Common Replenishment Strategies in Supply Chain under Uncertainty Demand Environment

This paper is based on the model proposed by Viswanathan and continue to analyze the benefit of supply chain inventories through the use of common replenishment epochs. We studied a one-vendor, multi-buyer supply chain for a single product under uncertainty demand environment. The vendor specifies common replenishment periods and asks all buyers to replenish only at those time period and the price discount to be offered by the vendor are determined by the solution to a Stackelberg game. A numerical study is conducted to evaluate the benefit of the strategy by simulation

One vendor and multiple retailers system in vendor managed inventory problem with stochastic demand

In many supply networks, the retailers are reluctant to share information about demand and inventory level to the vendor. This might lead to many difficulties for the vendor in establishing his own order/production plan. Vendor managed inventory (VMI) policy can help to solve that problem. By applying VMI, information sharing is not really a problem for the vendor anymore and this policy have been proven to help reduce total inventory cost as well as improve customer service level in the supply network. In this research, a VMI model for the system with one vendor and multiple retailers will be developed. The main target of the model is to determine the retailer’s lot size, the vendor’s lot size, the retailer cycle time, and the number of replenishments in a vendor cycle so as to minimise the total system cost. For solution purpose, simulation-optimisation technique using genetic algorithm is employed to help find optimal solutions for the decision variables. Numerical experiments are conducted to show the applicability of the proposed model. Sensitivity analysis is also conducted to examine the effects of some input parameters on the optimal solution

A coordination framework for distributed supply chains

Supply chain is a network of cooperating organizations that are involved through upstream and downstream linkages. Such a complex system cannot be modeled simply by mathematical equations without simplification of the problem. Recently, Multi-Agent System (MAS) is proposed as a modeling technique to represent supply chain networks. In fact, many application problems in MASs that are concerned with finding a consistent combination of agent actions can be formalized as Distributed Constraints Satisfaction Problem (DCSP). The main purpose of this paper is to propose a novel coordination framework by adopting the DCSP philosophy for distributed supply chains, which are modeled by MASs, subjected to uncertainties. Simulation results indicate that the proposed mechanism outperforms traditional stochastic modeling in solving supply chain dynamics in terms of total cost and fill rate of the system. © 2004 IEEE.published_or_final_versio

Analysis of simple inventory control systems with execution errors: Economic impact under correction opportunities

Cataloged from PDF version of article.Motivated by recent empirical evidence, we study the economic impact of inventory record inaccuracies that arise due to execution errors. We model a set of probable events regarding the erroneous registering of sales at each demand arrival. We define correction opportunities that can be used to (at least partially) correct inventory records. We analyze a simple inventory control model with execution errors and correction opportunities, and demonstrate that decisions that consider the existence of recording errors and the mechanisms with which they are corrected can be quite complicated and exhibit complex tradeoffs. To evaluate the economic impact of inventory record inaccuracies, we use a simulation model of a (Q,r) inventory control system and evaluate suboptimalities in cost and customer service that arise as a result of untimely triggering of orders due to inventory record inaccuracies. We show that the economic impact of inventory record inaccuracies can be significant, particularly in systems with small order sizes and low reorder levels. (C) 2010 Elsevier BM. All rights reserved

Vendor-Buyer Coordination in Supply Chains

Collaboration between firms in order to coordinate supply chain operations can lead to both strategic and operational benefits. Many advanced forms of collaboration arrangements between firms exist with the aim to coordinate supply chain decisions and to reap these benefits. This dissertation contributes to the understanding of the conditions that are necessary for collaboration in such arrangements and the benefits that can be realized of such collaboration arrangements. This dissertation focuses on the vendor-buyer dyad in the supply chain. We identify and categorize collaboration arrangements that exist in practice, based on a review of the literature and combine this with formal analytical models in the literature. An important factor in the benefits of collaboration is the benefit of reduced costs of transport, by realization of economies of scale in the context of capacity-constrained trucks. As a contribution to the understanding of the dependence of transport costs on the volume transported, we demonstrate how transport tariffs for orders of less-than-a-truckload in size on a single link can be deduced from a basic model. The success of a collaboration arrangement depends on agreement about the distribution of decision authority and collaboration-benefits. We study a collaboration arrangement in which the vendor takes responsibility for managing the buyer's inventory and makes it economically attractive to the buyer by offering a financial incentive, dependent on the maximum level the buyer permits to be stocked. This dissertation demonstrates that this incentive alignment leads to considerable cost savings and near-optimal supply chain decisions

