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840 G Li et al Computers Industrial Engineering 56 2009 839 853. 2001 Lamming Johnsen Zheng Harland 2000 Stuart Deckert McCutcheon Kunst 1998 research. ers are still in an early stage of understanding how a SN behaves and evolves Some studies investigated the. factors that in uence the SN evolution in di erent environments For example Hur Hartley and Hahn 2004. identi ed six factors that have in uence on the structure of SNs in di erent industries Choi and Hong 2002. found that the SN of Honda was controlled centrally by the nal assembler while the SN of DaimlerChrysler. was decentralized Choi et al 2001 and Chung Yam and Chan 2004 proposed that a SN emerged and. evolved in a way of its own The emergence of cooperation networks was largely an endogenous process driven. by the complex and dynamic interplay between institutions products technologies markets and innovative. actors Bruce 2000 What are the salient factors and the general principles that shape a SN No one can. answer this question with any degree of certainty Harland Zheng Lamming Johnsen 2002 This is. due to the lack of our understanding of evolutionary aspects of SNs Surana Kumara Greaves Raghavan. This study will investigate the general principles involved in the evolution of SNs It proposes a SN evolu. tion model based on CAS complex adaptive system Holland 1995 and tness landscape theory to model. the dynamic behavior of the SN evolution with the dynamic interaction among the rms and the environment. This approach underscores the importance of the model in which di erent entities in the SN operate subject to. their own local strategies constraints and objectives With the simulation of the evolution model based on. multi agent the dynamic behavior of the rms and the SN can be analyzed from a variety of organizational. perspectives It nds that the evolution of SNs is a self organization and identi es the salient factors that con. trol the evolution Also a case study which explores the evolution of a SN in China for more than 30 years. validates the ndings Finally some managerial insights are proposed in the paper. The remainder of the paper is organized as follows Section 2 reviews the relevant literature Section 3 pre. sents the system model and simulation of SN evolution based on CAS and tness landscape theory The prin. ciples and some salient factors that in uence the SN evolution are discussed In Section 4 a China case study. is presented to validate the ndings in Section 3 In Section 5 we present some propositions and managerial. implications about the evolution of SN Finally concluding remarks and future research directions are. pointed out in Section 6,2 Literature survey, A useful paradigm for supply chain management taking into consideration of the dynamic interaction of. the rms in the supply chain is to view it as a supply network Surana et al 2005 Most of the researches in. the past decades viewed the SN as a static system Analytical models simulation methods and empirical. approaches have been employed to enhance the knowledge of SNs and optimize the system decision Pathak. Dilts Biswas 2007a Based on analytical and simulation methods most of researches focused on the. design and optimization of SNs They tended to assume that a SN is an integrated and static organization. Gunasekaran Ngai 2005 Min Zhou 2002 Whang 1995 Empirical studies were employed to under. stand the strategic issues managerial perceptions and measurements of key operational issues of SNs Choi. Although the structures and collaboration mechanisms of a SN are static in a short term they evolve in the. long run The optimal network structure and collaboration mechanism for a SN which takes researchers. many e orts based on the assumption of static structure may become invalid as the SN evolves To better facil. itate the management of SNs we need to understand more about the dynamic behaviors of the rms and SNs. For example how can di erent rms form a SN structure How does the SN evolve over time To answer. these issues we need to understand the evolution dynamics of the formation adaptation and evolution of. There exists a body of literature that deals with the supply chain as a dynamic system These approaches are. often based on various simulation methods to examine the dynamic behavior of SNs They can generate. results about large scale systemic behavior in ways that are analytically intractable and nd how various. improvement e orts a ect the dynamical behavior Forrester 1961 was the rst one who examined system. dynamics within a supply chain by the simulation method Illuminating results have been generated from this. line of approach Berry Naim Towill 1995 Larsen Morecroft Thomsen 1999 Marquez Blanchar. G Li et al Computers Industrial Engineering 56 2009 839 853 841. 