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| demand forecasting in inventory management: Demand Forecasting and Inventory Control Colin Lewis, 2012-05-23 This practical book covers the forecasting- and inventory control methods used in commercial, retail and manufacturing companies. Colin Lewis explains the theory and practice of current demand forecasting methods, the links between forecasts produced as a result of analysing demand data and the various methods by which this information, together with cost information on stocked items, is used to establish the controlling parameters of the most commonly used inventory control systems. The demand forecasting section of the book concentrates on the family of short-term forecasting models based on the exponentially weighted average and its many variants and also a group of medium-term forecasting models based on a time series, curve fitting approach. The inventory control sections investigate the re-order level policy and re-order cycle policy and indicate how these two processes can be operated at minimum cost while offering a high level of customer service. |
| demand forecasting in inventory management: Service Parts Management Nezih Altay, Lewis A. Litteral, 2011-03-24 With the pressure of time-based competition increasing, and customers demanding faster service, availability of service parts becomes a critical component of manufacturing and servicing operations. Service Parts Management first focuses on intermittent demand forecasting and then on the management of service parts inventories. It guides researchers and practitioners in finding better management solutions to their problems and is both an excellent reference for key concepts and a leading resource for further research. Demand forecasting techniques are presented for parametric and nonparametric approaches, and multi echelon cases and inventory pooling are also considered. Inventory control is examined in the continuous and periodic review cases, while the following are all examined in the context of forecasting: • error measures, • distributional assumptions, and • decision trees. Service Parts Management provides the reader with an overview and a detailed treatment of the current state of the research available on the forecasting and inventory management of items with intermittent demand. It is a comprehensive review of service parts management and provides a starting point for researchers, postgraduate students, and anyone interested in forecasting or managing inventory. |
| demand forecasting in inventory management: Demand Forecasting for Inventory Control Nick T. Thomopoulos, 2014-12-04 This book describes the methods used to forecast the demands at inventory holding locations. The methods are proven, practical and doable for most applications, and pertain to demand patterns that are horizontal, trending, seasonal, promotion and multi-sku. The forecasting methods include regression, moving averages, discounting, smoothing, two-stage forecasts, dampening forecasts, advance demand forecasts, initial forecasts, all time forecasts, top-down, bottom-up, raw and integer forecasts, Also described are demand history, demand profile, forecast error, coefficient of variation, forecast sensitivity and filtering outliers. The book shows how the forecasts with the standard normal, partial normal and truncated normal distributions are used to generate the safety stock for the availability and the percent fill customer service methods. The material presents topics that people want and should know in the work place. The presentation is easy to read for students and practitioners; there is little need to delve into difficult mathematical relationships, and numerical examples are presented throughout to guide the reader on applications. Practitioners will be able to apply the methods learned to the systems in their locations, and the typical worker will want the book on their bookshelf for reference. The potential market is vast. It includes everyone in professional organizations like APICS, DSI and INFORMS; MBA graduates, people in industry, and students in management science, business and industrial engineering. |
| demand forecasting in inventory management: Service Parts Management Nezih Altay, Lewis A. Litteral, 2011-04-08 With the pressure of time-based competition increasing, and customers demanding faster service, availability of service parts becomes a critical component of manufacturing and servicing operations. Service Parts Management first focuses on intermittent demand forecasting and then on the management of service parts inventories. It guides researchers and practitioners in finding better management solutions to their problems and is both an excellent reference for key concepts and a leading resource for further research. Demand forecasting techniques are presented for parametric and nonparametric approaches, and multi echelon cases and inventory pooling are also considered. Inventory control is examined in the continuous and periodic review cases, while the following are all examined in the context of forecasting: • error measures, • distributional assumptions, and • decision trees. Service Parts Management provides the reader with an overview and a detailed treatment of the current state of the research available on the forecasting and inventory management of items with intermittent demand. It is a comprehensive review of service parts management and provides a starting point for researchers, postgraduate students, and anyone interested in forecasting or managing inventory. |
| demand forecasting in inventory management: Demand Forecasting for Inventory Control Nick T. Thomopoulos, 2014-12-31 |
| demand forecasting in inventory management: Intermittent Demand Forecasting John E. Boylan, Aris A. Syntetos, 2021-06-02 INTERMITTENT DEMAND FORECASTING The first text to focus on the methods and approaches of intermittent, rather than fast, demand forecasting Intermittent Demand Forecasting is for anyone who is interested in improving forecasts of intermittent demand products, and enhancing the management of inventories. Whether you are a practitioner, at the sharp end of demand planning, a software designer, a student, an academic teaching operational research or operations management courses, or a researcher in this field, we hope that the book will inspire you to rethink demand forecasting. If you do so, then you can contribute towards significant economic and environmental benefits. No prior knowledge of intermittent demand forecasting or inventory management is assumed in this book. The key formulae are accompanied by worked examples to show how they can be implemented in practice. For those wishing to understand the theory in more depth, technical notes are provided at the end of each chapter, as well as an extensive and up-to-date collection of references for further study. Software developments are reviewed, to give an appreciation of the current state of the art in commercial and open source software. “Intermittent demand forecasting may seem like a specialized area but actually is at the center of sustainability efforts to consume less and to waste less. Boylan and Syntetos have done a superb job in showing how improvements in inventory management are pivotal in achieving this. Their book covers both the theory and practice of intermittent demand forecasting and my prediction is that it will fast become the bible of the field.” —Spyros Makridakis, Professor, University of Nicosia, and Director, Institute for the Future and the Makridakis Open Forecasting Center (MOFC). “We have been able to support our clients by adopting many of the ideas discussed in this excellent book, and implementing them in our software. I am sure that these ideas will be equally helpful for other supply chain software vendors and for companies wanting to update and upgrade their capabilities in forecasting and inventory management.” —Suresh Acharya, VP, Research and Development, Blue Yonder. “As product variants proliferate and the pace of business quickens, more and more items have intermittent demand. Boylan and Syntetos have long been leaders in extending forecasting and inventory methods to accommodate this new reality. Their book gathers and clarifies decades of research in this area, and explains how practitioners can exploit this knowledge to make their operations more efficient and effective.” —Thomas R. Willemain, Professor Emeritus, Rensselaer Polytechnic Institute. |
