مدل تصمیم گیری تصادفی عملگرا برای پشتیبانی انتقال محموله کالاها در یک محیط زنجیره تامین

This paper develops a set of decision rules to assist wholesalers to decide whether it is more cost effective to trans-ship urgent outstanding retailer orders from other wholesalers, very fast but at a higher purchase cost, or to order from their suppliers. By considering the uncertainty in demand, it models the total cost encountered by wholesalers, including purchasing, backordering and holding costs in the inventory replenishment process. Unlike previous models that are complex, highly mathematical and difficult to apply, this model provides a pragmatic and less complex method adoptable by ordinary logistics managers and requires input data that are accessible from the previous transaction records of an organization. The application of the proposed decision rules are illustrated considering different scenarios of wholesaler–supplier combinations.

Ever increasing market competition forces wholesalers to be ever cost conscious and responsive to the changing needs of the market. A consequence is that wholesalers maintain a low inventory for the purpose of buffering in order to minimize the possible costs. However, the criticality of losing the sales due to stock outages with consequent possible lost profit and decreased customer satisfaction are no less important. To manage these potentials, wholesalers adopt a mix of urgent order lateral trans-shipments from other wholesalers at a higher cost, while at the same time backordering from their usual suppliers to meet the continuing stochastic retailer demands — this produces flexibility in the inventory system. Rules that assist wholesalers' decision making processes have practical importance for inventory management. Supply chain management concepts that streamline the flow of goods have been the focus of research for a considerable time. Previous research into wholesaler inventory management has presented complex criteria drawn from diverse information sources that are problematic to adopt in real-world practice. There remains a need for simpler and more readily applicable rules for lateral trans-shipment decisions. In a multi-location setting under a continuous review (R,Q) ordering policy Axsäter [3] considers a decision rule for determining how many units should be trans-shipped, depending on the complete state of the system. The decision is optimal and can be repeatedly used as a heuristic. He puts a significant focus on future cost difference for a certain initial state and the highly mathematical analysis and probability assumptions may not be easily understood by ordinary managers. The models proposed by Evers [9] and Minner et al. [18] consider the case of lost sales rather than backordering under (R,Q) policies. Olsson [21] has considered an optimal (R,Q) ordering policy under complete pooling but due to the problem complexities the optimal solution is restricted to systems with only two locations. Chiu and Huang [6] considered a system with more than two echelons. However, they then restricted focus to a single location of each echelon. Our study considering a multi-location setting, develops decision rules for reactive lateral trans-shipments of urgent demands that cannot be satisfied from the stock on hand. The decision rules for determining whether it is more cost effective to trans-ship urgent orders or to backorder all outstanding orders from suppliers, the size of trans‐shipment, the favorite wholesaler and the favorite supplier. Our approach handles the cost difference issue raised above using an alternative approach mainly based on predicted holding and backorder costs in different time periods. We find that the total cost function against the number of units trans-shipped has a linear relationship and presents a close estimation reflecting the cost variations, in real case, it is not a completely linear relation though. This new approach does not undervalue previous scholarly work, it builds on it by proposing a more pragmatic decision model for supply chain environment in wholesaler system. The model can be applied to a real context with multiple wholesalers, and multiple suppliers with variable lead times. It is less complex in calculations and the data requirement that can be fulfilled with previous transaction records data of the organization enables adoption of this model by an ordinary manager. The proposed approach is validated through the illustration of a practical application of the model in different scenarios. The next section reviews the relevant literature, Section 3 then goes on to describe and develop the proposed mathematical model for total costs, including purchasing costs, backordering costs and holding costs that are encountered in inventory replenishment by wholesalers and develops the decision rules for trans-shipments. Section 4 shows how these decision rules could be applied in practice and implications for management. Section 5 concludes with a discussion of the effectiveness and limitations of the decision model, with suggestions for further research.

The decision support system developed in this article serves the inventory management of wholesaler operations in making decisions on whether to trans-ship outstanding urgent retailer demands or back order from suppliers in full. The main advantage of this decision support system is the ease of application by wholesaler inventory management. The decision is driven by the important cost minimization objectives, the simplicity of the rules and the need for less cumbersome data inputs to the model, underpin the ease of adoption. The main decision rule needs only the unit purchasing cost from suppliers, unit trans-shipment cost from another wholesaler, own unit backordering cost, and the expected lead time from its suppliers. The corollary decisions on deciding the favorite other wholesaler and supplier, derived in this article can also be easily adopted by inventory management without performing complex mathematical calculations. Moreover, common spreadsheet software such as MS Excel can be used for these calculations as shown above. We are aware of the limitations of this study. First, we approximated that the probability distribution of retailer arrival at the wholesaler has a Compound Poisson distribution. Second, the suggested method of estimating backorder costs and holding costs was based on previous cost records. We assumed that the estimates based on old records would hold true during the forecasting period but the current dynamic business environment challenges that assumption. Furthermore, we assumed that the selected other wholesaler is able to supply the trans-shipment order in full thus no partial orders with multiple wholesalers were considered. However, wholesalers can place partial orders of trans-shipments with multiple other wholesalers for reasons of risk mitigation. Future research has the opportunity to accommodate the situation of partial orders with multiple suppliers and other wholesalers and to further develop the proposed decision support system in the present study. There is potential for developing this model into a software program for wide adoption by logistics organizations. In a standalone program, the user interface should allow the input fields for organization-specific dynamic parameters such as previous demand records, unit backordering, holding and purchasing costs and supplier lead times etc. In a wider scale, the proposed decision rule algorithm can be integrated as a module of the existing enterprise resource planning applications with the capability of extracting data from previous transaction records in the internal databases, thereby extending the capability of achieving more reliable and accurate expected costs in inventory management operations. Such extended software applications should be scalable to handle different scenarios and upgradable to accommodate the intended potential future developments of this approach covering partial order situations.