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Int. J. Production Economics

Int. J. Production Economics 127 (2010) 278–291

0925-52

doi:10.1

� Cor

E-m

journal homepage: www.elsevier.com/locate/ijpe

Case study: Optimizing order fulfillment in a global retailsupply chain

Yousef Amer �, Lee Luong, Sang-Heon Lee

School of Advanced Manufacturing and Mechanical Engineering, University of South Australia, Mawson Lakes, SA 5095, Australia

a r t i c l e i n f o

Article history:

Received 31 December 2008

Accepted 12 August 2009Available online 27 August 2009

Keywords:

Supply chain management

Performance measurement

Process control

Order fulfillment

Design for six sigma

Fuzzy logic

73/$ - see front matter & 2009 Elsevier B.V. A

016/j.ijpe.2009.08.020

responding author.

ail address: Yousef.amer@unisa.edu.au (Y. Am

a b s t r a c t

This paper demonstrates a model which uses an adaption of Design for six sigma and fuzzy

logic to optimize, monitor and control the order fulfillment process in the supply chain of a

global retail firm. Even though the firm has advanced SCM practices in place retail stores

suffer from stock outs of popular items and excessive stock of lower selling items. The

order fulfillment optimization model presented enhances supply chain integration and

collaboration across supply chain partners through effective monitoring and controlling of

supply chain variables. It considers the critical to customer requirements at the supply

chain design onset making it a useful model for dealing with customer differentiation and

channel separation. With 47% of firms only focusing on developing smooth internal

processes this model offers opportunities for external supply chain improvements and

partial integration across the chain to improve business outcomes.

& 2009 Elsevier B.V. All rights reserved.

1. Introduction

A key issue facing companies today is how to monitorand control performance across the supply chain. Takingincremental steps towards supply chain integration canlead to competitive gains and focusing on key supplychain processes can be a starting point for improvedperformance and collaboration between chain members(Aryee and Naim, 2008; Lummus et al., 2008; Bask andJuga, 2001; Lambert et al., 1998). However, there is a lackof managerial techniques and tools for SCM which may beinhibiting the uptake of SCM amongst firms. Branch(2009) states that most companies, 47% of them, areworking to create seamless processes within their ownorganization only, 34% of companies focus on integrationwith first tier suppliers, 11% of companies focus onintegration with key customers and only 9% integratinglogistics up and down stream between customers andsuppliers. Hence there is great scope to develop new

ll rights reserved.

er).

approaches that encourage greater uptake of supply chainmanagement, integration and process improvement(Branch, 2009; Xu et al., 2009).

One of the difficulties in managing a supply chain istheir complexity, having to deal with multiple marketsegments (McKay, 2003) with each segment a system initself, but having to closely interconnect with dependantsubsystems. Supply chain models such as the globalsupply chain forum (GSCF) model (Croxton et al., 2001;Croxton, 2003), the supply chain operational reference(SCOR) model (Supply-Chain Council, 2009) and theCollaborative Supply Chain Framework (CSCF) (Simatupangand Sridharan, 2005) take an holistic approach to SCM butremain generic in nature and one size does often not fit all.Another consideration is that while these models refer tothe development of supply chain metrics they do notdescribe how to determine specific metrics for individualcompany circumstances. The SCOR, relies on benchmark-ing and has recommended level one metrics but there issome criticism that the model is too manufacturingcentric (source, make, deliver, return), that there is notenough emphasis on process control (Bolstorff, 2003a–c)and an overreliance on benchmarking which can inhibit

Y. Amer et al. / Int. J. Production Economics 127 (2010) 278–291 279

innovation. Gunasekaran et al. (2004) defines supplychain metrics as strategic, tactical, and operational andgives generic measures, but the task still remains formanagers to determine what the most appropriatemeasures for their specific circumstances are. This oftendepends on the configuration of the chain and the positionof the firm within the chain. The model presentedprovides a means for creating specific targeted metricsand a way of isolating where process and qualityimprovement efforts should be focused from any positionwithin the chain and is not reliant on any specific supplychain configuration to implement.

The purpose of this paper is to demonstrate a real caseapplication of the order fulfillment model which uses anadaption of design for six sigma (DFSS) (Amer et al.,2007a) and fuzzy logic (Amer et al., 2008). In the casestudy the model was applied at a large international retailfirm which has an extensive supply chain. Through theapplication of the model by a cross functional supplychain design team the order fulfillment subprocesses andkey performance indicators (KPIs) are identified and theorder fulfillment variables as key process input variables(KPIVs) and key process output variable (KPOV) isidentified, showing the ‘‘perfect order’’ as one of thecritical to customer requirements (CCRs). A transferfunction was then developed for the perfect order and amembership function assigned to measure the gapbetween the current performance and the perfect order.The presented transfer function of ‘‘perfect order’’ wasformulized and refined using fuzzy logic. A greaterunderstanding of the global supply chain was grasped byparticipants and a response was triggered across the chainwhich aided further collaboration for process control andimprovement in supply chain outcomes. The demon-strated model can be applied to any supply chain process(Amer et al., 2007b) and from any position within thesupply chain making it a very flexible model for supplychain management.