| demand forecasting in inventory management: Retail Analytics Anna-Lena Sachs, 2014-12-10 This book addresses the challenging task of demand forecasting and inventory management in retailing. It analyzes how information from point-of-sale scanner systems can be used to improve inventory decisions, and develops a data-driven approach that integrates demand forecasting and inventory management for perishable products, while taking unobservable lost sales and substitution into account in out-of-stock situations. Using linear programming, a new inventory function that reflects the causal relationship between demand and external factors such as price and weather is proposed. The book subsequently demonstrates the benefits of this new approach in numerical studies that utilize real data collected at a large European retail chain. Furthermore, the book derives an optimal inventory policy for a multi-product setting in which the decision-maker faces an aggregated service level target, and analyzes whether the decision-maker is subject to behavioral biases based on real data for bakery products. |
| demand forecasting in inventory management: Inventory Analytics Roberto Rossi, 2021-05-24 Inventory Analytics provides a comprehensive and accessible introduction to the theory and practice of inventory control – a significant research area central to supply chain planning. The book outlines the foundations of inventory systems and surveys prescriptive analytics models for deterministic inventory control. It further discusses predictive analytics techniques for demand forecasting in inventory control and also examines prescriptive analytics models for stochastic inventory control. Inventory Analytics is the first book of its kind to adopt a practicable, Python-driven approach to illustrating theories and concepts via computational examples, with each model covered in the book accompanied by its Python code. Originating as a collection of self-contained lectures, Inventory Analytics will be an indispensable resource for practitioners, researchers, teachers, and students alike. |
| demand forecasting in inventory management: Demand Forecasting and Inventory Control Colin David Lewis, Institute of Operations Management, 1997 A practical guide to the forecasting and inventory control methods used in commercial, retail and manufacturing companies. Colin Lewis explains the theory and practice of demand forecasting methods, the links between forecasts produced as a result of analyzing demand data and the various methods by which this information, together with cost information on stocked items, is used to establish the controlling parameters of the most commonly-used inventory control systems. |
| demand forecasting in inventory management: Demand Management Best Practices Colleen Crum, George E. Palmatier, 2003-06-15 Effective demand management is becoming critical to acompany's profitability. Demand Management BestPractices: Process, Principles, and Collaborationprovides best practice solutions that will improveoverall business performance for supply chain partnersand all functions within a company impacted by the demandmanagement process. The ...... |
| demand forecasting in inventory management: Sales Forecasting Management John T. Mentzer, Mark A. Moon, 2004-11-23 Incorporating 25 years of sales forecasting management research with more than 400 companies, Sales Forecasting Management, Second Edition is the first text to truly integrate the theory and practice of sales forecasting management. This research includes the personal experiences of John T. Mentzer and Mark A. Moon in advising companies how to improve their sales forecasting management practices. Their program of research includes two major surveys of companies′ sales forecasting practices, a two-year, in-depth study of sales forecasting management practices of 20 major companies, and an ongoing study of how to apply the findings from the two-year study to conducting sales forecasting audits of additional companies. The book provides comprehensive coverage of the techniques and applications of sales forecasting analysis, combined with a managerial focus to give managers and users of the sales forecasting function a clear understanding of the forecasting needs of all business functions. New to This Edition: The author′s well-regarded Multicaster software system demo, previously available on cassette, has been updated and is now available for download from the authors′ Web site New insights on the critical area of qualitative forecasting are presented The results of additional surveys done since the publication of the first edition have been added The discussion of the four dimensions of forecasting management has been significantly enhanced Significant reorganization and updating has been done to strengthen and improve the material for the second edition. Sales Forecasting Management is an ideal text for graduate courses in sales forecasting management. Practitioners in marketing, sales, finance/accounting, production/purchasing, and logistics will also find this easy-to-understand volume essential. |
| demand forecasting in inventory management: Inventory Planning with Forecasting Expenditure Sanjay Sharma, 2022-03-06 In industrial or business cases, purchasing and procurement are significant functions. Usually, a procurement plan is prepared based on certain predictions of consumption patterns or demand. When this plan is implemented, the benefit is obtained corresponding to forecast accuracy. In the available literature, forecasting accuracy is frequently discussed. A need is established to link forecasting accuracy with forecasting expenditures. After an explicit inclusion of the forecasting expenditure, this book describes inventory planning for procurement and production. FEATURES Discusses forecasting expenditure in detail Provides an analysis of reduction and increase in forecasting expenditures Highlights advanced concepts that include procurement inventory, production planning, and priority planning in detail Examines an approach in relation to the inclusion of an explicit cost of forecasting Covers total cost formulation, modified total cost, relevant index, threshold value, and cost of forecasting in a comprehensive manner with the help of examples Inventory Planning with Forecasting Expenditure is useful for undergraduate and postgraduate students in engineering and management and has potential for elective and supplementary core courses. |
| demand forecasting in inventory management: Complex System Maintenance Handbook Khairy Ahmed Helmy Kobbacy, D. N. Prabhakar Murthy, 2008-04-18 This utterly comprehensive work is thought to be the first to integrate the literature on the physics of the failure of complex systems such as hospitals, banks and transport networks. It has chapters on particular aspects of maintenance written by internationally-renowned researchers and practitioners. This book will interest maintenance engineers and managers in industry as well as researchers and graduate students in maintenance, industrial engineering and applied mathematics. |