2004 Swaminathan Smith Sadeh 1998 Towill 1996 Towill Evans Cheema 1997 Wikner Towill. Naim 1991 Besides system dynamics other simulation methods such as discrete event simulation arti cial. intelligence etc has been employed to model the dynamical behavior Alfaro Sepulveda 2006 Holmstrom. Hameri 1999 and possible improvements of SNs Holweg Bicheno 2002 However most of these stud. ies were still based on the assumption that the SN structure was deterministic and stable They could not give. insight for the understanding of the evolution of SN structure and coordination mechanism Only a few stud. ies took consideration of the dynamic structure of SNs but they proposed di erent conclusions For example. Akkermans 2001 Schieritz and GroBler 2003 used system dynamics and multi agent to study the emer. gence and stability of SNs while their conclusions were quite di erent. Since the seminal contribution on supply chains as a CAS by Choi et al 2001 there emerges some liter. ature on the evolution of SN in the supply chain management discipline Pathak Day Nair Sawaya Kris. tal 2007b provided a good review of these literatures on CAS and proposed some critical issues and challenges. in the study of SNs based on CAS Surana et al 2005 argued that supply chains should be treated as a CAS. and proposed how various methods used in the study of CAS can be exploited to characterize and model SNs. Like any CAS some combination of control theoretic agent based and discrete event modeling approaches. might be applied to study the evolution of SNs Choi et al 2001 Pathak and David 2002 Pathak David. and Gautam 2003 Pathak et al 2007a developed a multi paradigm dynamic system simulator based on. CAS and showed that certain environmental and rm level factors might impact the evolution of SN struc. tures As to the empirical methods only a few studies viewed the SNs as CAS and proposed some arguments. for the understanding of the structure and operations of SNs Carbonara Giannoccaro Pontrandolfo. 2002 Choi Hong 2002 With the limited literature on SNs as CAS no one could give de nite answers. to how SNs behave and evolve, In the dynamic environment SNs do not always keep in constant but evolve over time The changes of the. environment the rms and the inter relationships within a SN coupled with the adaptive capability of rms. responding to such changes make the complexity of the SN evolution Due to the evolving nature of SNs the. challenge is to develop an e ective analysis tool to model the behaviors of rms and the entire network to. unveil of the evolution complexity of SNs The complexity of SNs evolution rules out the use of a single. approach A combination of approaches is necessary to adequately explore di cult issues such as nonlinear. ities cyclical feedback mechanisms emergence and path dependencies Pathak et al 2007b Based on Choi. et al s propositions 2001 that internal mechanisms the environment and co evolution are the three key foci. for SN research this paper proposes a dynamic evolution model of SNs based on CAS and tness landscape. theory and simulates the model using multi agent technology Pathak David 2002 Pathak et al 2003 The. simulation results disclose the general principles and salient factors that dominate the formation adaptation. and evolution of SNs Also a case study validates these ndings. 3 Modeling and simulation of SNs evolution,3 1 Modeling SNs evolution. Camazine et al 2001 de ned self organization as A process in which the pattern at the global level of a. system emerges solely from numerous interactions among the lower level components of the system More. over the rules specifying interactions among the system s components are executed using only local informa. tion without reference to the global pattern, As argued in the evolutionary theory self organization can be understood on the basis of the same varia. tion and natural selection processes compared with other environmentally driven processes of evolution. Firms collaborate with others to ful ll the demand generated by the market where they populate A SN. emerges and evolves in a bottom up way from the ongoing patterns of collaborations among rms The col. laboration relationship is like a weighted bi directional graph where the rms are the nodes and the relation. ships are the edges see Fig 1 in graph theory The weight of an edge represents the collaboration preference. between two rms With the interaction of rms those rms survive if they adapt to the environment and the. network structure emerges and evolves if it facilitates the collaboration. 842 G Li et al Computers Industrial Engineering 56 2009 839 853. Environment Evolving SNs,Firmi W N N,Dynamical W,interactions W.
Firmk W N N,N firm W weight,Fig 1 The SN evolution model. To model the self organizing evolution of SNs this paper not only takes account of the in uence of the. environment on rms behaviors and the evolution of SNs but also the in uence of rms internal mechanisms. e g strategies capability product etc on rms behavior and the evolution of SNs This model can better. map the dynamic behavior of an SN and the rms from the evolutionary aspects than the models of Pathak. and David 2002 Pathak et al 2003, According to Melody s 1994 contributions a rm can be modeled as an eight tuple. Firm E S OC RC OS BP P F 1, where they stand for the environment E Strategy S Organization Culture OC Resource Constraints. RC Organizational Structure OS Business Processes BP Products P and Fitness F for a rm. respectively, The environment places expectations on rms Firms need to balance between environmental expectations. placed on them with the resources and capability available in them Luhmann 1995 McCarthy 2004 Firms. co evolve with the environment via the concept of tness landscapes Kau man 1993 Kau man and Mac. Ready 1995 The environment factors are classi ed into two groups 1 The Macro Environment which spec. i es the economy politics culture laws regulations and market etc are exogenous to all rms 2 The. Micro Operation Ecology which speci es the demand the supply the price the lead time and the competitors. for each individual rm The expectations that the environment places on rms are evaluated by the environ. ment tness landscape Fc 0 Fc 6 1 A higher Fc indicates that rms are more di cult to t to the environ. ment In summary the environment is modeled as, E Macro Enironment Micro Operation Ecology F c jF c 2 0 1 2.
