Sixteenth Annual Conference of POMS, Chicago, IL, April 29 - May 2, 2005

No. 003-0236

SUPPLY CHAIN MANAGEMENT AND PLANT PERFORMANCE

- An Empirical Analysis of the Fisher Model

Jörn-Henrik Thun

Industrieseminar, Mannheim University Schloss S 205, 68131 Mannheim, Germany

thun@is.bwl.uni-mannheim.dephone: ++49 621 181 15 84,

fax: ++49 621 181 15 79

Keywords Supply Chain Management, High Performance Manufacturing, Fisher Model, Performance

Abstract

In this paper Supply Chain Management will be analyzed empirically based on the data of the

“High Performance Manufacturing”-Project, a research cooperation of universities from different

countries. The aim of the paper is to investigate the relation between Supply Chain Management

and plant performance. The empirical analysis investigates the Fisher Model which distinguishes

two different kinds of Supply Chains: physically efficient vs. market-responsive Supply Chains.

Differences concerning plant performance in terms of efficiency and responsiveness can be

shown.

Introduction

For many industries Supply Chain Management has become one of the most important concepts

for improving the flow of material and information. The potential of this concept has often been

mentioned in the academic literature (see e.g. Chopra and Meindl, 2001). The basics of Supply

Chain Management are introduced by Forrester when he describes the phenomenon of an

amplifying demand between different supply tiers (Forrester, 1958). This phenomenon

commonly referred to as the “bullwhip”-effect has become popular from the observation of

logistic executives at Procter & Gamble. While the customers demand diapers at a steady state,

the logistic executives observed an increased order variability of disposable diapers moving up

the supply chain. Supply Chain Management can mitigate the negative influences of this effect

by coordinating information and material between companies (Lee et al., 1997).

In the academic literature there is no standard definition of the term Supply Chain Management.

Jayram and Bechtel distinguish between schools with different approaches of Supply Chain

Management, i.e. the ‘Functional Awareness School’, the ‘Linkage/Logistics School‘, the

‘Information School’, the ‘Integration School’, and the ‘Future School’ (Bechtel and Jayaram,

1997). The ‘Functional Awareness School’ stresses the flow of material beginning at the supplier

and ending at the customer, whereas the ’Linkage/Logistics School’ goes one step further aiming

to investigate how linkages among the functional areas of a supply chain, i.e. purchasing,

manufacturing, and distribution, can be exploited for competitiveness. The ‘Information School’

focuses on the meaning of a bidirectional information flow within the chain, whereas the

‘Integration/Process School’ overcomes the view of a unidirectional flow of material and stresses

the integration of business processes. Following the ‘Future School’ the term Supply Chain

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Management should be replaced by the expression “seamless demand pipeline” with the

customer as the activator of the chain.

Despite the different approaches of the supply chain schools some definitions will show the

nature of Supply Chain Management. A definition related to the ‘Linkage/Logistic School’ is

provided by Chopra and Meindl (2001): “Supply Chain Management involves the management

of flows between and among stages in a Supply Chain to maximize total profitability.” Handfield

and Nichols (1999) define Supply Chain Management as “… the integration of [..] activities

through improved supply chain relationships, to achieve a sustainable competitive advantage.”

Both definitions mention implicitly the meaning of management of relationships between

different institutions within a chain, i.e. suppliers and customers.

Christopher notes explicitly suppliers and customers and defines the term Supply Chain

Management as the “… management of upstream and downstream relationships with suppliers

and customers to deliver superior customer value at less cost to the supply chain as a whole.”

(Christopher, 2004) This means that the focus of Supply Chain Management is upon the

management of relationships in order to achieve a more profitable outcome for all parties in the

chain. Christopher argues that the term supply chain management, although it is widely used,

should actually better be renamed.

First, the aspect that the chain has to be driven by the market is to be stressed. Christopher

suggests the term ‘demand chain management’. Second, he argues that the word ‘chain’ is

irritating since there will normally be multiple suppliers and customers. So the term ‘network’

seems to be more appropriate. Following Christopher a supply chain can simply be defined as a

“... network of connected and interdependent organizations mutually and co-operatively working

together to control, manage, and improve the flow of materials and information from supplier to

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end user.” (Christopher, 2004) This network will include several stages within the chain, i.e.

following the Supply-Chain Council “… from your supplier’s supplier to your customer’s

customer… .” Additionally, it can be argued that also the term ‘network’ can better be replaced

by the term ‘acyclic graph’ since there will be a flow of material without cyclic processes. For

this paper is important to note that supply chain management involves different institutional

entities which have to be coordinated in terms of information and material in order to generate a

mutual benefit.

