DOCSLIB.ORG

A Single Item Lot Sizing with Backorder and a Finite Replenishment Rate in MRP

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read 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. In 1990, B.Lev algorithm, assures an optimal or minimum cost solution and Zhang Jian [22] discussed the problem of EOQ for time-varying demand patterns and copes with inventory problem with the permission of two cost quantity discount. And Zangwill(1966), Blackburn and changes and three equal finite time horizons. And in 1997 Kunreuther (1974) et al extended the Wagner-Whitin Zhang Jian [23] discussed the same problems with algorithm by following demand to go unsatisfied during multiple time periods and multiple cost changes. Some of some period, provided it is satisfied eventually by these papers also discussed the backorder inventory production in some subsequent period. R. M. Hill (1997), problem in certain degree, and had important effect on Stanislaw Bylka, Ryszarda Rempala (2001) give the development of inventory theory. But some of the dynamic programming formulation to decide lot sizing assumptions such as continuous demand made in regard for a finite rate input process. But the Wagner-Whitin to classical inventory models (economic order quantity algorithm and its extensions commonly are criticized as (EOQ), economic production quantity (EPQ), and being difficult to explain and compute because the economic order interval (EOI)) are inappropriate for algorithms are complicated dynamic programming demand that varies from period to period. In this direction, algorithms. In this paper, we propose a series of the EOQ used in MRP ordering is to ignore the variation inventory models in which backorder and a finite and apply the EOQ formulation with an average demand replenishment rate are considered according to the rate. The indiscriminate use of these methods can result characteristics in MRP ordering and the optimal in larger than necessary inventory costs for these solutions can be obtained by using general-purpose conditions [15]. linear program solver, like EXCEL, LINDO, etc. The other research direction began with a different approach provided by Manne [14] and by Wagner and 1. Introduction Whitin [21] in 1958; they divided time into discrete periods and assumed that the demand in each period is It is important to decide the order quantities in the known in advance. Since 1958, the Manne-Wagner MRP system. MRP ordering has the features such as -Whitin model has received considerable attention, and multiple time periods, finite time horizon, time-varying several hundred papers have directly or indirectly costs according to the time periods, and discrete demand, discussed this model; most of these papers have either etc. There are two research directions to this practical extended this model or provided efficient algorithms for problem: one is the expansion of the classic inventory production problems that arise in it. The references given theory, such as EOQ model; the other gives models or here and those given by Bahl, Ritzman and Gupta [1] algorithms directly from the characteristics of the MRP provide only some of the papers related to the system. Manne-Wagner-Whitin model. Today, even an The first research direction focus on how to exceed introductory operations research textbook is likely to include a chapter on the Manne-Wagner -Whitin model production capacity or supply capacity from vendors and and on some of its extensions (See, for example, Johnson any interrupt between the replenishment incur setup cost. and Montgomery 1974 [10], Wagner 1975 [20], Denardo The order quantity may not satisfy the demand in 1982 [4], Richard J. Terine. 1988 [15], and Hax and time, and the shortages or stockouts are considered. But Candea 1984 [8].). Because of the immense interest in stockout in the end of the time horizon is prohibited. economic lot size models, a considerable amount of Items requirements in a period are withdrawn from research effort has been focused on establishing the inventory at the beginning of the period. Thus, the computational complexity of various economic lot size holding cost is applied to the end-of-period inventory and problems. (In particular, see Florian, Lenstra and is only applied to inventory held from one period to the Rinnooy Kan 1980 [6], Bitran and Yanasse 1982 [2], next. Items consumed during a period incur no holding Luss 1982 [13], Erickson, Monma and Veinott 1987 [5], cost. and Chung and Lin 1988 [3].). As the extensions of MRP is a rolling schedule and projected on hand Wagner-Whitin algorithm, Zangwill(1966), Blackburn being not zero is considered. and Kunreuther (1974) et al extended the Wagner-Whitin Suppose that there are I periods in MRP system, we algorithm by allowing demand to go unsatisfied during introduce the following problem parameters: some period, provided it is satisfied eventually by i={1,…,I},the index set of periods, production in some subsequent period [18]. R. M. Hill Ri = requirements in units for period i, (1997) [16], Stanislaw Bylka, Ryszarda Rempala (2001) OCi = fixed ordering cost or setup