nedaa agami, mohamed saleh, and mohamed...

IEEE SYSTEMS JOURNAL, VOL. 6, NO. 3, SEPTEMBER 2012 469

A Hybrid Dynamic Framework for Supply ChainPerformance Improvement

Nedaa Agami, Mohamed Saleh, and Mohamed Rasmy

Abstract—Improving supply chain performance has becomea critical issue for gaining a competitive edge for companies.Many critical drawbacks prevent the existing performance mea-surement systems from making a significant contribution to thedevelopment and improvement of supply chain management andthus, several research is still needed in this area. In an attempt tofill this gap, an enhanced process-based approach for measuring,managing, and hence improving supply chain performance ispresented. The proposed framework is dynamic, continuous,and hybrid. It integrates various sciences, methodologies, andtools, namely systems thinking, strategic planning, optimization,balanced scorecards, supply chain operations reference model,and theory of constraints thinking processes into a cohesiveperformance measurement system. In this paper, a comparisonbetween the proposed approach and currently existing systems isprovided highlighting how each methodology contributed in theenhancement.

Index Terms—Optimization, performance improvement,supply chain, systems thinking, theory of constraints thinkingprocesses (TOCTP).

I. Introduction

IN MODERN BUSINESS environments characterized byever increasing competition and economy globalization,

companies have been exploiting innovative technologies andstrategies to achieve and sustain competitive advantage. Nowa-days, they face an increasing pressure of customers’ require-ments in product customization, quality improvement, anddemand responsiveness more than ever. On the other hand,they need to reduce production cost, shorten lead time, andlower inventory level to ensure profitability. As an effectivebusiness philosophy, supply chain management (SCM) hasgained a tremendous amount of attention from both industriesand researchers’ communities in the recent years in orderto help enterprises survive under continuous pressures andachieve the common goal of enhanced customer satisfaction.

Manuscript received June 3, 2011; revised September 17, 2011; acceptedNovember 11, 2011. Date of publication December 28, 2011; date of currentversion August 21, 2012.

N. Agami and M. Rasmy are with the Department of Operations Researchand Decision Support, Faculty of Computers and Information, Cairo Uni-versity, Giza 12613, Egypt (e-mail: [email protected]; [email protected]).

M. Saleh is with the Department of Operations Research and DecisionSupport, Faculty of Computers and Information, Cairo University, Giza 12613,Egypt, and also with the System Dynamics Group, University of Bergen,Bergen 5096, Norway (e-mail: [email protected]).

Color versions of one or more of the figures in this paper are availableonline at http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/JSYST.2011.2177109

Over the last decade of the evolution of SCM, a steadystream of research and articles dealing with the theory andpractice of SCM have been published, but the topic of perfor-mance measurement has not received adequate consideration.As an indispensable management tool, performance mea-surement provides the necessary assistance for performanceimprovement in pursuit of supply chain (SC) excellence.However, many critical drawbacks prevent the existing per-formance measurement systems from making a significantcontribution to the development and improvement of SCMand thus several research is still needed to fill this gap[1]–[4].

In this paper, an enhanced approach for measuring, man-aging, and hence improving supply chain performance thataddresses and attempts to overcome many challenges that arecurrently existing, is presented. The proposed framework isdynamic, continuous, and hybrid. It integrates various sci-ences, methods, and tools namely systems thinking, strategicplanning, optimization, balanced scorecards, supply chain op-erations reference (SCOR) model and theory of constraintsthinking processes, into a cohesive performance measurementsystem.

This paper is organized as follows. Section II brieflyarticulates the recent literature on the works done in thearea of supply chain performance measurement (SCPM). InSection III, the research gap highlighting the motivation behindconducting this research is discussed. Then in Section IV, theresearch methodology in terms of the methods and sciencesused to develop this hybrid integrated approach is explained. InSection V, the proposed approach is presented and elaborated.Finally in Section VI, this paper is summarized and concluded,the characteristics of the proposed approach versus those ofthe currently existing ones are outlined, and suggestions aregiven for future research.

