Improving the Vehicle Fill Rate for Procter & Gamble -...

Eindhoven, September 2011

BSc Industrial Engineering — Bilkent University 2009Student identity number 0730345

in partial fulfilment of the requirements for the degree of

Master of Sciencein Operations Management and Logistics

Supervisors:Prof. Dr. T. Van Woensel, TU/e, OPACDr. Ir. H. Reijers, TU/e, ISD. Jammes, P&G, SNIC

Improving the Vehicle Fill Rate forProcter & Gamble

byTugce Tali

2

TUE. School of Industrial Engineering.Series Master Theses Operations Management and Logistics

Subject headings: vehicle fill rate, vehicle capacity utilization, outbound transportation,retailer, logistics costs, service, inventory

3

AbstractThis thesis aims to improve the Vehicle Fill Rate (VFR) of P&G outbound deliveries in orderto achieve a win/win/win solution for the manufacturer/retailer/consumer. For this purpose,processes of P&G outbound deliveries were analyzed and the impacts of increased VFR onservice level and logistics costs were assessed. It was found that increasing VFR without anyother changes in the supplier-retailer chain has negative impacts in most cases, specifically onthe inventory level. Afterwards, it was shown that reducing Minimum Order Quantities(MOQs) while increasing the VFR changes the direction of the impact to positive. Thisprovides an opportunity to increase the VFR while keeping the balance with service level andlogistics costs; and even improving them in most cases.

4

PrefaceThis is the master thesis of Tugce Tali, presented on 16 September 2011 at the EindhovenUniversity of Technology in partial fulfilment of the requirements for the degree of Master ofScience in Operations Management and Logistics.

I would like to take this opportunity to express my gratitude to all the people who havesupported me throughout my thesis project.

Firstly, I would like to thank Prof. Tom van Woensel, my first supervisor at TU/e, for hiscontinuous support and patience. His extensive knowledge as well as insightful andhumorous approach were very valuable for me. Secondly, I would like to thank Hajo Reijers,my second supervisor at TU/e, for his constructive comments and opinions.

Furthermore, I would like to thank David Jammes, my supervisor at P&G. His experience,guidance and professional support helped me a lot throughout my project. I also would like tothank all my colleagues at SNIC for their contributions to my project and for creating such apleasant work environment.

I would like to thank all my friends for their continuous support and encouragement. Youmade my last two years in the Netherlands and Belgium an adventure.

Finally, I would like to express my deepest gratitude to my family for their unconditionallove and support.

Anneciğim,babacığım ve Buse’ciğim, koşulsuz sevginiz ve desteğiniz için teşekkür ederim.

Tugce Tali

Eindhoven, the Netherlands, 2011

5

Executive SummaryThis project was performed in the context of an internship in the Supply Network InnovationCenter (SNIC) of Procter & Gamble (P&G) in Brussels, Belgium.

Market competition requires firms to respond to customer needs in a quick, cost effective andsustainable manner maintaining acceptable service levels. Hence, companies continuouslysearch ways to improve their operations. Pibernik (2006) states that in recent years companieshave been looking for ways to achieve increased supply chain efficiency with the help ofhigher resource utilization.

The vehicle fill rate (VFR) is defined as the ratio of the actual capacity used to the totalcapacity available in terms of weight and volume (McKinnon, 2010). It is a measure of howefficiently the freight sector is transporting goods with its vehicles. If it can be improved,then the same goods can be carried with fewer vehicle movements thereby leading to reducedcongestion, emissions, accidents and other environmental impacts of freight transport(European Environment Agency, 2010). As the European Energy Agency (2006, 2008)reports that the average VFR of trucks in Europe is below 50%, there is an obviousimprovement opportunity.

The challenge in increasing VFR is to use the full vehicle capacity while keeping the balancewith service and cost through the end-to-end supply chain. The opportunity to deliver moreproducts with each vehicle has a positive effect in terms of transportation cost and externalcosts (i.e. congestion, emissions, accidents and other environmental impacts). On the otherhand, this changes the dynamics of the supplier-retailer chain, raising the concern about thepotential impact on customer service and/or inventory levels.

Considering the abovementioned facts, the research assignment was set as follows:

Assess the impacts of increased VFR on the other performance measures of the system; andthen, to come up with potential decisions to improve the VFR in outbound transportation in

order to achieve a win/win/win solution for the manufacturer/retailer/consumer.

The scope of the study is the P&G outbound deliveries to the customers in Western Europe. Itcontains the P&G DC, the Customer DC and the transportation of goods from the P&G DC tothe Customer DC.

In the study, firstly, the drivers that lead to low VFR were categorized as productcharacteristics, vehicle characteristics, supply chain characteristics and health and safetyregulations. Among these, supply chain characteristics were determined to be investigatedfurther.

P&G supply chain characteristics were analyzed for outbound deliveries in order to observethe behavior of the system while the VFR is increased, identify the risks and negative impactsof increased VFR; and develop insights to mitigate the negative impacts and improve the

6

system. For this purpose, an overall mapping of the outbound delivery elements was providedwith their interactions. The analysis revealed the following risks and opportunities:

Higher VFR leads to delivery of more goods per shipment. For the total volume, this inducesfewer shipments which will cut part of the transportation cost, administration cost and CO2emissions. On the other hand, a negative impact is anticipated on customer’s inventory levelas more goods will be pushed to Customer DC. Meanwhile, reducing the Minimum OrderQuantities (MOQs) will be an opportunity to mitigate the negative impacts of increased VFRon customer’s inventory. If the MOQs are reduced, higher number of SKUs can be deliveredwithin the same vehicle each with lower volumes. As a result, a decline can be expected inthe total inventory. The target service level (i.e. the product availability at the customer’s siteto the downstream orders) was assumed to be constant throughout the study, as service levelis thought to be critical for the success of business.

The study continued with the redesign. The VFR is increased and MOQs were reduced. Twocomparative analyses were performed to quantify the extent of the impacts:

1. Current truck load (CTL) vs. Full truck load (FTL)2. Current truck load (CTL) vs. Full truck load with lower minimum order quantities

(FTL with lower MOQs)

Firstly, the VFR was increased; CTL is modified to FTL. The impacts of increased VFR onservice level and logistics costs were assessed for several business scenarios (i.e. variousshipment frequencies/business volumes on a lane, various numbers of SKUs on a lane,various forecast accuracy levels). Calculations were performed using the ‘Retailer InventoryModel’ (RIM) of P&G - an Excel based tool developed in 2008. Verification and validationwere carried out to confirm that the model and the calculations were reliable and representedthe actual system.

The results revealed that the extent of the impacts on performance measures differed mainlyaccording to the volume/frequency of the lane and the number of SKUs on the lane. It wasfound that increasing VFR without any other changes in the supplier-retailer chain hasnegative impacts, specifically on the inventory level. Improving the outbound deliveries fromCTL to FTL can only be reasonable for high frequency lanes.

Afterwards, MOQs were reduced; CTL is modified to FTL with lower MOQs. It was foundthat reducing MOQs while increasing the VFR changes the direction of the impact on theinventory level from negative to positive. Besides, improving the outbound deliveries fromCTL to FTL with lower MOQs was reasonable for most of the scenarios. The performancemeasures were improved especially for medium and low frequency lanes.

Clearly, increasing VFR meanwhile lowering MOQs is an opportunity to improve the VFRwhile keeping the balance with service level and logistics costs; and even improving them inmost cases.

This study contributes to the relevant research area by providing an example of increasing theVFR in outbound transportation. Firstly, it provides a categorization of the outbound delivery

7

elements as well as an overall mapping of them with their interactions. These can be used as acheck-list in related studies; and/or can facilitate detecting the indirect dynamics. Afterwards,the study presents the possible outcomes of increasing the VFR. This helps to identify therelated risks and opportunities. Furthermore, the study suggests an approach which willmitigate the possible negative impacts of increased VFR and improve the logistics costsfurther.

The study contributes to the company in several ways. Firstly, the categorization of theoutbound delivery elements as well as the overall mapping of them with their interactions canprovide guidance for people who are not involved in supply chain operations inunderstanding the part of supply chain dynamics. Moreover, the company can use this studyin determining the type of businesses that exhibit the VFR improvement potential. Similarly,when the VFR of a specific lane is considered to be increased, the company can consult theresults of the relevant scenarios of this study. Furthermore, displayed mutual benefits ofimproving CTL to FTL with lower MOQs will motivate both the suppliers and retailers toincrease the VFR.

It should be noted that this was not a financial study. The focus was on the goods flow. Thespecific technical solutions, possible investments and the absolute changes in terms of costwere not analyzed.

8

ContentsAbstract………………………………………………………………………………………..3Preface…………………………………………………………………………………………4Executive Summary…………………………………………………………………………...5Contents………………………………………………………………………………………..8List of Figures………………………………………………………………………………..10List of Tables………………………………………………………………………………....11List of Abbreviations…………………………………………………………………………12Chapter 1……………………………………………………………………………………..13

Introduction..........................................................................................................................131.1 Research Motivation ......................................................................................................131.2 Problem Description and Research Assignment............................................................141.3 Company Description ....................................................................................................14

1.3.1 P&G ........................................................................................................................141.3.2 SNIC .......................................................................................................................151.3.3 Supply Chain Operations ........................................................................................15

1.4 Project Scope and Approach..........................................................................................161.5 Outline............................................................................................................................17

Chapter 2……………………………………………………………………………………..18Description of the Vehicle Fill Rate ....................................................................................182.1 VFR Definition and Measures .......................................................................................182.2 VFR in P&G ..................................................................................................................182.3 Drivers of low VFR .......................................................................................................19

Chapter 3……………………………………………………………………………………..22Analysis of the P&G Supply Chain Characteristics ............................................................223.1 Outbound Delivery Processes and Overall Mapping.....................................................223.2 Structural Mechanisms in Outbound Deliveries ............................................................243.3 Performance Measures...................................................................................................293.4 Summary of the findings................................................................................................30

Chapter 4……………………………………………………………………………………..32Redesign: Increasing the Vehicle Fill Rate and Reducing the Minimum Order Quantities 324.1 Typology........................................................................................................................324.2 Assumptions...................................................................................................................334.3 Methodology..................................................................................................................34

4.3.1 Sample Data Building .............................................................................................344.3.2 Scenario Building....................................................................................................36

9

4.3.3 Retailer Inventory Model (RIM) and Calculations .................................................394.3.4 Verification and Validation.....................................................................................42

4.4 Results............................................................................................................................43CTL vs. FTL ........................................................................................................................44CTL vs. FTL with lower MOQs ..........................................................................................46

Chapter 5……………………………………………………………………………………..49Conclusion and Recommendations......................................................................................495.1 Conclusions....................................................................................................................495.2 Recommendations..........................................................................................................49

References……………………………………………………………………………………51Appendices…………………………………………………………………………………...53

Appendix 1: An overview of P&G data...............................................................................53Appendix 2: Outbound delivery elements ...........................................................................56Appendix 3: Retailer Inventory Model ................................................................................60Appendix 4: Results.............................................................................................................61

10

List of FiguresFigure 1: SNIC in P&G............................................................................................................15Figure 2: Supplier-retailer chain of P&G.................................................................................16Figure 3: Current truck load.....................................................................................................19Figure 4: Drivers of low VFR..................................................................................................21Figure 5: Supplier-retailer chain processes..............................................................................22Figure 6: # SKUs vs Daily Shipment Frequency of investigated lanes ...................................23Figure 7: Full truck load ..........................................................................................................24Figure 8: Shipment frequency & time interval in between shipments.....................................25Figure 9: Dynamics of the outbound delivery system .............................................................31Figure 10: Visualization of the typology .................................................................................33Figure 11: Example for sample data building..........................................................................35Figure 12: CO2 emission levels vs load weight .......................................................................41Figure 13: Impacts on the customer inventory cost as a result of improving CTL to FTL .....45Figure 14: Impacts on the customer inventory cost as a result of improving CTL to FTL withlower MOQs.............................................................................................................................47Figure 15: The lane selected for analysis.................................................................................55Figure 16: Mapping of the elements in outbound deliveries ...................................................56Figure 17: RIM user interface..................................................................................................60

11

List of TablesTable 1: VFR measures............................................................................................................18Table 2: H/M/L shipment frequency........................................................................................24Table 3: Relationship matrix of structural mechanisms ..........................................................28Table 4: Selection criteria of structural mechanisms...............................................................28Table 5: Performance measures of outbound deliveries ..........................................................29Table 6: Assumptions of the quantitative calculations ............................................................34Table 7: Scenarios considered in quantitative calculations .....................................................34Table 8: Necessary data for quantitative calculations..............................................................35Table 9: Categorization of the standard deviation of the forecast error ..................................35Table 10: values according to forecast accuracy scenarios ........................................38Table 11: Inputs of RIM ..........................................................................................................39Table 12: Outputs of RIM........................................................................................................40Table 13: Verification - number of trucks needed to deliver the total volume when CTL wasimproved to FTL/FTL with lower MOQs................................................................................42Table 14: Verification - number of trucks needed to deliver the total volume according todifferent scenarios in CTL case ...............................................................................................43Table 15: Behaviors of performance measures when CTL is improved to FTL .....................44Table 16: Number of trucks needed to deliver the total volume according to differentscenarios (CTL vs FTL)...........................................................................................................44Table 17: Behaviors of performance measures when CTL is improved to FTL with lowerMOQs.......................................................................................................................................46Table 18: Number of trucks needed to deliver the total volume according to differentscenarios (CTL vs FTL with lower MOQs).............................................................................47Table 19: VFR in P&G ............................................................................................................53Table 20: Max allowed legal weight........................................................................................53Table 21: P&G plant locations in Western Europe (as of 30 June 2010) ................................54Table 22: VFR (Inbound deliveries vs Outbound deliveries in Western Europe) ..................55Table 23: Categorization of the elements in outbound deliveries (P&G Operations) .............57Table 24: Categorization of the elements in outbound deliveries (Customer Operations) ......58Table 25: Categorization of the elements in outbound deliveries (System characteristics) ....59Table 26: Increase in inventory level for all scenarios when the CTL was improved to FTL 61Table 27: Example for the dynamics between the cycle stock and safety stock .....................62Table 28: Increase in inventory level for all scenarios when the CTL was improved to FTLwith lower MOQs ....................................................................................................................63

12

List of Abbreviations

BPS Business problem solvingCF Corporate functionCTL Current truck loadCustomer DC Customer distribution centerD DistributedFMCG Fast moving consumer goodsFTL Full truck loadGBS Global business serviceGBU Global business unitH HighL LowM MediumMDO Market development organizationMOQ Minimum order quantityOPAC Operations planning, accounting, and controlP&G Procter & GambleP&G DC Procter & Gamble distribution centerR&D Research & developmentRIM Retailer inventory modelSNIC Supply network innovation centerSNO Supply network organizationSKU Stock keeping unitTU/e Eindhoven University of TechnologyVFR Vehicle fill rate

13

Chapter 1

IntroductionThis report finalizes the MSc Operations Management and Logistics program of theEindhoven University of Technology (TU/e), in the sub-department of Operations Planning,Accounting and Control (OPAC). The project was performed in the context of an internshipin the Supply Network Innovation Center (SNIC) of Procter & Gamble (P&G) in Brussels,Belgium.

