Model retraining and information sharing in a supply chain with long-term fluctuating demands

Demand forecasting based on empirical data is a viable approach for optimizing a supply chain. However, in this approach, a model constructed from past data occasionally becomes outdated due to long-term changes in the environment, in which case the model should be updated (i.e., retrained) using the latest data. In this study, we examine the effects of updating models in a supply chain using a minimal setting. We demonstrate that when each party in the supply chain has its own forecasting model, uncoordinated model retraining causes the bullwhip effect even if a very simple replenishment policy is applied. Our results also indicate that sharing the forecasting model among the parties involved significantly reduces the bullwhip effect.

Ordering Policies of a Deteriorating Item in an EOQ Model under Upstream Partial Order-Quantity-Dependent Trade Credit and Downstream Full Trade Credit

In the classical inventory systems, the retailer had to settle the accounts of the purchased items at the time they were received. But in practice, the supplier applies some strategic tools, such as trade credit contract, to enhance his sales channel and offers delay period to his customers to settle the account. Any member of the supply chain may offer full or partial trade credit contract to his downstream level. Full trade credit is the case that the latter is allowed to defer the whole payment to the end of the credit period. In partial trade credit, however, the downstream supply chain member must pay for a proportion of the purchased goods at first and can delay paying for the rest until the end of the credit period. This paper considers a two-level trade credit, where the supplier offers order-quantity-dependent partial trade credit to a retailer, who suggests full trade credit to his customers. An economic order quantity (EOQ) inventory model of a deteriorating item is formulated here, and the Branch and Reduce Optimization Navigator is applied to find the optimal replenishment policy. The sensitivity of the variables on different parameters has been analyzed by applying some numerical examples. The data reveal that increasing the credit periods of the retailer and the customers can decrease and increase the retailer’s total cost, respectively.

Integrated Inventory Model for Single Vendor Multi-Buyer with a Single Item by Considering Warehouse and Capital Constraint

Integrated inventory management coordinates all party's replenishment policies to provide optimal benefits. Many models have been developed, but none of them have considered capital and warehouse constraints comprehensively. It may cause the model which cannot be applied, since it has exceeded the capacity. This study developed an integrated inventory model that consisted of one vendor, multi-buyer, and one type of item. The main objective was to minimize the joint total expected cost by considering warehouse, capital, and service level constraint. The optimal formula was constructed by using the Lagrange multipliers method. The results showed that with an increment in holding cost, the vendor tends to reduce lot size to minimize joint total expected cost. It is vice versa to the increment in set up cost. An increment in buyer service level can increase lot size and reduce order frequency. The buyer capacity is essential to determine its capability to apply the optimal replenishment policy.

REPLENISHMENT POLICY FOR A PROCESSING SYSTEM WITH STOCHASTIC CONSTRAINTS, DISCOUNT AND IMPERFECT ITEMS

In this paper, a processing system with multiple products, single-vendor and single-buyer is considered to maximize the inventory system’s profit. In order to be more suit for real-world applications, this model contains five stochastic constraints including backordering cost, space, ordering, procurement, and available budget. It is assumed that orders are subjected to quantity discount and also imperfect goods are permitted. The price of the perfect and imperfect goods are assumed to be different. The imperfect goods are assumed to be returned to the system for rework process. The objective is to find the optimal order quantities of products such that the total inventory profit to be maximized while satisfying all the constraints. The problem is formulated as a mixed integer nonlinear programming problem. Two algorithms, based on GA and GRASP are developed to solve the resulting model. Performance of the algorithms are analyzed based on 45 numerical examples with different sizes.