II. Literature Review

Research in the contemporary literature on the topic ofSCPM is numerous [2], [3]. Many attempts have been madeto measure SC performance using conventional approaches.They are generally classified into two classes: financial andnonfinancial. Financial approaches are traditional accountingmethods that mainly focus on financial indicators and ignoreimportant strategic nonfinancial ones such as customer loy-alty, and service quality. The two most common frameworksreferred to in the literature as pure financial approaches for

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470 IEEE SYSTEMS JOURNAL, VOL. 6, NO. 3, SEPTEMBER 2012

SCPM are activity-based costing (ABC) and economic valueadded (EVA).

The ABC approach was developed by Kaplan and Bruns[5] as one of the very first attempts to link financial measuresto operational performance. Within the SC context, the mainobjective is to allow for better assessment of the true produc-tivity and costs of a supply chain process. Few years later,Stern et al. [6] developed the EVA approach to correct thedeficiency of traditional accounting methods which focusedonly on short-term financial results and hence, provide littleinsights into the success of an enterprise toward generatinglong-term values to its shareholders. Despite having somesuccessful applications, EVA metrics fail to reflect operatingSC performance.

Although financial measures are essential for assessingwhether or not operational changes are improving the financialhealth of an organization, they prove insufficient to measureSC performance for the following reasons [4], [7]:

1) they tend to be short-term, internally focused, and his-torically oriented;

2) they do not relate to important strategic, non-financialperformance indicators such as customer satisfaction andproduct quality;

3) they do not directly tie to operational effectiveness andefficiency.

A variety of performance measurement approaches, referredto in the literature as non-financial performance measurementsystems (NFPMS), were developed to address the previouslystated limitations of pure financial systems. In a recent litera-ture survey, Agami et al. [4] classified the NFPMS into ninecategories as follows:

1) function-based measurement systems (FBMS);2) dimension-based measurement systems (DBMS);3) hierarchical-based measurement systems (HBMS);4) interface-based measurement systems (IBMS);5) perspective-based measurement systems (PBMS);6) efficiency-based measurement systems (EBMS);7) supply chain balanced scorecard (SCBS);8) SCOR;9) generic performance measurement systems (GPMS).

FBMS were developed by Christopher [8]. They cover thedetailed performance measures applicable at individual func-tions (departments) but ignore top level measures that coverthe entire SC, and thus results in localized benefits. DBMS arebased on the premise that any SC can be measured on dimen-sions, such as resources (R), output (O), and flexibility (F), asoriginally suggested by Beamon [9]. Gunasekaran et al. [10]developed HBMS in which measures are classified as strategic,tactical, or operational in order to assign measures where theycan be best dealt with by the appropriate management level.IBMS was primarily proposed by Lambert and Pohlen [11].They attempt to link the performance at each stage with theSC. However, this link-by-link approach requires openness andtotal sharing of information at every stage, which is eventuallydifficult to implement.

Otto and Kotzab [12] developed PBMS. It focuses on mea-suring the SC performance based on six main perspectives that

they identified, namely system dynamics, operations research,logistics, marketing, organization, and strategy. They presentedsix unique sets of metrics, one for each perspective, to measureperformance of SCs.

Many approaches were developed to measure the SC per-formance in terms of efficiency [13]–[18]. Most of them wereDEA-based [19]–[21], some used game theory [18], and othersused fuzzy logic [22], [23] or Choquet integral operator [24].Agami et al. [4] grouped these methods into one category thatthey called EBMS.

The SCOR model was introduced by the Supply ChainCouncil [25], [26] as a framework for examining the SC indetail through defining and categorizing the processes thatmake up the chain, assigning metrics to such processes andreviewing comparable benchmarks. Since then, SCOR is con-sidered one of the most commonly applied models for SCPM.

SCBS was developed by Kaplan and Norton [27]. Though itwas not developed specifically for measuring SC performance,it is widely used for this purpose. It allows managers toobserve a balanced view of both operational and financialmeasures at a glance on four main perspectives: financial,customer, internal business processes, and innovation andlearning.

