Rejuvenating the Supply Chain by Benchmarking using Fuzzy Cross-Boundary Performance Evaluation Approach

Transcription

1 ICSI International Journal of Engineering and echnology, Vol.2, o.6, December 2 ISS: Rejuvenating the Suly Chain by Benchmarking using uzzy Cross-Boundary erformance Evaluation roach RU SUIL BIDU, 2 B. B. HUJ bstract Indian comanies are yet to leverage the suly chain for cometitive advantage and as such there are no initiatives to measure the erformance of their existing suly chain systems. However, many multi-nationals dealing in MCG are fully exloiting the benefits and are also moving towards web-enabled suly chains. In view of globalization and liberalization of the economy, Indian comanies are being forced to change their ways of doing business to meet the cometitive ressure. In the recent ast, many rogressive comanies are re-engineering their business rocesses to challenge the ever-increasing cometitive ressures in the market lace. In this context, Suly Chain Management initiatives could be a cometitive tool and measuring the erformances against industry standards would go a long way in achieving International standards. However the existing erformance measurement methods fail to rovide significant assistance in suly chain develoment. he objective of this aer is to roose an innovative crossboundary erformance measurement method from a system ersective. uzzy set theory is introduced to address the real situation in the judgment and evaluation rocesses. his ractice will definitely hel in imroving the suly chain erformance. Index erms-cross-boundary aroach, Weighted average aggregation method, uzzy set theory, erformance measurement systems (MSs), rocess based model, System ersective, rocess and erformance measurement hierarchy (MH), erformance measurement team (M). Subsets of U are, B Row weight vector X M Weight matrix of elements by M evaluators f (μ) Degree of membershi of element μ in G inite universe of discourse of fuzzy erformance grade (, B, C, D, E, ) x (μ) Degree of membershi of value of μ in G erformance grade matrix of each erformance measures by all evaluators 6X erformance grade matrix of erformancemeasures ssoc. rof., Deartment of Mechanical Engineering, DYCOE, kurdi, une, India rof. and Head, Deartment of roduction Engineering, Govt.College of Engg., une, India-45 rsbindu3@ rediffmail.com, bba@ rod.coe.org.in 547 otations - G (μ), erformance grade set in G r reference entry of air wise comarison of element i over j, in the form of a triangular fuzzy number ( r l r m r u ) R Judgment matrices with fuzzy entries of air wise comarison by evaluators (l, m,u) rile that denotes a triangular fuzzy number where l, m, u stand for the lower, moderate and uer values resectively of the suort of the triangular fuzzy number U Universe of discourse W Weight vector of the M evaluator s oinions ( w, w 2,.,.w M ) α α i Discrete integers in [, 2] that exress the degree of fuzziness of the evaluators ormalized imortance weight of each element (α il α im α iu ) Discrete integers in [-6, 6] that quantify the qualitative reference of element i over j λ ny real number in [, ] μ Element in U I. IRODUCIO he Council of Logistics Management defines Suly Chain Management as the rocess of lanning, imlementing and controlling efficient and cost effective flow of materials, in-rocess inventory, finished goods and related information from oint-of-order to oint-ofconsumtion, for the urose of conforming to customer requirements. he fundamental objective of a high erformance of suly chain is to roduce roducts to match customers demand cycle, while roducing the greatest value ossible to the customers. number of technologies and managerial attention has gone into imroving suly chain erformance. he increasingly cometitive environment calls for seedy, cost efficient, accurate and reliable suly chains. Suly chain

2 ICSI International Journal of Engineering and echnology, Vol.2, o.6, December 2 ISS: management is no longer a matter of oerational and functional areas of the firm. oday, it is a strategic issue demanding to-level management attention. he suly chain can have huge leverage on the creation of customer value. Suly chains will fight the new battle for market dominance; as such measurements around the suly chain are critical. If we look at cometition today, it is suly chain versus suly chain. his brings out a situation that cometitors might focus on develoing suerior suly chain erformance. ccordingly, comanies will have to find or develo metrics to measure erformance of suly