Macro Enironment Economy Politics Culture Laws Regulations Market F c 3. Micro Operation Ecology Demand Supply LeadTime Price Competitors 4. To survive a rm has to t to the environment This is a process of matching the environmental t and the. internal t Hamel Prahalad 1994 Miller 1992 In this process rms need to identify and realize appro. priate strategies organization structure culture resources business process and products which are the. aspects of rm s internal mechanism A rm s tness is denoted as tnessi t representing the capability of. rm i to survive in one or more populations and imitate and or innovate combinations of capabilities which. will satisfy rm s objectives and market needs and be desirable for competing rms at time t McCarthy. 2004 tnessi t is represented as a real number in 0 1 The higher the value is the higher the rm s ability. to adapt to the environment, tnessi t is usually measured by customer satisfaction and is evidenced in pro t or loss It is in uenced by. many factors from the environment and the internal mechanism First if a rm can ful ll the demand it. makes pro t and its tness increases Therefore it has more resource to improve its products capabilities. etc which further gives it more opportunity to win in the competition Second the rm s tness decays over. time too If a rm cannot get any order in a demand cycle its tness decreases Third the environment has. in uence on the decay speed of tness A fast changing world has a higher Fc which results the greater decay. speed of rm s tness Fourth the decay speed of rms tness is in uenced by social culture If the rms in a. market prefer the short term relationship e g rms in USA the decay speed is greater than that of the rms. preferring the long term collaboration relationship e g rms in Japan China In summary the tness. evolution function of a rm is, G Li et al Computers Industrial Engineering 56 2009 839 853 843. fitnessi t 1 fitnessi t Df cos ti t reward i t Ddi t 5. Df costi t rewardi t is tness variation caused by the rm s ful llment of the demand at time t costi t is. the cost to ful ll the demand and rewardi t is the return at time t If a rm cannot ful ll the demand rewar. di t is negative Ddi t is the decay of rm s tness which is related to Fc and the social culture preference. The internal elements of rms evolve too In this model we take account of the evolution of rm s capacity. and manufacturing cost If the rm ful lls an order it makes pro t and invests in manufacturing capacity. which results its manufacturing cost be reduced,MC i t 1 MC i t DMC i t 6. CS i t 1 CS i t DCS i t 7, where DMCi t is the change of manufacturing capacity and DCSi t is the change of manufacturing cost at. time t When a rm ful lls an order rm s capability and cost is improved Namely both DMCi t and DCSi t. are positive otherwise they are negative On the other hand if a rm cannot get any order in a demand cycle. its capacity decreases and its cost increases, The process of interaction among rms and between rms and the environment involves natural selection.
If tnessi t is higher than Fc rm i survives Otherwise rm i is eliminated from the environment There is a. positive feedback between rm s tness and the probability to win in the competition A rm with the higher. tness has more opportunities to win the competition Therefore the goal for a rm is to improve its tness by. responding to the challenges and opportunities posed by the environment. In summary the evolution model of a SN is represented as. SN t f environment firmi firmm G t V t G Et G P t G 8. where there are m rms residing in the environment With the dynamic interaction of rms there emerges the. evolving SN which is like a bi directional graph G t Vt G Et G and Pt G are the collections of nodes edges. and collaboration preference of rms at time t respectively. 3 2 Simulations of SNs evolution and analysis, The evolution model is simulated based on multi agent technology In the simulation each rm is designed. as an autonomous agent which has strategies rules resource constraints business process products and t. ness The culture factors are integrated in the rm s strategy and operation rules Firms who manufacture the. same product can be a buyer as well as a seller The market is set as a Bertrand model where rms compete in. two stages 1 In the rst stage the market is a monopoly where the rm with the highest tness wins the. global demand from the market and manufactures the product subjected to its capacity 2 the second stage. is a competition market where the winner in the rst stage subcontracts the remaining demand to the rest. rms The winner selects its subcontractor according to its collaboration strategy price priority short term. or long term collaboration strategy and the rest rms compete in price for the remaining demand Firms. operation rules are 1 competing for demand without considering of capacity constraint 2 when the. demand is greater than its capacity it subcontracts the remaining demand to the others At the beginning. of the simulation each rm s internal elements tness capacity cost and the elements of the environment. demand are initiated randomly At the end of each demand cycle each rm s tness capacity and cost. are updated And then the environment evaluates each rm s the tness and eliminates the rms whose tness. are less than Fc, There are eight rms numbered from 0 to 7 involving in the experiments The demand in each. demand cycle is generated randomly in accord with a Uniform distribution U Dmin Dmax where Dmin. and Dmax are the lower and upper limits of the market demand respectively The initial capacities of each. rm are generated randomly in accord with a Uniform distribution U Cmin Cmax Also rms initial costs. are generated randomly in accord with a Uniform distribution U 50 150 The setting of these parameters. indicates that the environment is of high uncertainty and the rms are of high variety The number of. runs i e demand cycles of the simulation is set as 100 and each demand cycle stands for one month. in the real world, 844 G Li et al Computers Industrial Engineering 56 2009 839 853. 3 2 1 Experiment 1 Evolving structure of SNs, There are two experiments a and b in experiment 1 where the eight rms adopt the cost priority strategy. and the rm with the lowest bidding price wins the remaining demand at the second stage Firms bidding. prices are generated by a price function as pi 1 bi Ci where pi is rmi s price Ci is rmi s cost and bi. is generated randomly in accord with a Uniform distribution U 0 1 The other test parameters are set as. Fc 0 35 Dmin 5000 Dmax 10 000 Cmin 25 and Cmax is calculated such that the mean cumulative rm. capacities is 95 of Dmax The initial tness of the eight rms is generated randomly which is in accord with a. Uniform distribution U 0 40 0 75 The decay speed of rm s tness is 1 in each demand cycle The decay. speed Ddi t at each cycle is 1, In experiment 1 a the eight rms make product 1 without subparts In experiment 1 b they make product.
