Supply Chain Performance Evaluation And Improvement .

4.7 Estimated company total fixed assets914.8 DMs’ relative importance on SCOR KPIs924.9 List of corresponding TIs and their implementation lead time934.10 Degree of influence of TIs on SCOR KPIs934.11 Current SCOR KPIs of the company944.12 Current revenue-cost structure of the company964.13 Derivation of the average importance rating, competitive analysis, and97final importance rating *4.14 Relative importance (weight) of WHATS ( Wm ) and the relationship score98 ( rmn )4.15 Final rating and ranking of TIs984.16 The new SCOR KPIs after improvement994.17 The total Revenue-Cost structure obtained from LP model after101performance improvementxiRef. code: 25605422300342CMU

List of FiguresFiguresPage1.1 Block diagram of the overall methodology102.1 The SCOR model with six management processes (APICS,2016)152.2 A hierarchical structure of SCOR162.3 Set A (top), and the crisp set A (bottom)372.4 A fuzzy set H372.5 A fuzzy set with λ cut382.6 A House of Quality (HOQ) (Left), and the HOQ with detailed description43(Right)2.7 The HOQ planning matrix (Bozdana, 2007)452.8 The typical 4 phases QFD model472.9 A triangular fuzzy number493.1 Block diagram of the SCOR KPIs evaluation procedure563.2 Structure of manufacturing system573.3 The proposed procedure to evaluate Upside Make Flexibility653.4 The proposed procedure to evaluate Upside Make Adaptability663.5 The proposed procedure to evaluate Downsize Make Adaptability673.6 The manufacturing process of a case study713.7 Graphical representation of the SCOR KPIs754.1 Block diagram of the research methodology804.2 Fuzzy QFD approach for managing SCOR KPIs814.3 Linguistic representation of U834.4 Relationship matrix between “Whats” and “Hows”864.5 Current production process of case study884.6 Graphical representation of current SCOR KPIs95xiiRef. code: 25605422300342CMU

4.7 Graphical representation of new SCOR KPIs100xiiiRef. code: 25605422300342CMU

Chapter 1IntroductionThe performance evaluation is defined as the process of quantifying the effectivenessof action. In the organization, the objective of performance measurement is to identifysuccess, to recognize process bottleneck, and to communicate the right messages forthe improvements. In the supply chain management, measuring the supply chainperformance can help the company to disclose the gap between planning and execution.The effective organization management requires the framework, information, and toolsto support a decision-making process and to identify the area for improvement. In thischapter, a basic definition of performance measurement system (PMS), and a role ofPMS in the supply chain management are presented. Then, the problems of current PMSare drawn as a dissertation’s problem statements, followed by research objectives, blockdiagram of the overall research methodology, and finally the research overview.1.1 Definition of the Performance Measurement System (PMS), and role of PMSin supply chain management.A performance measurement system (PMS) is defined as a process to measure theeffectiveness of action (Neely et al., 1995). The performance measures and metrics areessential in the business management because they provide the information that isnecessary for organizations to make decision and take action, especially in acompetitive economy. Parker (2000) identified the purpose of measuring organizationalperformance as follows;(1) to measure the business success;(2) to determine whether the customer needs are satisfied;(3) to help the organization to understand its process;(4) to identify the problems and point out the area for improvement wherenecessary, and1Ref. code: 25605422300342CMU

(5) to ensure that decisions are made based on facts, not the intuition.In the supply chain management (SCM), a PMS facilitates the inter-understandingamong supply chain members, and provides the outlook to identify success, as well asthe potential activities (Chan and Qi, 2003). Therefore, the PMS makes a considerablecontribution to the field of SCM in a decision-making process, especially in the businessredesign, and reengineering process. Regarding to the literature, the research topic inthis area is not new. According to Akyuz and Erkan (2010), more than hundreds ofarticles on the PMS and metrics were published during 1997-2009. Most of the articleswere discussed about the PMS design, and metric selection. The study from Shepherdand Gunter (2006) revealed that the PMS in the supply chain system can be categorizedaccording to the following characteristics.Table 1.1: PMS in the supply chain system as categorized by Akyuz and Erkan (2010).1. Balanced Scorecard perspective Kaplan and Norton (1997)2. Component of measuresBeamon (1999), Gunasekaran et al., (2001),De Toni and Tonchia (2001), Chan (2003),and Chan and Qi (2003)3. Decision levelsGunasekaran et al., (2001)4. Supply chain processChan and Qi (2003), Huang et al., (2005), Liet al., (2005), Lockamy and McCormack(2004)Firstly, Kaplan and Norton (1997) proposed a Balanced Scorecard (BSC)performance system which is built upon 4 perspectives of financial, internal businessprocess, customers’ satisfaction, and the learning and growth. BSC presents theperformance measurement in a balanced framework of the total business performance;however, prioritization of these different perspectives for a firm is an issue that needsto be addressed. Secondly, for the component of measures, it means that the PMS isclassified into groups. Beamon (1999) categorizes performance measures in two distinctgroups, namely; a qualitative and quantitative measures. Beamon (1999) identified three2Ref. code: 25605422300342CMU