The paper is organized as follows. A literature review ofsix sigma and DFSS is given to develop an understandingof the approach taken. The order fulfillment model isdescribed followed by the research methodology, a briefdescription of the case firm and the application of themodel. In conclusion the results and findings are dis-cussed and recommendations made for further research.

2. Literature review

There are two major improvement methodologiesunder six sigma umbrella. Define, measure, analyse,improve and control (DMAIC) is used for continuousimprovement of already existing products/processes andDFSS is used for designing new product/processes. Sixsigma is a highly disciplined, data-oriented, top-downapproach to quality management that brings togetherelements from past quality initiatives and aims to reducevariability, focus on what the customer wants andimprove core processes (Klefsjo, et al. 2001; Summers,2007). A key feature of six sigma is the use of statisticaltechniques in a systematic way to reduce variation and

improve processes with a strong focus on results. Toincrease system reliability and reduce failure rates, thecomponents utilized in the complex system and productshave to have individual failure rates approaching zero(Summers, 2007).

Six sigma stands for a measure of customer quality aswell as a philosophy of giving customers what they wanteach and every time i.e. zero defects. Anything less thanideal is an opportunity for improvement; defects costsmoney and understanding processes and improving themis the most efficient way to achieve lasting and costeffective results (Summers, 2007; Brusse, 2006; El Haikand Roy, 2005). It is also a methodology that can be usedto change processes and company culture. It is based onthe premise that all actions are a process. Each process hasa start, a stop, inputs i.e. from supplier and outputs i.e. tocustomers, and actions that happen during the processsteps, i.e. subprocesses. Each process has measurablecharacteristics as each other processes change entities andthis change can be measured. Measurements can be ofinput or output characteristics such as number, weight, ortype. They can also be process characteristics at variousstages such as count and time taken. Measurements canbe of continuous data items such as time, money, size orthey can be of discrete data items such as integer counts.

The process itself will have requirements for the inputs,and the customer will have requirements of the outputs.Measures follow a frequency distribution showing howmany measures fall within a given range of data such as ina frequency histogram. Under DMAIC the problem is firstdefined, and quantified, measurement data are collectedthough root cause analysis and solutions to the root causesare determined. Finally improvements undergo monitor-ing and control to prevent reoccurrence.

A key limitation of the six sigma DMAIC methodology isthat it is focused on defect correction and is a reactivemethodology. It is often used when the existing processesdo not satisfy the customers or are not able to achievestrategic business objectives (Eckes, 2001). DMAIC isincreasingly being applied in supply chain improvementprojects for this purpose (Yang et al., 2007; Zhang et al.,2007) and there is interest in combining DMAIC with leantechniques termed ‘‘lean six sigma’’ (Martin, 2007;Bolstorff and Rosenbaum, 2007; El Haik and Al Aomar,2006; George, 2003). It has also been incorporated withSCOR to address the lack of root cause analysis and processimprovement in that model (Bolstorff and Rosenbaum,2007; Bolstorff, 2003a–c, 2004). However, these remain asquality improvement initiatives. The reality is even if zerodefects are achieved, it does not mean the product orservice will satisfy the customer’s requirements. Forexample if you are producing the perfect product i.e.water bottles with pure and high quality water but if thecustomer perceived a scratch on the water bottle as adefects then the product is not going to sell well.

In contrast, the second six sigma methodology, DFSS, isproactive as it focuses on getting things right the first timeand continuing to do them right all the time. El Haik andRoy (2005) state DFSS is the same whether it is used toaccommodate high customer satisfaction externally orwhether it is focusing on an internal process facing the

Y. Amer et al. / Int. J. Production Economics 127 (2010) 278–291280

employees, it can proactively produce highly consistentprocesses with very low variation in performance. Giventhe limited uptake of supply chain frameworks andmodels amongst firms the design of supply chains inmany industries exhibit deficiencies like modest levels ofquality, unawareness of what the customer really wants,and too much complexity. Such shortcomings add aconsiderable amount of non-value added activities toSCM and can mainly be attributed to the lack of a systemsdesign methodology.

Implementation of DFSS has successfully deliveredquality products to customer satisfaction for many indus-tries. DFSS has successfully been implemented for productdesign in a number of leading manufacturing firms such asMotorola, GE, Allied Signals, Honeywell, and Seagate, andnow there is increasing interest in designing a service suchas a supply chain through DFSS (Amer et al., 2007a, 2008;El Haik and Al Aomar, 2006; El Haik and Roy, 2005).

3. Order fulfillment model using DFSS and fuzzy logic

While DFSS is an open methodology with numerousinterpretations suiting varying situations (El Haik and Roy,

Fig. 1. Order fulfillment model sho

2005; Brue and Launsby, 2003; Yang and El Haik, 2003), ageneralized DFSS flow follows the steps of identify, define,design, optimize and verify or validate (IDDOV). Theinputs can be customer needs, business needs, rawmaterials, constraints and so on. The outputs can bequality products, processes or services designed to reachsix sigma levels. Going through these phases each CCR ispresented as a function (transfer function) of a number ofKPIVs, which can be used to predict the performance levelof the CCR. The following sections present a briefintroduction to the sequence of major activities coveredunder IDOV phases of DFSS in the order fulfillment modelas shown in Fig. 1.