chain. Over the last decade of evolution of SCM, a steady stream of articles dealing with the theory and ractice of SCM has been ublished, but the toic of erformance measurement does not receive adequate attention. s an indisensable management tool, erformance measurement rovides necessary assistance for erformance imrovement in ursuit of suly chain excellence. However many critical drawbacks resent in the existing erformance measurement systems revent it s significant contribution to the develoment and imrovement of SCM. In this aer an effective Holistic erformance measurement aroach is roosed. II. REVIEW O HE ERORMCEMESUREME I SCM erformance measurement is defined as the rocess of quantifying effectiveness and efficiency of action into readable symbols to reort [], [2]. oday s erformance measurement assumes a far more significant role than quantification and accounting. erformance measurement can rovide imortant feedback information to enable managers to monitor erformances, to reveal rogress, to enhance motivation and communication and diagnose roblems [3], [4]. It also rovides insight to reveal the effectiveness of suly chain strategies and to identify the success and otential oortunities. It makes indisensable contribution to decision making in SCM, articularly in redesigning the business goals and strategies and reengineering rocesses. here is very little literature available, dealing with system design and measures selection [5]. erformance measurement authority examined best-in-class industry erformance of customer-facing and internal-facing measures in suly-chain management. Customer-facing measures, such as roduction flexibility and delivery erformance, quantify how well a suly chain delivers roducts to the customer. Internal-facing measures, such as total suly-chain costs and cash-to-cash quantify how effectively an organization uses resources in creating value for the customer. he Metrics can also be classified as ) on-financial e.g. Cycle time, Customer Service Level, Inventory Levels, Resource utilization 2) inancial e.g. Cost of raw material, Revenue from goods sold, ctivitybased costs such as Material handling, Manufacturing, ssembling, Inventory holding costs, ransortation costs etc. raditional MSs that are financially focusssed have already received wide criticism on short term and rofit orientation, encouraging local otimization failing to suort continuous imrovement and one dimensional measures [ 6], [7], [8]. lso some major drawbacks of existing MSs [6], [9], [] are) not in line with strategies 2) no balanced integration of financial and non-financial measures 3) absence of holistic ersective 4) local aroach leading to local otima and lack of global aroach and hence global otima. Given the crossfunctional nature of many suly imrovements, the system ersective is lacking in the existing MSs. III. HE ROOSED ROCH his roosed innovative aroach is very well exlained here with the hel of real field data of an existing Suly Chain of an industry from an automobile sector for which the roject of rejuvenation is undertaken. suly chain should be viewed as an integrated entity, and all the members should be functionally coordinated as an extended enterrise [6], []. he suly chain erformances should be measured beyond the organizational boundaries rather than focusing locally [9]. oday's marketlace is shifting from individual comany erformance to suly chain erformance: the entire chain's ability to meet end-customer needs through roduct availability and resonsive, on-time delivery. Suly chain erformance crosses both functional lines and comany boundaries. o achieve the goal, you need erformance measures, or "metrics", for global suly chain erformance imrovements. herefore it is roosed to develo a simlified model to analyze the ractical suly chains when measuring the erformance. igure : Information and Material Management Six core rocesses are linked. hese six rocesses are called key rocesses. hese key rocesses can be further decomosed into sub rocesses and activities to address their detailed erformances. hese rocesses form a hierarchy of suly chain model, which is the framework of the roosed MS. he rocess based model enables the MS to locate the roblems easily and facilitates rocess reengineering. his cross boundary system ersective and rocess based method san the whole suly chain, but rather blurs the organization and boundaries of the involved firms and deartments. he erformances should cover the areas like ) those of critical concern to suly chain common goals and strategies 2) those of inter influence and of common concern among the suly chain artners and 3) those concerned with both internal artners and external customers. he existing erformance measures are criticized as too many and too isolated. Here identification of multile dimensions is achieved. ny rocess assumes articular functions at the cost of articular resources, and it ursues the lanned goals in it s articular inuts and oututs. Inut dimensions are time and costs. angible 548