This chapter explains the motivation of the research, introduces the problem description andthe research assignment, provides brief information of the company, and then presents theproject scope and approach as well as the report outline.

1.1 Research Motivation

Market competition requires firms to respond to customer needs in a quick, cost effective andsustainable manner maintaining acceptable service levels. Hence, companies continuouslysearch ways to improve their operations. Pibernik (2006) states that in recent years companieshave been looking for ways to achieve increased supply chain efficiency with the help ofhigher resource utilization.

Transportation is a significant factor in the supply chain economic system as in the EU itaccounts for about 5% of the GDP (European Comission, 2011). It links the producer andconsumer as well as all the other actors in the chain and provides goods flow. Contrary tocommon belief, due to inefficiencies transportation costs dominate the logistics costs in asupplier-retailer chain with its 30% share (Van Der Vlist, 2007). In addition, transportationactivities result in significant other external costs such as accidents, noise, air pollution,traffic congestion and climate change (INFRAS, 2004). The transportation sector isresponsible for the 26% of CO2 emissions (Chapman, 2007). Within the context of rising oilprices and increased concerns about sustainability by consumers, the abovementioned factsindicate a challenge for both the profitability and the public image of a company.

Vehicle utilization is a measure of how efficiently the freight sector is transporting goodswith its vehicles. If the vehicle utilization can be improved, then the same goods can becarried with fewer vehicle movements. This helps to reduce total freight vehicle traffic,measured as vehicle-km, thereby leading to reduced congestion, emissions, accidents andother environmental impacts of freight transport (European Environment Agency, 2010). Asthe European Energy Agency (2006, 2008) reports that the average weight utilization oftrucks in Europe is below 50%, there is an obvious opportunity to improve the vehiclecapacity utilization level (or the vehicle fill rate (VFR)).

The challenge in increasing VFR is to use the full vehicle capacity while keeping the balancewith service and cost through the end-to-end supply chain. This research focuses on the

14

outbound lanes of P&G - Retailer (i.e. Customer) chains where it is significant to explore theinteractions between the supply chain drivers for both P&G and the Customers while theVFR is modified (increased).

1.2 Problem Description and Research Assignment

VFR is defined as the ratio of the actual capacity used to the total capacity available in termsof weight and volume (McKinnon, 2010). In P&G, the loading of a vehicle is optimizedconsidering the use of floor area. However, in many cases, there is still space in terms ofvolume available within the legal weight constraints (Table 19 and Table 20in Appendix 1).

The opportunity to deliver more products with each vehicle has a positive effect in terms oftransportation cost and external costs (i.e. congestion, emissions, accidents and otherenvironmental impacts). On the other hand, this changes the dynamics of the supplier-retailerchain, raising the concern about the potential impact on customer service and/or inventorylevels. The impacts can be very different for each supplier-retailer chain depending upon thefeatures of it such as the volume of the business in between.

These indicate the significance of exploring the influences of increased VFR on the otherelements of the supplier-retailer system; in order to achieve a profitable balance withreasonable service levels. Meanwhile, the characteristics of the business depending on thecustomer being served should also be considered.

Regarding the problem description, research assignment is set as follows:



1.3 Company Description

This thesis is a result of the research carried out at P&G in Brussels, as the industrial partnerin this project. This section introduces P&G, including information about the SNIC and thesupply chain operations.

1.3.1 P&G

P&G, founded in 1837, is one of the leading companies in the fast moving consumer goods(FMCG) industry, with net sales of $78,938 million and net earnings of $12,736 million in2010 (P&G Annual Report, 2010). The company serves consumers in more than 180countries with 127,000 employees and more than 300 brands in Health & Well Being, Beauty& Grooming, and Household Care categories.

P&G is structured under four main divisions: The Global Business Unit (GBU) operates onproduct categories, and is responsible for the innovation pipeline, profitability andshareholder returns of the businesses. Market Development Organization (MDO) is the

15

business unit that acts locally. It is in charge of knowing consumers and retailers in eachmarket where P&G competes. Besides, it integrates the innovations flowing from the GBUsinto business plans for each market. Global Business Service (GBS) is responsible forproviding business support services to the other business units; and finally, CorporateFunction (CF) has a support role in every specific department that ensures ongoing functionalinnovation and capability improvement.

1.3.2 SNIC

SNIC is a multi-skilled team of P&G, based in Brussels, organized under the Research andDevelopment (R&D) section of the GBU and Supply Network Operations (SNO) section ofthe MDO (Figure 1).

Figure 1: SNIC in P&G

SNIC leverages research in supply network operations in order to build a comprehensivevision of the future; as well as to explore robust improvement opportunities and innovativesolutions for P&G based on knowledge and experience. The research areas cover shopper andcustomer solutions, transportation, warehousing, customization and end-to-end supply chains.

1.3.3 Supply Chain Operations

Characteristics of the supply chain in P&G show differences depending on the customer typebeing served. Deliveries to large retailers comprise diverse product categories; on the otherhand, deliveries to smaller customers (i.e. pharmacies, perfume shops etc.) consist ofparticular products. Hence, the latter requires a more customized supply chain.

In this project, supplier-retailer chains were examined, as they constitute the biggest shareamong all P&G business. The main characteristics of the supplier-retailer chain are explainedbelow (Figure 2).

Products are produced in P&G plants, located at the regional (e.g. Belgium) or continentallevel (e.g. Europe) (Table 21in Appendix 1 provides a list of P&G plants in Western Europe).Production is performed according to forecasts. Then, the products are shipped to P&GDistribution Centers (P&G DCs) with a lead time of 1-5 days by trains and/or trucks; andproducts are stored there until the customer order arrival.

GBUs - Product oriented

R&D

MDOs - Customer oriented

SNO

Global Business Services and Lean Corporate Functions

SNIC

16

Upon customer order, pallets are prepared and delivered to the Customer Distribution Center(Customer DC) with a lead time of 1-3 days by trucks; and stored there until the store orderarrival. A Customer DC is generally served by a single P&G DC, rarely two or three P&GDCs due to category (for ex: personal beauty care and fabric care) difference reasons. P&G isthe owner of the goods until the delivery is received by the Customer DC. Althoughtransportation is outsourced, planning is being carried out by P&G.

Figure 2: Supplier-retailer chain of P&G

1.4 Project Scope and Approach

The scope of the study is the outbound deliveries to the customers in Western Europe. Theinbound deliveries are left out of scope. The VFR of the outbound deliveries is significantlylower than the inbound deliveries (Table 22 in Appendix 1); and hence it is more important toelaborate the VFR in outbound deliveries primarily.

The scope contains the P&G DC, the Customer DC and the transportation of goods from theP&G DC to the Customer DC. Each supplier-retailer chain can be considered as a singleechelon multi item system; since numerous shipments with multiple stock keeping units(SKUs) are being performed on an outbound trade lane between P&G DC and Customer DC.

This project was conducted in collaboration with a company. Thus, it is a Business ProblemSolving (BPS) project. A BPS project, as Van Aken et al. (2007) indicates, has as purpose ofdesigning a sound solution along with the realization of performance improvement throughplanned changes. In BPS settings a specific problem situation within the company providesthe starting point of the research. From a relevance perspective the project is client-centered,performance-focused and design oriented. On the other hand, rigor of the project isstrengthened by taking the theory as a basis and by justification (Van Aken, 2007).Specifically, the BPS project considers all service and performance measures inunderstanding the current system, coming up with solution alternatives and identifyingimprovements of proposed solutions.

17

BPS projects follow classic problem-solving cycle (elaborated as regulative cycle in VanStrien (1997)) considering the setup (Van Aken, 2007). The steps of this cycle are given as:

- Problem definition- Analysis and diagnosis- Plan of action- Intervention- Evaluation

This project covered all steps of the regulative cycle except intervention. In order to come upwith a clear problem definition, the literature was reviewed and several interviews wereperformed with the company supervisor. The observations and insights obtained wereconverted to a problem definition and research assignment. In the analysis and diagnosis step,the processes were investigated in detail to map the relationships that exist between theelements of the system. Then, these were used to design the action plan. Intervention (orimplementation) step was left out of scope due to timing reasons. Although a physicalintervention was not possible, still, the evaluation step was covered by a quantitativecomparison of the situation before and after the redesign.

1.5 Outline

The rest of the report is structured as follows: Chapter 2 provides a description of the VFR.Chapter 3 explains the analysis of the P&G supply chain characteristics. Chapter 4demonstrates the redesign steps of the study and the results. Finally, in Chapter 5 conclusionsare presented; limitations of the study and the possible directions for future work arediscussed.

18

Chapter 2

Description of the Vehicle Fill RateBefore starting the analysis, it is necessary to understand the VFR concept. For this purpose,this chapter provides a clear definition of the VFR and its measures. Moreover, it presents theVFR in P&G; and explains the drivers of low VFR.

2.1 VFR Definition and Measures

The VFR refers to the extent to which a vehicle is loaded compared to its maximum carryingcapacity. A 0% fill rate means the vehicle is carrying no loads; and 100% fill rate means thevehicle is travelling with loads bringing the vehicle to its maximum carrying capacity.

There exist five measures of VFR (Table 1) in the literature (McKinnon, 2010).

Table 1: VFR measures

Deck-area coverage(i.e. Floor fill)

Net floor area covered by load/ Floor area per vehicle type(In P&G: Pallets loaded to a truck/ Total available palletspots)

Weight-based loading factor(i.e. Weight fill)

Net weight of load (excluding pallets/gaps/fillers)/ Max legalweight per specific lane & vehicle type

Volumetric loading factor(i.e. Cube fill)

Net volume of load (excluding pallets/gaps/fillers)/ Volumeper vehicle type

Level of empty running The proportion of truck-kms run emptyTonne-km loading factor Net weight of load (excluding pallets/gaps/fillers)/ Max legal

weight per specific lane & vehicle type(This measure allows weight fill to vary during the journey.)

Level of empty running and tonne-km loading factor were not assessed further. The reasonwas that empty running and varying loads are not a part of direct outbound shipments fromthe supplier to the customer.

2.2 VFR in P&G

P&G keeps track of the floor fill, the weight fill and the cube fill in its outbound shipments(Table 19 in Appendix 1). The weight fill and the cube fill are significantly lower than thefloor fill. The reason is, a full pallet spot on the floor is only explained by the occupation ofthe concerned spot by a pallet; and not necessarily that the spot is occupied till the ceilingand/or it carries its full weight capacity.

This study assumes a vehicle type of a standard semitrailer truck, commonly used by P&G.The vehicle is 2.4 m high, 2.45 m width and 13.6 m length; and it has 33 pallet spots on itsfloor (Figure 3).

19

In line with the current practice, the Current Truck Load (CTL) in this study is 33 full palletspots in a vehicle each with 1.8 m high pallets (Figure 3). Although 1.8 m high pallets in a2.4 m high vehicle theoretically means 75% cube fill, in practice actual cube fill is around50% since 1.8 m is the maximum height of a pallet and not all the pallets are filled up to 1.8m.

Figure 3: Current truck load

2.3 Drivers of low VFR

Although the objective is to exploit the full vehicle capacity, filling the cube and soincreasing the VFR is not always possible. The efficiency of an outbound delivery is severelyconstrained by the requirements of stakeholders. As a result of interviews in P&G the driversthat lead to low VFR were gathered in four categories (Figure 4): Product characteristics,vehicle characteristics, supply chain characteristics and health and safety regulations. Thecategorization is supported by the findings of literature study wherever it is appropriate.

Product characteristics as a driver of low VFR

- P&G has a wide product assortment with differing weights and volumes. Vehicleswill reach the maximum allowed legal weight with heavy loads before the cube isfilled (i.e. weight-out); or, vehicles will be filled with voluminous loads beforereaching the maximum allowed legal weight (i.e. cube-out) (Department forTransport, UK, 2007). Besides, it is inevitable to end up with empty spaces inbetween stacks when various types of P&G products are loaded to a vehicle.

- Packaging protects the products from damage in transit (McKinnon, 1999); poorpackaging strength may prevent stacking and so using the full vehicle space. Besides,in FMCG industry, packaging is mostly used to draw shopper’s attention; and thus,essential for value creation. However, in many cases, it consumes a significant portionof the vehicle space because of the empty space within the packages and the resultingspaces in between stacks.

