Comparison of Fuzzy Analytic Hierarchy Process and Analytic Hierarchy Process Based Decision Support for a Lean Performance Measurement System

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1 Journal of Oean, Mehanial and erospae -Siene and Engineering-, Vol. 9 Marh 0, 06 Comparison of Fuzzy nalyti Hierarhy Proess and nalyti Hierarhy Proess ased Deision Support for a Lean Performane Measurement System nita Susilawati, a,* and John Tan, b a) Mehanial Engineering, Universitas Riau, Indonesia b) Mehanial & Constrution Engineering, Northumbria University, Newastle Upon Tyne, United Kingdom *Corresponding author: anita.susilawati@unri.a.id Paper History Reeived: 0-Marh-06 Reeived in revised form: 0-Marh-06 epted: 0-Marh-06 STRCT performane measurement framework for a ompany that adopted lean manufaturing is needed to measure their ahievement in the implementation of lean manufaturing system. omprehensive design of the performane measurement framework requires an understanding of all the elements inluding performane of perspetives and key indiators of measurements, and deision support methods suh as Fuzzy nalyti Hierarhy Proess (FHP) and nalyti Hierarhy Proess (HP). The omparison of original HP and FHP methods in term of deision making for lean manufaturing performane measurement system was performed in this paper. benhmarking to ompute performane based on weight between FHP and HP was done as a ase study in automotive ompany. The result of the weight values of HP and FHP methods indiated almost similar among performanes of lean perspetives and indiators for the ompany s ase study. KEY WORDS: Lean manufaturing PMS, HP, FHP..0 LEN MNUFCTURING PERFORMNCE MESUREMENT FRMEWORK Companies use frameworks to identify an appropriate set of performane measures to assess their performane. ording to Neely et al. [], the reasons for implementing performane measurement systems usually fall into five general ategories: monitoring of performane, identifiation of areas that are in need of attention, enhaning motivation, improving ommuniations and strengthening aountability. Several authors have onduted researh on performane measurement systems for lean manufaturing, inluding nand and Kodali [] and [], Wong and Wong [4], and gus and Iteng [5]. gus and Iteng [5] developed a framework for lean manufaturing, whih is linked to business performane. Their framework onsists of two Lean prodution dimensions, namely: Just-In-Time (JIT), and Tehnology and Innovation (T&I). The framework also has two indiators of business performane i.e. Return On Sales (ROS) and Return On Investment (ROI). nand and Kodali [] developed a oneptual lean manufaturing framework for small and medium-sized ompanies. Their framework has 4 pillars/perspetives i.e. ustomer foused, respet to humanity, elimination of waste, and ontinuous improvement. nand and Kodali []also developed lean manufaturing framework used 5 pillars/perspetives with adding supplier perspetive to their previous framework in []. Wong and Wong [4] developed a lean manufaturing framework for the Malaysian Eletrial and Eletronis Industry. Their framework onsists of three parts, namely: (a) the foundation, whih serves as a basi ondition for improvement to be arried out, (b) the improvement praties whih start with the urrent state to the defined ideal state, and () the indiators for ontinuous improvement in the key areas of lean manufaturing. However, there is limitation of the lean performane measurements as disussed, suh as only a measure of nonfinanial dimensions. In ontrast, Kennerley and Neely [6] suggested that a framework has to be multi-dimensional and have the balane between finanial and non-finanial measures. The performane framework shown does not reflet the ompany proess and speifi goals (targets) whih are strongly suggested 8 Published by International Soiety of Oean, Mehanial and erospae Sientists and Engineers

2 Journal of Oean, Mehanial and erospae -Siene and Engineering-, Vol. 9 Marh 0, 06 by Lynh and Cross [7], and Fortuin [8]. omprehensive design of the Performane Measurement System (PMS) framework requires an understanding of all the elements that an affet performane measurement, as well as potential subsequent ations, inluding the dimensions and levels of measurement. ording to several authors on the subjet [9- ], the development of a PMS framework should: - be derived from strategy, - be simple to understand, - provide timely and aurate feedbak, - reflet the business proess and relate to speifi goals, - establish more speifi performane riteria at eah level, and - periodially re-evaluate the appropriateness of the established performane measurement system in view of the urrent ompetitive environment. The development and the harateristis of a lean manufaturing PMS framework suggest the need for appropriate Key Performane Indiators (KPIs) [8]. The lean KPIs