3 ICSI International Journal of Engineering and echnology, Vol.2, o.6, December 2 ISS: oututs include semi finished roducts and finished roducts. here are a variety of intangible added values or oututs. hese can be measured by assessing their functional erformances with their missions. hen there are some comosite measures such as roductivity, efficiency and utilization which are widely used to assess the oututs in comarison with the inuts or exectation. ll the comosite measures should be well defined and normalized before their use. he methodology measures the erformance of key suly chain rocesses. he roosed methodology facilitates deeer insight of the rocess erformance from inuts and oututs asect than the financial accounting method does. hese oerational dimensions of the rocess erformance rovide more visual information about the managerial effectiveness. or each rocess and sub rocess, the corresonding erformance measures are identified resectively. hen the associated measures are resectively groued into the hierarchy of the rocesses, thus building a rocess and erformance measures hierarchy (MH). he MH for the Industrial suly chain under study is shown in igure2. he riorities of various dimensions of erformances should differ from each other due to changing strategies and goals. ccordingly it is necessary to set relatively different weights for each measure when aggregating the global measurement results of the holistic erformances. Similarly individual decomosed rocesses are assigned relative weights to denote their various riorities when their measurement results are aggregated. It is a challenging task beyond any individual to assess the comrehensive erformances of the whole suly chain. In order to obtain the objective assessment of the holistic erformances, channel- sanning articiation of erformance measurement activity is required. In this roject, a erformance measurement team (M) is suggested. he M is comosed of the reresentatives from the various management areas of suly chain members. hese reresentatives can be sho floor oerators, rocess suervisors, deartment managers, lant managers etc. Members of the M serve mainly as the evaluators and rovide a variety of oinions based on the measurement activity. he team members have wide backgrounds and exeriences therefore reresent wide range of views. When incororating their oinions, the relative weights of the evaluators oinions must be assigned. Measures and rocesses he measures of every rocess or sub rocess must be weighted in order to address the changing objectives of the suly chains. he traditional air wise comarison method by Saaty [2], called as nalytical Hierarchy rocess (H), has some drawbacks. ) It creates a very unbalanced scale of weight comarisons. lthough this scale is resumably most widely used in ractice, it s unbalanced disadvantage yields an unequal distribution of comarison ratios [3]. 2) Since the method uses discrete nos. for judgments, it eliminates the uncertainty associated with the maing of the human ercetion and judgment to a number. here are naturally essential fuzziness and ambiguity in human judgments. he traditional comarison ratio scale with the cris nos. as roosed in Saaty s H fails to address the fuzziness [4]. herefore a geometric scale of triangular fuzzy numbers [5] is emloyed in this study to quantify the comarison ratios. Let U be the universe of discourse U ( μ, μ 2, μ 3,, μ n ). fuzzy number is a fuzzy subset in the universe of discourse, U that is both convex and normal. he fuzzy number has the meaning about. It is the natural generalization of a real cris number, thus allowing formal reresentation for inexact concets, subjective judgment and all tyes of evaluation. mong various membershi functions, the triangular fuzzy no. is the most oular function emloyed in the engineering alications. he curve of the membershi function is resented in igure 3. ccording to Zadeh [ 6 ], a fuzzy set of U is a set of ordered airs { ( μ, f (μ ) ), ( μ 2, f (μ 2 ) ),..( μ n, f (μ n )) }, where f : U [, ] is the membershi function of, and reresents the degree of membershi of μ i in. (l, m, u) () f (μ i ) IV. HE EW MESUREME LGORIHM Once the measurement results of each erformance measure have been obtained, a Weighted verage ggregation method is used to incororate the erformance of the whole rocess. Similarly, the measurement results of all the sub rocesses can be aggregated into the result of their arent rocess. With this ggregation method, the erformance of each rocess on each level can be assessed. a) Method to ssign Relative Weights to erformance igure 3: riangular uzzy umber 549