3.1. Identify

The major activity in this phase is the collection andtranslation of often vague and abstract customer require-ments also known as voice of the customer (VOC). Thesupply chain design team identifies the customerspotential needs, enlists and prioritizes their expectationsthrough data collected from customer interviews, focusgroups, surveys, complaint data and enhancement request

wing IDOV phases of DFSS.

Y. Amer et al. / Int. J. Production Economics 127 (2010) 278–291 281

or through market research. Using tools like qualityfunction deployment (QFD), VOC is translated into designspecifications termed as CCRs, comprehendible to thesupply chain design team (Buss and Ivey, 2001). WhileQFD is the major tool in identify phase, there are othertools that may be used, such as kano model, paretoanalysis, affinity diagram, brainstorming and benchmark-ing (Rath and Strong, 2002). QFD is a structured approachto defining customer needs or requirements and translat-ing them into specific plans to produce products/servicesto meet those needs. This is accomplished by a crossfunctional or multidisciplinary ‘‘supply chain design’’team who can use a series of approaches to deploy theCCRs throughout the design development (Yang, 2008;Taguchi et al., 2005). Customer voices are diverse. Hence,there may be a variety of different needs and there caneven be multiple customer voices within an organization.This demonstrates the ability of six sigma tools and QFDto address the needs of complex and variable supply chainenvironments. A supplier may be supplying to multiplefirms, each with their own requirements. By applying QFDto each channel, specific customer needs can be met withthe aim of achieving zero defects through DFSS. Wherebenchmarking occurs as in the SCOR model, for example, acompany may benchmark their delivery time as matchingtheir competitors results of ‘‘zero defect’’. However, theircustomers may have different requirements to thecustomers of the firm they are benching markingthemselves against. Where benchmarking is used in SixSigma, it is combined with QFD (Summers, 2007) whichallows for innovation to occur along with comparison tocompetitors.

3.2. Design

After having translated the VOC into engineering termsas CCRs, the supply chain design team has the task ofproposing and evaluating conceptual solutions to addressthe above CCRs. Use of the ‘‘functional design’’ approach ofsystems engineering and multiple functional systemmodels (or conceptual solutions) are proposed in thisphase. Each solution is presented as a hierarchy offunctional subsystems. The functional concept designsare evaluated to narrow down solution options. Measure-ment metrics and methods to inspect the design areverified prior to detail designing. Having narrowed downthe number of conceptual solutions, requirements arefollowed down from top to lowest hierarchical level of thefunctional system models. As a result of this exercise, theteam is able to translate the high level system CCRs tolower level KPIVs.

3.3. Optimize

Performance of real world products/services is alwaysshadowed with variation. DFSS therefore, stresses pre-dicting and optimizing the probability of the design tomeet the required targets (CCRs) given environmentalvariation, manufacturing variation, and usage variation.Statistical analysis and optimization of the design is

necessary to achieve a robust product/service design. Atransfer function is a mathematical representation of therelationship between the input and output of a system ora process. It facilitates the DFSS optimization of processoutput by defining the true relationship between inputvariables and the output. Optimization in this contextmeans minimizing the requirement variability and shift-ing its mean to some desired target value specified by thecustomer. The transfer function is formulized and refinedwith the use of fuzzy logic. Usually transfer functionsresulting from DFSS implementation are expected to havea mathematical representation. However, in this modelthe refined transfer function is presented as sets (se-quences) of fuzzy logic rules evaluated using the fuzzylogic toolbox in MATLAB (Amer et al., 2008).

3.4. Validate

At the final stage validations are performed to checkthat the process is complete, valid and will meetrequirements in practice. It involves verification of thedesign to ensure that it meets the set requirements;assessment of performance, reliability, capability, etc. Atthis stage simulation may be used to test the orderfulfillment design. If this stage suggests that the designdoes not or may not meet the required capability, then it isnecessary to retract back through the earlier stages ofdesign or optimize. If the validation results are satisfac-tory, development of a process control plan for the meanand variance of CCRs (Rath and Strong, 2002) are initiated.

4. Case study methodology

A case study application of the model was conductedover a period of four months at the retail store of CompanyA. This involved an initial survey of functional managers toobtain a picture of the current performance measurementsystem and supply chain relationships. Secondly, the stockcontrol (SC) and goods administration manager, logisticsmanager, finance manager and operations manager werethen interviewed to obtain a picture of the supply chainstructure and order fulfillment process. The DFSS andfuzzy logic model was applied by a small supply chaindesign team consisting of the logistics manager, stockcontrol manager and replenishment manager. The infor-mation gathered through this process, company docu-ments and historical data are now presented.

4.1. Description of case Company A

As an international retailer the firm, Company A, in thefiscal year of 2005, had sales of $17.4 billion. It employsover 90,000 people in some 44 countries. Company Aowns or franchises approximately 220 stores in 24countries in Europe, North America, Asia and Australia.The case retail store is a franchise and employs over 400people with an annual turnover of approx. $14,500,000.The company must secure that the customer promisesof lowest price, availability and quality are fulfilled.Company A is divided into several different companies

Y. Amer et al. / Int. J. Production Economics 127 (2010) 278–291282

and divisions worldwide. Fig. 2 shows the firms supplychain structure with distribution methods.

