DOCSLIB.ORG

Inventory Management: Modeling Real-Life Supply Chains and Empirical Validity Full Text Available At

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Inventory Management: Modeling Real-Life Supply Chains and Empirical Validity Full Text Available At Full text available at: http://dx.doi.org/10.1561/0200000057 Inventory Management: Modeling Real-life Supply Chains and Empirical Validity Full text available at: http://dx.doi.org/10.1561/0200000057 Other titles in Foundations and Trends R in Technology, Information and Operations Management Matching Supply and Demand for Hospital Services Diwakar Gupta and Sandra J. Potthoff ISBN: 978-1-68083-108-5 Quantity Discounts: An Overview and Practical Guide for Buyers and Sellers Charles L. Munson and Jonathan Jackson ISBN: 978-1-60198-888-1 Management Systems Standards: Diffusion, Impact and Governance of ISO 9000, ISO 14000, and Other Management Standards Pavel Castka and Charles J. Corbett ISBN: 978-1-60198-884-3 Designing and Controlling the Outsourced Supply Chain Andy A. Tsay ISBN: 978-1-60198-844-7 Full text available at: http://dx.doi.org/10.1561/0200000057 Inventory Management: Modeling Real-life Supply Chains and Empirical Validity Ton de Kok School of Industrial Engineering Eindhoven University of Technology, The Netherlands [email protected] Boston — Delft Full text available at: http://dx.doi.org/10.1561/0200000057 Foundations and Trends R in Technology, Informa- tion and Operations Management Published, sold and distributed by: now Publishers Inc. PO Box 1024 Hanover, MA 02339 United States Tel. +1-781-985-4510 www.nowpublishers.com [email protected] Outside North America: now Publishers Inc. PO Box 179 2600 AD Delft The Netherlands Tel. +31-6-51115274 The preferred citation for this publication is T. de Kok. Inventory Management: Modeling Real-life Supply Chains and Empirical Validity. Foundations and Trends R in Technology, Information and Operations Management, vol. 11, no. 4, pp. 343–437, 2018. ISBN: 978-1-68083-417-8 c 2018 T. de Kok All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, mechanical, photocopying, recording or otherwise, without prior written permission of the publishers. Photocopying. In the USA: This journal is registered at the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923. Authorization to photocopy items for internal or personal use, or the internal or personal use of specific clients, is granted by now Publishers Inc for users registered with the Copyright Clearance Center (CCC). The ‘services’ for users can be found on the internet at: www.copyright.com For those organizations that have been granted a photocopy license, a separate system of payment has been arranged. Authorization does not extend to other kinds of copying, such as that for general distribution, for advertising or promotional purposes, for creating new collective works, or for resale. In the rest of the world: Permission to photocopy must be obtained from the copyright owner. Please apply to now Publishers Inc., PO Box 1024, Hanover, MA 02339, USA; Tel. +1 781 871 0245; www.nowpublishers.com; [email protected] now Publishers Inc. has an exclusive license to publish this material worldwide. Permission to use this content must be obtained from the copyright license holder. Please apply to now Publishers, PO Box 179, 2600 AD Delft, The Netherlands, www.nowpublishers.com; e-mail: [email protected] Full text available at: http://dx.doi.org/10.1561/0200000057 Foundations and Trends R in Technology, Information and Operations Management Volume 11, Issue 4, 2018 Editorial Board Editor-in-Chief Charles Corbett UCLA, Anderson School of Management United States Editors Fernando Bernstein Duke University Cheryl Gaimon Georgia Institute of Technology Uday Karmarkar University of California, Los Angeles Sunder Kekre Carnegie Mellon University Panos Kouvelis Washington University Michael Lapré Vanderbilt University Karl Ulrich University of Pennsylvania Luk van Wassenhove INSEAD Full text available at: http://dx.doi.org/10.1561/0200000057 Editorial Scope Topics Foundations and Trends R in Technology, Information and Operations Management publishes survey and tutorial articles in the following topics: • B2B Commerce • New Product & Service Design • Business Process Engineering • Queuing Networks and Design • Reverse Logistics • Business Process Outsourcing • Service Logistics and Product • Capacity Planning Support • Competitive Operations • Supply Chain Management • Contracting in Supply Chains • Technology Management and • E-Commerce and E-Business Strategy Models • Technology, Information and • Electronic markets, auctions Operations in: and exchanges – Automotive Industries • Enterprise Management – Electronics Systems manufacturing • Facility Location – Financial Services • Information Chain Structure – Health Care and Competition – Media and Entertainment • International Operations – Process Industries • Marketing/Manufacturing – Retailing Interfaces – Telecommunications • Multi-location inventory theory Information for Librarians Foundations and Trends R in Technology, Information and Operations Management, 2018, Volume 11, 4 issues. ISSN paper version 1571-9545. ISSN online version 1571-9553. Also available as a combined paper and online subscription. Full text available at: http://dx.doi.org/10.1561/0200000057 Contents 1 Introduction2 2 Modeling inventory systems9 2.1 A brief history of inventory management research ..... 9 2.2 Empirical validity of inventory models ........... 11 2.3 The practice of inventory management: human intervention and correlations ....................... 12 2.4 Intervention frequency and inventory system performance . 13 2.5 Modeling time between order release and order receipt .. 