| demand forecasting in inventory management: Managing Supply Chain And Logistics: Competitive Strategy For A Sustainable Future Ling Li, 2014-07-18 Managing Supply Chain and Logistics: Competitive Strategy for a Sustainable Future explores practical ways of investing in a sustainable future through real-world cases which demonstrate various supply chain management strategies and tactics. By applying viable value creation strategies, operational models, decision-making techniques, and information technology, the author provides in-depth analyses of new initiatives such as collaborative planning, forecasting, and replenishment (CPFR); demonstrates competitive approaches to managing flows of material, information and fund in supply chain; and illustrates creative methods to apply data science and business intelligence. This book also promotes cross-functional decision-making, problem solving skills and offers a feasible approach to managing a volatile business. Readers will find this book a valuable resource to solve supply chain management practical problems with a sustainable future in mind. |
| demand forecasting in inventory management: Demand Forecasting and Inventory Control , This practical book covers the forecasting- and inventory control methods used in commercial, retail and manufacturing companies. Colin Lewis explains the theory and practice of current demand forecasting methods, the links between forecasts produced as a result of analysing demand data and the various methods by which this information, together with cost information on stocked items, is used to establish the controlling parameters of the most commonly used inventory control systems. The demand forecasting section of the book concentrates on the family of short-term forecasting models based on the exponentially weighted average and its many variants and also a group of medium-term forecasting models based on a time series, curve fitting approach. The inventory control sections investigate the re-order level policy and re-order cycle policy and indicate how these two processes can be operated at minimum cost while offering a high level of customer service. |
| demand forecasting in inventory management: Rapid Modelling for Increasing Competitiveness Gerald Reiner, 2009-06-13 A Perspective on Two Decades of Rapid Modeling It is an honor for me to be asked to write a foreword to the Proceedings of the 1st Rapid Modeling Conference. In 1987, when I coined the term “Rapid Modeling” to denote queuing modeling of manufacturing systems, I never imagined that two decades later there would be an international conference devoted to this topic! I am delighted to see that there will be around 40 presentations at the conference by leading researchers from aroundthe world, and about half of these presentationsare represented by written papers published in this book. I congratulate the conference organizers and program committee on the success of their efforts to hold the ?rst ever conference on Rapid Modeling. Attendees at this conferencemight?nd it interesting to learn about the history of the term Rapid Modeling in the context it is used here. During the fall of 1986 I was invited to a meeting at the Headquarters of the Society of Manufacturing Engineers (SME) in Dearborn, Michigan. By that time I had successfully demonstrated s- eral industry applications of queuing network models at leading manufacturers in the USA. Although in principle the use of queuing networks to model manufact- ing systems was well known in the OR/MS community and many papers had been published,the actual use of suchmodelsby manufacturingprofessionalswas almost nonexistent. |
| demand forecasting in inventory management: Inventory and Supply Chain Management with Forecast Updates Suresh P. Sethi, Houmin Yan, Hanqin Zhang, 2006-03-30 Real problems are formulated into tractable mathematical models, which allow for an analysis of various approaches. Attention is focused on solutions. Provides a unified treatment of the models discussed , presents a critique of the existing results, and points out potential research directions. |
| demand forecasting in inventory management: Inventory Optimization Nicolas Vandeput, 2020-08-24 In this book . . . Nicolas Vandeput hacks his way through the maze of quantitative supply chain optimizations. This book illustrates how the quantitative optimization of 21st century supply chains should be crafted and executed. . . . Vandeput is at the forefront of a new and better way of doing supply chains, and thanks to a richly illustrated book, where every single situation gets its own illustrating code snippet, so could you. --Joannes Vermorel, CEO, Lokad Inventory Optimization argues that mathematical inventory models can only take us so far with supply chain management. In order to optimize inventory policies, we have to use probabilistic simulations. The book explains how to implement these models and simulations step-by-step, starting from simple deterministic ones to complex multi-echelon optimization. The first two parts of the book discuss classical mathematical models, their limitations and assumptions, and a quick but effective introduction to Python is provided. Part 3 contains more advanced models that will allow you to optimize your profits, estimate your lost sales and use advanced demand distributions. It also provides an explanation of how you can optimize a multi-echelon supply chain based on a simple—yet powerful—framework. Part 4 discusses inventory optimization thanks to simulations under custom discrete demand probability functions. Inventory managers, demand planners and academics interested in gaining cost-effective solutions will benefit from the do-it-yourself examples and Python programs included in each chapter. Events around the book Link to a De Gruyter Online Event in which the author Nicolas Vandeput together with Stefan de Kok, supply chain innovator and CEO of Wahupa; Koen Cobbaert, Director in the S&O Industry practice of PwC Belgium; Bram Desmet, professor of operations & supply chain at the Vlerick Business School in Ghent; and Karl-Eric Devaux, Planning Consultant, Hatmill, discuss about models for inventory optimization. The event will be moderated by Eric Wilson, Director of Thought Leadership for Institute of Business Forecasting (IBF): https://youtu.be/565fDQMJEEg |
| demand forecasting in inventory management: Demand Forecasting and Order Planning in Supply Chains and Humanitarian Logistics Taghipour, Atour, 2020-09-18 In a decentralized supply chain, most of the supply chain agents may not share information due to confidentiality policies, quality of information, or different system incompatibilities. Every actor holds its own set of information and attempts to maximize its objective (minimizing costs/minimizing inventory holdings) based on the available settings. Therefore, the agents control their own activities with the objective of improving their own competitiveness, which leads them to make decisions that maximize their local performance by ignoring the other agents or even the final consumer. These decisions are myopic because they do not consider the performance of all the partners to satisfy the consumer. Demand Forecasting and Order Planning in Supply Chains and Humanitarian Logistics is a collection of innovative research that focuses on demand anticipation, forecasting, and order planning as well as humanitarian logistics to propose original solutions for existing problems. While highlighting topics including artificial intelligence, information sharing, and operations management, this book is ideally designed for supply chain managers, logistics personnel, business executives, management experts, operation industry professionals, academicians, researchers, and students who want to improve their understanding of supply chain coordination in order to be competitive in the new era of globalization. |