The Head Company is the core of the organization andis situated in Europe. The supply process commences hereconsisting of forecasting future demand, ordering, produ-cing and delivering the offer to the sale floor (retail) andultimately to the customer. The head company has the topresponsibility of sourcing and contracting suppliers. It isalso responsible for setting correct maximum and mini-mum level of inventory of the distribution centres (DCs).The Trading Company is responsible for all the tradingdone by the Head Company. The trading company isdivided into 16 different trading areas worldwide. Thetrading company works with over 1500 suppliers in over55 countries. Each trading area is responsible for ageographic area and the suppliers within that area. Theyare in daily contact with the suppliers. They ensure thatthe supplier is able to produce the products in terms ofbest quality and function at the lowest price. The TradingCompany office for the case retail store is in Singapore.The DCs stock the entire product on the stock list with the

Headquarters, design and marketing

Supplier

Distribution

Centre

Retailer

TradingCompany

Customer

VMI

Direct Delivery (DD)

Fig. 2. Company A’s supply chain structure with distribution methods.

retail store S1 min 99S2 min 95S3 min 90

(6 week lead time) -DC delivery

-DD deliveries

95%95%

+- 4 day

3

of ordequantity

≤1%

1 Service levels in

KPI’s Goals

2 Lead times

Load quality <1%

4 Load quantity 5% either w

5 Manifest accuracy

Fig. 3. Current KPIs

strategy to provide a high service to the stores simulta-neously with low transportation costs. The DCs work withthe retail stores to shorten lead times and make themstable. The DC is responsible for keeping track of theinventory level at the DC therefore their inventory level issent to the database. Good communication is essential toenable the distribution of the required volume of productsat the right time to the right store. The DC for the caseretail store is in Singapore. Company A is using otherdifferent types of distribution systems; order pointdistribution centres (OPDC) and direct delivery (DD) aswell as VMI. The case retail store is focused on sellingproducts. The information that is sent by the store to theHead Company database is the inventory level and sales.

4.2. Current KPIs of Company A

The current KPIs are indicated in Fig. 3. Once thedelivery is received order quality, quantity are checked;however, bad quality product are still accepted andtransferred to the recovery department where the teamtries to assemble the product for it to be sent to thediscount department to be sold as a defect product.Whether the order quantity is over or under the requestedorder the delivery will still be accepted and no action istaken. The delivery time is often late and even if on theother hand it arrives earlier than requested, it is stillaccepted and no action is taken. Finally most of the ordersreceived are missing high demand requested productwhilst carrying excess of the low demand products.

The lead time goal is that 95% of all orders aredelivered within the agreed lead time of six weeks witha window of 74 days. The total lead time is the lead-timemeasured from the date the order is created until date it isreceived in the store. Lead-time takes into considerationof retailer conditions, handling time at wholesaler, fillingrate of transports and transport structure. Manifest

% % %

Sales Manager

weekly

s

Business Support Manager

weekly weekly

Managers monthly

r

Manager

Manager Eachorder

Responsible Freq.

Logistic

Logistics monthly ay

Logistics

Company A.

Y. Amer et al. / Int. J. Production Economics 127 (2010) 278–291 283

accuracy consists of delivered quantities to be equal to thenotified quantities on the delivery note (manifest) r1%.Exceptions to the order quantity are the ‘‘rounding up’’parameter. If the order quantity is more or equal to 70% of afull pallet, a full pallet will be delivered. The minimum orderquantity is always a multi-pack. If the ordered quantity isless than a multi-pack a full pallet will be delivered.

5. Implementing the order fulfillment model

5.1. Identify

The supply chain design team identified the currentproblems at the retail store that need to be considered as

1.

Excess inventory of low demand product. 2. Stock outs occur on many top selling product lines. 3. Whilst KPIs are in place there are not acted upon. 4. There is no continuous improvement focus.

5.2. Translation of VOC

The goal of Company A is that the ‘‘range offer’’ has to beavailable at all times in all stores and to all customers, withthe lowest total supply cost at point of sale. The primaryconcern of the team is to meet their customer needs byreducing/eliminating the stock outs. The KPIs for customersatisfaction for product availability have been averaging 66%,with product quality at 70%. The store manager has requestedeveryone work towards improving these figures but has notgiven any direction as to how this can occur. The otherconcern is that there is an over abundance of low demandproduct. The team chose to initially focus on the internal and

Kan

o C

lass

ifica

tion

Impo

rtanc

e (1

-5)

Direction of ImprovementOrder FulfillmentCostProduct QualityQuality ServiceEnvironment friendly 1