4 ICSI International Journal of Engineering and echnology, Vol.2, o.6, December 2 ISS: igure 2: rocess and erformance Measurement Hierarchy for the Suly Chain under Study he geometric scale, whose echelons exhibit a discrete geometric rogression, takes the following form of a triangular fuzzy number with exonential functions: r ex [.5 ( -α )] l r m ex [.5 ( )] r u ex [.5( α )] (2) where quantify the qualitative reference ratios: arameter hel the evaluators to quantify their reference and judgments. he other integers between these defined values can be used to reresent the moderate oinion between their meanings resectively. he arameter α exress the degree of fuzziness. he evaluators can resectively select the suitable fuzziness for their judgment. s a simle model for the judgment statements, all α are denoted with a valueα, where α : no fuzziness α : moderate fuzziness α 2: significant fuzziness ssume that there are measures of a rocess to be weighted.i.e. i,j, 2,.,. Each evaluator is asked to rovide his judgment concerning each air of the measures : no reference of i over j 2: weak reference of i over j 4: strong reference of i over j 6: very strong reference of i over j Similarly - denotes some reference of j over i. hese discrete integers ranging from -6 to 6 of the (i, j), for i < j, in the form of fuzzy numbers r ( r l, r m, r u ). s a consequence, via air wise comarison by the evaluators, x judgment matrices with fuzzy entries will be obtained as follows. Here r ji ( / r ) and r ii (,, ). r r R 2... r r r r2 (3) r r 2... r ccording to the method of Buckley, the normalized imortance weight of each measure will be derived as follows: α i ( α il α im α iu ) 55

5 ICSI International Journal of Engineering and echnology, Vol.2, o.6, December 2 ISS: (r ) / (r ) / (r ) / l m u j j j,, / / (r ) / (r ) (r ) u m l i j i j i j (4) hese six grades, B, C, D, E, denote the gradational measurement results ranging from the erfect to the worst. ll these grades are defined by the triangular fuzzy numbers of erformance scores as shown in igure 4. s a result, the relative imortance weights of the measures of this rocess can be written in a row weight vector as follows: a a (5) (,,... ) 2 When imlementing this, the evaluators need to rovide only the ordered air of arameters (, α ) to denote each air wise comarison between the i th and j th measures. b) Measurement Scale and uzzy erformance Grade It is meaningless to assess any erformance without it s associated context of objective and history [7]. Existing MSs obtain this arameter just by dividing the current erformance by the exectation. So existing systems have itfalls like ignoring associated oeration context and losing imortant information arising from the uncertainty of human judgments. herefore a measurement algorithm based on fuzzy set theory is roosed here. Measurement scale is originally determined by the evaluators. When assessing the erformance, the evaluators consider it s erformance goal and history as well as associated oeration environments, and then set the measurement scale in the form of an interval [bottom, erfect]. By the nature of human reasoning, the measurement scales are calibrated by dividing the interval roortionately. Similarly, the judgments of measurement scales contains much fuzziness and imrecision. herefore the measurement results that are obtained through comaring current erformances against their corresonding measurement scales are fuzzy too. In this roject, this situation is taken care of by denoting the measurement results by triangular fuzzy numbers. Comared with the measurement scales, the current erformances are denoted by erformance scores ranging from to, which corresond roortionally to the measurement scale intervals. he number denotes the worst erformance and number denotes the erfect erformance. fuzzy erformance grade set is defined as the fuzzy measurement result, which is denoted by a fuzzy vector G {, B, C, D, E, }. ssume that there are M evaluators in the M who assess one rocess