| demand forecasting in inventory management: Inventory Control Sven Axsäter, 2015-07-06 This third edition, which has been fully updated and now includes improved and extended explanations, is suitable as a core textbook as well as a source book for industry practitioners. It covers traditional approaches for forecasting, lot sizing, determination of safety stocks and reorder points, KANBAN policies and Material Requirements Planning. It also includes recent advances in inventory theory, for example, new techniques for multi-echelon inventory systems and Roundy's 98 percent approximation. The book also considers methods for coordinated replenishments of different items, and various practical issues in connection with industrial implementation. Other topics covered in Inventory Control include: alternative forecasting techniques, material on different stochastic demand processes and how they can be fitted to empirical data, generalized treatment of single-echelon periodic review systems, capacity constrained lot sizing, short sections on lateral transshipments and on remanufacturing, coordination and contracts. As noted, the explanations have been improved throughout the book and the text also includes problems, with solutions in an appendix. |
| demand forecasting in inventory management: Demand Forecasting for Managers Stephan Kolassa, Enno Siemsen, 2016-08-17 Most decisions and plans in a firm require a forecast. Not matching supply with demand can make or break any business, and that's why forecasting is so invaluable. Forecasting can appear as a frightening topic with many arcane equations to master. For this reason, the authors start out from the very basics and provide a non-technical overview of common forecasting techniques as well as organizational aspects of creating a robust forecasting process. The book also discusses how to measure forecast accuracy to hold people accountable and guide continuous improvement. This book does not require prior knowledge of higher mathematics, statistics, or operations research. It is designed to serve as a first introduction to the non-expert, such as a manager overseeing a forecasting group, or an MBA student who needs to be familiar with the broad outlines of forecasting without specializing in it. |
| demand forecasting in inventory management: Forecasting Tourism Demand Douglas Frechtling, 2012-05-23 'Forecasting tourism demand' is a text that no tourism professional can afford to be without. The tourism industry has experienced an overwhelming boom over recent years, and being able to predict future trends as accurately as possible is vital in the struggle to stay one step ahead of the competition. Building on the success of 'Practical Tourism Forecasting' this text looks at 13 methods of forecasting and with a user friendly style, 'Forecasting Tourism Demand' guides the reader through each method, highlighting its strengths and weaknesses and explaining how it can be applied to the tourism industry. 'Forecasting Tourism Demand' employs charts and tables to explain how to: * plan a forecasting project * analyse time series and other information * select the appropriate forecasting model * use the model for forecasting and evaluate its results Ideal for marketing managers and strategic planners in business, transportation planners and economic policy makers in government who must project demand for their products among tourists. Executives who rely on forecasts prepared by others will find it invaluable in assisting them to evaluate the validity and reliability of predictions and forecasts. Those engaged in analysing business trends will find it useful in surveying the future of what has been called the largest industry in the world. |
| demand forecasting in inventory management: Business Forecasting Michael Gilliland, Len Tashman, Udo Sglavo, 2021-05-11 Discover the role of machine learning and artificial intelligence in business forecasting from some of the brightest minds in the field In Business Forecasting: The Emerging Role of Artificial Intelligence and Machine Learning accomplished authors Michael Gilliland, Len Tashman, and Udo Sglavo deliver relevant and timely insights from some of the most important and influential authors in the field of forecasting. You'll learn about the role played by machine learning and AI in the forecasting process and discover brand-new research, case studies, and thoughtful discussions covering an array of practical topics. The book offers multiple perspectives on issues like monitoring forecast performance, forecasting process, communication and accountability for forecasts, and the use of big data in forecasting. You will find: Discussions on deep learning in forecasting, including current trends and challenges Explorations of neural network-based forecasting strategies A treatment of the future of artificial intelligence in business forecasting Analyses of forecasting methods, including modeling, selection, and monitoring In addition to the Foreword by renowned researchers Spyros Makridakis and Fotios Petropoulos, the book also includes 16 opinion/editorial Afterwords by a diverse range of top academics, consultants, vendors, and industry practitioners, each providing their own unique vision of the issues, current state, and future direction of business forecasting. Perfect for financial controllers, chief financial officers, business analysts, forecast analysts, and demand planners, Business Forecasting will also earn a place in the libraries of other executives and managers who seek a one-stop resource to help them critically assess and improve their own organization's forecasting efforts. |
| demand forecasting in inventory management: Inventory Management Explained David J. Piasecki, 2009-01-01 Inventory Management isn't easy. If it were, more companies would be good at it. But being competent at managing your inventory isn't all that difficult either. Inventory Management Explained helps readers build a solid understanding of the key planning aspects of inventory management. It does this by clearly explaining what inventory management is, but then goes well beyond typical inventory management books by tearing apart the calculations and logic we use in inventory management and exposing the hidden (or not so hidden) flaws and limitations. It then builds on this by showing readers how they can use their understanding of inventory management and their specific business needs to modify these calculations or develop their own calculations to more effectively manage their inventory. The emphasis on practical solutions means readers can actually use what they've learned.For those new to inventory management, the author includes highly detailed explanations and numerous examples. Instead of archaic mathematical syntax, the author explains the calculations in plain English and uses Excel formulas and spreadsheet examples for many of them.For the experienced practitioner, the author provides insights and a level of detail they likely have not previously experienced. Overall, Inventory Management Explained does actually explain inventory management, and in doing so, exposes the good, the bad, and the ugly aspects of it. But more importantly, it leaves the readers knowing enough to be able to start making smart decisions about how they manage their inventory. |