% Weight

WHATs / Inputs

HOWs / Outputs

Importance in next level 51

Importance of the HOWs 6

234 35 9

Fig. 4. Supply chain de

external aspects of order fulfillment. Internally, the storecustomer wants to be able to walk into the store andpurchase their chosen product, take it home on the day inperfect condition at the right price. The store wants to receive,from an external source, in this case from the DC, a perfectorder, arriving on time, in good quality and in the correctquantity. The team proceeded to develop a QFD. Also knownas ‘‘house of quality’’, QFD is a matrix based tool that takescustomer ‘‘whats’’ as input and transforms them into designteam’s ‘‘hows’’. The prioritized list of ‘‘whats’’ is correlatedwith the proposed list of ‘‘hows’’, while each relation is scoredon the basis of relation strength. The cumulative score of‘‘hows’’ covers their mutual interactions and delivers aprioritized list of ‘‘hows’’, which can be used as the inputfor a next QFD iteration. The final outcome of a QFD exerciseshould be a prioritized list of CCRs as mentioned above (Rathand Strong, 2002). Indeed the technical knowledge, experi-ence and innovativeness of the design team cannot beoverlooked as they would form the basis of ‘‘hows’’. Fig. 4depicts a first level QFD output translating the subjective VOCinto meaningful ‘‘hows’’ for supply chain designers. In theQFD matrix, the relation intensity (Rij) of ‘‘whats’’ withpotential ‘‘hows’’ is indicated with numbers 1, 3 and 9, where1 shows a very low, 3 a moderate and 9 indicates a significantrelation. A blank relation entry depicts no existing relation ofcorresponding ‘‘whats’’ and ‘‘hows’’. The cumulative weight(CWj) for each ‘‘how’’ is indicated as ‘‘importance of hows’’ inFig. 4 and is calculated using the relation depicted in Eq. (1).Where Wi indicate the importance assigned to each ‘‘what’’on the scale of 5–1 (5 being highest):

CWj ¼Xn

i¼1

WiRij; j ¼ 1; . . . ;m ð1Þ

Del

iver

y on

tim

e

Und

amag

ed d

eliv

erie

s

No

extra

cos

ts

Mee

t urg

ent d

eman

ds

Acc

urat

e in

voic

es

Ord

er fl

exib

ility

Mee

t dem

and

quan

tity

Tran

spor

t

9

5 4 3 1 2 5 38 17 14 10 5 17 1266 63 51 36 18 21 62 45

3 3 9 3 11 9 1 3

3 9 1 3 39 3 1 3 9 3

sign teams QFD.

Purchase suggestion order

Authorize order

Place order TC receive

orderTC process order

For DD supplier fills order

DC fills order

Orderreceived warehouse

Fig. 5. Order fulfillment process flow, moving externally.

Perfect order

Promotional product ordering

(PPO)

Purchase suggestion order

(PSO)Direct Delivery (DD)

from supplier

Fishbone analysis of making order

Check Navision ordering

parameters

Fulfill vol., weight, order

window

Order quantities alignment

Navision parameters

CFO authorizes order

Stock controller (SC)Creates order,

Authorize order

Place order

Create order

SC places PSO

LM reviews and signs off order

Stock control/adminSends order

Placing order

SC notifies Logistics manager (LM) and Chief financial Officer (CFO),

SC Receives and completesinternal transaction

form from Sales

SC evaluates the forecastedquantity increase

SC obtains confirmation

from CRS

SC orders using ordering routine

with wish list

2

1

3

Fig. 6. Fishbone of making an order with actions noted.

Y. Amer et al. / Int. J. Production Economics 127 (2010) 278–291284

Through the QFD the team determined order fulfillment asCCR and delivery time, product quality, quantity of deliveredgoods and manifest (invoice) accuracy as the key aspects as tohow this will be achieved.

5.3. Design order fulfillment system structure

The team then considered process flow maps andfishbone arrangements to determine where improvementefforts should be focused and what are the KPIVs. Theinitial flow was of the order fulfillment system segment

order. The order fulfillment process which moves exter-nally was then mapped see Fig. 5.

As the major involvement internally in this processflow was making the order it was further analysedthrough a fishbone structure, see Fig. 6. The areas foraction were identified as

1.

placing order; 2. placing direct delivery (DD) order and 3. promotional product ordering.

procedures to ascertain if procedure time could be

reduced

Logistics Manager

2 weeks Order dwell time

Order processing

time

reviewed with Head office and Trading centre

Operations Manager parameter

accuracy %Order Quantity accuracy

quantity increase, review current practise

Stock controller and Sales and

Marketing Manager

quantity accuracy

Action Responsible Timeframe KPIV

1 Review placing order

2 Navision parameters to be 1 month %Navision

3 Evaluation of forecasted 4 weeks % order

Fig. 7. Actions determined by design team for making order.

ASIS Repair Defect product

Goods Receiving

Replenishment Orders

Buffer Pick location replenishment

Customer pick order

Customer picks order

Scrap

Fig. 8. Internal order fulfillment process flow.

Y. Amer et al. / Int. J. Production Economics 127 (2010) 278–291 285

Order accuracy on low demand product was ques-tioned. Given the ‘‘rounding up’’ parameter the designteam considered if this was what was leading to increasedinventory of these products. Given the stock outs of highdemand products forecast accuracy from the retail endwas in question, along with internal order fulfillment andthe supplier’s ability to meet demand. Actions to be takento further investigate these areas were agreed. KPIVs weredetermined and goals set as seen in Fig. 7.

