یک مدل موجودی منابع متعدد تحت ریسک اختلال
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|20860||2014||10 صفحه PDF||سفارش دهید||9666 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : International Journal of Production Economics, Volume 149, March 2014, Pages 37–46
Interruptions in supply can have a severe impact on company performance. Their mitigation and management is therefore an important task. Reasons for interruptions can be machine breakdowns, material shortages, natural disasters, and labour strikes. Sourcing from multiple suppliers is a strategy to deal with and reduce supply disruption risk. We study a supply chain with one buyer facing Poisson demand who can procure from a set of potential suppliers who are not perfectly reliable. Each supplier is fully available for a certain amount of time (ON periods) and then breaks down for a certain amount of time during which it can supply nothing at all (OFF periods). The problem is modeled by a Semi-Markov decision process (SMDP) where demands, lead times and ON and OFF periods of the suppliers are stochastic. The objective is to minimize the buyer's long run average cost, including purchasing, holding and penalty costs. In a numerical study, we investigate the trade-off between single and multiple sourcing, as well as keeping inventory and having a back-up supplier. The results illustrate the benefit from dual souring compared to single sourcing and show the influence of the suppliers’ characteristics cost, speed and availability on the optimal policy. Further, the value of full information about the supplier status switching events is analyzed and the performance of the optimal policy is compared to an order-up-to-S policy. As the optimal policy is very complex, a simple heuristic providing good results compared to the optimal solution is developed.
For successful supply chain management, a buyer has to consider that suppliers may not always be available. Temporary supply interruption can occur due to machine breakdowns, labour strikes, natural disasters, terror events etc. As these disruptions can have a severe impact on the supply process, a buyer may source from more than one supplier to protect against supply risk. One example for a supply disruption where a dual sourcing strategy resulted in high cost savings is the Nokia–Ericsson case in 2000, where a fire shut down Philips’ semiconductor plant in New Mexico which supplied both buyers Nokia and Ericsson for several weeks. Due to the disruption, Ericsson lost $400 million, while Nokia managed to source from alternative suppliers, minimizing the negative impact of the disruption (Latour, 2001). In April 2010, the car manufacturer BMW had to stop production in three German plants because electronic components, normally air-shipped, could not be delivered due to the ash cloud over Europe (Friese et al., 2010). Recently, the earthquake in Japan in March 2011 caused companies around the world to rebuild their supply chains to cope with supply disruption and search for new suppliers to avoid running out of components that had been previously obtained from Japan (Hookway and Poon, 2011). These real life examples show that buyers can reduce the risk of supply shortfalls by sourcing from multiple suppliers when the supply process is subject to failure. Our approach incorporates a multiple sourcing inventory system with stochastic demand, lead times and reliability of the suppliers, varying in cost, speed, and reliability. In order to compute the optimal decisions regarding supplier selection and reorder quantities for each state of the system, we formulate a semi-Markov decision process (SMDP) under Poisson demand, exponentially distributed lead times and exponentially distributed periods where a supplier is available (ON) and unavailable (OFF). A state consists of the buyer's inventory level, the outstanding orders, and the availability of the suppliers. As suppliers typically differ in service and cost, we investigate the optimal sourcing strategies depending on supplier characteristics like cheap/expensive, fast/slow, and reliable/unreliable. This paper is organized as follows: In Section 2 we review relevant literature. In Section 3 we give a detailed description of the model assumptions and the semi-Markov decision process (SMDP) formulation. In Section 4 we present numerical results discussing the optimal sourcing strategy dependent on the states of the inventory system for the dual sourcing case. In Section 5 we summarize the results and give concluding remarks.
نتیجه گیری انگلیسی
We have studied an inventory system where a buyer orders from multiple suppliers with different cost and reliabilities which are subject to temporary failures. A SMDP was formulated to optimize the order decisions of the buyer depending on the supplier's availabilities for a lost sales model and a backorder model, assuming Poisson demand, exponentially distributed lead times and exponentially distributed ON and OFF periods. The solution of the SMDP provides the optimal sourcing strategy depending on the actual inventory, the outstanding orders and the supplier status for both models. The numerical examples show that the optimal policy is rather complex and illustrate the benefit of dual sourcing compared to single sourcing when supply is subject to failure. In an illustrative example we analysed the optimal policy for the lost sales and backorder setting. Computational experiments show that the benefit of dual sourcing over single sourcing is high especially when penalty costs are high and disruption periods are long. We analyse the value of having full information about the suppliers becoming available and unavailable, which is very important when the supplier availabilities are low and disruption periods are frequent. Further, we compared the optimal policy with a simple order-up-to-S policy where S may not be equal for all supply bases and depends on the supplier characteristics of the actual supply base. A simple heuristic is developed providing good results compared to the optimal order-up-to-S policy with individual S. Simulation results indicate that our model is sensitive with respect to more general ON and OFF and lead time distributions. Further, our model is compared to a more dramatic supply breakdown scenario. Possible extensions to the current model would be to generalize to more general distributions and to relax the assumption of independent lead times and allow for batch ordering. Future research should include the investigation of the impact of different shapes of distribution for the demand, lead time and ON and OFF times.