Vehicle characteristics as a driver of low VFR

- If the vehicle design does not ensure the stability of the payload or it requires specificequipments in loading/unloading which are not available within the warehouse, uplayering or multiple stacking of the goods within the vehicle may not be possible(Department for Transport, UK, 2007).

20

- The design of the vehicle, specifically the number of the axles it has, plays a decisiverole in the legal maximum payload weight.

Supply chain characteristics as a driver of low VFR

- Building new facilities or adding capacity to the current ones requires hugeinvestments; therefore, the capacity of a warehouse remains the same for a long time.Limited storage and loading/unloading area at the customer’s site might lead toinefficient loads and lower VFR causing a physical limitation on the delivery amount.

- Lead time can be strictly short for some customers; thus, not letting to consolidate theloads in time in order to fill the vehicle.

- Customers release orders on an ‘as required’ basis for the smooth flow of materialsthrough a traditional supply chain; which is likely to result in less than full truck load(FTL) consignments (Disney et al., 2003; McKinnon, 1999). In case of highuncertainty of demand, the vehicle utilization deteriorates further.

- Economies of scale reasons force an agreed minimum order quantity for each of theSKUs between the supplier and the customer. This may influence VFR negatively intwo ways: Firstly, each resulting order quantity possesses the unfit risk to a partlyloaded vehicle, which might cause lower VFR eventually; and secondly, partial ordersthat will fill the empty space in the vehicle cannot be released.

Health and safety regulations as a driver of low VFR

- Equipment-related regulations may limit the payload due to stability reasons duringloading/unloading and/or transportation (Department for Transport, UK, 2007).

- Traffic regulations allow a defined maximum payload weight limit according to thecharacteristics of the vehicle.

Although there are several drivers of low VFR, it was not possible to analyze them all withinthe scope of this project.

Product-related drivers of low VFR are under the control of product design and packagingdesign departments. Vehicle-related drivers of low VFR can be handled by assessing theadvantages and disadvantages of various vehicle types; and then by investing further in thevehicles if it is worthwhile. Health and safety-related limitations require the intervention oflegal authorities or extra investments on the loading/unloading equipment. If supply chain-related drivers of low VFR are considered, customers are in charge of their storage capacities;and the lead time is a result of the supply chain process design.

21

Figure 4: Drivers of low VFR

Customer ordering behavior and the minimum order quantities (MOQs) are linked to eachother. Any changes applied to both do not require any further investment on the equipment,the intervention of the other parties and a supply chain process redesign. Thus, they werecontemplated to be a reasonable starting point to the analysis and determined to beinvestigated further.

21



21



22

Chapter 3

Analysis of the P&G Supply Chain CharacteristicsThis chapter analyzes the P&G supply chain characteristics for outbound deliveries in orderto observe the behavior of the system while the VFR is increased, identify the risks andnegative impacts of increased VFR; and detect the ‘customer ordering behavior and MOQs’-related redesign options to mitigate the negative impacts and improve the system.

This chapter provides four elements: A short overview of the end-to-end supply chainprocesses followed by an overall mapping of the outbound delivery elements with theirinteractions; a list of supply chain mechanisms which might appear while the VFR driver ismodified; the performance measures of the outbound deliveries identified in the analysis; andfinally the summary of the findings which also explains the direction of the following steps.

3.1 Outbound Delivery Processes and Overall Mapping

Figure 5: Supplier-retailer chain processes

The end-to-end supplier-retailer chain has several processes and stakeholders. The goodsmove along the production plant, P&G DC, Customer DC and store before it reaches to theshopper (Figure 2). Figure 5 provides a list of the end-to-end supplier-retailer chain processes

P&G

Plant

Warehousing

Pallet loading

Vehicleloading

Inboundtransportation Warehousing

Vehicleunloading

Stocking

Orderaccept/deliver

Order picking

Pallet loading

Vehicleloading

22

Chapter 3






P&GDC

Warehousing

Vehicleunloading

Stocking

Orderaccept/deliver

Order picking

Pallet loading

Vehicleloading

Outboundtransportation

Customer

Cust.DC

Warehousing

Vehicleunloading

Stocking

Orderaccept/deliver

Order picking

Pallet loading

Vehicleloading

Ordering

22

Chapter 3






Transportation

Consumer

Store

Vehicleunloading

Stocking

23

and highlights the ones that are directly related to the outbound deliveries in line with thescope of this study.

A sample outbound trade lane was considered in identifying the outbound delivery processes.To select that, data of 13 weeks of deliveries from P&G DCs to the Customer DCs in Francewere investigated (i.e. 29 lanes). The data revealed that the shipment frequencies and thenumber of SKUs of the lanes cover a wide range (Figure 6). One of these lanes was selectedfor further analysis (Figure 15 in Appendix 1). The selected lane is a good representative ofan outbound trade lane: It has a regular delivery cycle; it does not possess any cross-dockingoperations; and the number of SKUs of the lane is at the average (i.e. 164). The shipmentfrequency of the lane (i.e. 1.11 shipments/day) is above the average; and this was anopportunity to derive different shipment frequency scenarios in the following phases of thestudy.

Figure 6: # SKUs vs Daily Shipment Frequency of investigated lanes

Deliveries on the selected lane are order driven. Demand occurs to the Customer DC and it issupplied from stock. The customer checks its inventory levels on a daily basis and releasesorders if necessary. At the P&G DC orders are prepared, loaded to pallets and vehicles (i.e.trucks); and then shipped to the Customer DC. The lead time of this process is 3 days on thelane considered. Normally it depends on the specific lane; and it is mainly between 1-3 days.Orders are unloaded at the Customer DC; and then stocked until a store order arrives.

The lane has a given volume of business (shipments) which defines a given frequency ofdeliveries. Even if the profile of deliveries is not flat, we would consider here for simplicitythat there is a standard delivery frequency. For that very same trade lane, a change in theVFR will have consequences on the profile of deliveries, and potentially on the frequency.

Figure 16 in Appendix 2 is a mapping of the elements involved in the outbound deliveries.Table 23, Table 24 and Table 25 in Appendix 2 categorizes decision, dependent andindependent variables as well as performance measures of the outbound delivery elements

24

(i.e. mapped in Figure 16 in Appendix 2) under the related processes; and provide a definitionof the elements where necessary.

3.2 Structural Mechanisms in Outbound Deliveries

This section clarifies the complex structural supply chain mechanisms in outbound deliverieswhich will happen when the VFR is increased. To analyze the increased VFR, a full truckload (FTL) was assumed for convenience. The FTL has 33 full pallet spots in a vehicle eachwith 2.4 m high pallets (Figure 7). The logic behind is that the vehicle is filled basically by uplayering the pallets in order not to waste the empty space on top of them without any otherchanges. Similar to the CTL case, although 2.4 m high pallets in a 2.4 m high vehicletheoretically means 100% cube fill, in practice cube fill is expected to be between 60-80%(Source: interviews within the company) since 2.4 m is the maximum level and not all thepallets could be filled up to 2.4 m.

Figure 7: Full truck load

Increasing the VFR leads to several structural changes in the system. These structuralmechanisms as a result of increased VFR are separated and listed to be able to understandwhen and how they will happen and impact the output measures.

Business volume & increased VFR

Increased VFR leads to delivery of more goods per shipment. For the total volume, thisinduces fewer shipments (trucks). If the theoretical impact of improving the VFR from 50%to 100% is considered, it would reduce the number of trucks needed to deliver the samevolume by two. However, the impact on the delivery frequency is not linear. A Customer DCdelivered by two trucks per day would have only one delivery per day after the VFRimprovement; thus, still a frequency of daily delivery. On the other hand, a Customer DCdelivered only once a week would then be delivered every two weeks. Therefore, tocategorize the impact of the business volume on the delivery frequency a typology ofhigh/medium/low (H/M/L) frequency was proposed (Table 2).

Table 2: H/M/L shipment frequency

Shipment frequency DefinitionH frequency lane From very frequent shipments until around 2 shipments/dayM frequency lane From around 2 shipments/day until around 2 shipments/weekL frequency lane From around 2 shipments/week until very infrequent cases

25

Note also that, as another consequence, the time interval in between shipments increases forM/L frequency lanes (Figure 8).

Figure 8: Shipment frequency & time interval in between shipments

Increased delivery time interval

The increased time interval in between shipments might prompt several impacts on thesupplier-retailer chain especially for M/L frequency lanes:

- Fewer replenishments each with more goods and longer cycle times drive higher orderquantities and as a consequence higher cycle stock levels.

Average cycle stock level = Q / 2 (Silver et al., 1998) (Eq 1)

Q: Order quantity in units

- Customers fill the orders of stores from their stock in between replenishments; andhence they try to foresee and stock their needs in advance. As the likelihood ofunexpected events is higher in a longer period of time, the forecast accuracy is mostlydecreasing when covering a longer forecast horizon to build the orders (Makridakisand Hibon, 2000; Van Der Vorst and Beulens, 2002; Van Der Vorst et al., 1998)Reacting to lower forecast accuracy requires to buffer against uncertainties and driveshigher safety stock levels (Eq 2). Failing to react to lower forecast accuracy maycause problems in product availability.

Safety stock level = k * (Silver et al., 1998) (Eq 2)

k: Safety factor

: Standard deviation of errors of forecasts over a replenishment lead time in units

25










k: Safety factor


25










k: Safety factor


26

- In a longer time period the likelihood of date constrained events such as promotions,which might force deliveries in between the regular delivery frequency, are alsohigher.

Urgent deliveries

Urgent deliveries are the quickest expediency to handle with unexpected incidentsparticularly for M/L frequency lanes; while they are more expensive and have low efficiencyin terms of VFR. Therefore, considering the advantages they bring in terms of service and thedisadvantages in terms of cost, it is a challenge to allow them.

If urgent deliveries are considered to be allowed, the consequence of the above mechanism(i.e. increased delivery time interval) will drive to more urgent (or partial) deliveries tomitigate the risk of product unavailability issues at the Customer DC.

Delivery timeliness

Delivery timeliness is essential for the smooth operation of the system. Poor deliverytimeliness might prevent having the right product at the right time and place; and createproduct availability concerns.

While the number of trucks delivered to the Customer DC will be reduced, the volumeshipped remains the same (assumption of neutrality on business). From a statistic point ofview, the delivery timeliness remains the same per truck: While one truck –which can be ontime or late- will deliver more products, there will be proportionally less trucks delivering thesame total amount of product. So the delivery timeliness at the SKU level will remain thesame.

Fewer trucks will positively impact the traffic density (considering at the industry level).Besides, fewer trucks will moderate the traffic and lighten the reception workload at theCustomer DC by reducing the administrative work needed; and this might enable to discussadditional opportunities with customer such as more flexible delivery time windows. Takingthese into account, increased VFR might provide opportunities to improve the deliverytimeliness.

MOQs

Customer orders are released based on MOQs per SKU. MOQ can be defined as theminimum order amount per SKU in terms of pallets/layers/cases. For instance, if the MOQ ofproduct A is agreed to be a full pallet, the customer cannot order less than a full pallet forproduct A.

Increased VFR leads to delivering more goods per shipment. The additional load in a vehicleas a result of increased VFR can contain either the same SKUs the vehicle already contains ordifferent ones. In order not to raise the inventory per SKU, it is preferable to load differentSKUs. Moreover, if the MOQs of each SKU are diminished, we can expect further mitigationon the impacts. Then, the customer will have the opportunity to order and replenish less per

27

SKU. This mechanism would be limited in case of M/L frequency lanes due to the increase inthe time interval in between shipments. As cycle time increases and forecast accuracydeclines, the customer will have a difficulty in determining the right ordering levelsaccurately and will not leverage the capability of a lower MOQ.

Customer inventory

Customer inventory can be examined in two parts: Cycle stock which results from economiesof transport; and safety stock which provides a buffer against demand uncertainties. Severalmechanisms will play positively and negatively on the inventory level as mentioned above.

Fewer replenishments each with more goods and longer cycle times drive higher cycle stocklevels (Eq 1). On the other hand, lower MOQ might lead to lower order quantities and solower cycle stock levels. Safety stock level depends on targeted service level and theuncertainties in the supply chain (Eq 2); and an increase in forecast horizon and resultingforecast accuracy issues will drive higher safety stock levels (Makridakis and Hibon, 2000;Van Der Vorst and Beulens, 2002; Van Der Vorst et al., 1998). The actual inventory levelwill need to be analyzed. It appears clearly that the frequency of the lane (H/M/L) is a keydriver here.

Handling

Handling costs are mainly measured by the time spent to perform the handling processes.Therefore, they constitute higher proportion in markets (or countries) where wages per hourare substantially high. Long distances or infrequent deliveries help to mitigate the impacts ofhandling costs; hence, it might be profitable to reduce handling particularly in H frequencylanes and where the customer is nearby.

Fewer shipments and so less number of vehicles may have a reducing impact on handlingboth at the P&G DC and the Customer DC unless loading/unloading practice is altered in away to require more time and effort (for ex: double decking). Nevertheless, loading moregoods and wider assortment to a vehicle may have an increasing impact on handling costs pershipment. Furthermore, if the contents of pallets get more complex with smaller deliveryquantities per SKU, the handling performed in order picking and pallet loading at the P&GDC as well as pallet splitting at the Customer DC will rise.

The separation and listing of structural supply chain mechanisms was significant as thefollowing steps of the project aimed at analyzing and understanding some of themquantitatively. Still, interactions were detected between the several mechanisms as presentedin Table 3 (A ‘+’ sign indicates a direct interaction and a ‘-’ sign indicates no directinteraction between the involved mechanisms.); and there might be joint effects that bringopportunities or risks depending upon each specific outbound lane.