an measure progress toward speifi goals and help in making the most appropriate deisions for lean manufaturing ativities. Lea and Parker [4] suggested that performane indiators must: be easy to understand, have visual impat, fous on improvements more than on variane and be visible to everybody. Fortuin [8] suggested that indiators must: provide quik feedbak, provide information, preisely represent what is being measured and be objetive not based on opinions. Therefore KPIs in lean manufaturing PMS framework an used to measure, monitor, evaluate and ontrol status i.e. atual and target and drive improvement by fat/not based on guesswork. They an help to prioritise deision making in improvement ativities and ontinue health heks for the ompany. number of perspetives and KPIs are to be onsidered in order to make deision making in the Lean PMS. Sine many perspetives and indiators are to be analysed, Multi Criteria Deision Making (MCDM) models present the best alternatives in the multiple, usually onfliting, deision riteria. The most popular methods in risp-based deision making are: Weight Sum Model (WSM), Weight Produt Model (WPM), nalyti Hierarhy Proess (HP), and NP (nalytial Network Proess). Fuzzy-based deision making is based on HP methods known as Fuzzy nalyti Hierarhy Proess (FHP). Furthermore, in this paper onduted study of omparison between Fuzzy nalyti Hierarhy Proess (FHP) and nalyti Hierarhy Proess (HP) based deision support for a lean performane measurement system in the ompany..0 NLYTIC HIERRCHY PROCESS (HP) ND FUZZY NLYTIC HIERRCHY PROCESS (FHP) The HP is one of the Multi-Criteria Deision Making (MCDM) methods. Saaty [5] introdued the HP method for analysing a omplex problem and oping with both the qualitative and quantitative aspets of deision-making indiators by organising them into a hierarhial model. The HP an support the identifiation and weighting of seletion indiators for deisionmaking proess, whih uses experts s to derive the priority sales. In terms of deision irumstanes, Saaty et al. [6] and Forman and Saul [7] stated that HP an be used for appliations suh as hoie, ranking, resoure prioritization, alloation, quality management, onflit resolution and performane measurement. The HP has a onsistent preferene struture, namely onsisteny ratios, whih are used to measure the onsisteny of the deision-making proess. If pairwise omparison is inonsistent, the pairwise omparisons an be repeated. The main steps of HP are [8]: (a) onstrut a hierarhial model aording to the problem, (b) make pairwise omparisons of indiators based on the HP sale, and () ompute the result by alulating the weighted priorities for eah of the deision elements, heking the onsisteny and aggregating the priorities elements to get a final priority ranking. n example of a hierarhial model an be seen in Figure. It onsists of the goal on level one, several perspetives (indiators) on level two and the alternative hoies on level three. The linking lines indiate orrespondene between the goal, perspetives and alternatives. Level one line linking onsists of the goal and pairwise omparisons between the four perspetives and resulted in four omparison matries at level. The alternatives for deision making are shown on the bottom linking lines between perspetives and alternatives. Finally, the result is omputed to obtain the priority alternatives at level three. The steps to alulate weights priorities are explained in setion result and disussion. Figure : n example of the hierarhial struture. The HP an be applied to many fields suh as engineering, siene, mediine, military [9], soial, politial, eonomi, finane, managements and performane measurement. Some examples of the HP appliation inlude its use in deision making, e.g. the evaluation of tehnology investment deisions [0] and prioritization of key indiators for improving ompany performane []. In addition, Vinodh et al. [] applied HP to the hoie of the best implementation of lean philosophy to inreasing business suess. The primary strength of HP in deision making lies in its ability to rank hoies in order of their effetiveness and effiieny. However, the original HP uses risp numbers in omparing one alternative with another. Comparison depends on human pereption, whih always ontain vagueness and impreision. The risp numbers often fail to apture this vagueness []. Moreover, Kabir and Hasin [4] desribed the disadvantages of the HP appliation to inlude: its use of an unbalaned sale of, its failure to apture the unertainty assoiated with the mapping of one's to a number, its ranking s relative inauray, and its results ontaining subjetive in terms of seletion and preferene of deision-makers. Meanwhile, In terms of measurement, evaluation and improvement of ompany performane, the FHP method an aommodate a wide range of qualitative and quantitative 9 Published by International Soiety of Oean, Mehanial and erospae Sientists and Engineers

