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| 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. |
| 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. |
| 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. |
| 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. |
| 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. |
| 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. |
| 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. |
| forecasting in inventory management: Focus Forecasting Bernard T. Smith, 1984 |
| 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. |
| 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. |
| 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. |
| 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. |
| 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 |
| 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. |
| 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. |
| 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. |
| 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. |
| forecasting in inventory management: The Definitive Guide to Inventory Management Matthew A. Waller, Terry L. Esper, 2014 Inventory management is a critical component of supply chain management, addressing how much inventory should be carried across the supply chain, where to carry it, and how much safety stock is required to meet the organization's cost and customer service objectives. Now, there's an authoritative and comprehensive guide to best-practice inventory management in any organization. Authored by world-class experts in collaboration with the Council of Supply Chain Management Professionals (CSCMP), this text gives students and practitioners a thorough understanding of each leading approach to managing supply chain inventories, and the variables that drive decisions about inventory levels. It discusses the fundamental need for inventory, how product value affects inventory decisions, how to determine inventory levels, how the number of inventory locations affects inventory levels, and new approaches to reducing inventory. Coverage includes: Basic inventory management goals, roles, concepts, purposes, and terminology, including periodic inventory, perpetual inventory, safety stock, cycle count, ABC analysis, carrying and stockout costs, and more Key inventory management elements, processes, and interactions Principles/strategies for establishing efficient and effective inventory flows The critical role of technology in inventory planning and management New approaches to reducing inventory including postponement, vendor-managed inventories, cross-docking, and quick response systems Understanding essential trade-offs between inventory and transportation costs, including the impact of carrying costs Requirements and challenges of global inventory management Best practices for assessing inventory management performance using standard metrics and frameworks |
| 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. |
| 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. |
| forecasting in inventory management: Fundamentals of Demand Planning and Forecasting Chaman L. Jain, Jack Malehorn, 2012 |
| 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. |
| 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. |
| 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. |
| 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. |
| forecasting in inventory management: Inventory Management Demystified A.D. Dear, 1990-02-28 Despite the widespread use of computer based inventory control systems, most companies are aware that they often cannot meet their customer demand, while still suspecting that their stock levels are higher than they should be. |
| forecasting in inventory management: Inventory management , 1987 |
| forecasting in inventory management: Bricks Matter Lora M. Cecere, Charles W. Chase, 2012-12-20 Get proven guidance to build a market-driven supply chain management system Supply chain management processes have gradually shifted from a supply-driven focus to a demand-driven one in order to better synchronize demand and supply signals. Bricks Matter shows you how you can identify market risks and opportunities and translate these into winning tactics. Business cases highlight how business leaders are winning through market-driven approaches. Helps you understand how to apply the emerging world of predictive analytics for the better management of value networks Includes business cases illustrating the market-driven approach Reveals how businesses can identify market risks and translate these into supply-side tactics As companies transition from demand-driven to market-driven approach, the focus in organizations shifts from one of vertical excellence to building strong market-to-market horizontal processes. Improve revenue by increasing market share, improve profit margins, and maintain high levels of customer service with the indispensable guidance found in Bricks Matter. |
| forecasting in inventory management: Forecasting for Inventory Control with Exponential Smoothing Ralph D. Snyder, Anne Koehler, Keith Ord, 1999 |
| forecasting in inventory management: Data Science for Supply Chain Forecast Nicolas Vandeput, 2018-11-12 Data Science for Supply Chain Forecast Data Science for Supply Chain Forecast is a book for practitioners focusing on data science and machine learning; it demonstrates how both are closely interlinked in order to create an advanced forecast for supply chain. As one will discover in this book, artificial intelligence (AI) & machine learning (ML) are not simply a question of coding skills. Using data science in order to solve a problem requires a scientific mindset more than coding skills. The story behind these models is one of experimentation, of observation and of constant questioning; a true scientific method must be applied to supply chain. In the data science field as well as that of the supply chain, simple questions do not come with simple answers. In order to resolve these questions, one needs to be both a scientist as well as to use the correct tools. In this book, we will discuss both. Is this Book for me? This book has been written for supply chain practitioners, forecasters and analysts who are looking to go the extra mile. You do not need technical IT skills to start using the models of this book. You do not need a dedicated server or expensive software licenses: you solely need your own computer. You do not need a PhD in mathematics: mathematics will only be utilized as a tool to tweak and understand the models. In the majority of the cases - especially when it comes to machine learning - a deep understanding of the mathematical inner workings of a model will not be necessary in order to optimize it and understand