Advanced Planning in Fresh Food Industries Contributions to Management Science R Flavell (Ed.) Modelling Reality and Personal Modelling 1993 ISBN 3-7908-0682-X W Bỗhler/H Hax/R Schmidt (Eds.) Empirical Research on the German Capital Market 1999 ISBN 3-7908-1193-9 M Hofmann/M List (Eds.) Psychoanalysis and Management 1994 ISBN 3-7908-0795-8 M Bonilla/T Casasus/R Sala (Eds.) Financial Modelling 2000 ISBN 3-7908-1282-X R L D'Ecclesia/S A Zenios (Eds.) Operations Research Models in Quantitative Finance 1994 ISBN 3-7908-0803-2 S Sulzmaier Consumer-Oriented Business Design 2001 ISBN 3-7908-1366-4 M S Catalani/G F Clerico Decision Making Structures 1996 ISBN 3-7908-0895-4 C Zopounidis (Ed.) New Trends in Banking Management 2002 ISBN 3-7908-1488-1 M Bertocchi/E Cavalli/S Komlếsi (Eds.) Modelling Techniques for Financial Markets and Bank Management 1996 ISBN 3-7908-0928-4 U Dorndorf Project Scheduling with Time Windows 2002 ISBN 3-7908-1516-0 H Herbst Business Rule-Oriented Conceptual Modeling 1997 ISBN 3-7908-1004-5 B Rapp/P Jackson (Eds.) Organisation and Work Beyond 2000 2003 ISBN 3-7908-1528-4 C Zopounidis (Ed.) New Operational Approaches for Financial Modelling 1997 ISBN 3-7908-1043-6 M Grossmann Entrepreneurship in Biotechnology 2003 ISBN 3-7908-0033-3 K Zwerina Discrete Choice Experiments in Marketing 1997 ISBN 3-7908-1045-2 G Marseguerra Corporate Financial Decisions and Market Value 1998 ISBN 3-7908-1047-9 WHU Koblenz Otto Beisheim Graduate School of Management (Ed.) Structure and Dynamics of the German Mittelstand 1999 ISBN 3-7908-1165-3 H M Arnold Technology Shocks 2003 ISBN 3-7908-0051-1 T Ihde Dynamic Alliance Auctions 2004 ISBN 3-7908-0098-8 J Windsperger/G Cliquet/ G Hendrikse/M Tuunanen (Eds.) Economics and Management of Franchising Networks 2004 ISBN 3-7908-0202-6 K Jennewein Intellectual Property Management 2004 ISBN 3-7908-0280-8 A Scholl Balancing and Sequencing of Assembly Lines 1999 ISBN 3-7908-1180-7 M J Thannhuber The Intelligent Enterprise 2005 ISBN 3-7908-1555-1 E Canestrelli (Ed.) Current Topics in Quantitative Finance 1999 ISBN 3-7908-1231-5 C Clarke Automotive Production Systems and Standardisation 2005 ISBN 3-7908-1578-0 Matthias Lỗtke Entrup Advanced Planning in Fresh Food Industries Integrating Shelf Life into Production Planning With 63 Figures and 31 Tables Physica-Verlag A Springer Company Series Editors Werner A Mỗller Martina Bihn Author Matthias Lỗtke Entrup Linienstraỷe 71 10119 Berlin mlutke@hotmail.com Diss., TU Berlin, D83 ISSN 1431-1941 ISBN-10 3-7908-1592-6 Physica-Verlag Heidelberg New York ISBN-13 978-3-7908-1592-4 Physica-Verlag Heidelberg New York Cataloging-in-Publication Data applied for Library of Congress Control Number: 2005927952 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, reuse of illustrations, recitation, broadcasting, reproduction on microfilm 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 Physica-Verlag Violations are liable for prosecution under the German Copyright Law Physica-Verlag is a part of Springer Science+Business Media springeronline.com Physica-Verlag Heidelberg 2005 Printed in Germany The use of general descriptive names, registered names, trademarks, etc in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use Softcover design: Erich Kirchner, Heidelberg SPIN 11493334 88/3153-5 Printed on acid-free and non-aging paper Foreword During the last three decades trade, industry and also academia became heavily involved in the development primarily intended towards more effective planning and control of logistical operations in supply chains Lately, these approaches began to be directed towards fresh food supply chains Competitive fresh food supply chains require that the aspects costs, quality, food safety and technology be taken into account simultaneously in a multidisciplinary way In recent years the issue of food safety got large emphasis in government, industry and society (consumers) The introduction of the General Food Law by the EU from January 2005 on even put more emphasis on the issue of food safety It turns out that Advanced Planning and Scheduling Systems (APS) can play an important and integrative role in supporting decision