3 Journal of Oean, Mehanial and erospae -Siene and Engineering-, Vol. 9 Marh 0, 06 measures. These would be based on an established set of evaluation performane indiators and deision alternatives. The set of indiators and alternatives are onstruted in hierarhy orresponding to deision maker values. The FHP method an alulate the weighting and rank priorities for eah of the deision indiators/alternatives in preise human. Hene, the vagueness in human for deision making in measurement, evaluation and improvement of the ompany performane ould be avoided. The omparison of HP and FHP in term of performane measurement is shown in Table. Table : The omparison of original HP and FHP methods by several authors uthors Original HP FHP Kabir and Hasin [4] Jaskowski et al. [5] Tang and eynon [6] Lee et al. [7] Tan et al. [8] Gupta and Nukala [] Using an unbalaned sale of ; HP ranking s relative inauray; and HP results ontaining subjetive in terms of seletion and preferene of deision-makers. Cannot improve quality of riteria prioritization. ased on individual deision-making to derive priorities on pairwise omparisons. Lak of pratie in real measurement when an unertain pairwise omparison environment exists..0 METHODOLOGY Cannot be used to aount for variations in degrees of onfidene; suh as nuanes/traes. Deision makers to present their referenes using risp numbers that often fail to apture vagueness. More balane sale of ; FHP ranking s relative auray; and FHP results not ontaining subjetive in terms of seletion and preferene of deision-makers. Improve quality of riteria prioritization. It allows group deision-making to derive priorities on pairwise omparisons. Can obtain the relative importane in real pratie where an unertain pairwise omparison environment exists and provide performane evaluation suggestion. Can be used to aount for variations in degrees of onfidene, suh as nuanes/traes. Deision makers to present their referenes using fuzzy numbers that allow within a reasonable interval. In order to develop a lean manufaturing PMS framework, it is better to have multi-dimensional perspetives and performane indiators that an aurately represent the dynami nature of a ompany and its speifi environment. learer definition of lean priniples was identified from various relevant industrial work ativities that support the lean implementation (praties). Then, the key performane indiators (KPIs) of lean ativities that would have a signifiant influene on ompany performane were identified. Furthermore, the proposed method was employed the hierarhy that onsist of goal at first level, perspetives at seond level and indiators/alternatives at third level. There are two methods was performed to determine the degree of importane of the perspetives and indiators or alternatives in the PMS proposed framework. Sub-sequenes, a lean manufaturing PMS framework based the PMS struture and proess methods suh as the HP and FHP were developed in this paper.. Original HP method In the original HP demonstrated by Saaty [5], a deision maker expresses about the importane of one alternative over another by using a risp number. Steps to alulate weights priorities in the HP method are following [5]:. Construt hierarhy of the problem. Pairwise omparison matrix The matrix C ontains pairwise omparison value ij for all i, j {,,, n}. Let n be the number of riterion and,,, n be their orresponding relative priority given by one deision maker. C =... n... n... n... n... n = nn n... n... nn nn For multiple deision makers: It is used aritmethi mean to defind a new matrix by eah deision maker. The number of deision maker is denoted as h and ijk is the pairwise omparison value of riteria i and j given by deision maker k, where k =,,, h. C ij = ij k+ ijk ijkn h n C ij = ij h k =. Calulate eigenvetor and eigen value for getting the weights λ and max The alulating of eigenvetor for matrix C: multiply eah member of the matrix row and power of /n where n is the number of olumns, then divided by. Eigenvtor C (Weights) = ( * * ) / n n / n / n ( * * ) ( * * ) n n () () () 0 Published by International Soiety of Oean, Mehanial and erospae Sientists and Engineers