Despite the fact that Supply Chain Management has become on of the most discussed issues of

production and operations management there is still a need for empirical work. Accordingly, it is

the aim of this paper to contribute an empirical analysis concerning the relationship between

Supply Chain Management and plant performance based on the data base of the international

research project “High Performance Manufacturing”.

Supply Chain Management and the Fisher Model

Performance Measures of Supply Chain Management

Although there exists a great variety of possible performance measures, in this paper two factors

will be identified subsuming the most important aspects for measuring the performance of supply

chain management. The performance measures are derived from the Fisher model (Fisher, 1997).

Fisher distinguishes between two different kinds of products: Functional products and innovative

products. Functional products are characterized by long product life cycles, low product variety,

high stability of demand, etc., whereas innovative products have relatively short product life

cycles, show a great variety of variants, and their demand is unpredictable. Following Fisher,

supply chains should be designed according to the type of product. The crux is matching supply

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chains with products, i.e. “functional products require an efficient process; innovative products, a

responsive process” (Fisher, 1997: 109).

In his approach Fisher distinguishes between physically efficient and market-responsive supply

chains. Physically efficient supply chains serve standard products for a market with stable,

predictable demand. Market-responsive supply chains offer a great variety of innovative products

and can change quickly without high switching costs due to flexible processes. They are close to

the market and respond quickly to unpredictable demand. Based on these two kinds of supply

chains performance measures can be identified.

The performance of a physically efficient supply chain can be measured by indicators like high

average utilization rate, low inventory, or in general low cost. Contrary to that a market-

responsive supply chain tries to match best with customers expectations in terms of speed,

flexibility, and quality. Relevant indicators are e.g. on-time delivery ratio or short delivery times.

Fisher claims that supply chains matching the particular type of product will have superior

performance, whereas supply chains with a mismatch tend to be the ones with problems. The

following figure depicts the Fisher Model.

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Type of Product

Functional Products

Innovative Products

Type

of S

uppl

y C

hain

Effi

cien

tS

uppl

yC

hain

match mismatch

Res

pons

ive

Sup

ply

Cha

inmismatch match

Figure 1: The Fisher Modell

Despite the great popularity of the SCOR model, supply chain management practices will be

discussed in the following in order to create a generic framework for supply chain management

from a institutional viewpoint. This framework will serve as a basis for the empirical analysis.

Practices of Supply Chain Management

In the literature there is a vast variety of approaches and practices for supply chain management.

Therefore, different aspects will be discussed in order to identify the most important practices,

some of which can be derived from the schools of supply chain management discussed in the last

section.

The basic idea of supply chain management is the planning and control of the material flow.

Therefore, cross-company-analysis of supply chains is reasonable, because material management

need not stop at the factory doors. The activities within a supply chain should be coordinated

between the participating partners thus the supply chain can be managed as a whole. A local

optimization of every company will generally not lead to the maximum of the whole supply

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chain. Here the ‘Functional Awareness School’ comes into play stressing the flow of material

beginning with the supplier and ending at the customer.

Another crucial aspect in terms of supply chain management is the relationship with suppliers. In

order to establish a sustainable cooperation suppliers must be regarded as partners rather than as

opponents. Accordingly, in terms of supplier relationships often the expression “durable arm’s

length relationships” (Dyer et al., 1998) is used. Supplier Partnership will be seen as one column

of supply chain management.

By involvement suppliers are included in supply chain related aspects. Thereby it is important to

establish efficient communication processes. Following supplier involvement, problems must be

shared with suppliers. Additionally the involvement of suppliers is important to reconfigure

processes within a supply chain. Accordingly, the ‘information school’ and the ‘integration

school’ build the foundation for supplier involvement.