15 2.6 Conclusion on modeling inventory system ......... 17 3 Single-item single-echelon inventory models 19 3.1 Deterministic demand .................... 19 3.2 Stochastic demand ..................... 24 4 Uncapacitated multi-item multi-echelon models 35 4.1 Material availability and stochastic lead times ....... 36 4.2 The example supply chain .................. 37 4.3 Modeling multi-echelon inventory systems ......... 39 4.4 Feasibility of order release quantities ............ 41 4.5 Synchronization and allocation ............... 45 4.6 Decision node structure for the case example ....... 55 Full text available at: http://dx.doi.org/10.1561/0200000057 4.7 Control policies for divergent MIME systems........ 60 4.8 Generalized Newsvendor equations for divergent MIME systems........................ 63 4.9 Performance of SBS policies ................ 65 4.10 Empirical validity of SBS policies .............. 70 4.11 Positioning inventory in the supply chain .......... 74 5 Capacitated inventory systems 78 5.1 Feasibility of order release quantities ............ 80 5.2 Comparison of rolling scheduling concepts ......... 81 5.3 Optimal policies for serial MIME systems .......... 82 5.4 Implicit modeling of finite capacity ............. 84 6 Conclusion 85 Acknowledgements 88 References 89 Full text available at: http://dx.doi.org/10.1561/0200000057 Inventory Management: Modeling Real-life Supply Chains and Empirical Validity Ton de Kok School of Industrial Engineering, Eindhoven University of Technology, The Netherlands; [email protected] ABSTRACT It is our intention to write a different overview of inventory models, from single item single echelon models to multi-item multi-echelon models, then is mostly provided in text books on Operations Management. We hope that this monograph provides complementary knowledge. Instead of starting with inventory models that are tractable from a mathematical point of view, we start from the inventory management prob- lem and the modeling challenges to be faced. We present the economic order quantity problem from the perspective of Return On Investment instead of from a cost perspective. We show that the Newsvendor fractile emerges from virtually any model with linear holding and penalty costs. And we dis- cuss the complexities of multi-item multi-echelon inventory systems by developing necessary and sufficient conditions operational control policies for such systems should satisfy. Ton de Kok (2018), “Inventory Management: Modeling Real-life Supply Chains and Empirical Validity”, Foundations and Trends R in Technology, Information and Operations Management: Vol. 11, No. 4, pp 343–437. DOI: 10.1561/0200000057. Full text available at: http://dx.doi.org/10.1561/0200000057 1 Introduction Inventory management has been a core topic of Operations Research since the 1950s. Inventory can be seen as a means to create efficiency in production and distribution: it enables scale by allowing to accumulate demand until a batch quantity is released that can be produced and shipped efficiently. This role of inventory is of great importance in process industries, where set-up times are considerable. Inventory can be seen as a means to ensure sufficient customer service: as demand is unpredictable we must hold inventory in case there are unexpected surges in demand. This role of inventory is of great importance in retail, as we expect a product to be available off-the-shelf or at our doorstep within 24 hours. Inventory can also be seen as a symptom of bad management, as waste of capital. Reduction of inventory capital has been high on the priority lists of CEOs over the last four decades. In the early 1980s, the Just In Time (JIT) philosophy proclaimed zero inventory as the key objective to ensure continuous improvement of processes, leading to less process variability, shorter processing time, smaller production and transportation batches, and higher product yield. In many businesses inventory is
Load more

Recommended publications
  • Theory of Inventory Management with a View of Stochastic Models
    Barbora FRANKOVÁ1, Vojtěch MEIER2 1Technical University of Ostrava, Czech Republic (e-mail: [email protected]) 2Technical University of Ostrava, Czech Republic (e-mail: [email protected]) 13. THEORY OF INVENTORY MANAGEMENT WITH A VIEW OF STOCHASTIC MODELS 13.1. Inventory All types of companies, both Czech and foreign, are struggling with the problem of the amount of inventory in stock for both raw materials and goods or products. It can be said that the supplier is unable to meet the demand immediately if he does not have enough inventory in stock (Winston 2004). Many authors are concerned with various inventory optimization models. The basic inventory management models are deterministic, but they are a decision-making model behind certainty that does not consider the variable consumption over time (Polanecký, 2018). Deterministic models are no longer used today. Many companies have a variable consumption over time and they do not know predict consumption in the future. Therefore, we will deal with stochastic models dealing with risk-taking in this article, so consumption is counted as a random variable. The aim of the article is therefore to optimize inventories using stochastic models. The main question is why company must manage its inventory? How does it manage? Inventory must manage because they take your many. If company does not manage inventory, it cannot meet the demand of its customers. The result may be a lack of money but a surplus of materials in stock. 190 MEET 2018 13.2. Stochastic models for inventory optimization If the company does not use inventory management, it may encounter several problems.
