A novel approach to safety stock management in an integrated supply chain with controllable lead time and ordering cost reduction using present value

This paper presents a novel approach to safety stock management and investigates the impact of lead time reduction within an integrated vendor–buyer supply chain framework using present value where lead time and ordering cost reductions act dependently. In particular, the cost of the safety stock is determined by adopting a logistic approximation to the standard normal cumulative distribution. The service level is formulated in relation to the dimension of the single shipment, to the average demand of the buyer and to the number of admissible stockouts. We first discuss the case where the lead time and ordering cost reductions with linear function, and then consider the logarithmic functional relationship. Numerical examples including the sensitivity analysis with some managerial insights of system parameters is provided to validate the results of the supply chain models. The main contribution of this paper is introducing various types of ordering cost reduction in Braglia et al. (Appl. Stoc. Mod. Bus. Ind. 32 (2016) 99–112) by handling a new approach.

Lead-time Dependent Ordering Cost Reduction and Trade-credit: A Supply Chain Model with Stochastic Demand and Rework

This study focuses on an integrated vendor-buyer supply chain model where the lead-time and ordering cost reduction act dependently. The lead time demand of a product follows a normal distribution. The manufacturing process is imperfect. During production run time, a certain percentage of defective products are produced, which are immediately reworked. Trade-credit financing has been taken into consideration. The goal of this study is to minimize the joint total expected cost by providing an inter-dependent reduction strategy of lead-time and ordering cost along with the determination of the optimal values of lead-time, number of deliveries, order lot size, ordering cost, lead-time crashing cost, and the joint total expected cost. A solution algorithm and a numerical example are presented to illustrate and establish the integrated model. This model can be used in textiles, automobiles and computers industries.