سازماندهی مشارکت در زنجیره تأمین به وسیله فرآیند تحلیل سلسله مراتبی (AHP)

The significance of collaboration among supply chain members has been sufficiently stressed in the recent literature as a powerful tool for increasing accuracy of demand forecasts and for consequent cost reductions. Since it has been recognized that naïve forecasting is no longer cost efficient, Supply Chain (SC) members have found it very important to exchange relevant information that will help improve accuracy of demand forecasting. This information differs widely in terms of their characteristics. For example, some information (e.g. historic sales data) that is cheap to exchange may not contribute to a great increase in forecast accuracy. Similarly, some information may not be very reliable (e.g. demand forecast by individual SC members). In general, there is a trade-off in the kind of information required and the kind of information exchanged. This study analyses these trade-offs using an Analytic Hierarchy Process (AHP) model. The model is then implemented based on case studies conducted in two manufacturing firms. The AHP model ranks available information in terms of their contributions to improve forecast accuracy, and can provide vital clues to SC partners for preparing exchangeable data. From the case studies using AHP model, it was proved that using the preferred SC data, the firms could enhance forecasts accuracy. This in turn can help the firms to make decisions on SC collaborative arrangements for information exchange.

In the past two decades, supply chain management has been recognized as a powerful business tool to survive in the competitive marketplace. Supply Chain (SC) operators have started considering the changing interests of consumers and their shifting loyalty whilst managing supply chain inventory, capacity and production, and delivery management. This is reflected in collaborative relationships between SC partners to avoid stock-outs and excess inventory [22] and [6]. Several collaborative SC tools such as Vendor Managed Inventory (VMI) and Collaborative Planning Forecasting and Replenishment (CPFR) are being increasingly adopted by SC operators to improve SC efficiency. Some manufacturers practicing Supply Chain Collaboration (SCC) and advanced information integration with retailers have realised cost reduction and increased revenue [25]. Many researchers have discussed the role of supply chain information and quality of information in improving supply chain performance [18] and [19]. Information Sharing (IS) among partners facilitates flow of goods in the supply chain [6] and also helps to forecast demand more efficiently. However, the benefits of IS are highly dependent on the context and proper use of available information [34]. Forecast information quality may be lower for upstream members in the supply chain, especially for manufacture-to-order suppliers [18], but effective and efficient handling of available data will enhance the performance of supply chain and yield more benefits [23]. All available information may not be equally useful for the purpose of forecasting or decision making for all SC partners [27] and [34]. For instance, demand or transaction information may be more important to retailers than manufacturers, while product or inventory information may be more important to the latter. Yu et al. [48] showed that centralized IS benefits manufacturers more than retailers. They also suggested some incentives to retailers in order to encourage their participation in information sharing. Ovalle and Marquez [33] classified information into three types: product information, customer demand and transaction information, and inventory information. This classification varies widely depending on the firms involved in SCC. An exceptional level of service can be achieved through integrated information [24]. Although the benefit of IS is not necessarily the same to all SC members, it is perceived as one of the critical success factors for collaborative supply chains [22]. This paper refers to the information exchange among members of SC collaboration as ‘Collaborative Information Exchange’ (CIE), and it is discussed with regard to improving demand forecasts. In contrast to the above literature, Småros [42] identified from case studies that manufacturers' initiative on establishing collaboration for IS and forecasting with downstream members such as retailers wholesalers and distributors is an uncommon practice. However, there is no systematic approach in the existing literature for identifying importance of IS under SC collaboration. This paper, through two exploratory case studies, tries to study and rank the information needs of manufacturing firms to improve forecast accuracy. This paper has two objectives: • To identify and prioritize the information need in CIE to attain forecast accuracy. As mentioned earlier, various kinds of SC information have differing levels of importance to SC operators. This study attempts to develop a new AHP-based framework to arrive at the relative importance of this information by eliciting opinions from SC operators. • To decide the level of collaboration in SC. Depending on the importance of information, some SCs may have to engage in very close collaboration, while some SCs may not need very close engagement. A framework is established to decide on the levels of collaboration using the AHP model. The rest of the paper is organized as follows. The literature on evolution of information exchange in SCC is discussed briefly in Section 2. Research methodology is detailed in Section 3. Two case studies are briefly discussed in Section 4. Based on the case study observations, an AHP model is developed and analyzed in Section 5. The findings of AHP analysis are discussed in Section 6. The final section concludes with the research observations. This section also discusses scope for future work.

The results of AHP analysis have been discussed with the case study companies and the reasons behind each result have been justified with their comments. Generally, the importance of data have been analyzed on the basis of their impact on forecasting accuracy. In practice, other factors affecting CIE are not directly dealt at the time of decision making. However, the results of AHP analysis has helped the firms to revisit their decisions on IS with their SC partners. In the initial stage of the case study, sales data and discount data were not found to be an integral part of CIE for PackCo but they were found to be important for TexCo. The results of the analysis have also supported the same observation. This was due to the fact that the products of shorter shelf life need to be more reactive to the market [28]; hence the textile products have required both the sales and the discount sales data for forecasting the demand. Final priorities (refer to Table 2) of CIE were considered a combination of all the criteria. The discount data were designated as the least preferred data for TexCo with the rank of ten. Meanwhile, readily available order data was used effectively in both the firms. PackCo used its readily available order data in the first instance for the purpose of forecasting, which is highly preferred with a maximum weight of 21%.The seasonal sales information was preferred equally by both firms at the time of non-availability of other sales data. Promotional sales information was found to be highly beneficial for TexCo, even if the cost involved in promotions is high. Historical data is not highly preferred by both the firms, and was ranked ninth. The basic reason for its lower popularity was that the historical data did not make much impact in improving the forecast, and incurred more cost in administration and data maintenance. However, historical data is seldom used for forecasting if it is owned by the firms. Usually, government policy on import, export, the use of raw materials, and any other specific changes is passed on to the firms through suppliers and customers, hence the basic communication between SC members will suffice to update this information. A competitor's information is another costly input to use in forecasting, but TexCo might risk their attractive business opportunities in the absence of competitors' information. The choice of competitor's information in CIE was ranked second by TexCo. Involvement of the third parties in this exercise seemed to inflate the cost of obtaining competitors' information. Meanwhile, the anticipated usability of competitors' information seemed to be higher than the cost incurred. PackCo preferred to obtain competitors' information, with the final weight of 0.1190 (ranked third). Local forecast was found beneficial for both companies, irrespective of the cost involved. The final priority of the local forecasts was 0.1047 and 0.0839 for TexCo and PackCo, respectively. Tracking the customers' inventory was found useful to PackCo with the final weight of 0.103. At the same time, TexCo updated their database with sales and discount information; hence inventory position was not deemed important for its demand forecasting and its final weight was 0.0575. From the results of the AHP analysis (refer to Table 2), it is clear that although both firms uses similar types of information for their planning and forecasting, they attached different priorities to the information. From this table, it is clear that TexCo preferred to use current data from their customers as it uses advanced technology for information sharing and forecasting (namely Blackberry and SAP). Hence, the firm preferred to have a high level of collaboration with customers. The forecast accuracy of textile products in the past three years was consistently in the range of 60–70%. However, PackCo claimed only 50–55% forecast accuracy. However, PackCo preferred to use the available data (order data) as it did not have the technical capability to obtain electronic sales data. PackCo seldom obtained details of promotions from retailers. Currently, they do not maintain higher levels of collaboration with the customers for CIE due to the cost involved. Both firms used their judgements in establishing a collaborative relationship with their partners based on the priorities of CIE and forecasting capabilities.