2 which has two parts Each rm can make the two parts and assemble them into the nal product Once a. rm wins the demand of the nal product in the rst stage it makes decisions on 1 manufacturing the whole. product by itself 2 subcontracting the whole product to other rms 3 subcontracting the parts and assem. bling the parts into the nal product by itself The solution with the lowest total cost among the three choices. will be selected, 1 Emergence In experiment 1 a a linear structure SN emerges from the interaction of the eight rms. for the ful lling of the stochastic demand In the 0th demand cycle there is no relationship at all among. the rms However with their competition and collaboration a linear structure SN emerges in each. demand cycle and the SN structure evolves over time see Fig 2 In the 98th demand cycle rm 0 sub. contracts its remaining demand to rm 1 and rm 1 subcontracts some of the order to rm 2 This linear. structure SN evolves into a new structure which includes rms 1 0 and 5 in the 99th demand cycle and. further evolves into a new linear structure SN including rm 5 0 1 and 2 in the 100th demand cycle In. experiment 1 b there not only emerges the linear structure SNs see Fig 3a but also emerges SNs with. a binary tree structure see Fig 3b, Experiment 1 indicates that di erent SNs emerge and evolve with the dynamic interaction among rms. without anyone s control A linear structure SN emerges when rms make product 1 But when a more com. plex product Product 2 is involved various SNs emerge and evolve over time Some SNs are of high hori. zontal complexity while some are of high vertical complexity The complexity of the SN is related to the. internal mechanisms of rms e g product structure tness cost capacity strategy and the environment ele. ments e g Fc market structure demand, 2 Path dependence As shown in Fig 4 when the rms collaborate to make product 1 the rm 0 subcon. tracts the remaining demand to the rm 4 for 56 times in the 100 demand cycles rms 3 subcontracts the. t 98 t 99 t 100,Fig 2 SN emergence and evolution Product 1. G Li et al Computers Industrial Engineering 56 2009 839 853 845. t 98 t 99 t 100,1 7 1 3 6 2,t 98 t 99 t 100, Fig 3 The emerged SN with various structures Product 2.
Preference,Fig 4 Path dependence, 846 G Li et al Computers Industrial Engineering 56 2009 839 853. remaining demand to the rm 0 for 49 times and the rm 5 subcontracts to the rm 7 for 51 times etc Those. rms who form a mutually compatible collaboration structure survive and the survivors lock in their part. ners The collaboration is self reinforcing and path dependent. 3 Multiple equilibrium and chaos As we repeat experiment 1 a for 100 times we nd four kinds of evo. lution patterns see Fig 5 which are dramatically di erent As shown in Fig 5 a the tness of the 8 rms. degrades gradually Firm 0 dies at the 36th demand cycle and all the other rms die gradually Finally all. the rms die at the 93rd demand cycle On the contrary the tness of the 8 rms increases over time and. the collaboration evolves into a win win pattern see Fig 5 b All the rms survive and thrive and their col. laboration achieves a superior equilibrium In Fig 5 c some rms tness increases while some declines over. time Those rms that their tness below Fc are eliminated from the environment which further results the. network structure evolves dynamically over time In Fig 5 d the tness of rm 6 decreases gradually in. the rst 71 demand cycles and falls in an inferior path But in the 72nd cycle it gets the remaining demand. with the lowest price and ful lls the demand successfully Further rm 6 escapes from the inferior path. and its tness increases in the following cycles In the repeated simulations we nd that most of the evolution. patterns match Fig 5 c However at the beginning of the experiment it is di cult to predict which pattern. will emerge although the experiments setting are the same The various evolving patterns of SNs indicate that. the evolution has multiple equilibrium states It is highly sensitive to the initial conditions Some initial con. ditions may drive the SN to evolve into a superior equilibrium and achieve system e ciency While some may. drive the SN to fall into an inferior equilibrium with system ine ciency Any slight di erence in the initial. conditions may drive the evolution into totally di erent patterns There is non deterministic chaos in the evo. lution which cannot be predicted precisely,3 2 2 Experiment 2 Stability of SNs. In Experiment 2 the stability of the evolving SN is studied Except for Fc Ddi t and rms strategies the. other parameters are the same as experiment 1 and the 8 rms make product 1 There are two steps in the. experiment, 1 At rst we test the stability of the evolving SN in di erent environments where Fc is di erent Fc 0 25. and Fc 0 40 The eight rms adopt the price priority strategy The decay speed of the of Ddi t at. Fc 0 40 is 1 6 times to the speed at Fc 0 25 The highest weight of collaboration preference in a. SN is used to evaluate the stability If the weight is small it indicates that the rms change their partners. frequently and the SN structure varies over time Otherwise they lock in their partners and the SN struc. ture is stable The means and the standard deviations of the data are shown in Table 1 The Hypothesis 1. Hypothesis 1 In di erent environments the stability of a SN is di erent. 