The team then reviewed the internal order fulfillmentprocess flow, see Fig. 8, identifying that considerableinternal problems were occurring at goods receivingwhich could be affecting access to received stock.Further fishbone analysis highlighted several areas thatwere targeted for action and KPIVs were noted.

5.4. CCR flow down of perfect order

As depicted in Fig. 9 the order fulfillment process wasdescribed by a sequence of subprocesses, such as purchasesuggestion order, authorize/place order, trading centre, direct

delivery/distribution centre, receiving and de-stuffing, AQISand stock control, buffer and DR, pick up and returns. DFSSwas used to develop performance targets related to CCRs ofthese subprocesses. Through brainstorming, the supply chaindesign team listed the KPIVs shown under each process step.KPOVs for each subprocess are also determined and listed ontop of the process flow. This process enabled a merging ofboth the internal and external aspects of the order fulfillmentprocess.

The overall KPOV for the order fulfillment process wasbest described as the ‘‘perfect order’’. Its elements, on-time delivery, quantity of delivered order, quality ofdelivered order, and manifest accuracy as identified inthe KPIVs, make the ‘‘perfect order’’ the key metric formonitoring the order fulfillment variables.

The perfect order for Company A is represented as

Perfect order

¼ f ½delivery time ðDTÞ; quality ðQualÞ;

quantity delivered; ðQtyÞ; manifest accuracy ðMAÞ� ð2Þ

Fig. 9. Order fulfillment process flow with KPIVs/KPOV.

Table 1Ranges for ‘‘order’’ output and linguistic value.

Linguistic value Scores

Optimum 0.90–1.00

Satisfied 0.70–0.89

Acceptable 0.50–0.69

Poor 0.30–0.49

Reject 0.00–0.29

Y. Amer et al. / Int. J. Production Economics 127 (2010) 278–291286

A marginally short or over order or a slight decline in thedelivered quantity, or slight delayed/early delivery mayraise alarm if the factors are measured in specific metrics.In real world metrics to measure the performance orcustomer’s level of satisfaction from the ‘‘perfect order’’,DT, Qty, Qual and MA is a subjective matter. Therefore theabove equation (transfer function relating perfect order toDT, Qty, Qual, MA) is optimized with use of fuzzy logic.

5.5. Transfer function

The transfer function presented in Eq. (2) of ‘‘perfectorder’’ was formulized and refined with the use of fuzzy logic.The refined transfer function is presented as sets (sequences)of fuzzy logic rules evaluated using the fuzzy logic toolbox inMATLAB (Amer et al., 2008). The steps for assessing the‘‘perfect order’’ using the fuzzy inference system werefuzzification, rule evaluation and defuzzification.

5.5.1. Fuzzification

The fuzzification process was performed during runtime and consists of assigning membership degreesbetween 0 and 1 to the crisp inputs of delivery time,quantity and quality and manifest accuracy.

5.5.2. Rule evaluation

The rule evaluation process consisted of using thefuzzy value obtained during fuzzification and evaluating

them via the rule base in order to obtain a fuzzy value forthe output. The rule evaluation followed the form of if(condition x) and (condition y) then (result z) rules areapplied. Basically the use of linguistic variables and fuzzyIF–THEN-rules utilize the tolerance for imprecision anduncertainty mimicking the ability of the human mind tosummarize data and focus on decision-relevant informa-tion and are generated from expert knowledge.

5.5.3. Defuzzification

The fuzzy inference system using Mamdani’s fuzzyimplication rule determined the appropriate fuzzy member-ship value. The defuzzification process consisted of combin-ing the fuzzy values obtained from the rule evaluation stepand calculating the reciprocal in order to get one and onlyone crisp value that the output should be equal to. Theoutput ‘‘order’’ was evaluated in relation to the crisp valueand translated into linguistic terms (see Table 1).

Fig. 10. Graphic layout of transfer function in fuzzy format.

Table 2Manifest accuracy.

Fuzzy set Linguistic term Range (% of defects)

1 Poor Below �4%

2 Acceptable From �1.5% to �3.5%

3 Optimum Above �1%

Y. Amer et al. / Int. J. Production Economics 127 (2010) 278–291 287

5.6. Optimize—fuzzification of perfect order transfer

function

Fig. 10 depicts the empirical transfer function fromEq. (2) as a fuzzy logic system with inputs and outputbeing fuzzified using appropriate membership functions.The following sections narrate the component of manifestaccuracy as an example.

5.6.1. Manifest accuracy

Manifest accuracy value was measured based on thedelivered quantities being equal to the notified quantities onthe delivery note (manifest). There are three categories of themanifest accuracy, namely, ‘‘poor’’, ‘‘acceptable’’, and ‘‘opti-mum’’ as shown in Table 2. Any defect rate equal or below�4% is considered ‘‘poor’’ and rejected. Any order defect ratefrom�1.5% to�3.5% is considered acceptable. Any defect rateabove �1% to 0 is optimum. The range between ‘‘optimum’’and ‘‘poor’’ is considered ‘‘acceptable’’. ‘‘Acceptable’’, showsthat the manifest accuracy needs to be improved aimingtowards achieving ‘‘optimum’’. For the ‘‘poor’’ standard, thedegree of membership is zero, while the ‘‘optimum’’ standardhas a degree of membership of one. The rest of the standard ispresented within the range 0–1 degree of membership. Theboundaries between the standards are graded which providesthe fuzzy set sensitivity to the membership function byproviding degrees of closeness to the required value of theperfect order.

