concepts. If production postponement is located and applied in the same facility as a

centralized DC, meaning logistics postponement, it is difficult to clearly distinguish the


69

effects of these two postponement strategies. In addition, future research could examine, if in

such a situation these two postponement strategies create a mutual positive effect.

Finally, this research could only draw conclusions on the effect of indicators on their LVs as

well as on the effect of LVs among each other. It could not draw clear conclusions on the

relation between indicators of different latent variables. Future research could accomplish this

by analysing mediation models, in which the relation between different indicators is mediated

by a latent variable, using a set of bootstrap methods as explained by Shrout and Bolger

(2002).

Despite these limitations, this study has provided valuable theoretical and practical

contributions and offers a good starting point for future research. Hopefully, this study will

foster further research efforts into the still widely unrevealed field of postponement strategies

and further support management activities in companies facing demand uncertainties.

Postponement success stories such as of the company Benetton are surely going to proliferate.

In light of the current progress in present literature on the topic of postponement facing

demand uncertainties, this thesis will help company decision making until future models can

be established.

Appendix

70

Appendix

Appendix I: Unstructured questionnaire for interviews

Guideline for interviews

1. Which product segments does your company serve?

2. How does the corresponding supply chain look like?

3. What degree of demand uncertainty is your division facing?

4. What are the reasons for this demand uncertainty?

5. How do you cope with this demand uncertainty?

6. Are you also using postponement strategies to cope with this demand uncertainty?

a. Or to cope with other phenomena?

b. Which postponement strategies?

c. Examples

7. In general, what do you think about the relations of the research model?

Appendix

71

Appendix II: Online questionnaire

Appendix

72

Appendix

73

Appendix

74

Appendix

75

Appendix

76

Uncertainty:

No uncertainty:

Appendix

77

Appendix

78

Appendix III: Variance Inflation Factors (VIF)

1.) Variance Inflation Factors for reasons underlying demand uncertainty

Uncertainty VIFA - Price fluctuations (compet. Product) 1.544B - Price fluctuations (material/component) 1.306C - chang. customer preferences (pr. line) 4.584D - chang. Customer preferences (new product) 5.125E - irregular purchases 1.205F - innovation 1.825G - change in market share 1.467H - many different customer groups 1.893I - high seasonality 1.795J - weather 1.525

2.) Variance Inflation Factors for postponement aims per strategy

Aim Price Logistics Production Purchasing Prod. Dev.A- Overall cost reduction 2.301 1.875 3.342 1.105 4.723B- Lead time reduction 6.906 1.302 2.657 1.691 2.24C- Inventory reduction 6.203 1.733 2.56 1.184 2.936D- Product price optimization 1.732 1.716 2.218 1.336 6.081E- Benefit from decreasing material prices 2.08 1.531 2.414 1.29 2.419F- Increased customer responsiveness (short-term) 1.279 1.642 1.819 2.606 1.289G- Increased customer orientation (long-term) 1.696 2.094 2.343 3.075 3.019H- Increased innovativeness 2.586 1.803 1.962 2.145 3.007

VIF

Appendix

79

Appendix IV: Sample Characteristics 1.) History of postponement application

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

< 3 years 3-5 years 5-10 years > 10 years

length of application

perc

enta

ge o

f res

pond

en

price postponement

logistics postponement

production postponement

purchasing postponement

product developmentpostponemnt

2.) Impact on company performance

0.00

0.100.20

0.30

0.400.50

0.60

0.700.80

0.90

verynegative

negative neutral positive verypositive

impact on company performance

perc

enta

ge o

f res

pond

e price postponement

logistics postponement

production postponement

purchasingpostponementproduct developmentpostponemnt

3.) Independent Samples T-Test

Appendix

80

Appendix V: Canonical Correlation Analysis

The canonical correlation results show that only the first two canonical roots are significant at

5-10% significance level. The remaining roots can therefore be neglected. By squaring the

first two canonical correlations, they are found to explain 51.6 % and 39.8 % respectively of

the variance in the corresponding dependent variable. To assure comparability of the

coefficients, which are also called canonical weights, standardized coefficients are used for

interpretations. Their magnitudes represent the relative contribution to the corresponding root.