| demand forecasting in inventory management: Next Generation Demand Management Charles W. Chase, 2016-08-01 A practical framework for revenue-boosting supply chain management Next Generation Demand Management is a guidebook to next generation Demand Management, with an implementation framework that improves revenue forecasts and enhances profitability. This proven approach is structured around the four key catalysts of an efficient planning strategy: people, processes, analytics, and technology. The discussion covers the changes in behavior, skills, and integrated processes that are required for proper implementation, as well as the descriptive and predictive analytics tools and skills that make the process sustainable. Corporate culture changes require a shift in leadership focus, and this guide describes the necessary champion with the authority to drive adoption and stress accountability while focusing on customer excellence. Real world examples with actual data illustrate important concepts alongside case studies highlighting best-in-class as well as startup approaches. Reliable forecasts are the primary product of demand planning, a multi-step operational supply chain management process that is increasingly seen as a survival tactic in the changing marketplace. This book provides a practical framework for efficient implementation, and complete guidance toward the supplementary changes required to reap the full benefit. Learn the key principles of demand driven planning Implement new behaviors, skills, and processes Adopt scalable technology and analytics capabilities Align inventory with demand, and increase channel profitability Whether your company is a large multinational or an early startup, your revenue predictions are only as strong as your supply chain management system. Implementing a proven, more structured process can be the catalyst your company needs to overcome that one lingering obstacle between forecast and goal. Next Generation Demand Management gives you the framework for building the foundation of your growth. |
| demand forecasting in inventory management: Demand-Driven Forecasting Charles W. Chase, 2009-07-23 Praise for Demand-Driven Forecasting A Structured Approach to Forecasting There are authors of advanced forecasting books who take an academic approach to explaining forecast modeling that focuses on the construction of arcane algorithms and mathematical proof that are not very useful for forecasting practitioners. Then, there are other authors who take a general approach to explaining demand planning, but gloss over technical content required of modern forecasters. Neither of these approaches is well-suited for helping business forecasters critically identify the best demand data sources, effectively apply appropriate statistical forecasting methods, and properly design efficient demand planning processes. In Demand-Driven Forecasting, Chase fills this void in the literature and provides the reader with concise explanations for advanced statistical methods and credible business advice for improving ways to predict demand for products and services. Whether you are an experienced professional forecasting manager, or a novice forecast analyst, you will find this book a valuable resource for your professional development. —Daniel Kiely, Senior Manager, Epidemiology, Forecasting & Analytics, Celgene Corporation Charlie Chase has given forecasters a clear, responsible approach for ending the timeless tug of war between the need for 'forecast rigor' and the call for greater inclusion of 'client judgment.' By advancing the use of 'domain knowledge' and hypothesis testing to enrich base-case forecasts, he has empowered professional forecasters to step up and impact their companies' business results favorably and profoundly, all the while enhancing the organizational stature of forecasters broadly. —Bob Woodard, Vice President, Global Consumer and Customer Insights, Campbell Soup Company |
| demand forecasting in inventory management: Demand and Supply Integration Mark A. Moon, 2018-04-09 Supply chain professionals: master pioneering techniques for integrating demand and supply, and create demand forecasts that are far more accurate and useful! In Demand and Supply Integration, Dr. Mark Moon presents the specific design characteristics of a world-class demand forecasting management process, showing how to effectively integrate demand forecasting within a comprehensive Demand and Supply Integration (DSI) process. Writing for supply chain professionals in any business, government agency, or military procurement organization, Moon explains what DSI is, how it differs from approaches such as S&OP, and how to recognize the symptoms of failures to sufficiently integrate demand and supply. He outlines the key characteristics of successful DSI implementations, shows how to approach Demand Forecasting as a management process, and guides you through understanding, selecting, and applying the best available qualitative and quantitative forecasting techniques. You'll learn how to thoroughly reflect market intelligence in your forecasts; measure your forecasting performance; implement state-of-the-art demand forecasting systems; manage Demand Reviews, and much more. |
| demand forecasting in inventory management: Fundamentals of Demand Planning and Forecasting Chaman L. Jain, Jack Malehorn, 2012 |
| demand forecasting in inventory management: Best Practice in Inventory Management Tony Wild, 2017-11-02 Best Practice in Inventory Management 3E offers a simple, entirely jargon-free and yet comprehensive introduction to key aspects of inventory management. Good management of inventory enables companies to improve their customer service, cash flow and profitability. This text outlines the basic techniques, how and where to apply them, and provides advice to ensure they work to provide the desired effect in practice. With an unrivalled balance between qualitative and quantitative aspects of inventory control, experienced consultant Tony Wild portrays the many ways in which stock management is more nuanced than simple number crunching and mathematical modelling. This long-awaited new edition has been substantially and thoroughly updated. The product of decades of experience and expertise in the field, Best Practice in Inventory Management 3E provides students and professionals, even those with no prior experience in the area, an unbiased and honest picture of what it takes to effectively manage stocks in a firm. |
| demand forecasting in inventory management: Demand and Supply Integration Mark A. Moon, 2013-01-14 Supply chain professionals: master pioneering techniques for integrating demand and supply, and create demand forecasts that are far more accurate and useful! In Demand and Supply Integration, Dr. Mark Moon presents the specific design characteristics of a world-class demand forecasting management process, showing how to effectively integrate demand forecasting within a comprehensive Demand and Supply Integration (DSI) process. Writing for supply chain professionals in any business, government agency, or military procurement organization, Moon explains what DSI is, how it differs from approaches such as SandOP, and how to recognize the symptoms of failures to sufficiently integrate demand and supply. He outlines the key characteristics of successful DSI implementations, shows how to approach Demand Forecasting as a management process, and guides you through understanding, selecting, and applying the best available qualitative and quantitative forecasting techniques. You'll learn how to thoroughly reflect market intelligence in your forecasts; measure your forecasting performance; implement state-of-the-art demand forecasting systems; manage Demand Reviews, and much more. For wide audiences of supply chain, logistics, and operations management professionals at all levels, from analyst and manager to Director, Vice President, and Chief Supply Chain Officer; and for researchers and graduate students in the field. |