cost per order for [21] give dynamic programming formulation to decide lot period i, sizing for a finite rate input process. But the HCi = holding cost per unit for period i, Wagner-Whitin algorithm and it extensions commonly BCi =backorder cost of an item for period i, are criticized as being difficult to explain and compute S0=stock on hand for period 1 start or for period 0 because the algorithms are complicated dynamic end, programming algorithms. And many other non-optimal M=a sufficiently large number. algorithms, such as lot-for-lot, periodic order quantity, Pi = replenishment quantity if the replenishment for Silver-Meal algorithm and part-period algorithm occurred the whole period i is continued. [15, p161-178]. Decision Variables In this paper, we proposed the model that can obtain Xi = whether start to order or not (0-1 variables) for the economic lot size in MRP ordering systems with the ith period, backorder and a finite replenishment rate. Then, we Yi = whether to replenish or not (0-1 variables) for transfer the model into linear programming, and deduce the ith period, three other models, i.e., the model considering a finite Qi = lot size or replenish quantity for the ith period. time rate, the model considering backorder and the Zi = whether backorder or not (0-1 variables) for model in which backorder is prohibited and an infinite period i. replenishment rate. Finally, to illustrate the proposed The problem, denoted by RBP, is to solve: models we give a numerical example. Problem BDP: The models proposed in the paper permit order cost, Minimize TC= holding cost, backorder cost, price break points, and item prices varying from period to period. Comparing with Ii the Wagner-Whitin algorithm and it extensions, the ∑∑S+0 (QRjj−⋅−⋅−⋅ )[] (1 ZHCZBC i ) ii i+ ij==11 models are easier to explain and compute because the models are linearized or linear models and the optimal I I solutions of them can be obtained easily by using ∑ XOCii⋅ + ∑ PQ⋅ i (1) general-purpose linear program solver, like EXCEL, i=1 i=1 LINDO, etc. Subject to I S0+ ∑()QRii− ≥ 0 (2) 2. Mathematical model of the backorder and i=1 finite replenishment rate i SQRMZ0 +−≤⋅−∑()(1)jj i i=1,2,…,I, (3) Assumption: j=1 The planning horizon is finite and composed of i SQRMZ() i=1,2,…,I. (4) several time periods. 0 +−≥−⋅∑ jj i j =1 The demand is known and occurs at the beginning of QPY i=1、2…I (5) each period, but may change from one period from iii≤⋅ another. X1=Y1 (6) Lead-time is fixed. 、 … Yi1−≤⋅YMXii− i=2 3 I (7) Each time period is characterized by finite Q(1)(1)≥−⋅−PM Y −⋅− M Y i=1、2…I-1 (8) replenishment rate, which may be caused by a fixed i1ii+ i In BDP, the objective function, i.e. equation (1) is to i minimize the total inventory cost which include holding HQijji−−≥−⋅S+0 ∑( Q R ) M Z i=1,2,…,I, (12) j =1 cost and backorder cost over I time periods, i.e. Ii i ∑∑S+0 (QRjj−⋅−⋅−⋅ )[] (1 ZHCZBC i ) ii i, ordering BQijji+−≤⋅−S+0 ∑( Q R ) M (1 Z ) i=1,2,…,I, (13) ij==11 j=1 I i cost, i.e., ∑ XOCii⋅ , and item purchase cost, i.e., i=1 BQijji+−≥⋅−S+0 ∑( Q R ) M ( Z 1) i=1,2,…,I, (14) j=1 I ∑ PQ⋅ i .
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]
  • Inventory Dynamics in the Financial Crisis: an Empirical Analysis of Firm Responsiveness and Its Effect on Financial Performance
    Inventory Dynamics in the Financial Crisis: An Empirical Analysis of Firm Responsiveness and its Effect on Financial Performance Kai Hoberg Maximiliano Udenio Jan C. Fransoo Beta Working Paper series 482 BETA publicatie WP 482 (working paper) ISBN ISSN NUR 804 Eindhoven August 2015 Working Paper - Under Revision Please do not cite without notifying authors Inventory Dynamics in the Financial Crisis: An Empirical Analysis of Firm Responsiveness and its Effect on Financial Performance Kai Hoberg Kühne Logistics University Großer Grasbrook 17 20457 Hamburg, Germany, [email protected] Maximiliano Udenio Eindhoven University of Technology Den Dolech 2, 5612AZ Eindhoven, The Netherlands, [email protected] Jan C. Fransoo Eindhoven University of Technology Den Dolech 2, 5612AZ Eindhoven, The Netherlands, [email protected] The collapse of Lehman Brothers in 2008 triggered the largest natural experiment to test firm’s inventory management capabilities to date. During the ensuing Financial Crisis, firms were suddenly confronted with an unprecedented collapse of demand. This critical situation, often with limited credit availability, put forward an extreme test of firms’s inventory responsiveness. Following the demand shock in September 2008, a number of firms managed their inventories responsively, maintaining relative inventory levels fairly constant. Others built up considerable surplus inventory, and others reacted aggressively cutting down stocks. In this paper, we use firm-level empirical data from 1,278 public U.S. manufacturing firms for the period 2005-2011 to understand the drivers behind inventory responsiveness and explore the link between said responsiveness and profitability, market performance, and financial health. On average, firms required six quarters to adapt to the situation.
    [Show full text]