5.6.2. Order output

Any value for order output between the values of 0 and0.29 will cause the order to be rejected, between 0.30 and0.49 indicates that the order is poor and needs majorattention and action. Although the order value from 0.50to 0.69 indicates acceptance of the order it shows a high

gap from the optimum and the processes and perfor-mance needs significant improvement. Order value be-tween 0.70 and 0.89 indicates a low level of satisfactionaiming to close the gap towards optimum. An orderoutput value between 0.90 and 1 indicates that the orderis fulfilling the requirements and requires the processes tobe monitored and maintained as shown in Table 1.

5.6.3. Fuzzy evaluation rules

Table 3 demonstrates examples from the 441 ruleswhich follow the format ‘‘if (condition x) and (condition y)then (result z)’’ corresponding to the combinations ofinput conditions. For example: if manifest accuracy is‘‘poor’’, if quality is ‘‘poor’’, if delivery time is ‘‘very early’’and if quantity is ‘‘low’’ then order is ‘‘reject’’. The rulesare determined through the expert knowledge of thedesign team and are further refined following real lifeapplication and reappraisal which will either confirmthem or require them to be modified.

5.6.4. Fuzzy solution results

A continuum of fuzzy solutions for Eq. (1) is presentedin Fig. 11 using the fuzzy tool box of Matlab. The fourinputs can be set within the upper and lower specificationlimits and the output response is calculated as a score thatcan be translated into linguistic terms as per Table 1 i.e. anorder output of 0.818 indicates ‘‘satisfied’’ linguistically.

A further graphical representation example as re-sponse surface for the order is presented in Fig. 12.However, given the limitation of Cartesian axes only twoof the four input variables can be selected to map theresulting variations in the output (order).

Fig. 12 illustrates the order resulting from the interac-tion of delivery time and quality. Again an early deliverytime is better than late, however, the best score iscorresponding to on target delivery. Moreover, it can beseen that quality represents a much higher influence ondeciding a score for the order.

The above example surface was developed based on theset of 441 fuzzy relational rules corresponding to the settingof the fuzzy membership function of each input/outputvariable. As described earlier the rules can be adjusted inaccordance to their suitability or appropriateness. Theplotted surfaces are intended to give an overall picture ofthe order performance rather than discrete values.

Table 3Fuzzy evaluation rules for manifest accuracy ‘‘poor’’.

Manifest accuracy ‘‘optimum’’

Quantity Delivery time

Very early Early Acceptable early Optimum Acceptable late Late Very late

Quality ‘‘poor’’

Very low Poor Poor Poor Poor Poor Reject Reject

Low Poor Poor Poor Poor Poor Reject Reject

Acceptable low Poor Poor Poor Poor Poor Reject Reject

Optimum Poor Poor Poor Poor Poor Reject Reject

Acceptable high Poor Poor Poor Poor Poor Reject Reject

High Poor Poor Poor Poor Poor Reject Reject

Very high Reject Reject Reject Reject Reject Reject Reject

Quality ‘‘good’’

Very low Poor Poor Poor Poor Poor Poor Reject

Low Poor Acceptable Acceptable Acceptable Acceptable Poor Poor

Acceptable low Poor Acceptable Acceptable Acceptable Acceptable Poor Reject

Optimum Poor Poor Acceptable Satisfied Acceptable Poor Reject

Acceptable high Poor Acceptable Acceptable Acceptable Acceptable Poor Reject

High Poor Poor Acceptable Satisfied Acceptable Poor Reject

Very high Poor Poor Poor Acceptable Poor Poor Reject

Quality ‘‘excellent’’

Very low Reject Poor Poor Acceptable Poor Poor Reject

Low Poor Poor Acceptable Acceptable Acceptable Poor Poor

Acceptable low Poor Poor Acceptable Acceptable Acceptable Poor Poor

Optimum Acceptable Acceptable Acceptable Optimum Acceptable Acceptable Poor

Acceptable high Acceptable Acceptable Acceptable Acceptable Acceptable Poor Poor

High Poor Poor Poor Acceptable Poor Poor Poor

Very high Poor Poor Poor Poor Poor Poor Poor

Fig. 11. Shows the rule viewer of the fuzzy inference system for an order output=0.818 indicating ‘‘satisfied’’ from Table 1.

Y. Amer et al. / Int. J. Production Economics 127 (2010) 278–291288

Fig. 12. Output surface of the fuzzy inference system for delivery time and quality.