making activities in fresh food supply chains by considering shelf life as an instrument to generate more added value and food safety Basically the work of Matthias Lỹtke Entrup is concentrated on two research questions: x x Which requirements must APS systems cover in order to efficiently and effectively support production planning in fresh food industries? How can shelf life be integrated into production planning? How can production planning contribute to optimizing shelf life output? In his study the author shows how these questions should be answered adequately His results and conclusions are of paramount importance for integrating the issue of shelf life into production planning The study provides a wealth of insights and results which are significant both from a practical as well as from an academic point of view The research starts with an overview of current APS systems and highlights the need of a new generation of planning software which aims at supporting decision making in supply chain management Although APS gain increasing acceptance in industry, a number of issues remain, in particular at the detailed planning and scheduling level, which are not satisfactorily covered by the decision models to be found in the standard APS software packages This is truly the case for the fresh food industries Undoubtedly, the most important planning issue regarding fresh food lies in the consideration of shelf-life So far, vendors of APS systems have taken many efforts to consider shelf-life issues in their planning systems, however, without covering all of the characteristics being important in Fresh Food Supply Chains (FFSCs) and fresh food production systems One of the main contributions of the study by Matthias Lỹtke Entrup is a comprehensive analysis of the planning requirements of fresh food industries on one hand and the decision support offered VI Foreword by typical APS systems on the other Software packages from leading players in the market are assessed looking at the scope of shelf life integration and its capabilities to generate plans that optimize shelf life output Based on the shortcomings of current APS systems, new quantitative planning models are developed and resolved These models consider shelf life planning problems in specific fresh food industries (yogurt production, sausage production and poultry processing) The models are based on the general block planning principle and are adapted to the needs of the specific fresh food planning applications Considerable care has been taken to obtain compact model formulations which can be solved very efficiently by use of standard optimization software Numerical experiments demonstrate the applicability of the planning models in realistic industrial settings As a result, the author makes clear that suppliers of APS software are currently unable to offer APS systems in which the integration of shelf life into production planning has been dealt with adequately Specifically, product freshness has been modeled by the author as part of the optimization and not as a constraint within the planning function This is indeed a new and creative contribution of Matthias Lỹtke Entrup to solving complex planning problems of considerable practical relevance The applications (case studies) have been selected carefully by the author in such a way that many other application fields in fresh food industries could benefit from his results Prof Dr Paul van Beek Prof Dr Hans-Otto Gỹnther Acknowledgement This research could not have been written without the support of many people Therefore, I would like to thank a number of them for their support and contributions, knowing that the list is, of course, incomplete First of all, I am indebted to my academic advisors Professor Dr Hans-Otto Gỹnther of the Chair of Production Management at the Technical University of Berlin and Professor Dr Paul van Beek of the Operational Research and Logistics Group at the Wageningen University (NL) Professor Dr Hans-Otto Gỹnther woke my interest in the field of Production Management and helped me to transform my ideas into a full research project Similarly, I am thankful to Professor Dr Paul van