28

Table 3: Relationship matrix of structural mechanisms

Structural mechanismsBusinessvolume &increasedVFR

Increaseddeliverytimeinterval

Urgentdeliveries

Deliverytimeliness

MOQs Customerinventory

Handling

Business volume & increased VFR + + - + + + +Increased delivery time interval + + + - - + -Urgent deliveries - + + - - - -Delivery timeliness + - - + - - -MOQs + - - - + + +Customer inventory + + - - + + -Handling + - - - + - +

When all the structural mechanisms were considered, ‘Business volume & increased VFR’,‘Increased delivery time interval’, ‘MOQs’ and ‘Customer inventory’ were decided to studyfurther. Table 4 demonstrates the criteria used in selection.

Table 4: Selection criteria of structural mechanisms

Structuralmechanisms

Dataavailability

Independence from technicalsolution/investment

Business volume & increased VFR + +Increased delivery time interval + +Urgent deliveries - +Delivery timeliness - +MOQs + +Customer inventory + +Handling + -

‘Urgent deliveries’ and ‘Delivery timeliness’ were left out of the scope of this study becauseof data unavailability. There was no differentiation between the regular and urgent deliveriesfor the analyzed outbound trade lane; and thus no ‘Urgent deliveries’ data was available. In-depth analysis of ‘Delivery timeliness’ needs traffic-related data which is not available withinthe company.

‘Handling’ was left out of scope, because comprehensive analysis of ‘Handling’ needspicking out a specific technical solution (for ex: double stacking and new handlingequipment) by analyzing the handling processes, equipments and investment opportunities.All the other mechanisms are based on goods flow and independent from technicalsolution/investment.

‘Urgent deliveries’, ‘Delivery timeliness’ and ‘Handling’ also found to possess lessinteraction with the rest of the structural mechanisms (Table 3).

‘Business volume & increased VFR’, ‘Increased delivery time interval’, ‘MOQs’ and‘Customer inventory’ are linked to each other with interactions (Table 3); they are

29

considerably in line with what was determined to be investigated further in Section 2.3 (i.e.customer ordering behavior and MOQs); and they provide sufficient evidence of thesignificant performance measures of outbound deliveries (Section 3.3).

3.3 Performance Measures

Logistics performance measures, ideally, should capture all stakeholders, measure andcompare the current and future performance, include all related activities along the process,recognize and allow for trade-offs between the different dimensions of performance, beunderstandable as well as provide a guide for the action to be taken (Caplice and Sheffi,1995). Performance measures of this project were identified based on this statement and thecareful consideration of outbound delivery elements. Table 5 provides a list of all theperformance measures considered and highlights the ones that were assessed in this project.

Table 5: Performance measures of outbound deliveries

Category Performance measureService P&G on time delivery performance

P&G product availabilityCustomer product availability

Cost P&G transportation costP&G administration costP&G inventory costP&G handling costCustomer administration costCustomer inventory costCustomer handling cost

External cost CO2 emissionsNoisePollutionTraffic density

Three categories were defined for the performance measures: Service, cost and external cost.For both P&G and customer it is significant to keep the balance with service and cost in theiroperations as well as to keep the impact of the operations to their environment at minimum.

Service as a performance measure

Improvement is anticipated in P&G on time delivery performance upon increasing the VFRas explained in the structural mechanism ‘Delivery timeliness’; however, in-depth analysisrequires more traffic data which is not available within the company. Therefore, it was notassessed in this project.

P&G product availability (i.e. fill rate) measures the fraction of customer orders satisfiedfrom inventory. To produce efficiently manufacturers have to produce in batches; and stockat manufacturer’s warehouse is mainly the result of producing goods in certain batches (Van

30

Der Vlist, 2007). P&G product availability is influenced mostly by the upstream operationsand not by the modifications in VFR. Thus, it was not assessed in this project.

Customer product availability (i.e. fill rate) measures the fraction of store orders satisfiedfrom inventory. It is not merely providing a product where and when it is wanted; butmeeting the customer’s needs in a manner which makes the customer want to do businessagain and again in preference to any other company (Livingstone, 1992). P&G aims todeliver superior quality service to its customers and the consumers as a part of its purpose(www.pg.com). Hence, the target customer product availability was assumed to be constant(i.e. 99%) throughout the study.

Cost as a performance measure

Daganzo and Newell (1993) categorized logistics costs as follows: transportation (freightrates), inventory (the opportunity cost and loss of value associated with items in possession),storage (the cost of the physical facilities needed to hold stationary items) and handling (thecost of loading and unloading, storing and retrieving, and otherwise manipulating the items inquestion). Administration cost of outbound delivery processes was also included to the listafter the analysis of the processes in the company.

P&G transportation, P&G administration, customer administration and customer inventorycosts were assessed in this project. Storage costs were included in inventory costs. P&Ginventory cost was not considered since that was found to be not directly related to the VFRmodification. P&G and customer handling costs were not considered because the impacts ofthe VFR modification on these measures depend on a specific technical solution (for ex:double stacking and new handling equipment) to be selected.

External cost as a performance measure

Transportation activities result in significant other external costs such as accidents, noise, airpollution, traffic congestion and climate change (INFRAS, 2004). CO2 emissions wereassessed in this project as they are quantifiable. To evaluate noise, pollution and trafficdensity more data is required which is not available within the company; still improvement isanticipated in all these measures upon increasing the VFR.

3.4 Summary of the findings

The previous sections provided an understanding of the P&G outbound delivery system suchthat the VFR is increased. Higher VFR leads to delivery of more goods per shipment. For thetotal volume, this induces fewer shipments which will cut part of the transportation cost,administration cost and CO2 emissions. On the other hand, a negative impact is anticipated oncustomer’s inventory level as more goods will be pushed to Customer DC.

Reducing the MOQs will be an opportunity to mitigate the negative impacts of increasedVFR on customer’s inventory. If the MOQs are reduced, higher number of SKUs can bedelivered within the same vehicle each with lower volumes. As a result, a decline can beexpected in the total inventory.

www.pg.com

31

The target service level (i.e. the product availability at the customer’s site to the downstreamorders) was assumed to be constant throughout the study, as service level is thought to becritical for the success of business. Figure 9 summarizes the explained dynamics.

Figure 9: Dynamics of the outbound delivery system

In the light of the identified dynamics, the following steps of the project aimed andredesigning the P&G outbound deliveries. Firstly, the VFR was increased; current truck loadis modified to full truck load. Afterwards, MOQs were reduced; full truck load is modified tofull truck load with lower MOQs.

32

Chapter 4

Redesign: Increasing the Vehicle Fill Rate andReducing the Minimum Order QuantitiesThis chapter presents two comparative analyses:

3. Current truck load (CTL) vs. Full truck load (FTL)4. Current truck load (CTL) vs. Full truck load with lower minimum order quantities

(FTL with lower MOQs)

Firstly, the VFR was increased; current truck load is modified to full truck load. Thecomparison CTL vs FTL provides a quantitative understanding of system behavior andreveals the changes in performance measures. Afterwards, MOQs were reduced; current truckload is modified to full truck load with lower MOQs. The comparison CTL vs FTL withlower MOQs also provides a quantitative understanding of system behavior and reveals thechanges in performance measures. Besides, it enables to explain the extent of improvement inP&G outbound deliveries when the VFR is increased and MOQs are reduced.

The typology, the assumptions and the methodology of the analyses are clarified below.Finally, results are presented.

4.1 Typology

CTL

The CTL in this study is 33 full pallet spots in a vehicle each with 1.8 m high pallets (Figure3 and Figure 10; further detail in Section 2.2).

FTL

The FTL has 33 full pallet spots in a vehicle each with 2.4 m high pallets (Figure 7 andFigure 10; further detail in Section 3.2).

FTL with lower MOQs

FTL with lower MOQs has 33 full pallet spots in a vehicle each with two pallets of 1.2 mhigh. As a consequence, the vehicle contains 66 pallets which are double-stacked (i.e. for theconvenience of the calculations) and which are smaller than the current practice (Figure 10).This approach leads to lower MOQs for the SKUs those with an MOQ of one full pallet.

The main idea of this approach is to have lower MOQ levels instead of double stacking.Hence, double stacking should not be interpreted as a solution proposal; instead it should beregarded as a way to visualize the lower MOQs.

33

Figure 10: Visualization of the typology

4.2 Assumptions

A sample outbound trade lane (Figure 15) was considered in calculations. A vehicle type of astandard semitrailer truck, commonly used by P&G was assumed. The vehicle is 2.4 m high,2.45 m width and 13.6 m length; and it has 33 pallet spots on its floor. VFR was targeted tobe modified from CTL (i.e. around 50%) to FTL (i.e. around 60-80%).

The analysis was based on goods flow and independent from any technical solution (for ex:double stacking and new handling equipment) and investments.

Retailer inventory model (RIM) of P&G was used as a tool in calculations (Furtherexplanation in Section 0).

Table 6 lists the assumptions used in quantitative calculations. 13 weeks of data was availableand that whole period was considered in calculations. The lead time and review period weretaken as 3 and 1 day(s) respectively, in line with the practice. The target service level (i.e. theproduct availability at the customer’s site to the downstream orders) was assumed to beconstant and 99% throughout the study.

34

Table 6: Assumptions of the quantitative calculations

A month: 4 weeksA week: 7 daysTotal analysis period: 13 weeksLead time of the shipments: 3 daysReview period: 1 dayTarget service level: 99%

In order to develop insights on different kind of businesses several scenarios were examined.Table 7 lists the scenarios considered in quantitative calculations (Further explanation inSection 0).

Table 7: Scenarios considered in quantitative calculations

Number of the SKUs: 25, 53, 159, 265, 371, 477, 583Shipment frequency: 3/day (i.e. 3 shipments/ day), 2/day, 1/day, 4/week, 3/week, 2/week,

1/week, 3/month, 2/month, 1/monthForecast accuracy: distributed, high, medium, low

4.3 Methodology

This section explains how the sample data that were used in calculations and the examinedscenarios were built; and then introduces the calculations and the model used in thecalculations as well as the verification and validation processes.

4.3.1 Sample Data Building

P&G has all the necessary data for quantitative analysis except the demand information(Table 8). The outbound shipments of P&G are order driven, which means shipments areperformed according to the orders of customers. Customers order according to the demandforecast and their stock levels; and apart from a few cases (for ex: vendor managedinventory), P&G does not have access to the base demand and forecast information ofcustomers. Therefore, shipments are the only way to presume demand information.In depth examination of selected lane data revealed a very high variation of historicalshipments (i.e. σ – can be interpreted as standard deviation of forecast error, Eq 3) ofsome SKUs, which might mean that the data is polluted with promotions, productintroductions, product endings etc. and the results obtained by using this data would bedistorted. Hence, cleaning or correction was necessary to remove the unusual events anddisturbances from the actual data.

= ( )( ) (Eq 3)

35

Table 8: Necessary data for quantitative calculations

General Total analysis periodOutbound lane level Lead time of the shipments

Review periodTarget service levelNumber of SKUs on the laneShipment frequency on the lane

SKU level DemandNumber of cases per palletNumber of cases per layer

Cleaning and correction of data

P&G categorizes the standard deviation of the forecast error (i.e. σ ) as in Table 9.Considering this categorization 36 of 164 SKUs on the selected lane which have σsmaller than 1 (i.e. regular products) were selected for the sample data set. SKU 81143837 inFigure 11 is an instance of this selection. Afterwards, 17 more SKUs were detected whichhave σ smaller than 1 for either the first 6 weeks or the last 6 weeks (i.e. productendings/introductions). For these SKUs the first/last 6 weeks of data were replicated for thelast/first 6 weeks; and they were also selected for the sample data set. SKU 83715007 inFigure 11 is an example. The remaining SKUs were cleaned (SKU 81108433 in Figure 11).As a consequence 53 SKUs were obtained for the sample data set.

Table 9: Categorization of the standard deviation of the forecast error

20% Best in class30% Automatic system based ordering50% Good forecast70% Default value100-120% Heavily promotional

Figure 11: Example for sample data building

35










35










36

4.3.2 Scenario Building

The investigation of 13 weeks of deliveries from P&G DC’s to Customer DC’s in Francerevealed a wide range of shipment frequencies and number of SKUs for the lanes (Figure 6).This illustrates that the type of business on each lane can have distinct characteristics.Furthermore, the information flow in between the supplier and the retailer will not have thesame efficiency level in each of the lanes; and thus the accuracy of the forecast can bedifferent on each lane.

Considering these facts, several scenarios were built and examined in order to developinsights on the different kinds of businesses. The impacts of increased VFR were analyzedfor:

- Various numbers of SKUs on a lane- Various shipment frequencies on a lane- Various forecast accuracy levels

Number of SKUs

The product assortment on P&G outbound trade lanes vary from little to ample. The basesample data set (i.e. 53 SKUs) was replicated to obtain larger data sets. (i.e. 159, 265, 371,477 and 583 SKUs) Meanwhile, the total volume of the business on the lane was keptconstant by a variable (For ex: A lane which had 53 SKUs and a frequency of 2shipments/week still had the same shipment frequency when the number of SKUs wasincreased to 159). Therefore, a higher number of SKUs should not be interpreted necessarilyas higher volumes. Additionally, in order to be able to examine very low number of SKUscase, 25 SKUs were selected randomly from the base 53 SKUs.

As a result 7 different scenarios were obtained to analyze various numbers of SKUs on alane. In fact, the main idea was to observe the behavior of the system at high/medium/low(H/M/L) number of SKUs. Differentiation points for the H/M/L number of SKUs were notdefined; since it was thought to be more reasonable to interpret the impacts along the low-high continuum of number of SKUs. Hence, the numbers presented here should not beinterpreted in their absolute values. The reader is suggested to capture the generalunderstanding.