Several generic performance measurement approachesare used for SCPM. The most commonly used ones are:performance prism [28], performance pyramid [29], andMedori and Steeple’s [30] framework. Being applied forSCPM purposes, Agami et al. [4] grouped them in onecategory that they named GPMS. They also summarized andhighlighted the criteria of measurement used according to theNFPMS type as indicated in Table I.

III. Research Gap

Effective performance measurement is known as the key torecognize the benefits and achieve efficient SCM. It providesthe necessary assistance for performance improvement inpursuit of SC excellence. In spite of its importance, there is adearth of research in the contemporary literature on the topicof performance measurement in the SCM context, especiallythose that deal with system design and measures selection[31]–[35].

Many approaches have been developed for SCPM. However,they received wide criticism for being inflexible and lackingcontinual improvement [33]–[35]. It was pointed out by Neely[36]–[38] that complexities in SCM have made these tech-niques cumbersome when dealing with a large amount of data.In addition, their inability to handle ambiguity, stochasticity,uncertainty, and inconsistencies inherent in SCPM makes themappropriate to a certain limitation and only in certain cases.Thus, contributions of already existing SCPM systems arediscounted by the existence of too many drawbacks that canbe summarized as follows [2]–[4]:

1) not connected with strategy;2) incompleteness and inconsistencies in performance

metrics;3) lack of balanced approach that incorporates financial and

nonfinancial measures;

AGAMI et al.: HYBRID DYNAMIC FRAMEWORK FOR SUPPLY CHAIN PERFORMANCE IMPROVEMENT 471

4) lack of holistic approach, i.e., a SC must be viewed asone whole entity and measured widely across the whole;

5) being short-term, profit oriented;6) encourages local optimization and thus, fails to support

continuous improvement;7) being too inward looking;8) insufficient focus on customers and competitors;9) large number of metrics, making it difficult to identify

critical few among trivial many.

The aforementioned flaws call for the need to develop morerobust approaches for the measurement of SC performance. Itis an established fact that in order to improve SC performanceand effectiveness and realize a smooth flow of resources withinit, there is a need to appropriately measure its performance.Hence, an effective performance measurement method remainsunder considerable debate and requires further research andexploration.

IV. Research Methodology

In this research, the aim is to develop an enhanced SCPMand management system that is characterized by being holistic,efficient and effective, process-based focused, strategy aligned,and provides fact-based feedback. It is intended to be inte-grated, dynamic, and continuous to allow for better manage-ment, and hence improvement, of the overall SC performance.

Achieving the above requirements, i.e., research objective,called for the integration of various methods, sciences, andthinking approaches, namely systems thinking, strategic plan-ning, and theory of constraints thinking processes (TOCTP).Each of them will be discussed, in detail, in the next subsec-tion.

A. Systems Thinking

Well-synchronized tunes in an orchestra create a melodywhile asynchronous ones create gibberish. The same holds truefor SCs. To be able to survive in today’s highly dynamic andcompetitive markets, it is important to realize the significanceof synergizing the strengths of all players that constitute aSC [39], [40]. Advocating this argument, Terwilliger [41]described SCs as living, breathing entities. Focusing on aspecific function, department or process will only lead to localsolutions whereby the SC as a whole misses the opportunity toachieve global optimality. This usually necessitates that indi-vidual players shift focus from their own organization-specificbusiness interests to SC interest, i.e., the common good. And asCottril [42] put it: “The SC’s economic value is best enhancedwhen the realization occurs that true competition is betweenSCs, wherein performance is measured using overall chainmetrics.” Thus, the objective of SCM is to create value to thewhole SC network, not only for some individual companies.Therefore, successful SC performance measurement systemsshould not focus on partial areas, but rather look across thewhole network.

In light of the previous literature, the proposed approachtaken suggests employing a holistic system-thinking per-spective that captures the essence of SCM by focusing on

TABLE I

Nonfinancial SCPMS and Their Criteria of

Measurement (Source [4])

Type of Measurement System Criteria of MeasurementFBMS Performance measures of functions

within each process of the supplychain.

DBMS Performance evaluation of pre-determined key dimensions acrossthe supply chain.