Beside the suppliers customers play an essential role for supply chain management as well. It is

important to stay in close contact with customers, respond quickly to their needs, and give

feedback about quality and delivery performance. Customer integration is a focal point of the

‘integration school’, whereby the integration of business processes with customers and suppliers

must be stressed. Also in the ‘Information School’ customer involvement is important in terms of

the bidirectional information flow within the chain. Customer involvement will be seen as the

forth column of supply chain management. Although there are other aspects like Information

Technology, from an institutional viewpoint, i.e. putting the stakeholders supplier, manufacturer,

and customer into consideration, supply chain management will be characterized by the columns

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Supply Chain Planning, Supplier Involvement, Supplier Partnership, and Customer Involvement.

Figure 2 gives an overview of the institutional view of supply chain management.

Figure 2: Columns of Supply Chain Management

An Empirical Analysis of the Fisher Model

The “High Performance Manufacturing”-Project

The empirical analysis of the impact of supply chain management on the performance of

manufacturing companies is based on data collected within the international empirical research

project “High Performance Manufacturing”. The “High Performance Manufacturing”-project is

an international cooperation with the purpose to evaluate critical success factors in operations

management. High Performance Manufacturing declares the ability of a production unit to reach

continuous improvements in the manufacturing area through integration and utilization of

different management concepts. Former analysis of previous rounds is done by Schroeder, Flynn

et al. (Schroeder and Flynn, 2001). The basic aim of the project, in which research groups from

the U.S., Japan, Germany, Sweden, Finland, and Korea participate, is to identify the management

Supply

Chain

Planning Sup

plie

r

Invo

lvem

ent Supplier

Partner-

ship

Customer

Involvement

Supply Chain Management

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practices pursued by plants which are commonly seen as being at the edge in their industry with

respect to performance.

The data base comprises qualitative and quantitative information of over 189 manufacturing

plants collected in the automotive supply industry, the electronics industry, and the machinery

industry in the six countries. In each plant, 23 persons from various levels within the hierarchy

and the important functional areas had to fill in the questionnaires ensuring a transversal image

of the plant. The persons being asked in each plant are the Plant Manager, the Inventory

Manager, the Plant Superintendent, the Product Development Manager, the Process Engineer,

the Manager of Accounting, the Human Resource Manager, the Production Control Manager, the

Quality Manager, the Information Systems Manager, 3 Supervisors, and 10 Direct Labors. Figure

3 gives an overview of the participating countries and the plants from the different industries.

Figure 3: Data Set of the “High Performance Manufacturing”-Project

Participating Countries

Japan

USA

Germany

South Korea

Finland

=

Sweden

9

13

19

11

10

7

69

11

11

13

10

6

10

61

Automotive Machinery Electronics

29

34

41

31

30

24

189

Total

9

10

9

10

14

7

59

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Within the fields Just-in-Time, Quality Management, Human Resources, Manufacturing

Strategy, Information Management, and Technology Management, also information about

Supply Chain Management are contained in the data base as well as indicators for the

competitive performance of the plants.

Operationalizing the Factors of Supply Chain Management

The asked items are answered based on a 7-point-Likert scale. Altogether 21 items have been

taken from the questionnaire of the “High Performance Manufacturing”-project in order to create

four factors reflecting the four institutional supply chain management columns discussed in the

previews section (see appendix for the items). The items have been aggregated by a factor

analysis. The following table shows the criteria for validity and reliability.

Factors Items Eigenvalue Var. expl. Alpha

Supply Chain Planning 5 2.893 57.86 0.816

Customer Involvement 6 3.129 52.15 0.805

Supplier Partnership 5 3.016 60.32 0.826

Supplier Involvement 5 2.659 53.19 0.765

Table 1: Validity and Reliability of the SCM-scales

The validity of the factors is tested by their Eigenvalues and the explained variance. Firstly, the

Eigenvalue of each factor must be greater than 1. Secondly, all loadings of a factor must be

greater than 0.5, which holds for every factor. Furthermore the explained variance should be

greater than 50%. The reliability of the factors is tested by Chronbach’s Alpha. Following

Nunnally (1978) a value of 0.7 for Chronbach’s Alpha is regarded as acceptable. All four factors

fulfill the criteria for validity and reliability and can be used for further analysis.

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To show differences concerning the degree of implementation of supply chain manufacturing a

cluster analysis was performed. For the clustering Ward’s method with the squared Euclidean

distance was used. Based on the cluster analysis the plants can be divided into cluster of high and

low degree of implementation. The following figure shows the average values of the factors for

the created cluster.