1 Secondly we analyze the in uence of rm s strategy on the stability of the SN in the same environment. Fc 0 25 At rst the 8 rms adopt the short term strategy And then the 8 rms adopt the long term. strategy With the short term strategy the decay speed of Ddi t is twice to the decay speed as rms hold. ing the long term strategy The tenderee rm subcontracts the remaining demand to the other rms. whose attraction is the highest In the short term strategy the attraction is decided by Eq 9 where. the bidder s collaboration preference with the tenderee in the history is weighted 25 and the price is. weighted 75 In the long term strategy attraction is calculated by Eq 10 The collaboration preference. and price are normalized into 0 1 The di erence of the weight on collaboration preference represents. the in uence of the social factors e g culture and rm s preference on collaboration strategy The. means and the standard deviations of the data are shown in Table 2 The Hypothesis 2 is made. attraction 0 25 collaboration preference 0 75 price 9. attraction 0 75 collaboration preference 0 25 price 10. G Li et al Computers Industrial Engineering 56 2009 839 853 847. Fig 5 Multiple equilibrium and chaos in the SN evolution. 848 G Li et al Computers Industrial Engineering 56 2009 839 853. Fig 5 continued, Independent sample t test of the means of the collaboration preference. Environment Sample size Mean Standard deviation P value. Fc 0 25 30 69 73333 13 40852 0 176,Fc 0 40 30 63 56667 20 59743.
Independent sample t test of the means of the collaboration preference. Firms strategy Sample size Mean Standard deviation P value. Long term strategy 30 94 70000 5 73044 0 000,Short term strategy 30 79 03333 17 75792. Hypothesis 2 A SN s stability is di erent when the rms adopt di erent strategies. We used the software package SPSS to analyze the data At rst the data in Tables 1 and 2 is proved that. they are subjected to the normal distribution And then an independent samples t test is used to test the. di erence between the means of the data The result is shown in Tables 1 and 2 The reject of Hypothesis 1. indicates that once there emerges a mutually compatible SN it keeps stable In the real world of low uncer. tainty the driving force of a manufacturer to change suppliers is low when the suppliers can ful ll orders as. required On the other hand in a volatile environment the transaction cost is too high to change partners. frequently Therefore once rms nd their mutually compatible partners they lock in each other and collab. orate path dependently, Hypothesis 2 is accepted Furthermore we can prove that a SN is more stable if the partners adopt the long. term strategy In the real world the long term collaboration relationship helps the partners to foster trust and. decrease the transaction cost For example TOYOTA builds the Keiretsu to manage the collaboration activ. ities with its suppliers They forge long term collaboration relationship and achieve the world class manufac. turing DELL adopts the VMI vendor managed inventory schema with its suppliers Long collaboration. boosts DELL evolving into the No 1 personal computer manufacturer and its suppliers e g Flextronics. FOXCONN become the world class EMS electronic manufacturer suppliers The long term relationship. reinforces the winner take all e ect in the collaboration which results in more di culties for new rms to join. in the SN and reinforces the path dependence e ect. G Li et al Computers Industrial Engineering 56 2009 839 853 849. 3 2 3 Experiment 3 Fitness evolution, In Experiment 3 we compare the evolution of rms tness as the rms adopt di erent strategies 1 The 8. rms adopt the short term strategy 2 the 8 rms adopt the long term strategy The other parameters are the. same as those of experiment 2 We repeat the simulation for 30 times and the means and the standard devi. ations of the tness of the 8 rms are shown in Table 3 We have Hypothesis 3. Hypothesis 3 The variance of the rms tness in the evolution is di erent when rms adopt di erent. strategies in the same environment, At rst the data in Table 3 is proved that they are subjected to the normal distribution And then an inde. pendent samples t test is used to test the means of the data Hypothesis 3 is accepted Furthermore we can. prove that the long term collaboration strategy is better for the rms to improve their tness In practice. many rms achieve high performance by adopting the long term collaboration strategy For instance Chrysler. adopted the short term strategy in SN collaboration in the early 1980s Su ered from the high procurement. cost and the long lead time in the SN collaboration Chrysler fell into the edge of bankruptcy To survive. Chrysler had to move to the long term collaboration strategy in the SN management In 1989 Chrysler forged. long term contracts with its partners and implemented the SCRE Supplier Cost Reduction E ort plan to. help the suppliers to reduce cost to share information etc Consequently suppliers performance the man. ufacturing cost delivery time and product R D Research and Design time of Chrysler were all improved. greatly Chrysler survived and thrived at the edge of chaos. 4 Case study, CHINT Group Co is a LVEA low voltage electric apparatus manufacturer in China and the largest rm.