Beek for his supervision of the work and his critical comments Working with both of them was a pleasure, they have always been accessible and created a stimulating research environment Additionally, I thank Professor Dr Kasperzak for assuming the chairmanship of the promotion committee I would also like to thank the entire team of the Chair of Production Management consisting of Hanni Just, Dr Martin Grunow, Matthias Lehmann, Ulf Neuhaus, Martin Schleusener, and Onur Yilmaz for their helpfulness and the fruitful discussions Their comments proved to be very useful and resulted in several improvements In addition, I am grateful to Thorben Seiler and Shuo Zhang for their support regarding the development and implementation of the models Furthermore, I thank my employer A.T Kearney for the possibility to conduct this research and the continual support In particular, I highly appreciate the contributions of Dr Antje Vửlker, Jan van der Oord and Ferdinand Salehi as well as of Dr Peter Pfeiffer and all other colleagues of the Consumer Industries and Retail Practice Dr Marianne Denk-Helmold and Judith Siefers deserve a special thanks for carefully reading and correcting the manuscript The last words are dedicated to my family I thank my parents for their encouragement and their trust in me during all the years Finally, I thank Kathrin for her backing and her care She made me realize that there are other things in life than yogurt, sausages and poultry May 2005 M Lỹtke Entrup Table of Contents Foreword V Acknowledgement VII Abbreviations XIII Introduction 1.1 Introduction to the Field of Research 1.2 Research Objectives 1.3 Dissertation Outline 1.4 Conclusion Advanced Planning and Scheduling Systems 2.1 Evolutionary Path of APS Systems 2.1.1 MRP I and MRP II 2.1.2 Assessment of the MRP Planning Concepts 2.1.3 Emergence of APS Systems 2.2 Structure of APS Systems 12 2.2.1 Overview 12 2.2.2 Strategic Network Design 14 2.2.3 Demand Planning 15 2.2.4 Supply Network Planning 17 2.2.5 Production Planning 18 2.2.6 Production Scheduling 19 2.2.7 Distribution Planning 20 2.2.8 Transport Planning 21 2.2.9 Available-to-Promise 21 2.3 APS Systems Market Overview 23 2.3.1 Available Market Studies 23 2.3.2 Market Size and Segments 24 2.3.3 Major Providers 25 2.3.4 Expectations for the Future 27 2.4 Implementation of APS Systems 27 2.4.1 Implementation Process Overview 27 2.4.2 Project Definition 28 2.4.3 Vendor Selection 30 2.4.4 Implementation 31 X Table of Contents 2.4.5 Implementation Risks 32 2.5 Assessment of APS Implementations 33 2.5.1 Benefits 33 2.5.2 Development Needs 34 2.6 Conclusion 35 Fresh Food Industries 37 3.1 Introduction 37 3.2 Definition and Segments 37 3.3 Characteristics of Fresh Food Supply Chains 38 3.3.1 Structures of Fresh Food Supply Chains 38 3.3.2 Economic Characteristics and Developments 41 3.3.3 Technological Characteristics and Developments 47 3.3.4 Social/Legal Characteristics and Developments 50 3.3.5 Environmental Characteristics and Developments 53 3.3.6 Summary 57 3.4 Characteristics of Fresh Food Production Systems 58 3.4.1 Overview 58 3.4.2 Formulation 59 3.4.3 Processing 60 3.4.4 Packaging 61 3.4.5 Storage and Delivery 62 3.4.6 Summary 63 3.5 Case Study 1: Yogurt Production 64 3.5.1 Market Segments and Case Study Overview 64 3.5.2 Raw Milk Collection 67 3.5.3 Raw Milk Preparation 69 3.5.4 Fermentation 70 3.5.5 Flavoring and Packaging 71 3.5.6 Storage and Delivery 72 3.6 Case Study 2: Sausage Production 72 3.6.1 Market Segments and Case Study Overview 72 3.6.2 Input of Ingredients 75 3.6.3 Grinding and Mixing 76 3.6.4 Chopping and Emulsifying 76 3.6.5 Stuffing and Tying 76 3.6.6 Scalding 77 3.6.7 Maturing and Intermediate Storage 78 3.6.8 Slicing and Packaging 78 3.6.9 Storage and Delivery 79 3.7 Case Study 3: Poultry Processing 80 3.7.1 Market Segments and Case Study Overview 80 3.7.2 Transport of Animals 82 3.7.3 Stunning and Bleeding 83 3.7.4 Scalding and Eviscerating 84 3.7.5 Chilling 84 'D\S /LPLWRISHULRG 'D\G G SGD\ 'HPDQG GHMG Fig 7.5 Variables of the MDB model for yogurt production Indices j,k J l L s S p P S dD S rR j J(r) l LR(r) l LJ(j) d D(s) s S(d) products lines days production days