HBMS Performance measures identified onthree levels of management: strate-gic, tactical, and operational.

IBMS Performance measures defined be-tween supply chain linkages, i.e.,stages.

PBMS Performances measures on six per-spectives of the supply chain: op-erations research, system dynamics,logistics, marketing, organization,and strategy.

EBMS Performance measures to evaluatethe supply chain efficiency.

SCOR model Performance measures along thefive main supply chain processes:plan, source, make, deliver, and re-turn.

SCBS Performances measures across foursupply chain perspectives: financial,customer, internal business pro-cesses, and innovation and learning.

Generic systems (GPMS) Performance measures are strategyaligned.

measuring and improving the system level, i.e., the whole,performance rather than the entity level performance whichhas been the usual practice for a long time.

B. Strategic Planning

For effective performance management, it is essential tohave a clear, well-defined strategy set for achieving the long-term SC goals and objectives. Since what cannot be measuredcannot be improved, then no strategy will work if it is notpossible to measure the progress being made. Therefore, itis important for a SC to determine the appropriate, balanced,performance metrics to evaluate the performance as a whole asit affects the successful attainment of strategic objectives [43],[44]. The degree to which each performance metric is satisfiedacross different players determines the degree of strategic fit.Hence to be able to identify and determine relevant perfor-mance metrics, a balanced process-based metrics identificationapproach proposed by Abu-Suleiman et al. [45] is used, whichutilizes the SCOR model as depicted in Fig. 1. Being process-based allows for simplifying practical SCs from their essenceand commonalities.

A process is generally defined as a structured set of activitiesdesigned to perform specific functions and produce specificoutputs. In the model presented, a process is referred toas a series of planned activities from original suppliers andmanufacturers to retailers that add value to the end customers.

In the proposed approach, it is assumed five core businessprocesses are of crucial importance for achieving SC strategic


Fig. 1. Balanced process-based metrics identification model (source: [40]).

TABLE II

TOCTP and Their Roles

No. Change RelatedQuestion

Purpose TOCTP Tool

1 What to change? Identify coreproblems

Current realitytree (CRT)

2 What to change to? Developpracticalsolutions

Future realitytree (FRT)

3 How to cause thechange?

Implementsolution

Prerequisitetree (PRT)

objectives: supply, manufacturing, distribution, retail, and theend customer. However, returns and recycling are sometimesincluded in other studies.

C. TOCTP

The TOCTP was originally developed by Goldratt [46], [47]who views a system as a chain composed of many links. Hestated that the overall system performance is limited by itsweakest link. Hence to improve its performance, the first stepmust be to identify the system’s weakest link, i.e., constraint,which when improved leads to the overall performance im-provement.

The TOCTP applies the cause-and-effect thinking processesused in the hard sciences to understand and improve allsystems. It comprises a suite of logic trees that provide aroadmap for change and guides the user through the decisionmaking process of problem structuring, problem identification,solution building, identification of barriers to be overcome,and implementation of the solution [48]–[50]. At its mostbasic level, TOCTP provides users with a set of tools thatguide the user to find answers to the main questions re-lated to change. Its tools and their roles are illustrated inTable II.

In this research, it is postulated that utilizing the TOCTPand its tools would very much help identify appropriateimprovement strategies for the bottleneck (critical) KPIs thatare said to limit the overall SC performance. The TOCTPframework within the context of this research is shown inFig. 2.

Fig. 2. TOCTP framework.

V. Proposed Approach

This section gives a detailed explanation and demonstra-tion of the proposed approach. This starts by discussing thetraditional performance management approach emphasizingits main limitations followed by illustrating the modificationsmade, i.e., the main research contribution, taking into con-sideration these drawbacks. Finally, a summary is providedby giving an overview of the newly modified performancemanagement and improvement system structure.

A. Traditional Performance Management Approach

Generally, the traditional performance management cycleis composed of five main processes as illustrated in Fig. 3.It starts with identifying SC time-bound, strategic objectives.Then, a metrics model is employed to identify and define aset of appropriate performance metrics that should be used toassess and monitor the whole SC performance. In the samestep, benchmarks, i.e., target values, for each metric are setto be evaluated against. According to Lapide [51], there arefour methods that can be used to set performance targets:historical based (with respect to historical baseline levels),external benchmarks (competition best practice), internalbenchmarks (internal “best in class” practice), and theoreticaltargets [51], [52].