in the Wenzhou city Zhejiang province the southeast of China Zhejiang province has been the freest market. since 1970s when the government began the market economy reform In the past 30 years CHINT SN. emerged adapted and evolved into a huge network which involved the nal assembler CHINT 786. rst tier suppliers and more than 3000 second tier suppliers in the year of 2005 The distribution network. had three tiers 11 rst tier regional distribution centers 31 second tier provincial sales o ces and more than. 2000 third tier sales agencies in small counties of China Besides CHINT had 5 sales o ces and 30 sales agen. cies outside China In 2005 the total annual sale of CHINT Co was RMB15 billions. The evolution of CHINT SN involves three stages emergence development and maturity see Fig 6 The. qualitative traits of the evolution can be captured by the dimensions of physical structure product network. technology product process information sharing strategy business objectives operation strategy and. organization formalization centralization Carbonara et al 2002 Choi Hong 2002. In the emergence stage the SN was characterized by the casual vertical dependent collaboration schema. In 1984 encouraged by government s free market reform more than 1000 rms were founded to make single. LVEA in Wenzhou city And the mother rm of CHINT called QJ was one of them The homogeneous prod. ucts that were of low complexity on product structure and technology fostered intensive competition in the. local market which forced QJ to adopt the make to order strategy built its plant and founded more than. 1000 sales o ces to get more demand The great demand exacerbated the scarce of the internal capacity of. CHINT and it had to adopt the buy to order strategy and subcontracted surplus demand to other rms. For partner selection cost was the overarching force that shaped the SN Subcontractors were only viewed. as passive doers for the supplement of CHINT s internal capacity The collaboration was short term oriented. and always based on oral agreements To reduce cost CHINT frequently changed suppliers which resulted. Independent sample t test of the means of the tness variance. Firms strategy Sample size Mean Standard deviation P value. Long term strategy 30 0 0588990 0 16921757 0 044,Short term strategy 30 0 0463550 0 22303856. The signi cance is 0 05, 850 G Li et al Computers Industrial Engineering 56 2009 839 853. Emergence Development Maturity,Distributors,Distributors. Distributors,Distributors,Distributors,Distributors. Distributors Distributors, CHINT Manufacturer CHINT Manufacturer Assembler CHINT Assembler Designer.
1st supplier,1st supplier,1st supplier,1st supplier. 1st supplier,1st supplier,1st supplier,1st supplier. 1st supplier,2nd t supplier,2nd t supplier,2nd t supplier. 2nd t supplier,2nd t supplier,2nd t supplier,2nd t supplier. 2nd t supplier,2nd t supplier, Casual Vertical Dependent Vertical Dependent Horizontal Vertical Dependent.
Fig 6 Evolution of CHINT SN, the great number of the suppliers in the same tier and the varying structure of the SN The intensive compe. tition further boosted QJ s improvement on product quality In 1990 the government closed 1268 rms with. poor quality products and issued production licenses to quali ed rms QJ was one of these rms and got a. great progress at that year In 1991 QJ was splited into 2 rms and one of them was CHINT. From 1994 to 2000 the CHINT SN evolved into the development stage which was characterized by the. vertical dependent collaboration schema where the horizontal and vertical complexity the centralization. and formalization of the SN became higher than those at the emergence stage In 1994 CHINT moved to. a new strategy that focused on di erentiation quality and exibility It began to make the whole set of LVEA. which was of higher complexity in structure and technology This resulted the increasing of the horizontal and. vertical complexity of the SN and the vertical dependence among rms in di erent tiers To innovate prod. ucts improve cost quality and exibility CHINT moved to the make to order and assemble to order. strategy It bought 48 local rms and founded the CHINT Group Co Those rms with the same products. families were merged into seven key parts suppliers CHINT only made 10 key parts in house and subcon. tracted 90 parts and sub assembly to other rms CHINT forged long term relationships with the suppliers. implemented the Grand CHINT Plan and the Second Party Quality Certi cation Plan and routinely. exchanged the market and technology information with the suppliers The collaboration became more central. ized and the SN became more stable, Since 2000 the CHINT SN has been evolving into the maturity stage which is characterized by the hor. izontal vertical dependent collaboration schema The intensi ed product di erentiation and the movement. to the assemble to order and design to order strategy resulted the higher level of horizontal and vertical. complexity of the SN the horizontal dependence among suppliers at the same tiers and higher level of cen. tralization and formalization CHINT began to design and assemble the whole set high voltage electric appa. ratus which were more complex than LEVA The increasing complexity of products resulted the higher. vertical and horizontal complexity of the SN and it further enhanced the horizontal dependence among sup. pliers at the same tiers by the horizontal subcontracting in the same tier Also the market internationalization. exacerbated the complexity of the distribution network To improve product cost quality technology and. reduce suppliers CHINT implemented the supplier bidding mechanism some incentive mechanisms long. G Li et al Computers Industrial Engineering 56 2009 