4 Journal of Oean, Mehanial and erospae -Siene and Engineering-, Vol. 9 Marh 0, 06 Where is: ( * * N / n ) + n ( * * / ) ( * * / ) n Sum = n n + n (4) Calulate largest eigenvalue ( λ max ) of matrix C: λ max = [( + + ) ( + + ) ( + + )...] n n n ( * * ) / n n Sum / n ( * * n) Sum / n ( * * n) Sum The alulating of onsisteny The onsisteny index (CI) and onsisteny ratio (CR) to verify the onsisteny of the omparison matrix. n is the matrix size and RI is random index (see table). CI and CR are defined as follow: ( λ n) /( ) CI = max n CR = CI / RI Table : RI index table [5]. Size (n) RI (5) The proposed framework of PMS using the original HP for lean ativities is omposed of the following steps: Step : Identify the perspetives and indiators/alternatives to be used in the PMS framework model that ould have a signifiant influene on ompany performane. Step : Construt the HP model hierarhially based on the perspetives and indiators/alternatives identified at Step (). Step : Determine the degree of importane, also known as weights of the perspetives and indiators/alternatives by using the HP method. Step 4: Collet the information of ahievement in the performane indiators of lean manufaturing ativities in the step one (). Step 5: Calulate lean performane based on weight in step () and soring of lean praties in step (4), and then it is ranked to deide the priority. Step 6: nalyse and established target improvement based lean performane priority ranking in step (5).. The FHP Method The FHP is more preise than the original HP to apture vagueness in human deision making. The proposed FHP deals with the vagueness of the by translating this to a fuzzy number with a triangular membership funtion with a entral value for the fuzzy number. This orresponds to the risp number given by the deision maker. The from several deision makers is then aggregated and the arithmeti mean operation of the fuzzy number is then used within a proedure to alulate the weight vetor. The FHP method was proposed to takle the vagueness and unertainty existing in the perspetives and the indiators, in the of the deision-makers in evaluating the lean (6) (7) manufaturing ativities alternatives. The steps for FHP used in this proposed method are as follows: Establish a deision group, Members of the deision groups make a on the importane of the lean manufaturing ativities, ggregate s of the deision maker, Chek onsistene, Calulate the weight. For detail alulation or formula and proedure to ompute weight values using the FHP method that available on Susilawati et al. [9]. The proposed framework of PMS using the FHP for lean ativities is omposed of the following steps: Step : Identify the perspetives and indiators/alternatives to be used in the PMS framework model that ould have a signifiant influene on ompany performane. Step : Construt the HP model hierarhially based on the perspetives and indiators/alternatives identified at Step (). Step : Determine the degree of importane, also known as weights of the perspetives and indiators/alternatives by using FHP method. Step 4: Collet the information of ahievement in the performane indiators of lean manufaturing ativities in the step one (). Step 5: Calulate lean performane based on weight in step () and soring of lean praties in step (4), and then it is ranked to deide the priority. Step 6: nalyse and established target improvement based lean performane priority ranking in step (5). 4.0 RESULT ND DISCUSSION In this setion, the appliation of the lean PMS model based the FHP or the original HP is presented as a ase study at the disrete manufaturing industry (automotive ompany) in the Indonesian manufaturing industry. The appliation of lean manufaturing PMS that onsists of identifying the perspetives and performane indiators; onstrution of the struture hierarhy; onduted a pairwise omparison and data olletion. Furthermore, this setion explains the analysis and reommendations for the ase study undertaken at the ompanies. 4. ppliation of the Lean Manufaturing PMS in Case Study Company The aim of the appliation of the lean performane measurement system in ase study ompany is to measure, evaluate and deide upon the potential improvement for the lean ativities, on the ompany s overall performane. The HP method was employed to deide upon the priorities of the lean ativities at an operational level, in order to link the ompetitive priorities to the business perspetives, whih impat on the ompany s overall performane. Then, it was done for the FHP method as well, and then the results were ompared. 4. Determination of Perspetives and Performane Indiators, and Constrution of a Hierarhy Struture The ompany has a strategi plan to make sure the prodution Published by International Soiety of Oean, Mehanial and erospae Sientists and Engineers