| demand forecasting in inventory management: The Quantitative Supply Chain Joannès Vermorel, 2018-01-26 The Quantitative Supply Chain represents a novel and disruptive perspective on the optimization of supply chains. It can be seen as a refoundation of many supply chain practices, in particular regarding inventory forecasting, and has been built to make the most of the latest statistical approaches and vast computing resources that are available nowadays. |
| demand forecasting in inventory management: Retail Analytics Anna-Lena Sachs, 2015 This book addresses the challenging task of demand forecasting and inventory management in retailing. It analyzes how information from point-of-sale scanner systems can be used to improve inventory decisions, and develops a data-driven approach that integrates demand forecasting and inventory management for perishable products, while taking unobservable lost sales and substitution into account in out-of-stock situations. Using linear programming, a new inventory function that reflects the causal relationship between demand and external factors such as price and weather is proposed. The book subsequently demonstrates the benefits of this new approach in numerical studies that utilize real data collected at a large European retail chain. Furthermore, the book derives an optimal inventory policy for a multi-product setting in which the decision-maker faces an aggregated service level target, and analyzes whether the decision-maker is subject to behavioral biases based on real data for bakery products. |
| demand forecasting in inventory management: Inventory Management Insights Mansoor Muallim, 101-01-01 Chapter 1: The Foundations of Inventory Management Characters: Jammy (Expert) and Canny (Enthusiast) Jammy: Hey there, Canny! I'm excited to share some valuable insights about inventory management with you today. It's a crucial aspect of any business, and I'm sure you'll find it fascinating. Canny: Hi, Jammy! I'm really eager to learn more. So, what exactly is inventory management? Jammy: Great question, Canny! Inventory management involves efficiently handling a company's stock of goods to ensure smooth operations. It's all about striking the right balance between having enough products to meet customer demand while avoiding overstocking that ties up unnecessary capital. Canny: I see. So, why is it essential for businesses? Jammy: Well, effective inventory management brings several benefits. First and foremost, it helps businesses maintain customer satisfaction. When you have products readily available, you can fulfill orders promptly, leading to happy customers. Moreover, it reduces holding costs, which are the expenses associated with storing excess inventory. Canny: That makes sense. How do companies decide how much inventory to carry? Jammy: Good question! There are various factors that influence this decision. One crucial aspect is demand forecasting. By analyzing historical sales data and market trends, businesses can estimate future demand and plan their inventory accordingly. Canny: Is there a specific method for managing different types of products? Jammy: Absolutely! Not all products are equal. Businesses often categorize their inventory based on demand and value. This categorization helps them apply appropriate management techniques. For instance, high-value items may require closer monitoring and tighter controls. Canny: Interesting! Are there any popular inventory control models? Jammy: Yes, indeed! One of the widely used models is the Economic Order Quantity (EOQ) model. It calculates the optimal order quantity that minimizes total inventory costs, including ordering and holding costs. Canny: Is there any way to handle unpredictable demand? Jammy: Definitely! Safety stock comes into play here. It's the buffer inventory kept to tackle unexpected spikes in demand or delays in supply. Safety stock acts as an insurance against stockouts. Canny: That sounds important. How can technology help with inventory management? Jammy: Technology plays a significant role in modern inventory management. Businesses use specialized software to automate various processes, such as order processing, tracking, and forecasting. This streamlines operations and enhances accuracy. Canny: Thanks for sharing all this valuable information, Jammy. It's been really enlightening. Jammy: You're welcome, Canny! Inventory management is an ever-evolving field, and there's always something new to learn. I'm glad I could help satisfy your thirst for knowledge! Key Takeaways: Inventory management is about efficiently handling a company's stock of goods to meet customer demand while minimizing holding costs. Demand forecasting is crucial for determining the right inventory levels. Categorizing inventory based on demand and value helps tailor management techniques. The Economic Order Quantity (EOQ) model is widely used for inventory control. Safety stock acts as a buffer against unexpected fluctuations in demand or supply. Technology, such as inventory management software, plays a significant role in streamlining processes and improving accuracy. |
| demand forecasting in inventory management: Managing Supply Chain Risk and Vulnerability Teresa Wu, Jennifer Vincent Blackhurst, 2009-08-20 Managing Supply Chain Risk and Vulnerability, a book that both practitioners and students can use to better understand and manage supply chain risk, presents topics on decision making related to supply chain risk. Leading academic researchers, as well as practitioners, have contributed chapters focusing on developing an overall understanding of risk and its relationship to supply chain performance; investigating the relationship between response time and disruption impact; assessing and prioritizing risks; and assessing supply chain resilience. Supply chain managers will find Managing Supply Chain Risk and Vulnerability a useful tool box for methods they can employ to better mitigate and manage supply chain risk. On the academic side, the book can be used to teach senior undergraduate students, as well as graduate-level students. Additionally, researchers may use the text as a reference in the area of supply chain risk and vulnerability. |
| demand forecasting in inventory management: Data Science for Supply Chain Forecasting Nicolas Vandeput, 2021-03-22 Using data science in order to solve a problem requires a scientific mindset more than coding skills. Data Science for Supply Chain Forecasting, Second Edition contends that a true scientific method which includes experimentation, observation, and constant questioning must be applied to supply chains to achieve excellence in demand forecasting. This second edition adds more than 45 percent extra content with four new chapters including an introduction to neural networks and the forecast value added framework. Part I focuses on statistical traditional models, Part II, on machine learning, and the all-new Part III discusses demand forecasting process management. The various chapters focus on both forecast models and new concepts such as metrics, underfitting, overfitting, outliers, feature optimization, and external demand drivers. The book is replete with do-it-yourself sections with implementations provided in Python (and Excel for the statistical models) to show the readers how to apply these models themselves. This hands-on book, covering the entire range of forecasting—from the basics all the way to leading-edge models—will benefit supply chain practitioners, forecasters, and analysts looking to go the extra mile with demand forecasting. |