839 853 851. term contract supplier performance assessment and operation information sharing with suppliers It also. helped suppliers to improve technology and management These exacerbated suppliers reliance on CHINT. and reinforced the centralization and formalization of the SN. 5 Results of the study propositions and managerial implications. From the above simulations and case study we propose the following propositions. Proposition 1 The economic policies government regulations and industry structures influence the evolution of a. SN from the external environment, Firms collaborate to ful ll the demand generated in the environment The changing of the economic pol. icies government regulations and industrial structure has in uence on rms dynamic interaction Those rms. adapting to the environment survive and grow up Those rms who could not adapt to the environment are. eliminated by the environment Furthermore more rms competing in the market lead to more intensive com. petition and further boost the evolution of the SN. Proposition 2 Firms strategies product structure complexity technology complexity quality and cost. considerations involved in the internal mechanisms of firms are the internal dominated forces to shape the SN. A SN emerges and evolves with the process of partner selection and their collaboration pattern afterwards. A rm s di erent strategies result di erent complexity in product structure and technology which are the sali. ent architects in the construction of a SN Firm s consideration of cost and quality a ects how rms select. suppliers and further in uences the stability centralization and formalization of the network. Proposition 3 The dynamic interaction of the environment factors and the internal mechanism factors results that. the evolution of a SN is highly sensitive to the initial conditions The evolution of a SN is path dependent Slight. perturbation of the environment and the internal mechanism of firms can drive the evolution into chaos and it is. difficult to predict precisely, Firms have to adapt to the environment and try to know trust connect and collaborate with others in. routine At the same time the costs of searching for new partners are reduced by focusing on and working. more closely with the previous connected rms However SNs that are too richly connected are in danger. of producing chaotic behavior that adversely a ects the SNs to evolve e ectively into a super equilibrium. Too many parts of the SNs are a ected by any change resulting in the di culty to predict precisely. The implications for management of this are that managers should not only investigate the dynamics. behavior of rms but also should investigate the SN as a whole To better manage the SN managers and pol. icy makers should use the order parameters for enacting large scale change For policy makers it is better to. develop institutions which are bene cial to rms collaboration rather than to control rms operations. directly Managers should learn how to adapt to the environment and form a mutual compatible SN to. improve their tness to the environment Any incorrect assumptions about the SN by managers and policy. makers can lead to unintended consequences due to the continuously evolving nature of the SN Policy makers. and managers should learn how to balance between control and emergence. 6 Conclusion, The review on supply chain management identi ed a gap in the study of the evolution of supply networks.
due to the absence of knowledge of the dynamics of the network structure collaboration mechanism and the. environment This paper proposed an evolution model of SN based on CAS and tness landscape theory to. gain understanding of the evolution of the SNs The simulation of the evolution model and the case study indi. cated that the evolution of SN is self organizing An SN emerges from the dynamic interactions among the. rms and evolves over time The evolution is self reinforcing and path dependent Slight perturbation of. 852 G Li et al Computers Industrial Engineering 56 2009 839 853. the environment and the internal mechanism of rms can drive the evolution into chaos and it is di cult to. predict The environment and the internal mechanisms of the rms are the origins of the self organization evo. lution of a SN, Several aspects of the evolution of SN warrant further investigation Our present research directions include. 1 incorporation of more adaptive rms that are capable of modifying their collaboration strategies during. simulation based on the evolving environment 2 incorporation of the dynamics of the environment that. are capable of changing Fc to model the dramatic changes of the environment For instance as a disaster hap. pens Fc may be changed greatly and 3 development of an evolution model of service supply chain which is. of great di erence to the traditional supply networks. References, Akkermans H 2001 Emergent supply networks System dynamics simulation of adaptive supply agents In Proceedings of the 34th. Hawaii international conference on system sciences pp 1 5 Hawaii. Alfaro M D Sepulveda J M 2006 Chaotic behavior in manufacturing systems International Journal of Production Economics. 101 1 150 158, Berry D Naim M M Towill D R 1995 Business process re engineering an electronic products supply chain IEE Process Science. Measurement Technology 142 5 395 403, Bruce K 2000 The network as knowledge Generative rules and the emergence of structure Strategic Management Journal 21 3. Camazine S Deneubourg J L Franks N R Sneyd J Theraulaz G Bonabeau E 2001 Self organization in biological systems. Princeton Princeton University Press, Carbonara N Giannoccaro I Pontrandolfo P 2002 Supply chains within industrial districts A theoretical framework.