with erformance measures. he weights for evaluators, W K (K,2,.,M) are denoted by the weight vector W ( w, w 2,, w M ) where w w 2.. w M. s roosed above in equation (5), the relative weights of the measures of this rocess can be written in a row weight vector by the K th evaluator as follows.,,... a ) () K ( K 2K K ll the weight vectors of elements in a articular branch, by all the evaluators of the M, comrise the following x M weight matrix XM:,,...,, 2 3 M XM [ ] igure 4: riangular uzzy Grades (8,, ), B (6, 8, ) C (4, 6, 8), D (2, 4, 6) E (, 2, 4) (,, 2) (6) his erformance grade set in the finite universe of discourse, G {, B, C, D, E, } is defined by a set of ordered airs as follows. G ( μ ) { x, μ), X, B,..., } (7) where x ( μ ) : G [, ] is a maing called the membershi function of the fuzzy set G, and x (μ) indicates the degree of belongingness or membershi value of μ in G. G (μ) can be written in the form of sum as follows B C D G B C D E E or X G ( μ ) (9) X G X hysically, this gradation reresents the quantification of the degree to which a articular erformance satisfies the erformance criteria set by the evaluators. hysically, this gradation reresents the quantification of the degree to which a articular erformance satisfies the c) ggregating the Measurement Results he resulting matrix is as shown below. a a... a 2 M... M () M hus the grou oinion on the weights of the measures will be aggregated by a multilying algorithm as follows. XM. W a M 2M M w. w 2 w M (8) 55

6 ICSI International Journal of Engineering and echnology, Vol.2, o.6, December 2 ISS: members comose the fuzzy erformance grade matrix as follows: w w M wm w w M wm w w M wm, 2,..., M ( μ) ( ), ( ),..., ( ) B μ B2 μ BM μ, 2,..., M (4) his erformance grade vector can also be written as the summary D B C E B C D E (6) ll these erformance grade vectors lso, the grou oinion on measurement results will be aggregated by the multilying algorithm as follows: (μ ). W,,...,. w 2 M,,..., w2 B B2 BM w M,,..., 2 M [ ] B,..., [ a,,... ], (5) 2 a (2) Similarly, the erformance grade by the K th ( K,2, M) evaluator can be written in the forms of the row vector K ( K, BK, CK, DK, EK, K )..(3) or each erformance, all the erformance grade vectors of measurement results given by the M comose the erformance grade matrix and then are aggregated with weighted average method again as follows:, B,, 2 B2 2 6., 2,..., B, B2,...,, 2,...,,...,,...,,..., B a. B 2 [ ], B,..., (8) Obviously, B,..., in the vector of equation (8) are in the form of triangular fuzzy numbers resectively, which means the individual function membershi of the triangular fuzzy number is also fuzzy. he most revalent and hysically aealing of all the defuzzification methods, the centre of gravity (COG), is used. he algebraic exression of the COG is stated as follows: he method is as illustrated below. μ. f. dμ ( ) f. dμ (9) 552 i ( i, Bi, Ci, Di, Ei, i ) ( i,2,3,..,) [,,... ] 6x 2, B, 2,..., B B (7), 2,...,, 2,..., he relevant formula is as given below. By using this formula, the fuzzy numbers,, B,..., in the vector are transformed into cris numeric values resectively: C D E B (2) B C D E hus this vector has the same form as the results in equation (8). It is the aggregated measurement result of the holistic erformances of this articular rocess by the M. With a similar algorithm, the measurement results of each rocess can be incororated layer by layer in the MH. ll these results can be defuzzified into the cris value in order to rovide benchmark for the managers. d) Defuzzifying the Measurement Results and Benchmarking he fuzzy grades, B,......, are used to denote the notional values of measurement results.. he defuzzified result is defined as the erformance index (I), by the following formula: I 8 B 6 B C C 4 D D 2 E E (2) hysically, the I indicates the synthetic assessment of the holistic erformances of the suly chain by the evaluator grou. It reveals how a business rocess erforms with resect to the lanned goals and histories [8]. his simle number rovides a concise means for analyzing and benchmarking the erformances in the suly chain systems for their managers. ut in the full range [, ], this result can be benchmarked. Because the measurement results of each rocess on the higher layer is aggregated from the measurement results of sub rocesses, the worst result can be tracked layer by layer in the MH. hus the strengths and weaknesses of suly chain rocess can be identified and located. Moreover the arallel rocesses can be comared and the roblematic nodes can be discovered.