Shipment frequency

The range of delivery frequency in P&G outbound trade lanes is wide; P&G delivers some ofits Customers’ DCs with several trucks per day, while some others receive one truck everytwo weeks or less. To simulate and observe the situation as we go through this range 10different scenarios were developed. (i.e. 3/day, 2/day, 1/day, 4/week, 3/week, 2/week,1/week, 3/month, 2/month, 1/month) Similar to the ‘Number of SKUs’, the main idea was toobserve the behavior of the system at H/M/L lane frequencies (Table 2); hence, thefrequencies presented here should not be interpreted in their exact values. The reader issuggested to capture the general understanding.

37

Forecast accuracy

Even though the disturbances such as promotions and product introductions/endings wereattempted to be cleaned from the 53 SKUs of sample data set, the resulting inventory levelswith these data were very much above the actual inventory levels of P&G. The reason wasfound to be the σ (Eq 3) values.

Increased VFR leads to less number of shipments (trucks) and an increased time interval inbetween shipments. This prompts significant impacts. The forecast accuracy decreases whencovering a longer horizon (Makridakis and Hibon, 2000; Van Der Vorst and Beulens, 2002;Van Der Vorst et al., 1998); since the likelihood of unexpected events is higher in a longerperiod of time. Acknowledging this fact, σ values needed to be arranged according todifferent scenarios.

Distributed (D) forecast accuracy

High frequency deliveries - several shipments per day- require a close relationship andefficient information flow between the supplier and retailer. Considering the fact that 0.2 iscategorized as best in class in Table 9, it was assigned to σ when the frequency of thelane is very high, specifically when the average time interval in between shipments is lessthan or equal to one day.

The rest of the σ values -for different average time interval in between shipments- weredeveloped by using Eq 4.σ , + σ , = σ , (Eq 4)

Derivation of Eq 4:

Var (X+Y) = Var(X) + Var(Y) + 2 * Cov(X,Y) (Ross, 2000). (Eq 5)

X: First replenishment time interval

Y: Second replenishment time interval

X+Y: Time interval which is equal to the sum of first and second replenishment timeintervals

Var (X): σ ,Var (Y): σ ,Var (X+Y): σ ,X and Y are independent random variables; hence Cov(X,Y) = 0σ , + σ , = σ , (Eq 4)

38

Table 10 lists the σ values. The resulting inventory levels with these σ values were inline with the actual inventory levels of P&G. Thus, this setting can be interpreted as valid forsimulating the current forecast accuracy levels. Still, in addition to this, H/M/L forecastaccuracy level cases were also derived to examine the other situations.

Table 10: values according to forecast accuracy scenarios

σ + σ = σ Linear increaseAvg time interval in btw shipments Distributed High Medium. Low

1 day 0.200 0.200 0.500 0.8002 day 0.283 0.210 0.510 0.8103 day 0.346 0.221 0.521 0.8214 day 0.400 0.231 0.531 0.8315 day 0.447 0.241 0.541 0.8416 day 0.490 0.252 0.552 0.8527 day 0.529 0.262 0.562 0.8628 day 0.566 0.272 0.572 0.8729 day 0.600 0.283 0.583 0.883

10 day 0.632 0.293 0.593 0.89311 day 0.663 0.303 0.603 0.90312 day 0.693 0.314 0.614 0.91413 day 0.721 0.324 0.624 0.92414 day 0.748 0.334 0.634 0.93415 day 0.775 0.345 0.645 0.94516 day 0.800 0.355 0.655 0.95517 day 0.825 0.366 0.666 0.96618 day 0.849 0.376 0.676 0.97619 day 0.872 0.386 0.686 0.98620 day 0.894 0.397 0.697 0.99721 day 0.917 0.407 0.707 1.00722 day 0.938 0.417 0.717 1.01723 day 0.959 0.428 0.728 1.02824 day 0.980 0.438 0.738 1.03825 day 1.000 0.448 0.748 1.04826 day 1.020 0.459 0.759 1.05927 day 1.039 0.469 0.769 1.06928 day 1.058 0.479 0.779 1.07929 day 1.077 0.490 0.790 1.09030 day 1.095 0.500 0.800 1.100

High forecast accuracy

To simulate high forecast accuracy within a supplier-retailer chain, values between 0.2 and0.5 were assigned to σ in a way to increase linearly as the average time interval inbetween shipments raises (Table 10). Eq 4 could not be used in this scenario in order to beable to keep the σ values strictly low.

Medium forecast accuracy

Similar to ‘High forecast accuracy’, to simulate medium forecast accuracy within a supplier-retailer chain, values between 0.5 and 0.8 were assigned to σ in a way to increase linearly

39

as the average time interval in between shipments raises (Table 10). Eq 4 could not be used inthis scenario in order to be able to keep the σ values in the determined interval.

Low forecast accuracy

Similarly, to simulate low forecast accuracy within a supplier-retailer chain, values between0.8 and 1.1 were assigned to σ in a way to increase linearly as the average time intervalin between shipments raises (Table 10). Eq 4 could not be used in this scenario in order to beable to keep the σ values strictly high.

In the end 280 different scenarios were examined for CTL, FTL and FTL with lowerMOQs: 10 different shipment frequency scenarios were tested for 7 different scenarios ofnumber of SKUs on a lane; and each of them were simulated for 4 different types of forecastaccuracy level.

4.3.3 Retailer Inventory Model (RIM) and Calculations

RIM

After the sample data set and the different scenarios were developed, calculations wereperformed using the ‘Retailer Inventory Model’ (RIM) of P&G which is an Excel based tooldeveloped in 2008. RIM considers a single supplier-retailer lane; and it provides outputsaccording to the descriptive characteristics of the lane as well the attributes of the SKUs onthat lane.

The tool calculates the stock levels of the customer and its orders. Orders are based on thebusiness need to avoid a stock-out in daily reviews and on additional quantities that areordered to load the truck at its maximum floor fill capacity. This means that the service levelis maintained and the loads are consolidated forward and not backward.

Table 11 and Table 12 present the inputs and outputs of the tool respectively. The specificadditions/adaptations performed on RIM for this study are presented below. Figure 17 inAppendix 3 presents the user interface of the tool.

Table 11: Inputs of RIM

Input Variable Unit and ExplanationGeneral Total analysis period weeks

Number of pallets per truck pl/truckOutbound lane level Lead time of the shipments days (order to delivery cycle time)

Review period days (between DC orders)Target service level percentage

SKU level Number of layers per pallet ly/plNumber of cases per layer cs/lyMSU/period cs/week (can be calculated by P&G data;

it is a measure of sales used to obtaindemand information)σ (Eq 3)

40

Table 12: Outputs of RIM

Output Variable Unit and ExplanationTotal Number of SKUs on the lane unitsTotal demand in pallets per period pl/weekTotal demand in pallets per day pl/dayNumber or trucks needed per period to deliver the total volume trucks/periodAverage replenishment interval daysCycle stock level daysSafety stock level daysTotal stock level days

Adaptations of RIM

RIM assesses the truck load by the number of full pallet spots in a vehicle. Hence, itcomputes the number of trucks needed by assuming a 33 pallets load (each with 1.8 m high)for each vehicle. This structure provides the results for CTL. To be able to simulate FTL andFTL with lower MOQs, the number of layers per pallet for each of the SKUs were renderedas configurable by a variable; so that the pallet height could be adapted according to theexamined case:

- For FTL: number of layers per pallet * 2.4 / 1.8; rounded to the closest integer- For FTL with lower MOQs: number of layers per pallet * 1.2 / 1.8; rounded to the

closest integer

For each of the 10 shipment frequency scenarios, 7 different number of SKUs scenarios wereexamined. To do so, it was required to keep the total shipped volume constant, while thenumber of SKUs was changing. Therefore, MSU/period values for each of the SKUs werealso rendered as configurable by a variable, to arrange the total volume.

Calculations

Calculations were performed with the help of RIM to assess the impacts of increased VFR onthe performance measures of P&G outbound deliveries. The details of calculations for eachof the performance measures are explained below.

P&G transportation cost

Unit P&G transportation cost is measured in €/kms. Its actual value depends on many factorssuch as the location of the DCs, oil prices, employee wages etc. Therefore, it is specific foreach different lane. The decline in P&G transportation cost can be calculated by multiplyingthis unit cost by the eliminated number of shipments through increasing VFR. RIM providesthe number of trucks needed per period to deliver the total volume; and, eliminated number ofshipments per period is the difference of this value between CTL case and FTL/FTL withlower MOQs case.

41

P&G administration cost

Increasing VFR eliminates significant amount of paperwork as well as related costs. P&Gadministration cost is measured in €/order/invoice. The standard industry average is €30/order/invoice; which means each eliminated shipment leads a €60 of reduction in thismeasure.

Customer administration cost

Similar to ‘P&G administration cost’, increasing VFR eliminates significant amount ofpaperwork as well as related costs of the customer. The Customer DC was assumed to incuranalogous administration costs; which means each eliminated shipment leads a €60 ofreduction in this measure.

Customer inventory cost

RIM provides both resulting cycle and safety stock levels. This enables to calculate the totalinventory cost and the amount of capital tied to inventory. In addition, allocation of the cycleand safety stock levels gives an opportunity to comprehend the drivers of the presentinventory.

Unit inventory holding cost can be taken as €10 /spot/month. Capital tied to inventory can betaken as 7-10% per year.

CO2 emissions

Figure 12 (Bilan Carbone, 2007) presents the CO2 emission levels as a result of energyconsumption for heavy duty trucks: The higher the load factor the higher the emission pervehicle (i.e. blue line); however, the higher the load factor, the lower the emission per tonne-km (i.e. red line). Therefore, although a slight increase in CO2 emissions will be observed pershipment due to a heavier payload, the increased VFR and so the reduction of the number oftrucks used will definitely reduce the total emissions.

Figure 12: CO2 emission levels vs load weight

42

In P&G, CO2 emissions of an average loaded truck is assumed to be 0.9 kg/km. Theimprovement in CO2 emissions can be calculated multiplying this value by the eliminatednumber of shipments. The increase in CO2 emissions due to a heavier payload is assumed tobe negligible.

The calculations can be performed for any scenario as long as all the input variables areknown. In this project, all developed scenarios were simulated using RIM; however theabsolute changes in total cost were not calculated. The reason was that some cost measuresdepend on the specific lane to be selected. Although the absolute changes in cost could not becalculated, the results (presented in Section 4.4) obtained by simulating the scenarios wereenough to develop insights.

4.3.4 Verification and Validation

This study involves simulating scenarios (Section 0) on an Excel based tool (Section 0) withnumerous assumptions (Section 4.2) in order to compare the CTL with alternative situations.This section explains the steps of verification and validation processes that are performed tocheck whether the analyses provide reliable results.

Verification

Verification is the process of ensuring that the model behaves in the way it was intendedaccording to the modeling assumptions made (Law and Kelton, 1991). In this study, it wasnecessary to determine whether the developed scenarios were correctly represented in themodel.

Higher VFR leads to delivery of more goods per shipment and for the total volume, thisinduces fewer shipments. The model was expected to provide smaller values for the numberof trucks needed to deliver the same volume when CTL was improved to FTL/FTL withlower MOQs. Table 13 shows that it is reliable to verify that higher VFR leads to fewershipments in the analyses.

Table 13: Verification - number of trucks needed to deliver the total volume when CTL was improved to FTL/FTL withlower MOQs

# of trucks needed

#SKUs Ship. Fr CTL FTLFTL w.MOQ

265 3/day 280 201 188265 3/week 39 28 26265 1/month 3 3 2

One of the assumptions in building the scenarios was to keep the total shipped volumeconstant while the number of SKUs was changing. A variable was added to the model inorder to arrange the volume in the described way. Table 14 presents the resulting number oftrucks needed to deliver the total volume according to the different scenarios in CTL case.Although there are minor differences in some values, it is reliable to affirm that the volumewas kept constant whenever needed in the analyses.

43

Table 14: Verification - number of trucks needed to deliver the total volume according to different scenarios in CTL case

Ship.Fr.

Number SKUs25 53 159 265 371 477 583

3/day 277 275 278 280 279 278 2773/week 39 39 39 39 39 39 391/month 3 3 3 3 3 3 3

Validation

Validation is the process of determining whether a built model is an accurate representationof the actual system. Thus, it requires confirming the outputs of the model with real lifeknowledge. Although statistical tests can be carried out in validation, a good dose of commonsense is also respected (Kelton et. al, 2003).

To validate the analyses, firstly, the outputs of the model were compared with the actualstatistics. In this study numerous scenarios were analyzed; however, it was not possible tocompare the results of the each scenario with the real data. Still, apart from some extremecases (i.e. low frequency and low forecast accuracy level scenarios) it was observed thatresulting inventory levels in CTL case stay within the limits of the actual inventory levelsthat statistics indicate. The deviated outcomes of the extreme cases were also assumed asvalid; because it was predictable to have very high inventory levels in CTL case when theshipments were infrequent and the time interval between replenishments were high as well aswhen the forecast accuracy level was low.

Afterwards, the model’s behavior was tested to validate whether it was parallel to real lifeexpectations (Law and Kelton, 1991). For this purpose, business volume, σ , reviewperiod, lead time and target service level were altered respectively.

Higher VFR led to fewer shipments and longer replenishment time intervals; as aconsequence cycle stock levels were also higher as anticipated. When σ values werealtered, the number of shipments stayed constant; because there was no change in businessvolume. On the other hand, lower σ values kept safety stock levels lower, while higherσ values led to higher safety stock levels in line with the expectations. Increased reviewperiod as well as increased lead time caused an increase in safety stock levels; becauseincreasing any of them reduces the flexibility of the goods flow. Lastly, alterations in targetservice level also induced presumed variations in safety stock level.