    [Show full text]
  • Lot Sizing with Inventory Gains
    Lot sizing with inventory gains Hamish Waterer Department of Engineering Science The University of Auckland [email protected] Draft: January 2006 Revised: April 2006 November 2006 Abstract This paper introduces the single item lot sizing problem with inventory gains. This problem is a generalization of the classical single item capac- itated lot sizing problem to one in which stock is not conserved. That is, the stock in inventory undergoes a transformation in each period that is independent of the period in which the item was produced. A 01 mixed integer programming formulation of the problem is given. It is observed, that by projecting the demand in each period to a distinguished period, that an instance of this problem can be polynomially transformed into an instance of the classical problem. As a result, existing results in the litera- ture can be applied to the problem with inventory gains. The implications of this transformation for problems involving dierent production capacity limitations as well as backlogging and multilevel production are discussed. In particular, it is shown that the polynomially solvable classical constant capacity problems become NP-hard when stock is not conserved. Key words: Lot sizing, production planning, inventory gains, perish- able inventory, deteriorating inventory, xed charge networks, generalized networks, networks with gains. 1 Introduction The single item lot sizing problem is a classical production planning and schedul- ing problem that has been extensively studied in the literature. An instance of the capacitated version of this problem consists of n time periods, and a pro- duction capacity Ct and a demand dt for a single item in each time period t ∈ {1, .
    [Show full text]
  • Chapter 19 Inventory Theory
    19 Inventory Theory “Sorry, we’re out of that item.” How often have you heard that during shopping trips? In many of these cases, what you have encountered are stores that aren’t doing a very good job of managing their inventories (stocks of goods being held for future use or sale). They aren’t placing orders to replenish inventories soon enough to avoid shortages. These stores could benefit from the kinds of techniques of scientific inventory management that are described in this chapter. It isn’t just retail stores that must manage inventories. In fact, inventories pervade the business world. Maintaining inventories is necessary for any company dealing with physical products, including manufacturers, wholesalers, and retailers. For example, manufacturers need inventories of the materials required to make their products. They also need inventories of the finished products awaiting shipment. Similarly, both whole- salers and retailers need to maintain inventories of goods to be available for purchase by customers. The total value of all inventory—including finished goods, partially finished goods, and raw materials—in the United States is more than a trillion dollars. This is more than $4,000 each for every man, woman, and child in the country. The costs associated with storing (“carrying”) inventory are also very large, perhaps a quarter of the value of the inventory. Therefore, the costs being incurred for the storage of inventory in the United States run into the hundreds of billions of dollars annually. Re- ducing storage costs by avoiding unnecessarily large inventories can enhance any firm’s competitiveness. Some Japanese companies were pioneers in introducing the just-in-time inventory sys- tem—a system that emphasizes planning and scheduling so that the needed materials ar- rive “just-in-time” for their use.