-1,0

-0,5

0,0

0,5

1,0

SCM-Planning SCM-CustomerInvolvement

SCM-SupplierInvolement

SCM-SupplierPartnership

SCM low SCM high

Figure 4: Mean values of the SCM-cluster

As it can be seen from the figure the cluster with a high implementation degree has higher

average values than the other cluster. These results are confirmed by t-tests. Accordingly, the

cluster can be used to identify those plants with a high implementation of the generated factors.

In the following only plants from the cluster with a high implementation degree will be

considered to guarantee that only those plants are investigated that actually do supply chain

management. Accordingly, almost 50% of the plants are excluded from the sample leaving 96

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plants for further analysis. The following figure depicts the average values of the performance

measures of the two clusters.

3,0

3,2

3,4

3,6

3,8

4,0

4,2

Unit co

st of

manufa

cturin

g

On tim

e deli

very

perfo

rman

ce

Fast

deliv

ery

Flexib

ility t

o cha

nge p

roduc

t mix

Flexib

ility t

o cha

nge v

olume

Inven

tory t

urno

ver

Cycle

time

SCM low SCM high

Figure 5: Mean values of the performance measures

As it can be seen from figure 5 the cluster “SCM high” shows a better performance in all criteria.

But the differences are not significantly high. A reason for this might be that there are plants in

the cluster “SCM high” pursuing a different supply chain strategy, i.e. some might aim for

efficiency and some for responsiveness.

A Comparison of Responsive and Efficient Supply-Chain-Plants

The plants from the cluster with a high implementation degree are separated concerning the type

of their products. This is done by creating a measure reflecting the customization or

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standardization of the product. For each plant this measure called “Degree of Customization”

will be calculated in order to distinguish between plants with functional or innovative products.

In terms of the type of supply chain another factor is calculated in order to distinguish between

plants which focus on responsiveness instead of efficiency called “Degree of Responsiveness”.

The relevant items are the manufacturing goals inventory turnover and volume flexibility which

are mentioned by Fisher as well (Fisher, 1997: 109). Both created factors have been standardized

by means of comparable results leading to a mean of 0 and a standard deviation of 1. The

resulting positioning of the plants in the Fisher Model is depicted in figure 6.

Type of Product

Functional Products

Innovative Products

Type

of S

uppl

y C

hain

Effi

cien

tS

uppl

yC

hain

Res

pons

ive

Sup

ply

Cha

in

Figure 6: Positioning of SCM-plants in the Fisher Model

The figure shows that the plants are positioned on or close to a diagonal. This leads to the

presumption that there exists indeed a relationship between the type of product and the

corresponding type of the supply chain like it has been claimed by Fisher. This presumption can

be tested by a linear regression analysis. For the linear regression analysis the following equation

with the factor “Degree of Customization” (DoC) as independent variable and the factors

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“Degree of Responsiveness” (DoR) as dependent variable results on a high level of significance

with p < 0.01:

DoR = – 0.47 * DoC

The linear regression equation indicates that the presumption can not be rejected. In the

following the impact on performance measures will be investigated.

Linking the Fisher Model with plant performance

For the empirical analysis of the relationship of the Fisher Model and plant performance the

plants are grouped according to their position in the matrix described in the previous section. In a

first step the plants are separated into two groups according to the type of product. The group

with functional products and an efficient supply chain contains 15 plants. In the group with

innovative products and a responsive supply chain there are 19 plants. In a second step a

comparison of means is done. For the comparison of the performance measures for efficiency

and responsiveness will be chosen, because the plants in the two groups aim at different goals.

Figure 6 gives an overview of the mean values of plants with an efficient supply chain.

3,0

3,2

3,4

3,6

3,8

4,0

4,2

Unit cost ofmanufacturing

Inventoryturnover

Cycle time

Mismatch Match

3,0

3,2

3,4

3,6

3,8

4,0

4,2

Fast delivery Flexibility tochange volume

On time deliveryperformance

Mismatch Match

Figure 6: Performance Measures of plants with an efficient supply chain

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Figure 6 shows that those plants matching functional products with an efficient supply chain

have a better performance in terms of manufacturing cost, inventory turnover, and cycle time,

whereas the significance level is not always satisfying due to the reduced number of plants in the

group. Furthermore, the plants matching functionality and efficiency do not show superior

performance in terms of the criteria of the other matching group (right part of figure 6).