types of measures which is resource, output, and flexibility. This categorization isfollowed by some researchers that try to address the issue in the SCM. For example,Van Landerghem and Persoons (2001) built a causal model related to the use of bestpractices to group the performance under four objectives which are; flexibility, reactiontime, quality, and cost. The PMS in this category is also proposed as a cost and noncost measures such as; quality, cost, delivery, and flexibility (De Toni and Tonchia,2001), and input, output, and composite measures (Chan and Qi, 2003). However, thismethod of categorization has received a criticism of not being connected with strategy,lack of a balance approach to integrate cost and non-cost measures, and mostimportantly, it losses of the supply chain context. Thirdly, for the decision levels inSCM system, Gunasekaran et al., (2001) classified a PMS as strategic, tactical, andoperational focuses, so this performance system can support each other to achieve theoverall business objectives, and to assist the company to make a right decision. Theconceptual design of PMS by Gunasekaran et al., (2001) was widely supported byseveral researchers to design the strategic tool that can align the supply chain from theoperational level to the firm’s strategy. For example, Lin et al., (2005) studied theoperational issues and develop a mathematical model to optimize performances througha supply chain redesign. Various techniques such as a deterministic model (Chen et al.,2005), a stochastic analytical model (Chiang and Monahn, 2005), and a simulationmodel (Huang et al., 2005) were developed in order to link supply chain strategy toobjectives, and to the operations. However, despite the popularity of the framework,some authors analyzed that the approach lacks of a systematic thinking for the SCM, asthe supply chain system must be viewed as the whole processes, and the PMS shouldspan to cover all business aspects.Therefore, a renowned framework, the Supply Chain Operations Referencemodel (SCOR), was originally developed by the Supply chain council in 1997. It hasbeen described as the most systematic approach for identifying, evaluating, andmonitoring the supply chain performance (Stephens, 2001). The proposed metrics allow3Ref. code: 25605422300342CMU

the company to manage performance on multiple dimensions in a hierarchical structurewhich is defined in the reference model. According to the SCOR model, a company’ssupply chain would be represented by 6 meta-level processes of plan, source, make,deliver, return, and enabler, in which all processes are managed under a SCORperformance metrics for a supply chain. A major advantage of this model is the creationof a common and standardized language among supply chain members, hence, itenables the companies to compare supply chain performance with others. Todemonstrate the applications of SCOR model, there are a number of publications thatpublished the works using the SCOR model as a reference framework. For example, inthe exploratory work of the SCOR model, Lockamy and McCormack (2004) were thefirst author who studied this model by investigating a relationship between supply chainmanagement planning, and the supply chain performance based on four decision areasof SCOR model of plan, source, make, and deliver. The result reveals that the importantplanning function such as the importance of collaboration, process measure,integration, and information technology are the enabler for success implementation.Afterwards, McCormack et al., (2008) integrated the SCOR model with the businessorientation maturity model using the previous study as a reference. The study providesa comparison between traditional versus innovative performance measurement systembased on a Brazilian company surveyed. The result puts forward a clear support for theneed of new performance measurement methodologies that emphasis the important ofbusiness maturity. In terms of practicality of the SCOR model in industries, Hwang etal., (2008) performed a case-based study for the Taiwanese TFT-LCD industry. The workcontains a comprehensive set of SCOR model that only emphasizes on a sourcingprocess and then perform a stepwise regression analysis to analyze the dependency ofdifferent performance measure. Li, et al., (2011) also adopted the SCOR model in orderto ensure a supply chain quality performance to help companies develop and maintaina supply chain process according to the quality standards. However, these are only thebrief applications of the SCOR model, where the full literature reviews are presentedin the next chapter. Despite the discussion of various PMS in the supply chain system,4Ref. code: 25605422300342CMU

this research found that whichever the PMS are used, the effective performanceevaluation should be practical, comparable to other organizations, and be able toprovide the feedback for performance improvements. These are the starting points ofdiscussion in this dissertation, where the issues of performance evaluation andimprovement is explained subsequently in the following chapters.1.2 Research problem statementsBased on the literature survey, most of the articles have proposed the PMS as a metricdesign, or a requirement to compose a good performance measurement system. Thereare only few papers that addressed the issue of performance evaluation such as themethod or the underlying mechanism for assessment. Neely et al., (1995) actuallydefined the term “performance evaluation” as a definition to measure, depending onhow it will be calculated, and where the data is obtained from. Therefore, a simplestway to determine performance based on Neely’s definition is to choose a preferredmetrics, collect the related information, and perform the assessment straightforwardly.However, this method has a drawback as it reveals only the performance from the past,where the future direction cannot be anticipated. A good performance measurementsystem and evaluation method, actually, should encourage the improvement rather thanjust monitoring. Therefore, the measurement method should also integrate a feedbackmechanism in order to tell the company, or the manager on the improvement areas andthe decision to move on. Moreover, the measurement mechanism should also be able toadjust overtime, as a company needs changes (Maskell, 1991), and be able to compareto similar organizations where the same performance criteria is applied. With thisreason, and based on the evidence from the literature survey that only the few reportsare focusing on this issue, it becomes the interest of this dissertation to pay attention onthe topic of performance evaluation and improvement in the supply chain system.Firstly, this thesis begin with the discussion and selection of the standard supply chainframework that is used throughout dissertation.5Ref. code: 25605422300342CMU