One step further in the performance evaluation process, themetrics current performance is monitored and the gap versustarget is calculated and captured on a KPIs scoreboard. Theperformance evaluation results for each KPI are plotted astrend lines on a dashboard and communicated to managementKPIs whose results do not match expected outcomes arehighlighted as bottlenecks. In light of the evaluation results,management is usually asked to take corrective actions anddevise improvement strategies to improve the performance atwhatever cost with no clear guidance.

B. Research Main Contribution

In an attempt to assist management make guided correc-tive decisions to improve the overall SC performance, an


Fig. 3. Traditional performance management cycle.

Fig. 4. Main contribution: Modified performance management approach.

intermediate optimization step “Pc” is added, i.e., additionalprocess, between performance evaluation (P4) and perfor-mance management (P5) processes of the traditional approach.The proposed optimization process is intended to minimizethe total improvement cost. This modification is the maincontribution. It is illustrated in Fig. 4.

Below the additional optimization step (Pc) shall be ex-plained along with how introducing it is expected to en-hance the traditional performance management process. How-ever, it is essential to highlight that in the second process(P2), a process-based metrics model that utilizes the SCORframework (Fig. 1) is used to identify a set of appropriatebalanced KPIs that addresses each and every process ofthe SC.

In the optimization step, an optimization procedure origi-nally devised by Cai et al.1 [53] is employed. They apply an

1For the detailed algorithm, the reader can refer to [53].

Fig. 5. Performance improvement model proposed by Cai et al. (source:[53]).

eigen structure analysis model to identify critical few KPIsamong those considered as bottlenecks and hence prioritizetheir improvement such that the total accomplishment costis minimized. Their optimization algorithm is basically com-posed of four steps as follows:

1) identify and define KPIs and the pair-wise relationshipsbetween them;

2) estimate the accomplishment costs of these KPIs andasses their dependencies;

3) optimize, i.e., minimize the total KPIs accomplishmentcost using an eigen structure analysis;

4) determine the critical bottleneck KPIs.

Though their algorithm is used in the proposed approach,Cai et al. [53] developed the optimization procedure for adifferent purpose and applied it in a different context. Theysuggested that it should be applied at a very early stage inthe performance management cycle, directly after identifyingthe SC KPIs to give management a quick feedback on criticalKPIs, in terms of accomplishment cost, such that those KPIsare the management main focus from the very beginning. Ac-cording to those KPIs operational plans are then set, ignoringother KPIs that might turn out to be crucial to the overall SCperformance at a later stage. The framework within which theyemploy their model is illustrated in Fig. 5.

On the contrary in the proposed approach, the optimizationalgorithm is implemented in the last stage to identify only theset of KPIs, i.e., bottlenecks, that turned out to be crucial to theoverall SC performance. And hence, avoid the possibility ofignoring any of them as a result of employing this mechanismat a very early stage in the performance management cycle.

After identifying the critical few KPIs, the three steps ofthe TOCTP are executed in the performance managementprocess (P5), to recommend improvement strategies for theseKPIs. Utilizing the TOCTP is introduced to formalize theprocess of recommending improvement strategies and makeit a structured, continuous one instead of the trial and errorapproach currently employed in an unstructured manner.


Fig. 6. Proposed continuous dynamic feedback improvement framework.

C. Modified Performance Management Approach

In Fig. 6, the complete modified approach is demonstratedas a continuous dynamic feedback performance improvementloop.

The proposed approach can be divided into three mainphases, each of which is composed of two steps. The completeapproach can be summarized in the following procedure toformally illustrate how it works.

1) Setting

a) Determine the vision, mission and time-boundstrategic objectives for the SC as a whole.

b) Identify a set of process-based KPIs accordingto which the overall SC performance is to bemeasured and assessed. Define a benchmark targetvalue for each.