To sum up, it can be concluded from above explanations that the model and the calculationswere reliable and represented the actual system.

4.4 Results

This section presents the results of two comparative analyses:

1. CTL vs. FTL2. CTL vs. FTL with lower MOQs

44

The results for each of the performance measures are explained separately.

CTL vs. FTL

This part explains the behavior of the performance measures when VFR was increased andthe CTL was improved to FTL (Table 15).

Table 15: Behaviors of performance measures when CTL is improved to FTL

Shipment frequency (C TL) 3/day 2/day 1/day 4/week 3/week 2/week 1/week 3/month 2/month 1/month

SAVING IN TOTAL VOLUME HIGH FREQ MIDDLE FREQUENCY LOW FREQUENCY

Cash Customer inventory

Cost

P&G transport

P&G admin

Customer inventory

Customer admin

External Cost CO2 emissions

P&G transportation cost

Eliminating the number of trucks required to deliver the total volume led to a positive impactin ‘P&G transportation cost’ in all of the scenarios (Table 15); the cost decreased and savingsrealized. The extent of the savings depended mainly on the frequency of the lane (i.e. thevolume of the business) as well as the distance travelled.

Table 16: Number of trucks needed to deliver the total volume according to different scenarios (CTL vs FTL)

Number SKUs

Ship. Fr.

25 53 159 265 371 477 583

CTL FTL CTL FTL CTL FTL CTL FTL CTL FTL CTL FTL CTL FTL

3/day 277 198 275 197 278 200 280 201 279 200 278 200 277 198

2/day 181 129 182 130 181 130 182 130 182 130 181 130 183 131

1/day 91 65 91 65 91 65 91 65 90 64 91 65 91 65

4/week 52 37 52 38 52 37 51 36 52 37 52 37 52 38

3/week 39 28 39 28 39 28 39 28 39 28 39 28 39 28

2/week 26 19 26 18 26 19 26 18 26 19 26 18 26 19

1/week 13 9 13 10 13 10 13 9 13 9 13 9 13 10

3/month 10 7 10 7 10 7 10 7 10 7 10 7 10 7

2/month 7 5 7 5 7 5 7 5 7 5 7 5 7 5

1/month 3 3 3 3 3 3 3 3 3 3 3 3 3 3

Table 16 presents the number of trucks needed to deliver the total volume according todifferent shipment frequency and number of SKUs scenarios. The values are the same for alldifferent forecast accuracy level scenarios; hence, they are not presented here separately. Itcan be seen that the higher the volume and frequency of the lane the higher the difference innumber of trucks needed to deliver the total volume between CTL and FTL and thus thesavings.

no impact

no impact

variable/other factors

variable/other factors

major negative impact

major negative impact

major positiveimpact


medium positiveimpact


minor positiveimpact


45

Moreover, the results show that the number of SKUs on a lane or the forecast accuracy levelsin the system did not show a significant impact on the ‘P&G transportation cost’.

P&G administration cost / Customer administration cost

Similar to ‘P&G transportation cost’, eliminating the number of trucks required to deliver thetotal volume led to a positive impact in the administration costs in all of the scenarios (Table15); the cost decreased and savings realized. The extent of the savings depended on thefrequency of the lane (i.e. the volume of the business); the higher the volume and frequencyof the lane the higher the savings.


When the CTL is improved to FTL, resulting total inventory level showed differencesdepending on the analyzed scenario. For instance, the impact on the inventory level and so onthe ‘Customer inventory cost’ was very minor positive when the shipment frequency of thelane was high; the number of SKUs on the lane was low; and the forecast accuracy was low.Figure 13 summarizes the observed impacts.

Figure 13: Impacts on the customer inventory cost as a result of improving CTL to FTL

When the frequency of the lane was low, inventory level increased critically (Table 26 inAppendix 4), because the longer time interval in between replenishments prompted highercycle stock. The impact was mainly major negative (Figure 13, Table 15); causing anincrease in ‘Customer inventory cost’. The impact on ‘Customer inventory cost’ was stillnegative at medium shipment frequencies (Figure 13, Table 15). The extent of this negativeimpact depended on the other drives (i.e. the number of SKUs and forecast accuracy level).Almost no impact (i.e. negligible very minor positive) was observed in high frequency lanes(Figure 13, Table 15) as the modified VFR did not change the time interval in betweenreplenishments -and thus cycle stock- in such lanes significantly.

The higher the number of SKUs on a lane, the more the increase in inventory level throughVFR modification (Table 26 in Appendix 4); because as more goods were pushed to theCustomer DC per SKU, the total piled amount proliferated with numerous SKUs. Therefore,negative impact on ‘Customer inventory cost’ turn into major negative impact gradually asthe number of SKUs rose (Figure 13).

46

Lower forecast accuracy level means a higher probability of unexpected events; andconsequently higher safety stock level. The inventory level was already high when theforecast accuracy was low in CTL case. Therefore, the increase in cycle stock level when thesystem was improved to FTL was not obvious. Contrarily, a negligible minor positive impactwas observed when the forecast accuracy was low, the shipment frequency of the lane washigh and the number of SKUs on the lane was low (Figure 13, Table 26 in Appendix 4). Thisis a consequence of the dynamics between the cycle stock and safety stock. As the cyclestock and so its daily demand coverage increases, it leads a decline in safety stock. In thiscase, the decline in safety stock surpassed the increase in cycle stock (Table 26 and Table 27in Appendix 4). It can be concluded that the forecast accuracy level was not one of the maindrivers of the changes observed in inventory level and related costs.

CO2 emissions

Similar to ‘P&G transportation cost’, ‘P&G administration cost’ and ‘Customeradministration cost’ eliminating the number of trucks required to deliver the total volume ledto a positive impact in ‘CO2 emissions’ in all cases (Table 15); the emissions reduced. Theextent of the savings depended mainly on the frequency of the lane (i.e. the volume of thebusiness); as well as the distance travelled. The higher the volume and frequency of the laneand the longer the distance to be travelled the higher the savings.

CTL vs. FTL with lower MOQs

This part explains the behavior of the performance measures when VFR was increased andMOQs were reduces; specifically when the CTL was improved to FTL with lower MOQs(Table 17).

Table 17: Behaviors of performance measures when CTL is improved to FTL with lower MOQs

Shipment frequency (C TL) 3/day 2/day 1/day 4/week 3/week 2/week 1/week 3/month 2/month 1/month

SAVING IN TOTAL VOLUME HIGH FREQ MIDDLE FREQUENCY LOW FREQUENCY

Cash Customer inventory

Cost

P&G transport

P&G admin

Customer inventory

Customer admin

External Cost CO2 emissions

P&G transportation cost / P&G administration cost /Customer administration cost / CO2

emissions

Eliminating the number of trucks required to deliver the total volume led to a positive impactin these costs (Table 17); they decline and savings realized. Table 18 presents the number oftrucks needed to deliver the total volume according to different shipment frequency andnumber of SKUs scenarios. The values are the same for all different forecast accuracy levelscenarios; hence, they are not presented here separately. Again, the extent of the savingsdepended on the frequency of the lane (i.e. the volume of the business). It can be seen that the

no impact

variable/other factors major positive impactno impact

variable/other factors major positive impactmajor positive

impact






47

higher the volume and frequency of the lane the higher the difference in number of trucksneeded to deliver the total volume between CTL and FTL with lower MOQs and thus thesavings. Improving to system from CTL to FTL with lower MOQs instead of FTL ledfurther savings (Table 16 and Table 18).

Table 18: Number of trucks needed to deliver the total volume according to different scenarios (CTL vs FTL with lowerMOQs)

Number SKUs

Ship.Fr.

25 53 159 265 371 477 583

CTL

FTL

CTL

FTL

CTL

FTL

CTL

FTL

CTL

FTL

CTL

FTL

CTL

FTLw.

MOQw.

MOQw.

MOQw.

MOQw.

MOQw.

MOQw.

MOQ

3/day 277 185 275 184 278 187 280 188 279 187 278 187 277 186

2/day 181 121 182 122 181 122 182 122 182 122 181 122 183 123

1/day 91 61 91 61 91 61 91 61 90 60 91 61 91 61

4/week 52 35 52 35 52 35 51 34 52 35 52 35 52 35

3/week 39 26 39 26 39 26 39 26 39 26 39 26 39 26

2/week 26 18 26 17 26 18 26 17 26 18 26 17 26 18

1/week 13 9 13 9 13 9 13 9 13 9 13 9 13 9

3/month 10 7 10 7 10 7 10 7 10 7 10 7 10 7

2/month 7 5 7 5 7 5 7 5 7 5 7 5 7 5

1/month 3 2 3 2 3 2 3 2 3 2 3 3 3 2


When the CTL is improved to FTL with lower MOQs, resulting total inventory levelshowed differences depending on the analyzed scenario. The impacts were almost theopposite of improving the system from CTL to FTL. For instance, the impact on theinventory level and so on the ‘Customer inventory cost’ was very minor negative when theshipment frequency of the lane was high and the number of SKUs on the lane was low; andthe forecast accuracy was high. Figure 14 summarizes the observed impacts.

Figure 14: Impacts on the customer inventory cost as a result of improving CTL to FTL with lower MOQs

When the frequency of the lane was low, inventory level decreased (Table 28 in Appendix 4),because the redundant part of the cycle stock could be eliminated through lowering the MOQ.

48

The impact was mainly major positive (Figure 14, Table 17); causing a decline in ‘Customerinventory cost’. The impact on ‘Customer inventory cost’ was still positive at mediumshipment frequencies (Figure 14, Table 17). The extent of the effect depended on the otherdrives (i.e. number of SKUs and forecast accuracy level). Almost no impact (i.e. negligiblevery minor positive) was observed in high frequency lanes (Figure 14, Table 17) as themodified VFR and reduced MOQs did not change the time interval in betweenreplenishments in such lanes significantly.

The higher the number of SKUs on a lane, the more the decrease in inventory level throughVFR modification (Table 28 in Appendix 4). More stock could be eliminated from the totalamount with numerous SKUs as part of the redundant cycle stock could be cut per SKU.Therefore, positive impact on ‘Customer inventory cost’ turn into major negative impactgradually as the number of SKUs rose (Figure 14).

The inventory level was already high when the forecast accuracy was low in CTL case.Therefore, the decrease in cycle stock level when the system was improved to FTL withlower MOQs was not obvious. Contrarily, a negligible minor negative impact was observedwhen the forecast accuracy was low and the shipment frequency of the lane was high (Figure14). This is a consequence of the dynamics between the cycle stock and safety stock. As thecycle stock and so its daily demand coverage decreases, it leads an increase in safety stock. Inthis case, the increase in safety stock surpassed the decrement in cycle stock (Table 27 andTable 28 in Appendix 4). It can be concluded that the forecast accuracy level was not one ofthe main drivers of the changes observed in inventory level and related costs.

The results indicated negative impacts, specifically on the inventory level, when the VFR wasincreased (i.e. CTL was improved to FTL) without any other changes in the supplier-retailerchain. Subsequently, it was shown that the direction of the negative impact changes topositive when the MOQs were reduced while increasing the VFR (i.e. CTL was improved toFTL with lower MOQs). Therefore, reducing the MOQs provides an opportunity to improvethe VFR while keeping the balance with service level and logistics costs; and even improvingthem in most cases.

49

Chapter 5

Conclusion and RecommendationsThis chapter summarizes the main findings of the project. Afterwards, it providesrecommendations.

5.1 Conclusions

This project was held in cooperation with SNIC, P&G. The research assignment was set asfollows:



Firstly, the VFR was increased; CTL is modified to FTL. The impacts of increased VFR onservice level and logistics costs were assessed. The analyses revealed that the extent of theimpacts on performance measures differed mainly according to the volume/frequency of thelane and the number of SKUs on the lane.

It was found that increasing VFR without any other changes in the supplier-retailer chain hasnegative impacts, specifically on the inventory level. Improving the outbound deliveries fromCTL to FTL can only be reasonable for high frequency lanes. In practice, this is tricky;because high frequency deliveries usually performed between the supplier and retailers wherethe DCs are nearby. Hence, it should be checked if the expected improvements in total costactually satisfy the modifications in VFR.

Afterwards, MOQs were reduced; CTL is modified to FTL with lower MOQs. It was foundthat reducing MOQs while increasing the VFR changes the direction of the impact on theinventory level from negative to positive. Besides, improving the outbound deliveries fromCTL to FTL with lower MOQs was reasonable for most of the scenarios. The performancemeasures were improved especially for medium and low frequency lanes.

Clearly, increasing VFR meanwhile lowering MOQs is an opportunity to improve the VFRwhile keeping the balance with service level and logistics costs; and even improving them inmost cases.

5.2 Recommendations

This study contributes to the relevant research area by providing an example of increasing theVFR in outbound transportation. Firstly, it provides a categorization of the outbound deliveryelements (Table 23, Table 24and Table 25) as well as an overall mapping of them with theirinteractions (Figure 14). These can be used as a check-list in related studies; and/or canfacilitate detecting the indirect dynamics. Afterwards, the study presents the possible

50

outcomes of increasing the VFR. This helps to identify the related risks and opportunities.Furthermore, the study suggests an approach which will mitigate the possible negativeimpacts of increased VFR and improve the logistics costs further.

The study contributes to the company in several ways. Firstly, the categorization of theoutbound delivery elements (Table 23, Table 24and Table 25) as well as the overall mappingof them with their interactions (Figure 14) can provide guidance for people who are notinvolved in supply chain operations in understanding the part of supply chain dynamics.Moreover, the company can use this study in determining the type of businesses that exhibitthe VFR improvement potential. Similarly, when the VFR of a specific lane is considered tobe increased, the company can consult the results of the relevant scenarios of this study.Furthermore, displayed mutual benefits of improving CTL to FTL with lower MOQs willmotivate both the suppliers and retailers to increase the VFR.

