Trade Credit Policies for Supplier, Manufacturer, and Retailer: An Imperfect Production-Inventory System with Rework

In this study, we developed a trade credit policy for a three-layer supply chain consisting of a supplier, a manufacturer and a retailer. We propose an optimal production rate and selling price for the manufacturer and the retailer under an imperfect production system. The suggested coordination policy optimizes the profit of each supply chain member. Two models were formulated for two real-life strategies respectively. The first one is a collaborative (integrated) system and the second one is a Stackelberg leadership system. Both strategies were analyzed for various credit periods, respectively offered by the supplier to the manufacturer, by the manufacturer to the retailer, and by the retailer to the customers, by considering price-sensitive demand and a certain replenishment rate. Finally, we concluded which strategy will be better for inventory management under the given restrictions in the form of propositions. The concavity property for the net profit function was established with respect to the selling price and the production rate, which was also described graphically and analyzed by numerical examples

Supply chain coordination and integration under yield loss

The primary objective of this dissertation is to develop analytical models for typical supply chain situations to help supply chain decision-makers under supply yield loss. We derive solution procedures for each model and present several managerial insights obtained from our models through numerical examples. Additionally, this research provides decision-makers insights on how to incorporate uncertainty in demand and supply and shortage information into a mathematical model. This study deals with three forms of integrated cost-profit models under different scenarios including coordination policy and supply yield loss in a two-stage supply chain involving a retailer and a supplier, dealing with a single product under deterministic condition. We compare the profits of the whole supply chain system under the coordinated policy with those of individual decision making approaches and demonstrate the efficiency of coordination. These models attempts to find the optimal solutions for the retailer’s order quantity, quality level, amount of emergency procurement, and the production and shipment decisions of the supplier, so that the resulting joint total profit for the entire supply chain is maximized. We illustrate our model and the potential benefits of outsourcing in a supply chain system through a numerical example. Extending the analyses obtained above, we then develop models for an integrated supplier–retailer supply chain under imperfect production and shortages, with the additional decision variable of market pricing on the part of the retailer. We assume that market demand is sensitive to the retailer’s selling price and study the combined operation and pricing decisions in the supply chain. We develop profit maximization models for the cases of independent and joint optimization. The results of obtained from our analyses demonstrate that the individual profit, as well as joint profit can be increased by our suggested model, under a non-linear price dependent demand function. In addition, the results with retailer-supplier coordination tend to be superior, which leads to illustrate that setting appropriately retailer’s selling price can increase market demand and the profits of both parties, as well as that of the supply chain. Finally, numerical examples are presented to illustrate these models, and the sensitivity analyses of a selected set of model parameters on the total profit is conducted. A major finding of this study is that coordination between the retailer and the supplier improves channel profit significantly. Furthermore, the possibility of external procurement tends to improve total system profitability as the price sensitivity of demand increases.Ph.D., Business Administration -- Drexel University, 201

A fuzzy periodic review integrated inventory model involving stochastic demand, imperfect production process and inspection errors

In this study, we investigate an integrated production-inventory system consisting of a single-vendor and single-buyer. The buyer manages its inventory level periodically at a certain period of time. We consider a fuzzy annual demand, imperfect production, inspection errors, partial backordering, and adjustable production rate in the proposed model. Additionally, it is assumed that the protection interval demand follows a normal distribution. The model contributes to the current literature by allowing the inclusion of fuzzy annual demand, adjustable production rate and imperfect production and inspection processes. Our objective is to optimize the number of deliveries from vendor to buyer, the buyer’s review period, and the vendor’s production rate, so that the joint expected total annual cost incurred has the minimum value. Furthermore, an iterative procedure is proposed to find the optimal solutions of the model. We also provide a numerical example and conduct a simple sensitivity analysis to illustrate the model’s behaviour and feasibility. The results from the sensitivity analysis show that the defective rate, type I inspection error, fuzzy annual demand, fixed production cost, variable production cost and setup cost give impacts to both the review period and production rate. Finally, it is concluded that the proposed model can be applied by managers or practitiones for managing inventories across the supply chain involving a vendor and a buyer

Detailed Inventory Record Inaccuracy Analysis

This dissertation performs a methodical analysis to understand the behavior of inventory record inaccuracy (IRI) when it is influenced by demand, supply and lead time uncertainty in both online and offline retail environment separately. Additionally, this study identifies the susceptibility of the inventory systems towards IRI due to conventional perfect data visibility assumptions. Two different alternatives for such methods are presented and analyzed; the IRI resistance and the error control methods. The discussed methods effectively countered various aspects of IRI; the IRI resistance method performs better on stock-out and lost sales, whereas error control method keeps lower inventory. Furthermore, this research also investigates the value of using a secondary source of information (automated data capturing) along with traditional inventory record keeping methods to control the effects of IRI. To understand the combined behavior of the pooled data sources an infinite horizon discounted Markov decision process (MDP) is generated and optimized. Moreover, the traditional cost based reward structure is abandoned to put more emphasis on the effects of IRI. Instead a new measure is developed as inventory performance by combining four key performance metrics; lost sales, amount of correction, fill rate and amount of inventory counted. These key metrics are united under a unitless platform using fuzzy logic and combined through additive methods. The inventory model is then analyzed to understand the optimal policy structure, which is proven to be of a control limit type