    [Show full text]
  • A-Primer-On-Inventory-Theory.Pdf
    Chapter 25 Page 1 Inventory Theory Inventories are materials stored, waiting for processing, or experiencing processing. They are ubiquitous throughout all sectors of the economy. Observation of almost any company balance sheet, for example, reveals that a significant portion of its assets comprises inventories of raw materials, components and subassemblies within the production process, and finished goods. Most managers don't like inventories because they are like money placed in a drawer, assets tied up in investments that are not producing any return and, in fact, incurring a borrowing cost. They also incur costs for the care of the stored material and are subject to spoilage and obsolescence. In the last two decades their have been a spate of programs developed by industry, all aimed at reducing inventory levels and increasing efficiency on the shop floor. Some of the most popular are conwip, kanban, just-in-time manufacturing, lean manufacturing, and flexible manufacturing. Nevertheless, in spite of the bad features associated with inventories, they do have positive purposes. Raw material inventories provide a stable source of input required for production. A large inventory requires fewer replenishments and may reduce ordering costs because of economies of scale. In-process inventories reduce the impacts of the variability of the production rates in a plant and protect against failures in the processes. Final goods inventories provide for better customer service. The variety and easy availability of the product is an important marketing consideration. There are other kinds of inventories, including spare parts inventories for maintenance and excess capacity built into facilities to take advantage of the economies of scale of construction.
    [Show full text]
  • Algorithmic Approaches to Inventory Management Optimization
    processes Article Algorithmic Approaches to Inventory Management Optimization Hector D. Perez , Christian D. Hubbs , Can Li and Ignacio E. Grossmann * Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, PA 15123, USA; [email protected] (H.D.P.); [email protected] (C.D.H.); [email protected] (C.L.) * Correspondence: [email protected]; Tel.: +1-412-268-3642 Abstract: An inventory management problem is addressed for a make-to-order supply chain that has inventory holding and/or manufacturing locations at each node. The lead times between nodes and production capacity limits are heterogeneous across the network. This study focuses on a single product, a multi-period centralized system in which a retailer is subject to an uncertain stationary consumer demand at each time period. Two sales scenarios are considered for any unfulfilled demand: backlogging or lost sales. The daily inventory replenishment requests from immediate suppliers throughout the network are modeled and optimized using three different approaches: (1) deterministic linear programming, (2) multi-stage stochastic linear programming, and (3) reinforcement learning. The performance of the three methods is compared and contrasted in terms of profit (reward), service level, and inventory profiles throughout the supply chain. The proposed optimization strategies are tested in a stochastic simulation environment that was built upon the open-source OR-Gym Python package. The results indicate that, of the three approaches, stochastic modeling yields the largest increase in profit, whereas reinforcement learning creates more balanced inventory policies that would potentially respond well to network disruptions. Furthermore, deterministic models perform well in determining dynamic reorder policies that are comparable to reinforcement learning in terms of their profitability.
    [Show full text]
  • Effect of Inventory on Supply Chain
    Effect of Inventory on Supply Chain Objectives of the study • When the firms improve their inventory management, what is the reaction of other Factors on supply chain? • How to improve the inventory from cost effective perspective? Method of Data Collection In order to meet the objectives of the study data will be collected from secondary sources. So the financial reports of a manufacturing company will be obtained. Review of literature The Effect of Inventory on Supply Chain Abstract: Supply chain management addresses the management of materials and information across the entire chain from suppliers to producers, distributors, retailers, and customers. In the past few decades, scholars gave ample attention about the impact of inventory on Supply Chain Management (SCM). Roughly speaking, research on supply chain management has been mainly focused on three major issues. One is the behavior of information flow; The second issue deals with inventory management; The third issue is orientated to planning and operations management. In this paper the second issue, namely inventory management will be discussed. The author will follow the phases of classifying inventory; Identify cost factors; Assess cost components; Calculate EOQ; Giving suggestion and effect of inventory on supply chain will be discussed. The result is going to become clear under the analysis of two alternatives by using MCDM (Multiple Criteria Decision Making) method. The conclusion is when optimizing the inventory management; both upstream and downstream activities will run effectively. The Impact of Inventory and Flow Planning Parameters on Supply Chain Performance: An Exploratory Study Abstract: The primary objective of this paper is to study the impact of selected inventory parameters and management techniques on the performance of an expanded and comprehensive retail supply chain.