It is important to note that the conclusions should be interpreted considering the assumptionsmade during the analysis. Furthermore, it should be noted that this was not a financial study.The focus was on the goods flow. The specific technical solutions, possible investments andthe absolute changes in terms of cost were not analyzed.

As a future research direction, handling costs can be incorporated into the study. Due to timelimitations, specific technical solutions (for ex: double stacking and new handling equipment)could not be assessed and handling costs could not be covered. Consideration of investmentson the technical solution as well as resulting handling costs will provide a more realisticoverall idea on the results.

This study mainly focuses on what will happen and what should be done when the VFR isincreased instead of how VFR can (optimally) be increased. Another potential research canfocus on the density of the payload for instance by mixing the heavy and light SKUs in orderto leverage the increased VFR and/or not to face with obstacles in practice.

During the study, it was recognized that the literature lacks a whole quantifiable model of theVFR in which all the aspects of the outbound deliveries are included. Taking the overallmapping of the outbound delivery elements as a basis (Figure 14), the relations of the VFRwith related elements can be quantified. Being aware of the high complexity in the supplychain, still, it will be worthwhile to consider modeling for the setting.

Lastly, while this project is focusing on the outbound trade lane scope, the connections withthe wider end-to-end perspective could be the scope of further studies.

51

ReferencesBilan Carbone. “Emission factor guide”. France; 2007.

Caplice, C, and Sheffi, Y. “A review and evaluation of logistics performance measurementsystems”. The International Journal of Logistics Management 1995; 6, 1; p. 61-74.

Chapman, L. “Transport and climate change: a review”. Journal of Transport Geography2007;.15; p. 354–367.

Daganzo, C.F, and Newell, G.F. “Handling operations and the lot size trade-off”.Transportation Research Part B: Methodological 1993; 27, 3; p. 167-183.

Department for Transport, UK. “Key performance indicators for food and drink supplychains”. Freight Best Practice Publications. UK; 2007.

Disney, S, Potter, A, and Gardner, B. “The impact of vendor managed inventory on transportoperations”. Transportation Research Part E: Logistics and Transportation Review 2003;39; p. 363–380.

European Commission. “White Paper: Roadmap to a single European Transport Area –Towards a competitive and resource efficient transport system”. Brussels; 2011.

European Energy Agency. “Climate for a transportation change, TERM 2007, indicatorstracking transport and environment in the European Union”. Copenhagen; 2008.

European Energy Agency. “Transport and environment: Facing a dilemma, TERM2005,indicators tracking transport and environment in the European Union”. Copenhagen;2006.

European Environment Agency. “Load factors for freight transport, TERM 030”. 2010.http://www.eea.europa.eu/data-and-maps/indicators/load-factors-for-freight-transport/load-factors-for-freight-transport-1. Last retrieved in 22.08.2011.

INFRAS. “Final report: External costs of transport”. Zurich/Karlsruhe; 2004.

Kelton, W.D, Sadowski, R.P , and Sturrock, D. T. Simulation with Arena. New York:McGraw-Hill; 2003.

Law, A.M, and Kelton, W.D. Simulation modeling and analysis. New York: McGraw-Hill;1991.

Livingstone, G. “Measuring customer service in distribution”. International Journal ofPhysical Distribution & Logistics Management 1992; 22, 6; p. 4-6.

Makridakis, S, and Hibon, M. “The M3-Competition: results, conclusions and implications”.International Journal of Forecasting 2000; 16; p. 451-476.

http://www.eea.europa.eu/data-and-maps/indicators/load-factors-for-freight-transport/

52

McKinnon, A. “A logistical perspective on the fuel efficiency of road freight transport”.Workshop “Improving fuel efficiency in road freight: The role of informationtechnologies”. Paris; 1999.

McKinnon, A. “Report prepared for the 15th ACEA scientific advisory group meeting:European freight transport statistics: limitations, misinterpretations and aspirations”.Brussels; 2010.

Pibernik, R. “Managing stock-outs effectively with order fulfillment systems”. Journal ofManufacturing Technology Management 2006; 17, 6; p. 721-736.

Procter&Gamble. “Annual Report”. 2010.

Procter&Gamble. “Sustainability Report”. 2010.

Procter&Gamble website, www.pg.com. Last retrieved in 20.08.2011

Ross, S.M. Introduction to probability and statistics for engineers and scientists. USA:Harcourt Academic Press; 2000.

Silver, E.A, Pyke, D.F, and Peterson, R. Inventory management and production planning andscheduling. USA: John Willy & Sons; 1998.

Van Aken, J.E, Berends, H, and Van der Bij, H. Problem-solving in organizations:Methodological handbook for business students. Cambridge: University press; 2007.

Van Der Vlist, P. “Synchronizing the Retail Supply Chain”. Erasmus University, RotterdamSchool of Management; ERIM Ph.D. Series Research in Management no. 110,http://hdl.handle.net/1765/1, ISBN 90-5892-142-0. Rotterdam; 2007.

Van Der Vorst, J.G.A.J, Beulens, A.J.M, De Wit, W, and Van Beek, P. “Supply chainmanagement in food chains: Improving performance by reducing uncertainty”.International Transactions in Operational Research 1998; 5, 6; p. 487-499.

Van Der Vorst, J.G.A.J, and Beulens, A.J.M. “Identifying sources of uncertainty to generatesupply chain redesign strategies”. International Journal of Physical Distribution &Logistics Management 2002; 32. 6; p. 409-430.

Van Strien, P.J. “Towards a methodology of psychological practice: the regulative cycle”.Theory Psychology 1997; 7; p. 683 – 700.

www.pg.com

http://hdl.handle.net/1765/1

53

AppendicesAppendix 1: An overview of P&G data

Table 19: VFR in P&G

CONFIDENTIAL

Table 20: Max allowed legal weight

Max allowed legal weight of a vehicle 24 tons of payload

The 24 tons of maximum allowed legal payload weight presented in Table 20 is the value fora standard semi-trailer truck which was assumed in this project.

54

Table 21: P&G plant locations in Western Europe (as of 30 June 2010)

Country City Plant Name Category OwnerBelgium Aarschot Aarschot DuracellBelgium Mechelen Mechelen Fabric CareFrance Amiens Amiens Fabric CareFrance Blois Blois Hair CareFrance Poissy Poissy Personal Beauty CareFrance Sarreguemines Sarreguemines Hair CareGermany Berlin Berlin Blades & RazorsGermany Cologne Cologne Personal Beauty CareGermany Crailsheim Crailsheim Feminine CareGermany Euskirchen Euskirchen Baby CareGermany Gross-Gerau Gross-Gerau Oral CareGermany Hünfeld Huenfeld Hair CareGermany Kronberg Kronberg BraunGermany Marktheidenfeld Marktheidenfeld Oral CareGermany Rothenkirchen Rothenkirchen Hair CareGermany Walldürn Wallduern BraunGermany Worms Worms Fabric CareIreland Carlow Carlow BraunIreland Nenagh Nenagh Personal Beauty CareIreland Newbridge Newbridge Oral CareItaly Campochiaro Campochiaro Fabric CareItaly Gattatico Gattatico Home CareItaly Pescara Pescara Feminine CareItaly Rome Pomezia Fabric CareNetherlands Coevorden Coevorden Pet CarePortugal Guifões Porto Fabric CareSpain Jijona Jijona Baby CareSpain Mataró Mataró Fabric CareSpain Mequinenza Mequinenza Baby CareSpain Montornès del Vallès Montornès Feminine CareUnited Kingdom London London Home CareUnited Kingdom Manchester Manchester Baby CareUnited Kingdom Reading Reading Personal Beauty CareUnited Kingdom Whitley Bay Seaton Delaval Personal Beauty Care

(Source: P&G Sustainability Report, 2010)

55

Table 22: VFR (Inbound deliveries vs Outbound deliveries in Western Europe)

CONFIDENTIAL

Figure 15: The lane selected for analysis

CONFIDENTIAL

56

Appendix 2: Outbound delivery elements

Figure 16: Mapping of the elements in outbound deliveriesIn Figure 16, the element at the back of an arrow has a direct influence on the element at the head of the very same arrow. For red elementsmeasurement unit could not be provided; more analysis is required. Table 23, Table 24 and Table 25 categorizes decision, dependent andindependent variables as well as performance measures of the outbound delivery elements in Figure 16 under the related processes and provide adefinition of the elements where necessary.

VFR (%)PG trans port cos t

(Euro's )

External cos t(Euro's )

Total lanebus ines s (cs )

Shopper demand perSKU (H/M/L) (cs )

Shopper demandvariability per SKU

(cs /SKU)

Smoothnes s

Promotion

Phas e-inPhas e-out

O ne timeintroduction

Seas onality

C us tomer's forecas thorizon (hrs -days -wks )

MO I per SKU(cs -ly-pl/SKU)

# of urgents hipments

(s hpmnts /wk)

Probability of anurgent s hipment (%)

C us tomer productavailability (%)

C us tomer totalinventory per SKU

(cs /SKU)

PG order picking &pallet loading cos t

(Euro's )

C us tomer orders plitting cos t (Euro's )

PG on timedelivery rate (%)

Probability of beinglate per s hipment (%)

Traffic dens ity

C us tomer's forecas taccuracy per SKU (%)

Urgent s hipmentallowed? (binary)

Flexibility of timewindows impos ed by

C us tomer

Lead time from PG DC toC us tomer DC

(hrs -days -wks )Delivery quantity for each

SKU per s hipment(cs -ly-pl/SKU)

C us tomer's forecas tper SKU (cs /SKU)

C us tomer's order perSKU (cs -ly-pl/SKU)

Trade terms

C us tomer cycle s tockper SKU (cs /SKU)

C us tomer s afety s tockper SKU (cs /SKU)

C us tomer pipelines tock per SKU (cs /SKU)

Review period(hrs -days -wks )

# ofs hipments (s hpmnts /wk)

Time interval in betweens hipments (hrs -days -wks )

# of SKUs on thelane (units )

Target s ervice level interms of productavailability (%)

PG vehicle loadingcos t (Euro's )

C us tomer vehicleunloading cos t (Euro's )

PG handling cos t(Euro's )

C us tomer handlingcos t(Euro's )

PG cas e fill rate(%)

Vehicle height(m)

Vehicle length(m)

Vehicle width (m)

Max legal weight(kgs )

C us tomer totalinventory holding cos t

(Euro's )

C us tomer s toragecapacity

C us tomer unit inventoryholding cos t (Euro's /pl

s pot/month)

Pallet width (m)

Pallet length (m)

# decks (units )

Pallet height (m)

Max # of pallets(pl)

# of pallets (pl)

C us tomer unit orders plitting cos t (Euro's /

pl)

C us tomer unit vehicleunloading cos t (Euro's /pl

/deck /s hpmnt)

P&G unit order picking &pallet loading cos t (Euro's

/pl)

P&G unit vehicle loadingcos t (Euro's /pl /deck

/s hpmnt)

Fixed vehicle us e cos t(Euro's /s hpmnt)

Variable vehicleus e cos t

Dis tancetravelled (kms )

External cos tparameters

Urgent s hipmentcos t parameters

SKU dimens ions(cs /pl/SKU)

SKU weight(kg/cs /SKU)

57

Table 23: Categorization of the elements in outbound deliveries (P&G Operations)

Variable(Definition)

(Unit)

P&G Operations

Warehousing Outbound transportation

Proc

ess

Step

s

OrderAccepting

Order Picking & PalletLoading Vehicle Loading Regular Transportation Urgent Delivery

Deci

sion

varia

bles Target VFR (Vehicle fill rate

in terms of weight andvolume) (%)

# of pallets (Also has theinformation of what apallet contains) (pl)

Inde

pend

ent v

aria

bles

P&G unit order picking &pallet loading cost(According to whatpallet contains) (Euro's/pl)

# of decks (units)

P&G unit vehicle loadingcost (Euro's /pl /deck/shpmnt)

Pallet height (m)Pallet length (m)Pallet width (m)

Vehicle height (m)Vehicle length (m)Vehicle width (m)Max legal weight (kg)

Fixed vehicle use cost (Euro's/shpmnt)

Variable vehicle use costparameters

Distance travelled (km)

External cost parameters

Time windows imposed bycustomer (The time intervalwhich the delivery can beperformed; the narrower thetime window the less theprobability to be on time)

Urgent shipmentallowed (Y/N)? (If thesystem allows urgentdeliveries, it is "Yes";otherwise it is "No")(1/0)

Urgent shipment costparameters

Depe

nden

t var

iabl

es

Delivery quantity foreach SKU per shipment(Delivery quantity perSKU a shipment containsin terms of cases, layersand pallets) (cs-ly-pl/SKU) Max # of pallets (pl)

# of shipments (shpmnt/wk)

Time interval between 2shipments (hrs-days-wks)

Lead time (Time in betweenan order release and deliveryof goods) (hrs-days-wks)

Probability of being late pershipment (%)

Probability of anurgent shipment (%)

# of urgent shipments(shpmnts/wk)

Perf

orm

ance

mea

sure

s

P&G case fillrate (Thepercentage ofcustomerorders thatare filled frominventory onhand) (%)

P&G order picking & pallet loading cost (handlingperformed in order picking & pallet loading) (Euro's)

P&G vehicle loading cost (handling performed invehicle loading) (Euro's)

P&G handling cost (Euro's)

P&G transport cost (Euro's)

P&G on time delivery rate(%)

External cost (including CO2and traffic related costs)(Euro's)

For red elements in Table 23 measurement unit could not be provided; more analysis isrequired.