5 Journal of Oean, Mehanial and erospae -Siene and Engineering-, Vol. 9 Marh 0, 06 proesses inrease and are effiient manner. Therefore, the ompany fouses on produt proessing has been enhaned. Consequently, the ompany needs to measure and improve the ompany s overall performane by ahieving the ompany s strategi goals, whih use six speifi perspetives and performane indiators of the lean ativities, in areas of the manufaturing effiieny proess, whih seleted performane indiators are required to investigate as follows: Manufaturing effiieny proess: Eliminate time spent on engineering hange Eliminate defets in produts Eliminate exessive lead time Eliminate exessive movement of workers Eliminate exessive srap Eliminate idleness of workers Eliminate mahine down time Subsequently, a hierarhy struture was onstruted based on the seleted perspetives and performane indiators of the lean ativities. The hierarhy onsists of: Level : the overall objetives that improve the overall performane of the ompany. Level : measurement perspetives: the finanial perspetive, supplier issues, ustomer issues, proess, people and future. Level : deision indiators/alternatives. ording to these elements outlined above, the hierarhy for the lean performane measurement of the ompany s ase study is presented in Figure. Figure : The lean manufaturing PMS hierarhy for ompany s ase study 4. Condut a Pairwise Comparison and Data Colletion The olletion of data using a strutured interview and a selfadministered questionnaire were undertaken by two Head of Department Setions. They identified weights (relatively important) of seleted lean manufaturing performane indiators using the pairwise omparison and the degree of onfidene in their making of the pairwise omparison. The olletion of data and the resulting data pairwise omparison for performane perspetives and performane indiators on the seleted manufaturing effiieny proess at the ompany s ase study were depited in Tables and 4 respetively. The lean perspetives Finanial perspetives Supplier issues Customer issues Proess People Future Deision making members Table : The pairwise omparison for the seond level of the hierarhy. Finanial perspetive Customers issues Supplier issues Proess People Future Confidene Confidene Confidene Confidene Confidene Confidene 5 /5 /5 / / 4 4 / /4 4 The lean indiators for the manufaturing effiieny proess Eliminate time spent on engineering hange Eliminate defets in produts Eliminate exessive lead time Eliminate exessive movement of workers Eliminate exessive srap Eliminate idleness of workers Eliminate mahine down time Table 4: The pairwise omparison for the third level of hierarhy in the manufaturing effiieny proess. Deision making members Eliminate time spent on engineering hange Sale Confidene degree on Eliminate defets in produts Sale /5 /4 Confidene degree on Eliminate exessive lead time Sale / Confidene degree on Eliminate exessive movement of workers Sale Confidene degree on Eliminate exessive srap Confidene /4 /4 Eliminate idleness of workers Confidene / / / /5 /5 / / / /4 Eliminate mahine down time Confidene / / / / /4 / / / / / / Published by International Soiety of Oean, Mehanial and erospae Sientists and Engineers

6 Journal of Oean, Mehanial and erospae -Siene and Engineering-, Vol. 9 Marh 0, Comparison of Weights between HP Method and FHP Method for Lean Manufaturing PMS The omparison of original HP and FHP proposed methods was performed in this setion. Steps to alulate weights priorities in the HP and FHP methods an be seen in Setion Methodology. The omparison results of weights between for original HP and FHP methods were presented in Table 5 of the lean performane perspetives) and Table 6 of the lean performane indiators of manufaturing effiieny proess). Demonstrated sample alulation and proedure of the original HP method.. Construt hierarhy of the problem The lean manufaturing PMS hierarhy for ompany s ase study was depited in Figure.. Pairwise omparison matrix for HP method = Finanial perspetives; = Supplier issues; = Customer issues; 4 = Proess; 5 = People; and 6 = Future. Deision making Deision making Combine matrix by eah deision maker using Equation () Calulate eigenvetor and eigen value for getting the weights and n = 6 λ max Eigenvtor C (Weights) = ( *4*.5*.5*4*.5 ) ( 0.67**0.*0.45*.5*0.65 ) ( 0.75*5**.5** ) / 6 ( 0.47*.55*0.75**.5* ) ( 0.5*0.75*0.*0.47** ) ( 0.75*0.5*0.75*0.5** ) / 6 / 6 / 6 / 6 / 6 = / 6 / 6 ( *4*.5*.5*4*.5 ) + ( 0.67**0.*0.45*.5*0.65 ) + ( 0.75*5**.5** / 6 ) + ( 0.47*.55*0.75**.5* / 6 ) / 6 + ( 0.5*0.75*0.*0.47** ) + ( 0.75*0.5*0.75*0.5** ) = Eigenvetor (weights) =.69/7.896 = / / / / /7.896 The weights:. Finanial perspetive Supplier issues Customer issues Proess People Future 0.7 Calulate largest eigenvalue ( λ max ) using Equation 5: λ max = λ max =6.67 [ ] The alulating of onsisteny Using Equation (6), C I= (6.67-6)/(6-) = From Table. RI table: n = 6, RI =.4. Then using Equation (7), CR = 0.075/.4 = / 6 Meanwhile, the alulation and proedure of weights for the FHP method was based on Susilawati et al. [9]. The FHP analysis onsisteny is presented in Table 5. The onsisteny ratios (CR) for eah member and group of deision makers are less than the 0.0 that was depited in Table 5. Therefore, it an be onluded that the lean ativities performane measurement is onsistent and aeptable. Table 5: Consistene Ratio (CR) for the FHP method. CR Hierarhy Deision Deision maker maker Group Level (perspetives) Level (indiators/alternatives) on proess of manufaturing effiieny Table 6: The omparison using the FHP proposed method and the original HP method for the lean performane perspetives in ompany s ase study The lean performane perspetives Finanial perspetive The FHP proposed method The original HP method Weight Rank Weight Rank Supplier issues Customer issues Proess People Future Published by International Soiety of Oean, Mehanial and erospae Sientists and Engineers