| demand forecasting in inventory management: Demand Prediction in Retail Maxime C. Cohen, Paul-Emile Gras, Arthur Pentecoste, Renyu Zhang, 2022-01-01 From data collection to evaluation and visualization of prediction results, this book provides a comprehensive overview of the process of predicting demand for retailers. Each step is illustrated with the relevant code and implementation details to demystify how historical data can be leveraged to predict future demand. The tools and methods presented can be applied to most retail settings, both online and brick-and-mortar, such as fashion, electronics, groceries, and furniture. This book is intended to help students in business analytics and data scientists better master how to leverage data for predicting demand in retail applications. It can also be used as a guide for supply chain practitioners who are interested in predicting demand. It enables readers to understand how to leverage data to predict future demand, how to clean and pre-process the data to make it suitable for predictive analytics, what the common caveats are in terms of implementation and how to assess prediction accuracy. |
| demand forecasting in inventory management: Computational Science and Its Applications – ICCSA 2018 Osvaldo Gervasi, Beniamino Murgante, Sanjay Misra, Elena Stankova, Carmelo M. Torre, Ana Maria A.C. Rocha, David Taniar, Bernady O. Apduhan, Eufemia Tarantino, Yeonseung Ryu, 2018-07-03 The five volume set LNCS 10960 until 10964 constitutes the refereed proceedings of the 18th International Conference on Computational Science and Its Applications, ICCSA 2018, held in Melbourne, Australia, in July 2018. Apart from the general tracks, ICCSA 2018 also includes 34 international workshops in various areas of computational sciences, ranging from computational science technologies, to specific areas of computational sciences, such as computer graphics and virtual reality. The total of 265 full papers and 10 short papers presented in the 5-volume proceedings set of ICCSA 2018, were carefully reviewed and selected from 892 submissions. |
| demand forecasting in inventory management: Essentials of Monte Carlo Simulation Nick T. Thomopoulos, 2012-12-19 Essentials of Monte Carlo Simulation focuses on the fundamentals of Monte Carlo methods using basic computer simulation techniques. The theories presented in this text deal with systems that are too complex to solve analytically. As a result, readers are given a system of interest and constructs using computer code, as well as algorithmic models to emulate how the system works internally. After the models are run several times, in a random sample way, the data for each output variable(s) of interest is analyzed by ordinary statistical methods. This book features 11 comprehensive chapters, and discusses such key topics as random number generators, multivariate random variates, and continuous random variates. Over 100 numerical examples are presented as part of the appendix to illustrate useful real world applications. The text also contains an easy to read presentation with minimal use of difficult mathematical concepts. Very little has been published in the area of computer Monte Carlo simulation methods, and this book will appeal to students and researchers in the fields of Mathematics and Statistics. |
| demand forecasting in inventory management: Essentials of Inventory Management Max Muller, 2011 Does inventory management sometimes feel like a waste of time? Learn how to maximize your inventory management process to use it as a tool for making important business decisions. |
| demand forecasting in inventory management: Fundamentals of Supply Chain Management John T. Mentzer, 2004-05-05 This book is an insightful, well-balanced, stimulating SCM Strategy book that clearly tells managers, consultants, as well as educators that the SCM concept is not a fad but a must strategy to gain competitive advantage in today′s dynamic global market place. There are three major strengths. First, it is an unprecedented interdisciplinary SCM strategy book that explains how companies obtain, maintain, and even enhance competitive advantages based upon a well-laid SCM strategy. Second, it provides readers a unique, well-balanced framework for SCM strategy formulation. Third, it is a valuable contribution in the area of SCM in that it does a good job in explaining such a complicated SCM strategy to readers in such a simple manner. —Soonhong (Hong) Min, University of Oklahoma Author of the bestselling text Supply Chain Management, John T. Mentzer′s companion book Fundamentals of Supply Chain Management: Twelve Drivers of Competitive Advantage has been developed as a supplemental text for any course dealing with strategy and supply chains. Written in an entertaining, accessible style, Mentzer identifies twelve drivers of competitive advantage as clear strategic points managers can use in their companies. Research from more than 400 books, articles, and papers, as well as interviews with over fifty executives in major global companies, inform these twelve drivers. The roles of all of the traditional business functions—marketing, sales, logistics, information systems, finance, customer services, and management—in supply chain management are also addressed. Complete with cases and real-world examples from corporations around the world, the book′s exemplars will help students and practicing managers to more effectively understand, implement, and manage supply chains successfully. |
AI-driven demand forecasting: Enhancing inventory …
Integrating AI-driven demand forecasting models into existing inventory management systems can significantly enhance their efficiency. By automating the replenishment process based on …
Forecast-Driven Inventory Management for the Fast-Moving …
Demand forecasting is crucial in the retail industry, influencing supply chain management, inventory control, and pricing strategies. Accurately predicting demand is essential for …
Inventory Management and Demand Forecasting - IJRPR
Analysing the customer base plays a huge role in demand forecasting to analyze which goods to under-stock and which goods not to overstock thus maintaining a healthy inventory. With the …
Demand Forecasting for Inventory Optimization - JETIR
Forecasting the inventory supplies and probable future demands is a necessary part of efficiently managing stocks and capital. In this competitive ecosystem, the philosophy of just-in-time …
AI-enhanced inventory and demand forecasting: Using AI to …
Inventory management and demand forecasting are critical components of supply chain management, directly influencing a company's operational efficiency, cost management, and …
Demand Forecasting for Inventory Management using Limited …
Inventory management plays a vital role within the business chain, which is the bufer between two processes, supply, and demand. This can be known or unknown, variable, or constant. The …
Chapter 1 Demand Forecasting for Inventory Control - Springer
Demand forecasting is a term more recently coined to define the forecast of de-mands for items in stock. The demands are from customers who want to purchase the item for immediate use. A …
Inventory Management using Demand Sales Forecasting