7 ICSI International Journal of Engineering and echnology, Vol.2, o.6, December 2 ISS: V. VLIDIO BY USIG REL IELDD By using this methodology, the erformance index of an existing suly chain of an industry from an automobile sector for which the roject of rejuvenation is undertaken, is calculated as follows. he whole suly chain consisting of manufacturer, distributor and dealer along with inter-node logistics (s shown in igure 2) is visualized as a single integrated rocess. here are three measures identified to indicate the three dimensions of it s erformances: otal suly chain Cost, Service factor, Reliability. our members are selected from different functional areas, to act as the members of M viz. General Manager of the Manufacturing lant, Head of the Distribution Centre, rocess Suervisor from the distribution comany, Salesman at the Dealer s sho. hese four evaluator s oinions are assigned relative weights deending on their functional imortance in the rocess as W (.5,.25,.5,.) resectively. irstly one evaluator makes the judgment of the measure cost. he cost estimated from the erformance history and resent status is Rs.7, 7,/- for an average inventory of 5 vehicles along the suly chain. Cost/unit is Rs.4. While the otimization of suly chain for minimum total suly chain cost and maximum service level has yielded the cost value as Rs. 6,42,7/- for an average inventory of 5 vehicles along the suly chain. Cost /unit is therefore Rs.2. Hence the first evaluator determines the measurement scale of cost as the interval [4, 2]. Similarly the service factor estimated from erformance history and resent status is.9. While the otimization rocedure yields a value of.7 at the minimum total suly chain cost. So the measurement scale of service factor is [.9,.7]. he reliabilities of the different sub rocesses as er the erformance history and current status are given in able I. While the reliability aimed at is.. herefore the measurement scale interval for this erformance arameter will be [.5, ].he current erformance on suly chain cost averages to Rs.3.69/unit then we calculate the erformance score and erformance grades as follows: hen the measurement results of these four evaluators with their resective weights are aggregated by alying equation (5). In a mathematical sense, this vector denotes the aggregated oinion of the measurement of cost erformance of the four evaluators. It takes the form of a fuzzy erformance grade set...5 ( μ ). W igure 5: erformance Score to erformance Grade erformance score is ( ) hus the erformance grade set can be written as follows: ( μ ) (,,,,.775,.225) his is the measurement result of total suly chain cost judged by the first evaluator. he erformance grade sets of the other three evaluators calculated in a similar way are given below. he erformance grade sets of the other three evaluators calculated in a similar way are ( μ ) (,,,.55,.45,) 2 3 ( μ ) (,,.95,.5,,) 4 ( μ ) (.,.9,,,,) hese four vectors comose the fuzzy erformance grade matrix with equation (4) as follows: ( μ ) [ ( μ ), 2 ( μ ), 3 ( μ ), 4 ( μ )] [.,.9,.425,.45,.5,.25] he erformance grades of the other two erformances i.e. the service factor and reliability are calculated in a similar way, by the members of M and are given below: 2 [.288,.325,.288,.375,.75,.375] 3 [.,.4,.225,.23,.35,.] or all these three erformance measures, the erformance grade vectors of this rocess comose the 6 x 3 erformance grade matrix of the rocess with equation (7) as follows: 6 3 [, 2, 3 ]