2) Performance measurement

c) For each KPI, calculate gap versus target.d) Plot the findings on a KPIs scoreboard. Identify

bottleneck KPIs.

3) Performance management (main contribution)

e) Implement the optimization algorithm to identifythe critical few KPIs among all the bottlenecks.

f) Apply TOCTP to recommend improvement strate-gies for those critical KPIs.

To summarize this section, the formal system structurediagram (SSD) of the proposed performance management andimprovement approach is plotted as shown in Fig. 7 andexplained briefly.

Generally, any system is composed of three main com-ponents: input(s), processing, and output(s). As for the in-put(s) part, the proposed approach takes three elements asan input: strategic objectives of the whole SC, process-basedperformance metrics and benchmark targets for these metrics,depicted in the SSD as I1, I2, and I3, respectively. Theseinputs are then processed in two stages each of which involvestwo steps. In the first stage, the performance metrics valuesare monitored and captured in a KPIs dashboard. Then the

Fig. 7. Proposed approach: SSD.

gap versus benchmark target values (previously identified) arecalculated to identify the bottleneck KPIs affecting the overallperformance. The previous two steps are highlighted in theSSD as P1 and P2, respectively. The second processing stageconstitutes the main contribution. It takes the output of thefirst stage as an input and processes it in two steps: Pc and P3.Pc employs Cai et al.’s [53] optimization procedure explainedearlier to identify critical few KPIs among those consideredas bottlenecks and hence prioritize their improvement suchthat the total accomplishment cost is minimized. Then inP3, TOCTP tools are utilized to recommend improvementstrategies for these KPIs in a formal structured manner.

The approach then gives as a final output the critical fewKPIs among those identified earlier as bottlenecks limitingthe progress of the overall SC performance and recommendedimprovement strategies to support and help the managementin taking the appropriate decisions. These two outputs arerepresented in the SSD as O1 and O2, respectively.

There is a dynamic feedback loop from the final output toall other system components to help continuously adjust, andhence improve, the overall SC performance. This feedbackloop constitutes the basic component for dynamic improve-ment.

VI. Practical Implications, Verification,

and Validation

Being composed of various scientific methodologies, theproposed approach has a solid theoretical foundation. It alsohas many useful practical implications for SC managers inany domain. The significance of the proposed approach tomanagers is summarized at the following points.

1) It evaluates and improves the performance of the SCas a whole rather than focusing on individual entitieswithin it, and thus helps focus on achieving the overallSC objectives.

2) It ensures that selected KPIs for measuring and track-ing performance are strategy aligned and represent allimportant processes within the SC.

3) It filters out only the critical few KPIs from thoseconsidered as bottlenecks for performance improvement,


TABLE III

PCTM: Case #1

KPI KPI A KPI B KPI CKPI A 0 0 0KPI B 0 0 0KPI C 0 0 0

TABLE IV

PCTM: Case #2

KPI KPI A KPI B KPI CKPI A 0 0 0KPI B 0.2 0 0.2KPI C 0 0.05 0.05

and therefore allows managers to focus only on relevantKPIs and improve their performance at the minimumcost.

4) It enables managers to not only measure performancebut also set structured improvement strategies for criticalKPIs through adopting the TOCTP.

5) It helps managers measure and track performance oncontinuous basis through the dynamic feedback compo-nent, and hence ensures continuous improvement.

A. Verification

Before testing the effectiveness of the proposed approach,the optimization procedure needed to be verified. Hence, itwas exposed to extreme condition testing where two caseswhose results are known beforehand were examined. The firstcase is when all bottleneck KPIs are independent in terms ofaccomplishment cost, i.e., had a parallel pair-wise relationship[53], whereas the second case is when they had coupled pair-wise relationship [53] and their accomplishment costs aredependent on each other where the accomplishment of one KPIaffected the accomplishment of the other in some way and viceversa. The KPIs accomplishment cost transformation matrices(PCTM)2 for the two cases are illustrated in Tables III and IV,respectively.

In the first case, the optimization procedure results showedno priority for one bottleneck KPI over the other since theyare already independent.

