برنامه ریزی زنجیره تامین واگرا مبتنی بر فعالیت برای مزیت رقابتی در محیط پر مخاطره جهانی : رویکرد برنامه ریزی آرمانی فازی DEMATEL-ANP

Supply chain management allows modern enterprises to relax their own capacities and produce in a more flexible manner for diversified consumer demands. However, for an enterprise with divergent supply chain (DSC) and multiple product lines, to plan the production allocation for higher competitive advantage in the risky global market is a challenging problem. The existing literature still has not address this problem very well. This paper is aimed to treat this problem by using an integrated approach of activity based costing (ABC) and management, five forces analysis, risk and value-at-risk analysis, decision making trial and evaluation laboratory (DEMATEL), analytic network process (ANP), and fuzzy goal programming (FGP). The proposed model can effectively incorporate the key factors of precise costing, managerial constraints, competitive advantage analysis, and risk management into DSC forecasting and multi-objective production planning. A case study of a consumer-oriented cell phone DSC is also presented. The sensitivity analysis shows that identifying and relaxing crucial constraints can play an important role in DSC planning for higher competitive advantage and lower risk.

Supply chain (SC) operations enable producers to break through their limits of production with much more flexibility and thus they can focus on consumers’ demands. In order to satisfy the diversified demands of consumers, manufacturers might produce various products in which some products may need common components for cost reduction, and form so-called divergent supply chain (DSC); for example, cell phone manufacturers might change the appearances and styles of their cell phones, and maintain the same basic inner-components (Fig. 1).A SC may be viewed as a DSC if a SC node has one predecessor, but more than one successors (Beamon & Chen, 2001). Mineral industries and consumer-oriented industries often form such type of SC. This type of SC is opposite to a convergent supply chain (CSC). In a DSC, the critical issues of product mix planning, supply chain constraints, forecasting and risk management, and competitive advantage may have relationships with each other. For instance, over production of high risk and low competitiveness products, which may be caused by lack of suitable measurement or not suitably relaxing constraints, could result in ineffectiveness and low capacity efficiency. Especially in today’s highly risky global market, how to address these issues and meanwhile achieve high cost-benefit performance has become an important research topic (Syntetos & Boylan, 2006). Fig. 2 shows the external environment and internal constraints for a supply chain (Robbins & Coulter, 2001). We can see that successful supply chain management relies on employing internal resources, finance, and strategy to achieve higher competitive advantage, avoid higher risk, and prepare for potential opportunity.n the literature of supply chain management, the planning of supply chain is frequently discussed, whereas the integration of precise costing, SC constraints, competitive advantage, and risk management for a DSC still has not been deeply explored. How these elements could be included in DSC planning remains a problem to be solved. To fill this gap, this research integrates activity based costing (ABC) and management, five forces analysis, risk and value-at-risk analysis, decision making trial and evaluation laboratory (DEMATEL), analytic network process (ANP), and fuzzy goal programming (FGP) for the DSC planning problem. This paper uses cost drivers at various levels (unit, batch, product, facility, supply chain) to measure activity amounts and costs, and identify the constraints of each activity in the supply chain. In measuring respectively the competitive advantage and risk of a product supply chain, DEMATEL is first used to analyze and determine the interdependence relationships between the criteria. Next, ANP evaluates the weights of the alternatives. Value-at-risk (VaR) is also used to measure the potential largest loss. Finally, FGP is used to obtain the optimal product mix and the worst profit. We found that this research develops a new approach that can not only improve DSC production planning, but also can efficiently strengthen DSC competitive advantage and risk management. In the sensitivity analysis of the case study, this paper shows that identifying and relaxing crucial constraints can effectively improve DSC planning for higher competitive advantage and lower risk. In this case, supply chain management time is more crucial than common component capacity constraint. When only the common component constraint is relaxed, the overall planning of product mix is not affected. However, if the supply chain management time is first relaxed, it will increase the production of the higher competitive advantage and lower risk products, meanwhile relaxing common component constraint will also come into effect. Hence, identifying and relaxing crucial constraint is a step with priority in DSC planning. This paper provides a fine application of ABC and DEMATEL-ANP-FGP model in a DSC, and also assists SC managers in making appropriate decisions to decrease the revenue risks of their product mix and raise the overall competitive advantage at the same time. The rest of this paper is organized as follows: Section 2 presents the literature review. Section 3 discusses the proposed DEMATEL-ANP-FGP model followed by a case study in Section 4. Section 5 concludes this study.

In the increasingly risky global market, the integration of precise costing, constraint control, competitive advantage and risk management is a critical problem for a divergent supply chain (DSC). However, existing papers have not yet proposed a very suitable planning approach for this problem. This paper suggests that this problem can be solved by applying an ABC system and a DEMATEL-ANP-FGP model. On the resource side, this research utilized the cost drivers at each level (unit, batch, product, facility, supply chain) to measure the activity amounts and costs, and built the constraints of total budget goals, gross revenue goals, the performance of response time and asset turnover time, SC management time, defect rate, late delivery rate, flexibility rate, and capacity. For measuring the competitive advantage and risk of the product supply chains, DEMATEL is first used to determine the interdependence relationships between each criterion, and then ANP determines the weights of the alternatives, and value-at-risk determines the largest losses. Finally, the FGP model of multi-objective decision making finds the optimal product mix, and the worst profit. In the sensitivity analysis of our case study, the constraint of supply chain management time is even more crucial than the common component capacity constraint, because only when the supply chain management time is first relaxed will the relaxing of common component constraint come into effect, and increase the production levels of the products of higher competitive advantage and lower risk; thus in this case, identifying and relaxing supply chain management time plays a critical role in DSC planning. In practice, this paper suggests that in the highly risky global market, the cost drivers and cost information of ABC can be used in DSC planning to identify the costs and constraints of the supply chain. Then by combining the evaluations of competitive advantage and risk with a DEMATEL-ANP-FGP model, a company can meet its budget and revenue goals, and achieve optimal planning for higher competitive advantage and lower risk. The contribution of this research is developing a sound model of ABC and DEMATEL-ANP-FGP for DSC forecasting and planning. In practice, the proposed model can help DSC managers in making better decisions for lower revenue risk and higher competitive advantage.