Our module leverages sophisticated statistical models like Holts winter, AR, ARMA, ARIMA & SARIMA available in Python Libraries. The module is made so that an average excel user will …
Demand Forecasting and Inventory Management for Spare Parts
Focusing in the demanding planning and inventory management in the spare parts context can lead to an increase in operational performance. Following the suggested demand forecasting …
Optimizing inventory management and demand forecasting …
Forecasting plays a critical role in inventory management as it helps companies anticipate future demand for their products and services. Forecasting involves analyzing historical sales data, …
Demand Forecasting, Planning, and Management - MIT …
What Are Demand Forecasting, Planning, and Management? What should we do to shape and create demand? What will demand be for a given demand plan? How do we prepare for and …
Demand Planning: Forecasting and Demand Management
Demand Planning Summary 1. Forecasting process choice is influenced by a variety of factors 2. Forecasts are judgment or statistical model based 3. Both accuracy and bias should be …
7 Essential Elements of Demand Forecasting, Planning
Oct 7, 2015 · forecasting and replenishment, inventory optimization, broader supply chain management and others. One IBM manager, Anders Herlitz, first documented a best-practice …
Optimizing Supply Chain Demand Forecasting and Inventory …
CHAINDemand forecasting: Predicting future product demand is the key to supply chain management. Large language models can analyze historical sales data, market trends, …
Demand Forecasting and Inventory Management for Spare Parts
Gerber Technology, a manufacturing company that sells industrial machines and the spare parts that support them, faces challenges in its spare parts demand forecast quality and inventory …
The integration of Artificial Intelligence in demand forecasting …
Demand forecasting and inventory management are two of critical areas in supply chain management, which is a veritable tool for promoting industrialization, manufacturing …
Demand Forecasting and Inventory Management for Spare Parts
How can we better forecast the demand in the Spare Parts contexts? How can we better categorize products for inventory management to capture business needs? Multi-criteria …
Optimizing Inventory Management through Demand …
Successful inventory management starts with a correct demand forecasting strategy which in turn, helps businesses predict customer requirements in the future and keeps their stock levels …
Leveraging AI and Machine Learning for Enhanced Inventory …
To explore how AI and ML enhance inventory management and optimization, this study adopts a qualitative research methodology, evaluating demand forecasting and efficiency in inventory …
International Journal of Core Engineering & Management …
transforming inventory optimization and demand forecasting in supply chain management. Through a comprehensive analysis, it demonstrated how advanced techniques such as …
AI-driven demand forecasting: Enhancing inventory …
Integrating AI-driven demand forecasting models into existing inventory management systems can significantly enhance their efficiency. By automating the replenishment process based on …
Forecast-Driven Inventory Management for the Fast-Moving …
Demand forecasting is crucial in the retail industry, influencing supply chain management, inventory control, and pricing strategies. Accurately predicting demand is essential for …
Inventory Management and Demand Forecasting - IJRPR
Analysing the customer base plays a huge role in demand forecasting to analyze which goods to under-stock and which goods not to overstock thus maintaining a healthy inventory. With the …
Demand Forecasting for Inventory Optimization - JETIR
Forecasting the inventory supplies and probable future demands is a necessary part of efficiently managing stocks and capital. In this competitive ecosystem, the philosophy of just-in-time …
AI-enhanced inventory and demand forecasting: Using AI to …
Inventory management and demand forecasting are critical components of supply chain management, directly influencing a company's operational efficiency, cost management, and …
Demand Forecasting for Inventory Management using …
Inventory management plays a vital role within the business chain, which is the bufer between two processes, supply, and demand. This can be known or unknown, variable, or constant. The …
Chapter 1 Demand Forecasting for Inventory Control - Springer
Demand forecasting is a term more recently coined to define the forecast of de-mands for items in stock. The demands are from customers who want to purchase the item for immediate use. A …
Inventory Management using Demand Sales Forecasting
Our module leverages sophisticated statistical models like Holts winter, AR, ARMA, ARIMA & SARIMA available in Python Libraries. The module is made so that an average excel user will …
Demand Forecasting and Inventory Management for Spare …
Focusing in the demanding planning and inventory management in the spare parts context can lead to an increase in operational performance. Following the suggested demand forecasting …
Optimizing inventory management and demand forecasting …
Forecasting plays a critical role in inventory management as it helps companies anticipate future demand for their products and services. Forecasting involves analyzing historical sales data, …
Demand Forecasting, Planning, and Management - MIT …
What Are Demand Forecasting, Planning, and Management? What should we do to shape and create demand? What will demand be for a given demand plan? How do we prepare for and …
Demand Planning: Forecasting and Demand Management
Demand Planning Summary 1. Forecasting process choice is influenced by a variety of factors 2. Forecasts are judgment or statistical model based 3. Both accuracy and bias should be …
7 Essential Elements of Demand Forecasting, Planning
Oct 7, 2015 · forecasting and replenishment, inventory optimization, broader supply chain management and others. One IBM manager, Anders Herlitz, first documented a best-practice …
Optimizing Supply Chain Demand Forecasting and Inventory …
CHAINDemand forecasting: Predicting future product demand is the key to supply chain management. Large language models can analyze historical sales data, market trends, …
Demand Forecasting and Inventory Management for Spare …
Gerber Technology, a manufacturing company that sells industrial machines and the spare parts that support them, faces challenges in its spare parts demand forecast quality and inventory …
The integration of Artificial Intelligence in demand forecasting …
Demand forecasting and inventory management are two of critical areas in supply chain management, which is a veritable tool for promoting industrialization, manufacturing …
Demand Forecasting and Inventory Management for Spare …
How can we better forecast the demand in the Spare Parts contexts? How can we better categorize products for inventory management to capture business needs? Multi-criteria …
Optimizing Inventory Management through Demand …
Successful inventory management starts with a correct demand forecasting strategy which in turn, helps businesses predict customer requirements in the future and keeps their stock levels …
Leveraging AI and Machine Learning for Enhanced Inventory …
To explore how AI and ML enhance inventory management and optimization, this study adopts a qualitative research methodology, evaluating demand forecasting and efficiency in inventory …
International Journal of Core Engineering & Management …
transforming inventory optimization and demand forecasting in supply chain management. Through a comprehensive analysis, it demonstrated how advanced techniques such as …