demand days recipes, blocks products based on recipe r lines that can process recipe r lines that can process product j demand days (to meet the demand on these days, lots produced on day s can be considered) production days (the lots produced on these days can be considered to meet the demand of demand day d) Parameters varcj slj B1, B2 capl sterl cll benj variable costs for the production of one unit of product j maximum shelf life of product j in days sufficiently large numbers capacity of line l, in units per day sterilization time of line l cleaning time of line l maximum additional benefit when meeting the maximum shelf life of product j, in per kg 154 Shelf Life Integration in Yogurt Production lossr loss of fermented plain yogurt of recipe r when cleaning the line, in kg costs for the cleaning loss of plain yogurt of recipe r, in per kg demand of product j on demand day d inventory of product j, produced on day s costs of utilization of line l, in per day minimum batch size to be processed, in kg packaging size of product j, in kg per unit fermentation time for recipe r, in hours fermentation capacity, in kg-hours per day minimum shelf life of product j required by the customer (as a fraction of maximum shelf life, applied to multiply the shelf life of product j) maturation time of product j overtime supplement for weekend production on line l, in per day percentage of plain yogurt contained in one unit of product j start of the first production day within the week (Sunday) start of the last production day within the week (Saturday) clossr dejd sjs cl mb psj ftr fc crj qj osl adj fdp ldp Decision Variables Srpl =1, if recipe r is set-up on production day p on line l (0, otherwise) units of product j produced on line l on production day p units of product j produced on production day s that is used to meet the demand of demand day d duration of recipe/block r on production day p on line l end time of recipe/block r on production day p on line l start time of line l end time of line l overtime at the end of scheduling horizon (Saturday) on line l overtime at the beginning of the scheduling horizon (Sunday) on line l Xjpl Zjds Lrpl ENDrpl ESTl LFTl SAOl SUOl Objective Function max Ư Ư Ư j J d D s S ( d ) ƯƯ ƯX j J pP lLJ ( j ) 1  cr sl  d  s 1  cr sl  Ư LFT  EST c Z jds ben j jpl j j varc j l j l l l L  Ư SAOl  SUOl osl  ƯƯ l L j ƯS pP r R l LR ( r ) rpl lossr clossr (7.1) 7.2 Model Formulations 155 6KHOI/LIH'HSHQGHQW 3ULFLQJ&RPSRQHQW EHQM ben j 1  cr sl  d  s 1  cr sl j j j j FUM VOM VOMGV VOM 6KHOI/LIH Fig 7.6 Shelf life and shelf life dependent pricing component The objective function aims at maximizing the contribution margin It contains the profit from the shelf life depending pricing component (benj) This benefit increases linearly between the minimum customer requirement on shelf life (crj) and the maximum possible shelf life (slj) since the benefits for the retailer increase with every additional day of residual shelf life (see Fig 7.6) As an example, supposing a product j with a total shelf life of 30 days (slj = 30) and a customer requirement on the minimum residual shelf life when being delivered of 66% of the total shelf life (crj = 0.66) Supposing further that the shelf life of the product starts on day (s = 6), the product is delivered to the retailer on day 10 (d = 10) and the maximum benefit for meeting the maximum shelf life of product j, benj is 0.30 per kg In this case, the manufacturer yields a financial benefit of 0.18 per kg of product j (60% of the maximum benefit) This financial benefit can be justified by several factors On the one hand, the manufacturer will probably yield a higher turnover as consumers tend to buy the product with a longer remaining shelf life (see Chapter 5.3.2) On the other hand, the retailer will yield financial benefits as well (e.g less write-offs due to stock obsolescence or less stock-outs) and parts of the benefits are transferred to the manufacturer by means of the shelf life dependent pricing component in order to cover its higher production costs Costs in the objective function include the variable costs for the input factors (varcj), the costs of the utilization of the packaging lines in regular (cl) and overtime (osl) mode and the set-up costs for cleaning losses of plain yogurt (lossr * clossr) Constraints to be considered are the following [...]