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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : International Journal of Production Economics, Volume 89, Issue 2, 28 May 2004, Pages 109–118
The Internet and related information and communication technologies (ICT) have recently enabled the cost-effective dissemination of information between disparate parties in the supply chain. New supply chain strategies, such as vendor managed inventory (VMI), collaborative planning, forecasting and replenishment (CPFR) and efficient consumer response (ECR), have begun to exploit these new communication channels, principally at the retail end of the supply chain. The impact of the e-business enabled supply chain on manufacturers and materials/component suppliers is, however, less well understood and exploited. This paper is aimed at establishing e-business enabled supply chain models for quantifying the impact of ICT, in particular its effect on dynamic behaviour. The paper concludes that simple, yet robust, models enable considerable quantitative insights into the impact of e-business on supply chain dynamic behaviour prior to their implementation.
While information and communication technologies (ICT) in the form of e-business is advocated as an enabler to the 1–2–1 enterprise (Peppers and Rogers, 1997) by allowing market place information to be shared by all businesses in the supply chain, there is little analytical or quantifiable evidence that it will actually improve the overall performance of the enterprise in delivering customer wants. It is usually proposed that passing information to all businesses in the supply chain via ICT will improve performance. In fact, recent research (Hong-Minh et al., 2000) has shown, via the supply chain “Beer Game” (Sterman, 1989), that simply passing information on to businesses can have a detrimental effect. This is due to the fact that, as well as having more information available, schedulers need to know what to do with it. There are many ways in which innovative information flows could be used within supply chains. Kiely (1998) provides a good starting point, specifically focusing on using demand data for forecasting purposes. In this paper we analyse the impact of four ICT enabled scenarios by investigating the bullwhip effect (Lee et al (1997a) and Lee et al (1997b)) using two different approaches and comparing them to a traditional supply chain. The first approach is based on an analysis of the results of a management flight simulator, the Beer Game. The second approach is based on a quantitative z-transform analysis using the tools highlighted by Disney and Towill (2002). The aim is to compare and contrast the two approaches qualitatively to assess the implications of their evaluations of e-business scenarios on supply chain dynamics. Bullwhip is an important measure, being symptomatic of a poorly performing supply chain (Jones and Simons, 2000). It is a surrogate measure of production adaptation costs (Stalk and Hout, 1990) and implies the inclusion of “just-in-case” stock holding to buffer against uncertainties. There is considerable empirical evidence of bullwhip including recent examples in the: • food sector where the supplier orders two tiers further upstream varied 10 times more than the electronic point of sales (EPOS) data (Jones and Simons, 2000). • automotive sector where the ratio of the variance between incoming orders and order to suppliers at just a single echelon in the supply chain was 1:2 (Naim et al., 2002). The five supply chain strategies considered are: •Traditional—in which there are four “serially linked” echelons in the supply chain. • e-Shopping—where the distribution network is by-passed and information and materials flow directly between the end consumer and the product suppliers. •Reduced—where an echelon in the supply chain had been removed. • Vendor managed inventory (VMI)—that is simulated by developing a protocol positioned between two businesses in the supply chain that gives the necessary inventory and sales information, authority and responsibility to the supplier in order to manage the customer's inventory. •EPOS—where information from the market place is transmitted to all enterprises in the supply chain.Although various e-business scenarios are available the above were chosen by four groups of four Masters Programme students based on their review of commonly quoted and/or implemented strategies in both the academic and practitioner literature. It was these Masters students who implemented the scenarios in the Beer Game.
نتیجه گیری انگلیسی
The results we have presented are based on single products or an aggregate product type. The results are just as valid when assessing the dynamic impact of ICT on multiple products. Previous research has indicated the need for appropriate categorisation policies so as to aggregate products into types based on dynamic marketplace behaviour (Naim et al., 2002). Different product types may then require different ordering policies (Evans et al., 1998) and/or information enrichment strategies (Mason-Jones and Towill, 1997). We have taken two different approaches to understand the impact of ICT on supply chain dynamics. The z-transform analysis indicates that there is an expectation that the innovative ICT will outperform the alternative strategies. But the Beer Game results have indicated that ICT adds a degree of complexity to human decision-making that is difficult to cope with even if well-defined protocols are provided. There is just too much information and too many calculations to manage. The Beer Game also indicates that poor management of the ICT protocols leads to increased inventory costs. We conclude that although the Beer Game is a simulated and simplified environment, much like the real-world people have to make decisions the consequences of which are not immediately known. As Sterman (1989) has indicated people are not good at making decisions in such an environment. While ICT offers the opportunity for greater supply chain transparency, it creates an even more complex environment so that when people do have to intervene, the decision-making is even more difficult. Between playing the Beer Game in traditional mode and implementing the e-business scenarios there may be a learning curve that the players go through. The results of the e-business scenarios suggest that if the learning curve did exist for the results indicated then the simpler the scenario the faster up the learning curve the players progress. There is also the possibility that the players in implementing the e-business scenarios were far too ambitious in their strategies. Again, this is not unlike the real world. Companies may often over stretch their capabilities and resources in implementing new technologies that are either too advanced for their needs or are inherently too resource intensive to implement and operate. As it is difficult to ensure that everybody is appropriately trained to manage new ICT protocols, there is a need to design robust systems that minimise human interaction. Alternatively, when human interaction is required it should be intuitively obvious what particular decisions have to be made and with which specific sources of information. At the same time, with current technology, it is difficult for ICT systems to handle exceptions, such as absenteeism or factory shut downs. Appropriate simplified procedures are therefore required to enable simple human interaction to manage the exceptions.