    [Show full text]
  • Inventory Control
    Dieter Bartmann Martin J. Beckmann Inventory Control Models and Methods Springer-Verlag Berlin Heidelberg New York London Paris Tokyo Hong Kong Barcelona Budapest Authors Prof. Dr. Dieter Bart mann Institute of Information Management University of St. Gallen for Business Administration. Economics, Law and Social Sciences Dufourstr. 50, CH-9000 St. Gallen Prof. Dr. Martin J. Beckmann Institute of Applied Mathematics and Statistics Technical University of Munich Arcisstr. 21. W-8000 München 2, FRG [Uni« .-üiölioihek I ISBN 3-540-55820-9 Springer-Verlag Berlin Heidelberg New York ISBN 0-387-55820-9 Springer-Verlag New York Berlin Heidelberg This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, re-use of illustrations, recitation, broadcasting, reproduction on microfilms or in any other way. and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9. 1965, in its current version, and permission for use must always be obtained from Springer-Verlag. Violations are liable for prosecution under the German Copyright Law. © Springer-Verlag Berlin Heidelberg 1992 Printed in Germany Typesetting: Camera ready by author/editor 42/3140-543210 - Printed on acid-free paper FOREWORD Inventory control is a major field in OR. Interest in the problems of optimal stock management at a scientific level goes back to the start of the 20th century. The most important impulse, however, came after the 2nd World War when scientists of the caliber of Jacob, Marschak, Kenneth Arrow, Samuel Karlin among others looked into the problem of optimal stocking under stochastic demand.
    [Show full text]
  • OPERATIONS RESEARCH Inventory Theory
    OPERATIONS RESEARCH Chapter 5 Inventory Theory Prof. Bibhas C. Giri Department of Mathematics Jadavpur University Kolkata, India Email: [email protected] MODULE - 1: Economic Order Quantity and EOQ Models without Shortage 1.1 Introduction The word ‘inventory’ means simply a stock of idle resources of any kind having an economic value. In other words, inventory means a physical stock of goods, which is kept in hand for smooth and efficient running of future affairs of an organization. It may consist of raw materials, work-in-progress, spare parts/consumables, finished goods, human resources such as unutilized labor, financial resources such as working capital, etc. It is not necessary that an organization has all these inventory classes but whatever may be the inventory items, they need efficient management as generally a substantial amount of money is invested in them. The basic inventory decisions in- clude: 1) How much to order? 2) When to order? 3) How much safety stock should be kept? The problems faced by different organizations have necessitated the use of scientific techniques in the management of inventories known as inventory control. Inventory control is concerned with the acquisition, storage, and handling of inventories so that the inventory is available whenever needed and the associated total cost is minimized. 1.2 Reasons for Carrying Inventory Inventories are carried by organisations because of the following major reasons : 1. Improve customer service- An inventory policy is designed to respond to indi- vidual customer’s or organization’s request for products and services. 2 2. Reduce costs- Inventory holding or carrying costs are the expenses that are in- curred for storage of items.
    [Show full text]
  • Inventory Models - Waldmann K.-H
    OPTIMIZATION AND OPERATIONS RESEARCH – Vol. IV - Inventory Models - Waldmann K.-H INVENTORY MODELS Waldmann K.-H. Universität Karlsruhe, Germany Keywords: inventory control, periodic review, continuous review, economic order quantity, (s, S) policy, multi-level inventory systems, Markov decision processes Contents 1. Introduction 2. The Basic EOQ Model 3. The Dynamic Economic Lotsize Model 4. Periodic Review Stochastic Demand Models 4.1. The Single-Period Model 4.2. The Finite Horizon Model 4.3. The Infinite Horizon Model 4.4. Generalized (s, S) Policies 4.5. Multi-Level Systems 5. Continuous Review Stochastic Demand Models 5.1. Poisson Demand 5.2. A Two-Level System 5.3. Extensions Glossary Bibliography Biographical Sketch Summary This article offers an introduction to the basic lines of research in inventory management: economic order quantity (EOQ) type models, dynamic economic lotsize models, periodic review stochastic demand models, and continuous review stochastic demand models. 1. Introduction UNESCO – EOLSS Inventory theory deals with the management of stock levels of goods with the aim of ensuring that demand for these goods is met. Most models are designed to address two fundamental decisionSAMPLE issues: when a replenishm CHAPTERSent order should be placed, and what the order quantity should be. Their complexity depends heavily on the assumptions made about demand, the cost structure and physical characteristics of the system. Inventory control problems in the real world usually involve multiple products. For example, spare parts systems require management of hundreds or thousands of different items. It is often possible, however, for single-product models to capture all essential elements of the problem, so it is not necessary to include the interaction of different items into the formulation explicitly.