... the food industry in general (e.g Wagner and Meyr 2002), however no author looks specifically at the requirements of fresh food industries Research question 2: How can shelf life be integrated into production planning? How can production planning contribute to optimizing shelf life output? The outcomes of the second part of the thesis are Mixed Integer Linear Programming (MILP) models that integrate shelf. .. by integrating several production sites, distribution centers, suppliers and customers into one planning model However, implementation numbers of APS systems in fresh food industries remain rather low, because many important requirements of these industries are not yet sufficiently covered One of the most distinctive factors to consider in fresh food production planning is the limited shelf life of the... Network Planning 100 4.6 Requirements for Purchasing & Materials Requirements Planning 101 4.7 Requirements for Production Planning and Production Scheduling 103 4.8 Requirements for Distribution Planning 109 4.9 Requirements for Transport Planning 111 4.10 Requirements for Demand Fulfilment and Available-to-Promise 114 4.11 Conclusion 116 5 Shelf Life in Fresh Food Industries. .. by means of MILP models how shelf life can be integrated into the production planning of three sample industries These models will allow fresh food producers to optimize product freshness with respect to specific products and customers 2 Advanced Planning and Scheduling Systems 2.1 Evolutionary Path of APS Systems 2.1.1 MRP I and MRP II The production planning and scheduling processes that have been... planning in fresh food industries? The scientific outcome of the first part of the thesis is a profile of three sample fresh food industries (yogurt, sausages and fresh poultry) with regard to APS systems These three case study industries cover the most important fresh food segments (dairy, processed and fresh meat) In addition, within each of the case study industries, the product with the most challenging... raw materials, intermediate and final products, fluctuating prices, or variable processing times and yields, production planning in fresh food industries is generally a challenging task In this environment, Advanced Planning and Scheduling (APS) systems can constitute significant means of support for the planner Driven by developments in Supply Chain Management (SCM) and Information Technology (IT), APS... shelf life into production planning and the solution of those models The models are built around the case studies from the three sample fresh food industries and will support providers of APS systems to develop tools that integrate shelf life With respect to literature, only very few authors integrated the shelf life of the products into their models The main contributions are concerned with inventory... Fresh Poultry 123 5.3 Shelf Life in Fresh Food Supply Chain Management 125 5.3.1 Literature Review 125 5.3.2 Role of Shelf Life in Fresh Food Supply Chains 127 5.4 Conclusion 128 6 Shelf Life Integration in APS-Systems 131 6.1 Introduction 131 6.2 SAP APO 131 6.2.1 System Overview 131 6.2.2 Shelf Life Integration 134... solve production planning problems has increased by six orders of magnitude since 1987, which is related to an increase by three orders of magnitude of both the algorithmic and the machine speed Advanced Planning and Scheduling systems aim in particular at supporting decision-making in SCM Some authors use the abbreviation “APS” for Advanced Planning Systems”; however, in this research APS refers to Advanced. .. customer demand All industries including process industries Planning: Demand, Manufacturing, Logistics, Supply Chain Bi-directional High High Flexible Available High Memory-resistant 12 2 Advanced Planning and Scheduling Systems The objective of production planning shifted from generating feasible plans to plans that are subject to company-specific optimization criteria Therefore, all planning parameters