اثر سیاست های موجودی بر عملکرد زنجیره تامین: یک مطالعه شبیه سازی پارامترهای تصمیم گیری حیاتی

This paper investigates the effects of information sharing and early order commitment on the performance of four inventory policies used by retailers in a supply chain of one capacitated supplier and four retailers. Model parameters and operating conditions are emulated from a local business supplying a standard product to its retailers. Through computer simulation and subsequent analyses, we found that the inventory policy used by the retailers, information sharing, and early order commitment can significantly influence the performance of the supply chain. Out of the four inventory policies examined, the economic order quantity rule is found to be the best for the retailers and the entire supply chain, but periodic order quantity and Silver–Meal provide the best performance for the supplier. The sharing of future order plans by the retailer and the supplier is also shown to be the most effective way for reducing the supplier’s cost and improving its service level; however, the magnitude of these benefits achieved is less for the retailers. In addition, early order commitment by the retailers is found to be beneficial to the supplier and retailers in reducing their total cost.

The coordination of logistics and inventory decisions in a supply chain has a significant effect on the supply chain performance. Any attempts to reduce supply chain costs and improve service levels must consider the complex interactions across a wide spectrum of business organizations and their individual replenishment decisions. Until recently, most businesses have primarily focused on improving their internal operations to better serve their immediate customers, and have paid little attention to improving the performance of the entire supply chain by examining the impact of their decisions on other members along the supply chain. Our research was motivated by a local manufacturer that supplied a standard product to four retailers at different locations. Each of the four retailers made their inventory decisions using simple lot-sizing rules based on their own demand forecasts. The manufacturer made its own production decisions using a capacitated lot-sizing rule, but wanted to investigate the impact of the inventory policies (IPs) used by the retailers on the performance of the supply chain and identify the best inventory ordering policy for the benefit of all members in the supply chain. Four IPs were identified for ease of implementation; these were economic order quantity (EOQ), periodic order quantity (POQ), Silver–Meal (SM), and part-period balancing (PPB). In addition, neither information sharing nor early order commitment was currently used, but these policies would be initiated if such efforts could have been justified by a significant reduction in total supply chain cost or improvement in service level. To gain a better understanding of the performance of the four IPs under different operational scenarios, we developed a computer simulation model that incorporated a variety of basic environmental and decision variables. The simulation model incorporated many scenarios that would affect the ordering decisions of the retailers and the production lot-sizing decisions of the manufacturer. While addressing the unique issues raised by the local manufacturer and its retailers, we also wanted to subject our simulation to a larger set of operating characteristics so that the results could be generalized and helpful to other supply chains. As a result, we used a combination of demand patterns, capacity, and cost structure variables throughout our simulation. By analyzing the findings from our simulation model, we have sought to provide managerial insights into the following questions: (1) How does the inventory policy used by the retailers influence the costs of the supplier, retailers, and the entire supply chain? (2) How does the inventory policy used by the retailers influence the service levels of the supplier and the retailers? (3) How does the inventory policy used by the retailers influence the value of information sharing? (4) How does the inventory policy used by the retailers influence the value of early order commitment? In addition, our findings can also serve as a building block for future work in this emerging area of research. In the following sections, we first review the related literature and then describe our research designs. Subsequently, we present the simulation model parameters and the results of the statistical analyses. Finally, we describe the managerial implications and conclusions.

Most retailers make their own inventory replenishment decisions based on their demand forecasts and their cost structures (i.e., inventory carrying cost and ordering costs). To improve the performance of the supply chain under conditions of demand uncertainty, all supply chain members are now encouraged to share information and coordinate orders with each other. While many models have been developed to determine the timing and quantity of orders, most of them do not consider the complex interactions between the retailers and the supplier. This study took the initiative to examine the impact of the retailer’s inventory policies (IPs) on the performance of a supply chain. As an exploratory study of its kind, we strived to gain more insight into how the IPs of the retailers influence the performance of the supply chain and how the different IPs influence the value of information sharing (IS) and early order commitment (EOC). Our analyses showed that a combination of IP, IS, and EOC can significantly influence the performance of the supply chain. Out of the four IPs examined, the EOQ policy provided the lowest cost for the supply chain. Sharing information on the future orders plan was shown to be the most effective way for cost and service level improvement. Any EOC is better than none at all, but its benefit will level off after it has reached a certain value. Although the findings from this simulation study shed some insights into the selection of IP, IS, and EOC between the supplier and the retailers, the limitations of the study must be noted. First, the supply chain paradigm used in this study represented a very simplified case, with one capacitated supplier and four identical retailers with one identical product. Second, we have only used a narrow range of parameters in the simulation experiment, so the results are only valid within these ranges of parameters used. Third, there are certainly plenty of alternative methods of sharing information and ordering coordination in practice, in addition to those we have proposed in this study. In order to better understand the complex relationships between IP, IS, and EOC, and their effects on supply chain performance, the above mentioned issues must be taken into consideration in future research.