    [Show full text]
  • Inventory Management: Information, Coordination and Rationality1
    Chapter 14 Inventory Management: Information, Coordination and Rationality1 Ozalp¨ Ozer¨ Management Science and Engineering Stanford University Stanford, CA 94305 [email protected] Abstract The success of a product in today’s global marketplace depends on capabilities of firms in the product’s supply chain. Among these capabilities, effective inventory management is a capability necessary to lead in the global marketplace. The chapter provides a discussion of four funda- mentals of effective inventory management. First, it requires managers to know how best to use available information. Second, managers need to quantify the value of information. Third, they need to coordinate decentralized inventory operations. Finally, effective inventory management requires decision tools that can be embraced by their users. The chapter’s emphasis is on the use of information, and the role of new information technologies in inventory management. Previous research on inventory management played an important role in the advancement and develop- ment of new technologies and processes. Today more research is needed because new technologies (such as RFID Radio-Frequency Identification) and new management methods (such as collab- orative forecasting and planning) are emerging and evolving faster than ever before. Inventory management and research will continue to play a central role in the success of a product and the firms in its supply chain. The chapter brings together separate but inherently related streams of research in inventory management. By doing so, we highlight potential research opportunities that lie on the boundaries. 1This manuscript will appear in the Handbook of Production Planning. (Eds) K. Kempf, P. Keskinocak and R. Uzsoy. 1 1 Introduction Inventory control problems have attracted researchers for many years2.
    [Show full text]
  • A Dynamic Lot Size Model for Clinical Trial Supply Chains
    Planning for Demand Failure: A Dynamic Lot Size Model for Clinical Trial Supply Chains Adam J. Fleischhackera, Yao Zhaob aDepartment of Business Administration, University of Delaware, Newark, DE 19716, USA bDepartment of Supply Chain Management & Marketing Sciences, Rutgers Business School { Newark and New Brunswick, 1 Washington Park, Newark, NJ 07102, USA This paper examines the optimal production lot size decisions for clinical trial supply chains. One unique aspect of clinical trial supply chains is the risk of failure, meaning that the investigational drug is proven unsafe or ineffective during human testing and the trial is halted. Upon failure, any unused inventory is essentially wasted and needs to be destroyed. To avoid waste, manufacturers could produce small lot sizes. However, high production setup costs lead manufacturers to opt for large lot sizes and few setups. To optimally balance this tradeoff of waste and destruction versus production inefficiency, this paper generalizes the Wagner-Whitin model (W-W model) to incorporate the risk of failure. We show that this stochastic model, referred to as the failure-risk model, is equivalent to the deterministic W-W model if one adjusts the cost parameters properly to reflect failure and destruction costs. We find that increasing failure rates lead to reduced lot sizes and that properly incorporating the risk of failure into clinical trial drug production can lead to substantial cost savings as compared to the W-W model without the properly adjusted parameters. 1. Introduction For every new drug that reaches a pharmacy's shelf, roughly 5,000 to 10,000 other po- tential medicines have failed to achieve commercialization (Pharmaceutical Research and Manufacturers of America 2009).
    [Show full text]
  • A Single Item Lot Sizing with Backorder and a Finite Replenishment Rate in MRP
    A Single Item Lot Sizing with Backorder and a Finite Replenishment Rate in MRP Liang Liang Yugang Yu School of Business, University of Science and Technology of China, Hefei Anhui 230026, China [email protected], [email protected] Hongying Wan Graduate School, University of Science and Technology of China, Hefei Anhui 230026, China Abstract the two basic hypotheses: fixed cost and infinite time horizon. In recent thirty years, many researches expanded There are the following characteristics in decision the application of economic order quantity equation by on lot size in material requirements planning (MRP) exceeding one or both of the two basic hypotheses. In systems: multiple time periods, a finite time horizon, 1972, Schwarz [17] suggested a model of EOQ with fixed discrete demand, and time-varying costs etc. In MRP cost and finite demand horizon. In 1967, 1982, 1985, B. system there are several different types of lot size Lev, Taylor [19] etc. presented respectively the EOQ techniques, such as the economic order quantity (EOQ), inventory model with one cost change and two time lot-for-lot, periodic order quantity, Wagner-Whitin horizons. B.Lev [11] concluded the results of former algorithm, Silver-Meal algorithm and part-period researches systematically and built a fluctuant EOQ algorithm. Although these lot size approaches focus on model with one cost change (increase or decrease) and controlling the cost of holding cost and order cost, none two time horizons (one finite time horizon adding one of them, with the exception of the Wagner-Whitin finite or infinite time horizon) in 1989.
    [Show full text]