Since a small sample size as well as multicollinearity can cause instability in canonical

coefficients, canonical loadings also need to be considered. The loadings of the first canonical

root demonstrate that Product Development Postponement represents by far the most

influential factor. Among the demand uncertainty factors, factors C, I, and J are most

influential. Factors that have negative coefficients equal each other out and represent positive

relationships. Loadings of root 2 show a high influence of Postponement Strategies B and C,

that are mainly affected by uncertainty factor G (positively related) and J (negatively related).

The cross-loadings indicated that 34.7 % of the variance in Postponement Strategy E is

explained by root 1 and 17.7 % and 18.8 % of the variance in Postponement Strategy B and C

are explained by root 2.

1.) Significance of Canonical Roots

Test that remaining correlations are zero:Canoncial Correlations Wilk's Chi-SQ DF Sig

1 0.718 0.183 77.251 45 0.0022 0.631 0.378 44.295 32 0.0733 0.469 0.627 21.221 21 0.4464 0.395 0.804 9.941 12 0.6215 0.218 0.953 2.211 5 0.819

Appendix

81

2.) Raw and Standardized Canonical Coefficients Raw Canonical Coefficients Standardized Canonical Coefficients

1 2 1 2reasuncA -0.007 -0.003 reasuncA -0.008 -0.004reasuncB -0.246 -0.147 reasuncB -0.258 -0.154reasuncC -0.356 -0.467 reasuncC -0.430 -0.564reasuncE 0.313 -0.135 reasuncE 0.349 -0.151reasuncF -0.129 0.426 reasuncF -0.172 0.570reasuncG -0.193 -0.397 reasuncG -0.240 -0.494reasuncH 0.132 -0.157 reasuncH 0.151 -0.179reasuncI -0.114 -0.153 reasuncI -0.162 -0.217reasuncJ -0.341 0.490 reasuncJ -0.501 0.719PosStrA 0.041 0.433 PosStrA 0.052 0.548PosStrB -0.294 -0.636 PosStrB -0.390 -0.843PosStrC 0.076 -0.439 PosStrC 0.103 -0.597PosStrD 0.357 0.000 PosStrD 0.462 0.000PosStrE -0.909 -0.282 PosStrE -1.009 -0.313

3.) Canonical Loadings and Cross Loadings Canonical Loadings Cross Loadings

1 2 1 2 sqrt(1) sqrt(2)reasuncA -0.400 -0.075 reasuncA -0.287 -0.047 8.24% 0.22%reasuncB -0.380 -0.274 reasuncB -0.273 -0.173 7.45% 2.99%reasuncC -0.692 -0.358 reasuncC -0.497 -0.226 24.70% 5.11%reasuncE 0.134 -0.172 reasuncE 0.096 -0.109 0.92% 1.19%reasuncF -0.398 0.095 reasuncF -0.286 0.060 8.18% 0.36%reasuncG -0.278 -0.572 reasuncG -0.200 -0.361 4.00% 13.03%reasuncH -0.175 -0.268 reasuncH -0.125 -0.169 1.56% 2.86%reasuncI -0.596 0.018 reasuncI -0.428 0.011 18.32% 0.01%reasuncJ -0.697 0.485 reasuncJ -0.501 0.306 25.10% 9.36%PosStrA -0.316 0.064 PosStrA -0.227 0.040 5.15% 0.16%PosStrB -0.038 -0.668 PosStrB -0.027 -0.421 0.07% 17.72%PosStrC 0.194 -0.687 PosStrC 0.140 -0.433 1.96% 18.75%PosStrD 0.333 -0.364 PosStrD 0.239 -0.229 5.71% 5.24%PosStrE -0.821 0.024 PosStrE -0.589 0.015 34.69% 0.02%

Appendix

82

Appendix VI: PLS Regression

Not taking the postponement variables for granted as latent variables, we can evaluate the

latent factors resulting from the PLS Regression instead. Analysing the table “proportion of

variance explained”, it can be concluded that it is sufficient to evaluate the first five latent

factors, because the adjusted R² decreases for the subsequent factors, indicating a penalty for

complexity. The values for the first five latent factors are shown in Table 1.