8 ICSI International Journal of Engineering and echnology, Vol.2, o.6, December 2 ISS: fter the erformance grade matrix and the local relative weights of the three erformances of this rocess have been obtained, the measurement result of this rocess can be aggregated by alying equation (8) as follows: ( (.63,.34822,.), (.25893,.7778,.23627), (.6932,.57479,.) ) 2 ( (.6798,.22728,.7566), (.57,.4293,.295), (.25787,.72978,.) ) 3 ( (.6647, ,.), (.8538,.9527,.42373), (.4642,.3789, ) ) 4 ( (.53987,.53564,.4434), (.3439, ,.), (.749,.4933,.)) benchmarked by alying equations ( 9 ) and ( 2): B C D E I his number reveals how the rocess erforms with resect to the lanned goals and histories. he global I resents clear measurement results in the form of brief numerical score for suly chain managers. hese four vectors comose the 3 x 4 weight matrix using equation (): 3 4 [, 2, 3, 4 ] With the relative weights of the evaluator s oinions, in the judgments of the erformance local weights are incororated by alying equation (2): 3 4. W [(.52,.32,.8832), (.399,.35,.396), (.836,.5745,.9962)] he next ste is to derive the local relative weights of the three measures. ccording to the method described in section 4, the four evaluators resectively rovide the ordered airs for the three measures. or e.g. ables II illustrates the judgments of the first evaluator. he four weight vectors of the three measures resectively by the four evaluators are obtained according to the calculation algorithm in equations (3) to (5): his result can be defuzzified into the cris value to be 554 VI. COCLUSIO rocess based model, aroriate erformance measures, teamwork evaluation and fuzzy measurement algorithm are used to measure and imrove the erformance of suly chain under analysis by using crossboundary measurement method from a system ersective. he introduction of fuzzy set theory in setting weights and measuring erformances is advantageous, because this fuzzy method addresses the real situation of human judgment with fuzziness in measurement activity without losing imortant information as the cris method does. he concise defuzzified results rovide easy assess for benchmarking the erformances and avoiding excessive roliferation of data. With this data, the suly chain managers can easily benchmark the erformance of the whole system and can analyse the effectiveness of their strategies leading to identification of the otential oortunities. REERECES [] eely,., Gregory, M. and latts, K. erformance measurement system design: a literature review and research agenda.int.j.os.r od.managmt,995,5(4), 8-6. [2] Lebas, M.J. erfor mance measurement and erformance Management. Int. J. rod.economics,995,4, [3] Rolstandas,. erformance measurement: business rocess benchmarking aroach,995 (Chaman and Hall,London) [4] Waggoner, D.B., eely,. D. and Kennerley, M.. he forces that shae organizational erformance measurement systems: an interdiscilinareview.rod.economics,999, 6, [5] Beamon,M.B. Measuring suly chain erformance.int. J. Os rod.managmt,999, 9(3), [6] Holmberg, S. system ersective on suly chain Measurement. Int.J. hys.distribution Logistics Managmt, 2, 3(), [7] Kalan, R.S.and ortan D.. he balanced scorecard- measures that drive erformance. Harvard Business Rev., January-ebruary 992, [8] De oni,. and onchia, S. erformance measurement Systems :models, characteristics and measures. Int. Os.rod. Managmt.,2, 2(-2), [9] Gunasekaran,., atel, C. and irtiroglu, E. erformance measurement and metrics in a suly chain environment Int. J. Os. rd. Managmt, 2, 2(-2), [] Van Hoek, R. I. Measuring the unmeasureable-measuring and imroving the erformance in the suly chain.int. J. Suly Chain Managmt, 998, 3(4), [] Lambert, D. M., Cooer, M.C. and agh, J. D. Suly Chain Managmt : imlementation issues and research oortunities. Int. J. Logistics Managmt, 998, 9(2), -9. [2] Saaty,. L. he nalytical Hierarchy rocess. 98, (McGraw-Hill, ew York).

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