However in the second case, the accomplishment of KPI Ahad no effect (extra cost) over the other two KPIs, while theaccomplishment of KPI B highly affected the accomplishmentof the other two KPIs and the accomplishment of KPI C hada low effect on both as shown in Table IV. Therefore, it isintuitive that the priority in terms of accomplishment shouldbe: KPI A followed by KPI C then KPI B comes at the end,which were also the priorities generated by the optimizationprocedure.

B. Validation

To our knowledge, the optimization procedure is the onlyformal approach for KPIs filtration process in SCPM. There-

2To construct the PCTM, the experts adopted a Likert scale: 0 for “no”dependency, 0.05 for “low” dependency (5% extra cost), 0.1 for “medium”dependency, and 0.2 for “high” dependency.

TABLE V

PCTM: Experts’ Estimates

KPI Rate of Stock Inventory On-TimeOuts Storage Deliveries

Rate of stock outs 0 0.2 0.05Inventory storage 0.05 0 0.1On-time deliveries 0 0.1 0

TABLE VI

Validation Results

Month % of Budget Allocated Gap PI (100)Rate of Inventory On-Time

Stock Outs Storage Deliveries1 20 25 55 02 40 35 25 3 853 25 15 60 2 554 15 10 75 2 605 35 25 40 2 756 20 25 55 0 657 50 15 35 3 858 45 35 20 3 609 25 20 55 2 4510 25 35 40 0 5511 15 25 60 0 8512 20 10 70 2 9013 65

fore, in order to validate the proposed approach, it had to becompared with the informal (heuristic) method applied in reallife. Hence, one year (monthly) historical data for bottleneckKPIs in the SC department of a famous telecommunicationsoperator in Egypt was used. The percentage of the budget (ded-icated to performance improvement) allocated every month toeach bottleneck KPI represented the actual priority in termsof criticality in improvement as assumed by the manager wascompared to the ideal (theoretical) priority generated by theoptimization procedure of the proposed approach. Given theKPIs PCTM shown in Table V whose values are estimatedby experts in the field and fixed for all months, the idealpriorities were found to be as follows: on-time deliveries as afirst priority, inventory storage as second priority, and rate ofstock outs as the last priority.

The gap, in terms of absolute difference, between the actualand ideal priorities (ranks) was calculated as indicated inTable VI. And finally, the gap in each month was plottedversus the performance index (PI) in the next month asshown in Fig. 8 since there is a one month lag betweenthe budget allocation for KPI improvement and the resultingperformance.

It is obvious from the previous figure that there is aninverse relationship between the gap in one month and theresulting performance in the following month. The Pearsoncoefficient between the two variables has a value of −0.92,which indicates a strong negative linear association. Hence,this experiment corroborates our hypothesis that the proposedapproach helps managers focus only on relevant KPIs andimprove performance at the minimum cost.


Fig. 8. Performance in month (m+1) versus gap in month (m).

TABLE VII

Proposed Approach: Main Contribution

No. Current Proposed Science Used to FillApproaches Approach the Gap

1 Lack of holism Holistic Systems thinking2 Not connected

with strategyStrategyaligned

Strategic planning

3 Static Dynamic Systems thinking (feed-back component)

4 Lack of continualimprovement

Continuousimprovement

TOCTP

5 Lack of balancedKPIs

Balancedprocess-based KPIs

Balanced scorecards andSCOR model

6 Large number ofKPIs

Critical fewKPIs

Optimization

7 Inward looking Not handled Future Research8 Short-term profit

orientedNot handled Future Research

9 Insufficient focuson customers

Not handled Future Research

VII. Summary, Conclusion, and Possible

Future Work

Throughout the different sections of this paper, the literatureon SCPM is reviewed and key issues in this area are high-lighted. The review revealed that most of the already existingSCPM systems are inflexible and lack continual improvement.Hence, a gap still exists between the research and applicationof current systems.

In an attempt to bridge this gap, a dynamic, continuous andhybrid SCPM system framework that integrates systems think-ing, strategic planning, balanced scorecards, SCOR model,TOCTP, optimization and eigen structure analysis into a co-hesive approach for improving SC performance is proposed.The conceptual framework of the proposed system, as well asits formal structure design in terms of inputs, processing andoutputs, is also discussed in detail.