The factor loadings indicate certain relations between postponement strategies and reasons

underlying demand uncertainty. A cut-off value of 0.4 is used for latent factors loadings,

which proved to be adequate for exploratory research purposes (Hulland 1999). These results

show that different postponement strategies may be influenced by the same uncertainty

factors.

As also the table “variable importance in the projection” illustrates, reason for uncertainty H

is of minor importance (all VIP < 0.8). Reason A also shows low VIPs. However, as the

values are only slightly below the cut-off value and prove to have a positive effect in the

loadings analysis, this factor should not be neglected.

Table 1

Appendix

83

Table 2

Table 3

Table 4

References

84

References

Alderson, W. (1950). "Marketing efficiency and the principle of postponement." Cost and Profit Outlook 3: 15-18.

AMR Research (2009). Factors impacting demand variability.

Bagozzi, R. P., C. Fornell, et al. (1981). "Canonical correlation analysis as a special case of a structural relations model." Multivariate Behavioral Research 16(4): 437.

Bernhardt, I. (1977). "Vertical integration and demand variability." The Journal of Industrial Economics 25(3): 213-229.

Biller, S., A. Muriel, et al. (2006). "Impact of price postponement on capacity and flexibility investment decisions." Production & Operations Management 15(2): 198-214.

Bollen, K. and R. Lennox (1991). "Conventional wisdom on measurement: A structural equation perspective." Psychological Bulletin 110(2): 305.

Boone, C. A., C. W. Craighead, et al. (2007). "Postponement: an evolving supply chain concept." International Journal of Physical Distribution & Logistics Management 37(8): 594-611.

Bowersox, D. J., T. P. Stank, et al. (1999). "Lean Launch: Managing product introduction risk through response-based logistics." Journal of Product Innovation Management 16(6): 557-568.

Bucklin, L. P. (1965). "Postponement, speculation and the structure of distribution channels." Journal of Marketing Research (JMR) 2(1): 26-31.

Chin, W. W. (1998). Issues and opinion on Structural Equation Modeling. MIS Quarterly: 1-1.

Chin, W. W. (1998). The Partial Least Squares Approach to Structural Equation Modeling, Lawrence Erlbaum Association.

Cooper, R. G. and E. J. Kleinschmidt (1987). "New products: What separates winners from losers?" Journal of Product Innovation Management 4(3): 169-184.

Dapiran, P. (1992). "Benetton - Global logistics in action." International Journal of Physical Distribution & Logistics Management 22(6).

Diamantopoulos, A. and J. A. Siguaw (2006). "Formative versus reflective indicators in organizational measure development: A comparison and empirical illustration." British Journal of Management 17(4): 263-282.

Duncan, R. B. (1972). "Characteristics of organizational environments and perceived environmental uncertainty." Administrative Science Quarterly 17(3): 313-327.

References

85

Esposito Vinzi, V., G. Russolillo, et al. (2009). "A joint use of PLS Regression and PLS Path Modelling for a data analysis approach to latent variable modelling." Retrieved 03-03, 2010, from http://www.statssa.gov.za/isi2009/ScientificProgramme/IPMS/0185.pdf.

Granot, D. and S. Yin (2008). "Price and order postponement in a decentralized newsvendor model with multiplicative and price-dependent demand." Operations Research 56(1): 121-139.

Guinot C., J. Latreille, et al. (2001). "PLS Path Modelling and multiple table analysis. Application to the cosmetic habits of women in Ile-de-France." Chemometrics and Intelligent Systems 58: 247-259.

Heskett, J. L. and S. Signorelli (1984). "Benetton (A)." Harvard Business School Cases.

Hsu, S.-H., W.-H. Chen, et al. (2006). "Robustness testing of PLS, LISREL, EQS and ANN-based SEM for measuring customer satisfaction." Total Quality Management & Business Excellence 17(3): 355-371.

Hulland, J. (1999). "Use of partial least squares (PLS) in strategic management research: A review of four recent." Strategic Management Journal 20(2): 195.