International Journal of Production Economics 76 2 159 176. Choi T Dooley K Rungtusanatham M 2001 Supply networks and complex adaptive systems Control versus emergence Journal. of Operations Management 19 3 351 366, Choi T Y Hong Y 2002 Unveiling the structure of supply networks Case studies in Honda Acura and DaimlerChrysler Journal. of Operations Management 20 5 469 493, Christopher M 1992 Logistics and supply chain management Strategies for reducing costs and improving service London Pitman. Chung W W C Yam A Y K Chan M F S 2004 Networked enterprise A new business model for global sourcing International. Journal of Production Economics 87 3 267 280, Forrester J W 1961 Industrial dynamics Cambridge MA MIT Press. Gunasekaran A Ngai E W T 2005 Build to order supply chain management A literature review and framework for development. Journal of Operations Management 23 5 423 451, Hamel G Prahalad C K 1994 Competing for the future Boston MA Harvard Business School Press. Harland C M Zheng J Lamming R C Johnsen T E 2002 A taxonomy of supply networks IEEE Engineering Management. Review 30 4 12 20, Holland J H 1995 Hidden Order How adaptation builds complexity Menlo Park CA Addison Wesley.
Holmstrom J Hameri A 1999 The dynamics of consumer response a quest for the attractors of supply chain demand International. Journal of Operations Production Management 19 10 993 1009. Holweg M Bicheno J 2002 Supply chain simulation A tool for education enhancement and endeavour International Journal of. Production Economics 78 2 163 175, Hur D Hartley J L Hahn C K 2004 An exploration of supply chain structure in Korean companies International Journal of. Logistics Research and Applications 7 2 151 166, Kau man S A 1993 The origins of order Self organization and selection in evolution New York Oxford University Press. Kau man S A MacReady W 1995 Technological evolution and adaptive organizations Complexity 1 2 26 43. Lamming R Johnsen T Zheng J Harland C 2000 An initial classi cation of supply networks International Journal of. Operations and Production Management 20 6 675 691, Larsen E R Morecroft J D W Thomsen J S 1999 Complex behaviour in a production distribution model European Journal of. Operational Research 119 1 61 74, Luhmann N 1995 Social systems CA Stanford Stanford University Press. Marquez A C Blanchar C 2004 The procurement of strategic parts analysis of a portfolio of contracts with suppliers using a. system dynamics simulation model International Journal of Production Economics 88 1 29 49. McCarthy I P 2004 Manufacturing strategy Understanding the tness landscape International Journal of Operations Production. Management 24 2 124 150, Melody A R 1994 Enterprise architecture De nition content and utility In Proceeding of the third workshop on enabling.
technologies Infrastructure for collaborative enterprises pp 106 111 Washington IEEE Computer Society Press. Miller D 1992 Environmental t versus internal t Organization Science 3 2 159 178. Min H Zhou G G 2002 Supply chain modeling past present and future Computers Industrial Engineering 43 1 2 231 249. G Li et al Computers Industrial Engineering 56 2009 839 853 853. Pathak S D David M D 2002 Simulating supply networks using complex adaptive systems theory In Proceeding of IEEE. international engineering management conference IEEE Catalog No 02CH37329 Cambridge UK. Pathak S D David M D Gautam B 2003 Multi paradigm simulator for simulating complex adaptive supply chain networks In. Proceedings of the 2003 winter simulation conference pp 808 816 Washington. Pathak S D Dilts D M Biswas G 2007a On the evolutionary dynamics of supply network topologies IEEE Transactions on. Engineering Management 54 4 662 671, Pathak S D Day J Nair A Sawaya W J Kristal M 2007b Complexity and adaptivity in supply networks Building supply. network theory using a complex adaptive systems perspective Decision Sciences Journal 38 4 547 580. Schieritz N GroBler A 2003 Emergent structures in supply chains a study integrating agent based and system dynamics modeling. In Proceedings of the 36th annual Hawaii international conference on system sciences pp 1 9 Hawaii. Stuart I Deckert P McCutcheon D Kunst R 1998 Case study A leveraged learning network Sloan Management Review 39 4. Surana A Kumara S Greaves M Raghavan U N 2005 Supply chain network A complex adaptive systems perspective. International Journal of Production Research 43 20 4235 4265. Swaminathan J M Smith S F Sadeh N M 1998 Modeling supply chain dynamics A multi agent approach Decision Sciences. 29 3 607 632, Towill D R 1996 Industrial dynamics modeling of supply chains Logistics Information Management 9 4 43 56. Towill D R Evans G N Cheema P 1997 Analysis and design of an adaptive minimum reasonable inventory control system. Production Planning Control 8 6 545 557, Whang S 1995 Coordination in operations A taxonomy Journal of Operations Management 12 3 413 422. Wikner J Towill D R Naim M M 1991 Smoothing supply chain dynamics International Journal of Production Economics 22 3.


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