7 Journal of Oean, Mehanial and erospae -Siene and Engineering-, Vol. 9 Marh 0, 06 Table 7. The omparison using the FHP proposed method and the original HP method for performane indiators of the manufaturing effiieny proess in ompany s ase study The lean performane indiators of manufaturing effiieny proess Eliminate time spent on engineering hanges Eliminate defets in produts Eliminate exessive lead time Eliminate exessive movement of workers Eliminate exessive srap Eliminate idleness of workers Eliminate mahine down time The FHP proposed method The original HP method Weight Rank Weight Rank The Tables 6 and 7 presented for the HP and FHP methods showed the weights were almost similar results. The data provided for both methods had been derived from two managerial levels in ompany s ase study. The data was gathered using questionnaires and strutured interviews, whih ontain the quantitative and the qualitative forms. The data results provided the extent of the degree of onfidene on their. In fat, both managers have onfidene in their that was shown in Table and 4. The FHP proposed method has the apability to ope with variations in degrees of onfidene; an obtain the relative importane in real pratie where an unertain pairwise omparison environment exists; and more exatly reflet human opinions. Meanwhile, it had not been aounted by the original HP. Speifially, the CR result produed by both methods was also almost similar (less than 0.). However, the FHP proposed method has apability to hek the CR twie as an individual and then aggregated by eah member group. Whilst, the original HP was only hek one as aggregated by eah member of group. Therefore, in this ase, the weights and the CR produed by the FHP method were more natural, pratial and aurate. 4.5 nalysis of the Data Weights In Table 6 and 7 an observed the value of weights ontribution improvement impated upon the overall performane of the lean ativities in the ompany s ase study. The ompany s business impat had a high priority for the finanial perspetives, with the weights at 0.97 for FHP method and 0.94 for original HP method. The lower priorities in the overall performane measurement of the ompany s ase study were awarded to perspetives for supplier issues of 0.07 for both the FHP and original HP methods. Table 7 presented the weights and priority ranking of the lean performane indiators/alternatives in the manufaturing effiieny impated upon the perspetives of the ompany. To eliminate the idleness of workers was the high priorities, whih the value of weights ontribution improvement impated at 0.67 for both the FHP and original HP methods. The lower priorities were awarded to the time spent on engineering hanges of for both the FHP and original HP methods. 5.0 CONCLUSION In this paper, the proposed framework of lean manufaturing PMS onsists of the indiators and perspetives of lean manufaturing ativity. The development of a framework to measure the ompany s performane used Fuzzy nalyti Hierarhy Proess (FHP) and nalyti Hierarhy Proess (HP) in term of deision making. For benhmarking omparison, both methods (HP and FHP) were applied as a ase study in automotive ompany, in Indonesian manufaturing industry. oth methods produed almost similar results to generate the weights and the CR. It an be noted if the data is ertainty (degree of onfidene = 00%) then the original HP method might be preferred. However, if the data is unertainty (degree of onfidene between 0% and 00%) then the FHP proposed method might be preferred. In this ase study, the deision makers have unertainty on their s (degree of onfidene was ). So in this ase, the FHP proposed method was more appliable and realisti than the original HP method to generate the weights and the CR. REFERENCE. Neely,.D., Mills, J.F., Gregory, M.J., Rihards,.H., Platts, K.W. and ourne, M.C.S. (996).Getting the Measure of Your usiness: Horton Kirby, Findlay Publiations.. nand, G. and Kodali, R. (008). Performane Measurement System for Lean Manufaturing: a Perspetive from SMEs, International Journal of Globalisation and Small usiness Vol., No. 4, pp: nand, G. and Kodali, R. (009). Development of a Framework for Lean Manufaturing Systems, International Journal of Servies and Operations Management, Vol. 5, No.5, pp: Wong, Y.C. and Wong, K.Y. (0). Lean Manufaturing Framework for the Malaysian Eletrial and Eletronis Industry, The rd International Conferene on Information and Finanial Engineering IPEDR, Vol.. 5. gus,. and Iteng, R. (0). 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