Kumar, S. and S. Craig (2007). "Dell, Inc.'s closed loop supply chain for computer assembly plants." Information Knowledge Systems Management 6(3): 197-214.

Kutner, M. H., C. J. Nachtsheim, et al. (2004). Applied Linear Regression Models, McGraw-Hill Irwin, inc, Boston.

Lee, H. L. (2002). "Aligning supply chain strategies with product uncertainties." California Management Review 44(3): 105-119.

Lee, H. L. and V. Padmanabhan (1997). "Information distortion in a supply chain: The bullwhip effect." Management Science 43(4): 546.

Lee, H. L. and C. S. Tang (1997). "Modelling the costs and benefits of delayed product differentiation." Management Science 43(1): 40.

Lee, H. L. and S. Whang (2001). "Winning the Last Mile of E-Commerce. (cover story)." MIT Sloan Management Review 42(4): 54-62.

Liker, J. K. and T. Y. Choi (2004). "Building deep supplier relationships." Harvard Business Review 82(12): 104-113.

Matthews, P. and N. Syed (2004). "The power of postponement." Supply Chain Management Review 8(3): 28-34.

Mullins, J. W. and D. J. Sutherland (1998). "New Product Development in rapidly changing markets: An exploratory study." Journal of Product Innovation Management 15(3): 224-236.

References

86

Olhager, J. (2003). "Strategic positioning of the order penetration point." International Journal of Production Economics 85(3): 319-329.

Pagh, J. D. and M. C. Cooper (1998). "Supply Chain Postponement and Speculation Strategies: How to choose the right strategy." Journal of Business Logistics 19(2): 13-33.

Petter, S., D. Straub, et al. (2007). "Specifying formative constructs in information systems research." MIS Quarterly 31(4): 623-656.

Prockl, G., A. Bauer, et al., Eds. (2004). Entwicklungspfade und Meilensteine moderner Logistik, Gabler.

Shrout, P. E. and N. Bolger (2002). "Mediation in experimental and nonexperimental studies: New procedures and recommendations." Psychological Methods 7(4): 422-445.

Spender, J. C. (1993). "Some frontier activities around strategy theorizing." Journal of Management Studies 30(1): 11-30.

Stigler, G. (1939). "Production and distribution in the short run." The Journal of Political Economy 47(3): 305-327.

Tenenhaus, M. (2005). "PLS methodology to study relationships between hedonic judgements and product characteristics." Food Quality and Preference 16: 315-325.

Tenenhaus, M., V. E. Vinzi, et al. (2005). "PLS path modeling." Computational Statistics & Data Analysis 48(1): 159-205.

Thompson, B. (1984). Canonical Correlation Analysis: uses and interpretations, Sage Publications, Thousand Oaks, CA , .

van Hoek, R. I. (2001). "The rediscovery of postponement a literature review and directions for research." Journal of Operations Management 19(2): 161-184.

Van Mieghem, J. A. and M. Dada (1999). "Price versus Production Postponement: Capacity and Competition." Management Science 45(12): 1631.

Vinzi, V. E., W. W. Chin, et al. (2010). Handbook of Partial Least Squares, Springer Handbooks of Computational Statistics.

Weele, A. J. v. (2005). Purchasing and Supply Chain Management. London, UK, Thomson Learning.

Wetzels, M., G. Odekerken-Schröder, et al. (2009). "Using PLS Path Modeling for assessing hierarchical construct models: guidelines and empirical illustration." MIS Quarterly 33(1): 177-195.

Yang, B. and N. Burns (2003). "Implications of postponement for the supply chain." International Journal of Production Research 41(9): 2075-2090.

References

87

Yang, B., N. D. Burns, et al. (2004). "Management of uncertainty through postponement." International Journal of Production Research 42(6): 1049-1064.

Yang, B., N. D. Burns, et al. (2005). "An empirical investigation into the barriers to postponement." International Journal of Production Research 43(5): 911-1005.

Yang, B., Y. Yang, et al. (2007). "Postponement: an inter-organizational perspective." International Journal of Production Research 45(4): 971-988.

Postponement Strategies in the Supply Chain

Documents

Transcript of Postponement Strategies in the Supply Chain