Non-cooperative joint replenishment under asymmetric information

Cataloged from PDF version of article.We consider jointly replenishing n ex-ante identical firms that operate under an EOQ like setting using a non-cooperative game under asymmetric information. In this game, each firm, upon being privately informed about its demand rate (or inventory cost rate), submits a private contribution to an intermediary that specifies how much it is willing to pay for its replenishment per unit of time and the intermediary determines the maximum feasible frequency for the joint orders that would finance the fixed replenishment cost. We show that a Bayesian Nash equilibrium exists and characterize the equilibrium in this game. We also show that the contributions are monotone increasing in each firm’s type. We finally conduct a numerical study to compare the equilibrium to solutions obtained under independent and cooperative ordering, and under full information. The results show that while information asymmetry eliminates free-riding in the contributions game, the resulting aggregate contributions are not as high as under full information, leading to higher aggregate costs. 2013 Elsevier B.V. All rights reserved

Stochastic Optimization Models for Perishable Products

For many years, researchers have focused on developing optimization models to design and manage supply chains. These models have helped companies in different industries to minimize costs, maximize performance while balancing their social and environmental impacts. There is an increasing interest in developing models which optimize supply chain decisions of perishable products. This is mainly because many of the products we use today are perishable, managing their inventory is challenging due to their short shelf life, and out-dated products become waste. Therefore, these supply chain decisions impact profitability and sustainability of companies and the quality of the environment. Perishable products wastage is inevitable when demand is not known beforehand. A number of models in the literature use simulation and probabilistic models to capture supply chain uncertainties. However, when demand distribution cannot be described using standard distributions, probabilistic models are not effective. In this case, using stochastic optimization methods is preferred over obtaining approximate inventory management policies through simulation. This dissertation proposes models to help businesses and non-prot organizations make inventory replenishment, pricing and transportation decisions that improve the performance of their system. These models focus on perishable products which either deteriorate over time or have a fixed shelf life. The demand and/or supply for these products and/or, the remaining shelf life are stochastic. Stochastic optimization models, including a two-stage stochastic mixed integer linear program, a two-stage stochastic mixed integer non linear program, and a chance constraint program are proposed to capture uncertainties. The objective is to minimize the total replenishment costs which impact prots and service rate. These models are motivated by applications in the vaccine distribution supply chain, and other supply chains used to distribute perishable products. This dissertation also focuses on developing solution algorithms to solve the proposed optimization models. The computational complexity of these models motivated the development of extensions to standard models used to solve stochastic optimization problems. These algorithms use sample average approximation (SAA) to represent uncertainty. The algorithms proposed are extensions of the stochastic Benders decomposition algorithm, the L-shaped method (LS). These extensions use Gomory mixed integer cuts, mixed-integer rounding cuts, and piecewise linear relaxation of bilinear terms. These extensions lead to the development of linear approximations of the models developed. Computational results reveal that the solution approach presented here outperforms the standard LS method. Finally, this dissertation develops case studies using real-life data from the Demographic Health Surveys in Niger and Bangladesh to build predictive models to meet requirements for various childhood immunization vaccines. The results of this study provide support tools for policymakers to design vaccine distribution networks

On the Benefit of Inventory-Based Dynamic Pricing Strategies

We study the optimal pricing and replenishment decisions in an inventory system with a price-sensitive demand, focusing on the benefit of the inventory-based dynamic pricing strategy. We find that demand variability impacts the benefit of dynamic pricing not only through the magnitude of the variability but also through its functional form (e.g., whether it is additive, multiplicative, or others). We provide an approach to quantify the profit improvement of dynamic pricing over static pricing without having to solve the dynamic pricing problem. We also demonstrate that dynamic pricing is most effective when it is jointly optimized with inventory replenishment decisions, and that its advantage can be mostly realized by using one or two price changes over a replenishment cycle.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/78685/1/j.1937-5956.2009.01099.x.pd