Y. Amer et al. / Int. J. Production Economics 127 (2010) 278–291 289

6. Validate

Due to time constraints and staff turnover the validatephase was not able to be undertaken in the cases study.Simulation may be a good option to consider. A pilot trial isrun in as realistic circumstances as possible being subjectedto as much ‘‘noise’’ as possible to determine if anyadjustments need to be made. If this stage suggests that thesupply chain design does not or may not meet the requiredcapability, then it is necessary to retract back through toearlier stages of design or optimize (Peplinski, 2004).

7. Conclusion and discussion

The case application of the order fulfillment modelpresented in this paper contributes to the qualitativeunderstanding of supply chain management and perfor-mance measurement. By developing a greater under-standing of their own internal processes through a crossfunctional approach the design team were able to seewhere their processes intercept with the processes of theirsupply chain partners’, the trading company and distribu-tion centre. The case application highlighted areas forlinkages across the chain in forecast evaluation and the‘‘rounding up’’ parameter which called for a mutualreview and closer monitoring to determine if these arethe areas impacting on stock outs of top selling items andexcessive inventory of low selling items.

The order fulfillment model enabled the developmentof KPIVs and KPOVs of order fulfillment to be determinedwhich added depth and focus to Company A’s perfor-mance measurement system building supply chain inte-gration through focusing on customer requirements,process monitoring and control and the application ofinternal and external order fulfillment process perfor-

mance measurement. It provides useful managerialtechniques for global SCM and is a practical quantitativemodel for operations managers to redesign/design, moni-tor and control the supply chain order fulfillment processusing DFSS and fuzzy logic. There is potential for themodel to design, monitor and control other supply chainprocesses with Amer et al. (2007b) approaching thedemand management process. Hence there are widerimplications for the model to provide further innovativeapproaches to supply chain management.

This paper has highlighted fundamental problems withcurrent supply chain models which refer to the impor-tance of a performance measurement systems to achievesupply chain strategy and business outcomes but do notelaborate on how to determine specific targeted measuresfor individual circumstances. The DFSS approach meantthat the case company’s strategy of satisfying theircustomer was at the forefront of order fulfillment designdue to the identification of CCRs through the VOC. Byconsidering this at the design onset the focus on designingto meet those requirements meant that they wereoptimized and the appropriate performance measureswere matched to the supply chain strategy and theparticular order fulfillment process structure.

As 47% of firms are currently only focusing ondeveloping a seamless flow of internal processes the orderfulfillment model presented holds great potential forthese firms to develop SCM and extend their focus acrossthe chain. Partial integration was advocated as analternative to the more holistic approaches of the SCORand GSCF models in the literature. One of the inhibitingfactors to such holistic approaches is the cost, and time toimplement such organizational changes which manySMEs may not be able to bare. This model provides agood alternative. It could be used to build a new supplychain as in the case of axiomatic design, to redesign an

Y. Amer et al. / Int. J. Production Economics 127 (2010) 278–291290

existing one, or just to analyse, understand and improvethe existing chain as was demonstrated in the caseapplication and the partial illustration of the model beingadapted to the demand management process.

The SCOR model analysed the ‘‘as is’’ state, and thenthrough the application of the SCOR template, the ‘‘to be’’state was benchmarked with other firms. The GSCFmodel is a comprehensive model but has limited uptakeamongst firms probably due to its comprehensivenessand depth making its implementation time consumingand expensive. The model presented in this thesis hasthe potential for supply chain design innovation that isadaptive to changing circumstances. It can be applied todifferent channels, addressing issues raised by Bask andJuga (2001) that supply chains can be obsolete by the timeholistic frameworks are in place and they therefore needto deal with multiple goals and strategies within a supplychain. The order fulfillment model presented makes aconsiderable contribution to improving supply chainintegration through an appropriate performance measure-ment system. The approach of DFSS allows for targetedmetrics that are relevant to the process performed and theorder fulfillment model can accommodate varying orderfulfillment structures thereby making the metrics relevantto that structure.

The application of fuzzy logic to the transfer functionalso addresses the variability of the four elements of theperfect order namely delivery time, quantity, quality andmanifest accuracy. The input of the expert design teammakes the measures relevant to their specific circum-stances as the rules are determined from the commonsense, human thinking and judgment of the design teammembers. The fuzzy transfer function enables a clearerlook at the relationships between the variables which canprovide more robust information for determining thesupply chain improvement focus.

The flexibility of the model lies in that it canaccommodate the many varieties and degrees of supplychain integration by providing a means to target someaspect of processes relationships or channels that is beingexamined and therefore provides a means for incrementalsteps for supply chain improvement.

8. Recommendations and further research opportunities

It is recommended that further research opportunitiesexist by the application of the presented order fulfillmentmodel in the following ways to further contribute to theunderstanding of its usefulness and validity:

�

The model be applied in small to medium sizedenterprises that do not have advanced supply chainmanagement practices in place. � That the model be used in different positions within a

supply chain and its use be extended further across achain.

� That the model be used in further research to other

supply chain processes.

� As the use of the model could necessitate the use of

specific software, research could develop suitable

platforms for the methodology which could make itmore accessible for commercial application.

� Consideration of the use of DOE to optimize process

parameters and process structures in the optimize phase.

� Using discrete event analysis and simulation in the

verification phase to verify the final design.

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