The Supply Chain Operations Reference (SCOR) Model is one of a well-establishedprocess reference model which is now supported by the APICS Supply Chain Council(APICS, 2016). It is organized into five main processes. SCOR Model is comprised ofperformance attributes and the measurement metrics in a hierarchical structure. Theseorganized features allow the framework to be widely adopted by the supply chainresearch, and practically adapted to various industries. According to the relatedpublications that work on the SCOR model, researchers and practitioners agreed thatthe SCOR model is a good reference model because; It provides the standard descriptions of each business process along the supplychain, which consists of “Plan”, “Source”, “Make”, “Deliver”, “Return”, and“Enabler”. The key performance indicators (KPIs) are classified by attributes, which aredependent on each business process, and lastly There are the best practices, which can be used as a guideline to achieve goodperformances.With the successful implementation of the SCOR model that appears broadly in theacademic literature, so in this dissertation, the SCOR model is employed as a referenceframework to work on the performance evaluation and improvement system. Although,the model has provided a definition that is ready to be used which is quick and easy,and it is possible to assess the values of these KPIs directly based on the businessoutcomes as agreed by Neely et al., (1995). The underlying disadvantages are, it lacksof a procedural methodology, and the obtained KPIs cannot be further analyzed.Moreover, when the SCOR KPIs are used, the indicators can only help to identifyproblems in the current situation, but the logical methodology to manage those KPIsfor further improvement is still unclear. Even though in the traditional method,managers have relied on experience and intuition to determine how to improve KPIs,which is a swift decision-making process, this method is still non-systematic andunexplainable.6Ref. code: 25605422300342CMU

From the statement above, even this dissertation is interested in exercising theSCOR model to address such issue, there are still problems that need to be clearlyexamined in order to create a reliable performance evaluation and improvement methodin the supply chain system by using the SCOR model. These points are clarified as theresearch problems in this dissertation as follows.Problem statements1. Currently, the SCOR model is only the reference model, so when theirperformance metrics (SCOR KPIs) are applied, there is no relationships betweenthe values of the SCOR KPIs and the system parameters under studied. Hence,it is not possible to predict the consequences of the SCOR KPIs when the systemis changed or improved.2. There are the agility measures in the SCOR KPIs which are difficult to evaluate.The agility measures determine flexibility of the system when the upside, ordownsize in demand occurs. Without a procedural methodology and a model,the evaluation of the agility measures is unclear and non-systematic.3. The SCOR KPIs compose of many aspects, so when the organization need toimprove the performance in the supply chain, they have wide ranges of directionand possibilities to be managed without a systematic approach.4. Since the SCOR KPIs compose of many metrics to be managed, so theimprovement of SCOR KPIs needs to be compromised. The company cannot bebest in all metrics, and there must be some reliable method for the company totrade off among the improved KPIs that can satisfy the need of the organization.5. Lastly, there is a complexity of interrelationship between the variables in theSCOR KPIs and the parameters of the system under studied, so the managementof KPIs for performance improvement needs a detailed methodology todetermine the direction of improvement.Based on the above research problems, it is an initiative of this dissertation to proposea model and a procedural methodology to assess these SCOR KPIs, and also themethodology to manage these KPIs for improvement. According to the supply chain7Ref. code: 25605422300342CMU

planning problem, and the work from the literature reviews, this research aims toaddress the problems (1) and (2) by contributing the new knowledge to evaluate theSCOR KPIs by using a predictive MILP model with fuzzy parameters. The completeliterature review of the SCOR model, application of the SCOR model in performanceevaluation are provided in Chapter 2 to support how the SCOR KPIs are defined andused as a fundamental theory in this research. Followed by the reviews of the MILPmodel in the supply chain planning problem, how the uncertainties in the supply chainsystem are defined and managed, and why it comes up with the fuzzy set theory (FST)to endorse the establishment of the MILP with fuzzy parameters that is used to evaluatethe current KPIs, and to predict the future performance in many what-if scenarios.Apart from the proposed method to assess the supply chain KPIs systematically andfrom the obtained outputs in Chapter 3, in order to meet the business objective whichis required by the organization, the procedural method that explain the outputs of theKPIs must also be able to identify the direction for improvement. This is addressed bythe research questions (3)-(5). Another aim of the dissertation is to propose the newapproach to manage the SCOR KPIs for improvement. The literature review of a Qualityfunction deployment (QFD), which is considered as a successful tool for systematicplanning of the new product development, is described in Chapter 2 to support therationale of why the QFD approach is appropriate to use as a tool to guide the managersfor performance improvement. QFD integrates the customer requ

2 2 3 SCOR model that decompose a problem into a hierarchical structure using Analytical Hierarchy Programming AHP 23 2 2 4 Case studies using SCOR model 24 . Ref. code: 25605422300342CMU vii 2 2 5 The relationship of SCOR model to other external factors 25 2 3 The MILP model