3,0

3,2

3,4

3,6

3,8

4,0

4,2

Fast delivery Flexibility tochange volume

On time deliveryperformance

Mismach Match

3,0

3,2

3,4

3,6

3,8

4,0

4,2

Unit cost ofmanufacturing

Inventoryturnover

Cycle time

Mismach Match

Figure 7: Performance Measures of plants with a responsive supply chain

Plants with an ‘innovative product’/‘responsive supply chain’-match have a better performance

with respect to flexibility, fast delivery, and on-time delivery, whereas the differences are not

significantly high. These plants have no superior performance in terms of manufacturing cost,

inventory turnover, and cycle time (right part of figure 7).

In both groups with plants matching their supply chain with their particular product no difference

concerning on-time delivery can be shown. A possible interpretation is that on-time delivery

functions as a key driver for efficient supply chains and responsive supply chains. A high on-

time delivery ratio supports both, efficiency and responsiveness.

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Conclusion and Further Research

From the results the conclusion can be drawn that supply chain management has an impact on

plant performance. The empirical investigation of the Fisher Model shows that supply chains

respond to the products they serve. Regarding the performance of the groups it can be stated that

those plants matching their supply chain with their product show a better performance. In terms

of the innovative/responsive group these differences are only small. A reason for this might be

that some plants have overcome the apparent trade off between innovativeness and efficiency.

One aspect for further research is the question whether a supply chain has to fulfil the

requirements of responsiveness in every single tier or if a hybrid approach might be more

promising in some circumstances. A hybrid approach indicates that supply chain processes will

focus on efficiency as far as it is possible and turn to a responsive focus after a certain

decoupling point. The development of a framework for hybrid supply chains is an interesting

question for further research as well.

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References

Bechtel, C., Jayaram, J., 1997. Supply Chain Management – A Strategic Perspective, The Inter-

national Journal of Logistic Management 8(1), 15-34.

Chopra, S., Meindl, P., 2001. Supply Chain Management – Strategy, Planning, and Operation.

Upper Saddle River.

Christopher, M., 2004. Logistics and Supply Chain Management: Strategies for Reducing Cost

and Improving Service. 3. ed., London.

Dyer, J. H., Cho, D. S., Chu, W. 1998. Strategic supplier segmentation: The next `best practice'

in supply chain management, California Management Review, (40) 2, 57-76.

Fisher, M.L., 1997. What is the Right Supply Chain for Your Product?, Harvard Business

Review, March-April 1997, 83-93.

Forrester, J.W., 1958. Industrial Dynamics: A Major Breakthrough for Decision Makers,

Harvard Business Review 36(4), 34-66.

Handfield, R.B., Nichols, E.L., 1999. Introduction to Supply Chain Management. Upper Saddle

River.

Lee, H.L., Padmanabhan, V., Whang, S., 1997. The Bullwhip Effect in Supply Chains, Sloan

Management Review 38(3), 93-102.

Nunnally, J. C. (1978), Psychometric Theory, New York.

Schroeder, R., Flynn, B., 2001. High Performance Manufacturing – Global Perspectives, New

York.

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Appendix

Supply Chain Planning Customer Involvement

We actively plan supply chain activities.

We frequently are in close contact with our customers.

We consider our customers’ forecasts in our supply chain planning.

Our customers give us feedback on our quality and delivery performance.

We strive to manage each of our supply chains as a whole.

Our customers are actively involved in our product design process.

We monitor the performance of members of our supply chains, in order to adjust supply chain plans.

We strive to be highly responsive to our customers’ needs.

We gather indicators of supply chain performance.

We regularly survey our customers’ needs.

Our customers seem happy with our responsiveness to their problems.

Supplier Partnership Supplier InvolvementWe maintain cooperative relationships with our suppliers.

We strive to establish long-term relationships with suppliers.

We provide a fair return to our suppliers We are comfortable sharing problems with our suppliers.

We help our suppliers to improve their quality.

In dealing with our suppliers, we are willing to change assumptions, in order to find more effective solutions.

We maintain close communications with suppliers about quality considerations and design changes.

We believe that cooperating with our suppliers is beneficial.

Our key suppliers provide input into our product development projects

We emphasize openness of communications in collaborating with our suppliers.

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