58

Table 24: Categorization of the elements in outbound deliveries (Customer Operations)


(Unit)

Customer operations

Warehousing Ordering

Proc

ess

Step

s

VehicleUnloading Order Splitting Stocking Forecasting & Ordering

Deci

sion

varia

bles

Inde

pend

ent v

aria

bles

Customerunit vehicleunloadingcost (Euro's/pl /deck/shpmnt)

Customer unit ordersplitting cost (Accordingto what pallet contains)(Euro's / pl)

Customer unit inventory holding cost(cost & cash) (Euro's/pl spot/month)

Customer storage capacity (units orvolume)

Review period (hrs/days/wks)Target service level in terms ofproduct availability (%)

Depe

nden

t var

iabl

es

Customer cycle stock per SKU (cs/SKU)

Customer safety stock per SKU (cs/SKU)

Customer pipeline stock per SKU (cs/SKU)

Customer's forecast horizon (hrs-days-wks)

Customer's forecast accuracy perSKU (That is due to forecast horizonand demand variability per SKU)(%)

Customer's forecast per SKU(cs/SKU)

Customer's order per SKU (cs-ly-pl/SKU)

Perf

orm

ance

mea

sure

s

Customer vehicle unloading cost(handling performed in vehicleunloading) (Euro's)

Customer order splitting cost (handlingperformed in order splitting) (Euro's)

Customer handling cost (Euro's)

Customer total inventory level per SKU(cs/SKU)

Customer total inventory holding cost(Euro's)

Customer product availability (Thepercentage of store/shopper demand thatare filled from inventory on hand) (%)


59

Table 25: Categorization of the elements in outbound deliveries (System characteristics)


(Unit)

System characteristics

Lane Product Other

Proc

ess

Step

sDe

cisio

n va

riabl

es

MOI per SKU (MOI: Minimum orderincrement, the minimum orderamount in terms of cs/ly/pl for anSKU) (cs-ly-pl/ SKU)

Inde

pend

entv

aria

bles

# of SKUs on the lane(units)

Total lane business(Amount of the deliverythat has been done withina specific time period for alane) (cs)

Trade terms

Shopper demand per SKU (Demandprofile per SKU, the distribution withits mean and variance, as well as thelevel of rotation, H/M/L) (cs)

Shopper demand variability per SKU(Demand variability per SKU based oncharacteristics of the product such assmoothness, seasonality, promotions,phase-ins, phase-outs, one timeintroduction) (cs/SKU)

SKU dimensions (To assess how manycases will fit to a pallet) (cs per pl perSKU)

SKU weight (kg/cs/SKU)

Traffic density (It has a special status since it isboth dependent on and independent from systemvariables)

Depe

nden

t var

iabl

esPe

rfor

man

cem

easu

res


60

Appendix 3: Retailer Inventory Model

Figure 17: RIM user interface

Yellow markings in Figure 17 indicate the inputs of the tool; green markings indicate the outputs of the tool. Blue markings indicate theadditions/adaptations performed on RIM for this study.

61

Appendix 4: Results

Table 26: Increase in inventory level for all scenarios when the CTL was improved to FTL

Distributed forecast accuracy levelFrequency 3/day 2/day 1/day 4/week 3/week 2/week 1/week 3/month 2/month 1/month

# SK

Us

25 0,00 0,00 1,52 1,29 1,22 1,23 3,67 3,18 5,13 1,5753 0,01 0,02 1,53 1,34 1,31 2,30 3,12 3,60 5,97 4,39

159 0,09 0,16 1,69 1,76 1,94 2,46 5,26 6,52 9,94 16,69265 0,17 0,34 1,94 2,34 2,68 4,32 8,14 10,14 15,24 27,36371 0,29 0,50 1,09 2,88 3,47 4,84 10,92 13,41 19,24 39,82477 0,42 0,70 2,53 3,50 4,34 6,80 13,58 17,61 26,40 51,63583 0,52 0,86 2,85 4,08 5,19 7,55 15,45 21,23 32,29 63,80

High forecast accuracy levelFrequency 3/day 2/day 1/day 4/week 3/week 2/week 1/week 3/month 2/month 1/month

# SK

Us

25 0,00 0,00 0,23 0,42 0,53 0,71 2,06 2,48 3,45 1,9453 0,01 0,02 0,28 0,48 0,65 1,22 1,95 2,91 4,69 4,99

159 0,09 0,16 0,58 1,03 1,38 2,10 4,45 6,07 9,04 17,58265 0,17 0,34 0,92 1,69 2,20 3,51 7,16 9,83 14,56 27,81371 0,29 0,50 1,22 2,27 3,02 4,58 10,02 13,14 18,62 40,90477 0,42 0,70 1,64 2,95 3,95 6,17 12,85 17,44 25,98 52,61583 0,52 0,86 2,02 3,57 4,85 7,36 15,05 21,09 31,98 64,67

Medium forecast accuracy levelFrequency 3/day 2/day 1/day 4/week 3/week 2/week 1/week 3/month 2/month 1/month

# SK

Us

25 -0,07 -0,03 -0,15 0,02 0,10 0,35 1,81 2,11 3,29 1,8053 -0,03 -0,09 -0,07 0,16 0,26 0,83 1,75 2,57 4,39 4,73

159 -0,13 -0,13 0,23 0,66 0,99 1,70 4,14 5,73 8,81 17,20265 0,03 -0,13 0,56 1,33 1,80 3,19 6,90 9,48 14,33 27,62371 -0,08 0,30 0,53 1,90 2,62 4,18 9,77 12,82 18,41 40,44477 0,08 0,24 1,28 2,58 3,55 5,84 12,59 17,12 25,75 52,20583 0,20 0,61 1,65 3,21 4,45 6,97 14,75 20,79 31,75 64,31

Low forecast accuracy levelFrequency 3/day 2/day 1/day 4/week 3/week 2/week 1/week 3/month 2/month 1/month

# SK

Us

25 -0,19 -0,09 -0,86 -0,69 -0,76 -0,26 1,28 1,45 2,93 1,5853 -0,10 -0,25 -0,71 -0,41 -0,42 0,13 1,34 1,99 3,95 4,36

159 -0,49 -0,59 -0,40 0,06 0,33 1,00 3,60 5,15 8,33 16,68265 -0,20 -0,89 -0,08 0,68 1,15 2,61 6,41 8,92 13,87 27,35371 -0,69 -0,02 -0,59 1,25 1,97 3,52 9,28 12,26 17,96 39,80477 -0,47 -0,51 0,60 1,93 2,90 5,26 12,09 16,59 25,32 51,62583 -0,34 0,21 1,01 2,55 3,76 6,33 14,23 20,28 31,33 63,78

62

Table 27: Example for the dynamics between the cycle stock and safety stock

For 159SKUs Distributed for. acc. High for. acc. Medium for. acc. Low for. acc.

CTLCyclestock

Safetystock

Cyclestock

Safetystock

Cyclestock

Safetystock

Cyclestock

Safetystock

3 /day 0,82 3,31 0,82 3,31 0,82 10,21 0,82 21,42 /day 1,01 3,24 1,01 3,24 1,01 9,99 1,01 20,931 /day 1,75 3 1,75 3 1,75 9,16 1,75 19,154 /week 3,07 3,82 3,07 2,78 3,07 8,24 3,07 17,033 /week 4,14 4,49 4,14 2,71 4,14 7,84 4,14 16,042 /week 6,21 4,79 6,21 2,52 6,21 7,13 6,21 14,481 /week 12,42 5,47 12,42 2,2 12,42 6 12,42 11,963 /month 17,07 6,22 17,07 2,12 17,07 5,62 17,07 11,052 /month 24,83 6,31 24,83 1,9 24,83 4,97 24,83 9,691 /month 54,63 7,82 54,63 1,8 54,63 4,3 54,63 7,87

FTLCyclestock

Safetystock

Cyclestock

Safetystock

Cyclestock

Safetystock

Cyclestock

Safetystock

3 /day 1 3,22 1 3,22 1 9,9 1 20,732 /day 1,29 3,13 1,29 3,13 1,29 9,59 1,29 20,061 /day 2,39 4,05 2,39 2,94 2,39 8,75 2,39 18,114 /week 4,19 4,47 4,19 2,7 4,19 7,79 4,19 15,983 /week 5,65 4,93 5,65 2,59 5,65 7,33 5,65 14,872 /week 8,47 4,99 8,47 2,35 8,47 6,56 8,47 13,221 /week 16,94 6,21 16,94 2,13 16,94 5,61 16,94 11,033 /month 23,3 6,51 23,3 1,97 23,3 5,13 23,3 9,982 /month 33,88 7,19 33,88 1,88 33,88 4,73 33,88 8,981 /month 72,55 6,59 72,55 1,47 72,55 3,58 72,55 6,63

FTL with lower MOQsCyclestock

Safetystock

Cyclestock

Safetystock

Cyclestock

Safetystock

Cyclestock

Safetystock

3 /day 0,73 3,35 0,73 3,35 0,73 10,35 0,73 21,712 /day 0,88 3,28 0,88 3,28 0,88 10,12 0,88 21,21 /day 1,6 4,29 1,6 3,11 1,6 9,28 1,6 19,224 /week 2,8 4,79 2,8 2,89 2,8 8,34 2,8 17,113 /week 3,77 5,32 3,77 2,79 3,77 7,9 3,77 16,032 /week 5,52 6,28 5,52 2,74 5,52 7,47 5,52 15,031 /week 11,04 7,64 11,04 2,58 11,04 6,59 11,04 12,753 /month 14,85 7,73 14,85 2,43 14,85 6,13 14,85 11,762 /month 21,6 8,76 21,6 2,43 21,6 5,83 21,6 10,881 /month 49,78 7,14 49,78 1,6 49,78 3,89 49,78 7,18

63

Table 28: Increase in inventory level for all scenarios when the CTL was improved to FTL with lower MOQs

Distributed forecast accuracy levelFrequency 3/day 2/day 1/day 4/week 3/week 2/week 1/week 3/month 2/month 1/month

# SK

Us

25 0,00 0,00 1,50 1,22 1,14 1,97 3,14 2,39 4,26 4,0953 0,00 -0,01 1,43 1,13 1,02 1,82 2,80 1,90 3,15 2,31

159 -0,05 -0,09 1,13 0,69 0,46 0,80 0,80 -0,71 -0,78 -5,54265 -0,11 -0,19 0,80 0,11 -0,25 -0,31 -1,51 -3,62 -5,25 -16,10371 -0,16 -0,32 -0,69 -0,30 -0,80 -1,11 -3,35 -5,71 -7,63 -21,55477 -0,24 -0,42 0,21 -0,79 -1,46 -2,20 -5,55 -8,66 -13,06 -32,00583 -0,33 -0,52 -0,07 -1,20 -2,03 -2,99 -6,85 -10,91 -17,79 -36,86

High forecast accuracy levelFrequency 3/day 2/day 1/day 4/week 3/week 2/week 1/week 3/month 2/month 1/month

# SK

Us

25 0,00 0,00 0,19 0,31 0,41 0,69 1,26 1,41 2,37 5,6353 0,00 -0,01 0,16 0,23 0,27 0,61 0,99 0,87 1,28 3,54

159 -0,05 -0,09 -0,05 -0,17 -0,29 -0,47 -1,00 -1,91 -2,70 -5,05265 -0,11 -0,19 -0,32 -0,74 -1,02 -1,55 -3,24 -4,75 -6,98 -16,03371 -0,16 -0,32 -0,69 -1,07 -1,48 -2,20 -4,82 -6,58 -9,01 -21,20477 -0,24 -0,42 -0,80 -1,55 -2,15 -3,28 -6,98 -9,58 -14,41 -32,69583 -0,33 -0,52 -1,02 -1,91 -2,67 -4,00 -8,19 -11,76 -19,43 -36,82

Medium forecast accuracy levelFrequency 3/day 2/day 1/day 4/week 3/week 2/week 1/week 3/month 2/month 1/month

# SK

Us

25 -0,01 -0,01 -0,09 0,02 0,05 0,50 1,22 1,23 2,31 4,9853 0,04 0,09 -0,04 0,03 0,05 0,39 0,98 0,77 1,30 3,02

159 0,05 0,00 -0,04 -0,17 -0,31 -0,34 -0,79 -1,72 -2,37 -5,27265 0,06 -0,07 -0,12 -0,53 -0,84 -1,30 -2,89 -4,51 -6,63 -16,02371 -0,06 0,04 -0,70 -0,97 -1,42 -2,06 -4,62 -6,48 -8,81 -21,35477 -0,04 -0,21 -0,58 -1,34 -1,96 -3,02 -6,68 -9,37 -14,14 -32,40583 -0,10 -0,31 -0,81 -1,70 -2,48 -3,76 -7,93 -11,56 -18,94 -36,84

Low forecast accuracy levelFrequency 3/day 2/day 1/day 4/week 3/week 2/week 1/week 3/month 2/month 1/month

# SK

Us

25 -0,02 -0,01 -0,62 -0,51 -0,71 0,07 1,01 0,86 2,11 4,0853 0,10 0,26 -0,43 -0,37 -0,38 -0,06 0,81 0,55 1,32 2,30

159 0,22 0,14 -0,08 -0,19 -0,39 -0,14 -0,59 -1,51 -2,05 -5,54265 0,33 0,13 0,13 -0,29 -0,61 -0,99 -2,51 -4,22 -6,25 -16,00371 0,10 0,61 -0,73 -0,91 -1,42 -1,94 -4,45 -6,52 -8,67 -21,56477 0,28 0,12 -0,33 -1,07 -1,69 -2,71 -6,34 -9,11 -13,83 -31,99583 0,27 0,00 -0,53 -1,45 -2,29 -3,46 -7,60 -11,33 -18,33 -36,86

Improving the Vehicle Fill Rate for Procter & Gamble -...

Documents

Transcript of Improving the Vehicle Fill Rate for Procter & Gamble -...