The point of departure was the challenges outlined byAgami et al. [4]. The aim of this research is to address thosechallenges. Thus, the proposed approach is characterized bybeing aligned with strategy, process focused and holistic in thesense that it focuses on improving the performance of the SCas a whole rather than just considering individual entities. It is

effective for managing and efficient for improving the overallSC performance in a dynamic environment.

A. Conclusion

In Table VII, the main contribution is summarized andconcluded. The characteristics of the proposed approach versusthose of the currently existing ones are outlined and specified.Also how each of the sciences used adds value to the overallapproach is highlighted.

To achieve this, the proposed framework is enhanced byincorporating two additional steps to the traditional SCPMprocess namely: optimization and TOCTP implementation.The former is applied at an intermediate stage between perfor-mance evaluation and management to determine critical fewKPIs amongst those identified as bottlenecks and thus allowmanagement to focus the use of resources on improving onlyrelevant KPIs. TOCTP tools are then used to set appropriateimprovement strategies for the KPIs identified as critical.

B. Possible Future Work

Despite the advantages of the proposed system, it stillhas some limitations and can be further enhanced in futureresearch as indicated below.

1) The optimization procedure depends on experts’ opin-ions, i.e., human judgment, about the KPI’s dependen-cies and accomplishment cost estimates. Therefore, thereis still a need to formalize the experts’ opinions in thismatter. Fuzzy logic is a recommended candidate [1].

2) The proposed framework can be implemented in a web-based context as an online decision support system.This would facilitate and speed up the performancemeasurement process and enable information sharing,which is crucial for appropriate performance measure-ment especially for SCs as it helps reduce inconsistency.

3) In theory, the structure and framework of the proposedapproach addresses and overcomes most of the flaws inthe currently existing performance measurement systemsfor SCs. However, there is still a need to test it and assessthe findings in practice, i.e., case studies.

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Nedaa Agami received the M.Sc. degree in opera-tions research from the Data Mining and ComputerModeling Center of Excellence, in conjunction withthe Ministry of Communications and InformationTechnology, Egypt, in 2008. She is currently pur-suing the Ph.D. degree with the Department ofOperations Research and Decision Support, Facultyof Computers and Information, Cairo University,Giza, Egypt.

She is currently an Assistant Lecturer with theDepartment of Operations Research and Decision

Support, Faculty of Computers and Information, Cairo University. She isalso a Business Development and Modeling Consultant with Vodafone Egypt,Cairo, Egypt. Her current research interests include performance measurementand management, systems thinking, supply chain management, simulation andmodeling, future studies, and scenario planning.


Mohamed Saleh received the M.Sc. degree fromBergen University, Bergen, Norway, in 1998, theM.B.A. degree from the Maastricht School of Man-agement, Maastricht, The Netherlands, in 1996, andthe Ph.D. degree in system dynamics from the Uni-versity of Bergen, Bergen, in 2002.

He is currently an Associate Professor with theDepartment of Operations Research and DecisionSupport, Faculty of Computers and Information,Cairo University, Giza, Egypt. He is also an Ad-junct Professor with the System Dynamics Group,

University of Bergen. His current research interests include mainly systemdynamics, simulation, supply chain management, future studies, and revenuemanagement optimization.

Mohamed Rasmy received the M.Sc. and Ph.D.degrees from Cairo University, Giza, Egypt, andNorth Carolina State University, Raleigh, both inoperations research, in 1973 and 1981, respectively.

He is currently a Professor with the Departmentof Operations Research and Decision Support, CairoUniversity. He held many positions such as the Deanand Vice Dean of the Faculty of Computers andInformation, the Head of the Operations Researchand Decision Support Department, and the Directorof the Decision Support and Futures Studies Center,

Cairo University. His current research interests include systems thinking,operations research and management sciences in general, and in artificialintelligence and decision support systems.

nedaa agami, mohamed saleh, and mohamed...

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