its limitations. Reviews In an age where analytics and machine learning are taking on larger roles in the business forecasting, Nicolas' book is perfect solution for professionals who need to combine practical supply chain experience with the mathematical and technological tools that can help us predict the future more reliably. Daniel Stanton - Author, Supply Chain Management For Dummies Open source statistical toolkits have progressed tremendously over the last decade. Nicolas demonstrates that these toolkits are more than enough to start addressing real-world forecasting challenges as found in supply chains. Moreover, through its hands-on approach, this book is accessible to a large audience of supply chain practitioners. The supply chain of the 21st century will be data-driven and Nicolas gets it perfectly. Joannes Vermorel - CEO Lokad This book is unique in its kind. It explains the basics of Python using basic traditional forecasting techniques and shows how machine learning is revolutionizing the forecasting domain. Nicolas has done an outstanding job explaining a technical subject in an easily accessible way. A must-read for any supply chain professional. Professor Bram Desmet - CEO Solventure This book is before anything a practical and business-oriented DIY user manual to help planners move into 21st-century demand planning. The breakthrough comes from several tools and techniques available to all, and which thanks to Nicolas' precise and concrete explanations can now be implemented in real business environments by any normal planner. I can confirm that Nicolas' learnings are based on real-life experience and can tremendously help on improving top and bottom lines. Henri-Xavier Benoist - VP Supply Chain Bridegstone EMEA |
| 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. |
| forecasting in inventory management: Inventory and Production Management in Supply Chains Edward A. Silver, David F. Pyke, Douglas J. Thomas, 2016-12-19 Authored by a team of experts, the new edition of this bestseller presents practical techniques for managing inventory and production throughout supply chains. It covers the current context of inventory and production management, replenishment systems for managing individual inventories within a firm, managing inventory in multiple locations and firms, and production management. The book presents sophisticated concepts and solutions with an eye towards today’s economy of global demand, cost-saving, and rapid cycles. It explains how to decrease working capital and how to deal with coordinating chains across boundaries. |
| 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. |
| 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. |
| 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. |
| 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 |
| forecasting in inventory management: Best Practice in Inventory Management Tony Wild, 2007-06-01 Good management of inventory enables companies to improve their customer service, cash flow and profitability. 'Best Practice in Inventory Management' outlines the basic techniques, how and where to apply them, and provides advice to ensure they work to produce the desired effect in practice. The book shows how inventory management techniques can be used in a wide variety of situations, particularly in stores where the inventory can be anything from fast moving products to slow moving spares. The discussion extends across distribution warehousing and manufacturers' operations. The text is based on best theory and practice, which has been gradually developed by the inventory management profession over the years. It covers the inventory control aspects included in the courses for the DPIM, COM, DLM, CPIM and other professional and academic qualifications. Readers develop their understanding of stock control by seeing the techniques explained logically and learn how inventory structuring, individual item control, forecasting and co-ordination provide the base for logistics management. This new edition has been up-dated throughout and the final chapter, The Future - Inventory and Logistics, has been re-written to reflect the developing applications of technology and changes in focus. |
| forecasting in inventory management: Supply Chain Metrics that Matter Lora M. Cecere, 2014-12-22 How to Conquer the Effective Frontier and Drive Improved Value in Global Operations Growth has slowed. Volatility has increased and the world is more global. Brands are defined by innovation and services. Supply chain excellence matters more than ever. It makes a difference in corporate performance. One cannot snap their fingers and deliver supply chain success. It happens over the course of many years. It is measured in inches not miles. In this book, the author evaluates the progress of over a hundred companies over the period of 2006-2013. Success drives value. The effective supply chain makes a difference in winning a war, saving a patient, and driving commerce; but it also makes a difference in a community having clean air, potable water, and a standard of living. Mistakes are hard to overcome. Supply Chain Metrics that Matter tells this story. The book links corporate financials to supply chain maturity. In the book, the author analyzes which metrics matter. The author Lora M. Cecere is a supply chain researcher as well as an authority in supply chain technology. She helps companies gain first mover advantage. In the book, Cecere provides concrete, actionable steps to align and balance the supply chain to drive value. The book explores the crossover between supply chain efficiency and financial growth with topics such as: Outlining the metrics that matter, the metrics that don't Progress in industry sub-segment in improving inventory, cash, productivity and margin The management techniques that improve performance Sharing insights on how metrics change as the supply chain matures The roadmap to improve performance. Today, supply chains are global and dynamic. They are rapidly evolving. Companies that constantly seek out new solutions and opportunities for improvement drive differentiation. In a market where growth is stalled and many companies are stuck in driving supply chain performance, this book provides a clear, concise framework for a more modern, effective supply chain. |
| 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. |
| forecasting in inventory management: Encyclopedia of Production and Manufacturing Management Paul M. Swamidass, 2000-06-30 Production and manufacturing management since the 1980s has absorbed in rapid succession several new production management concepts: manufacturing strategy, focused factory, just-in-time manufacturing, concurrent engineering, total quality management, supply chain management, flexible manufacturing systems, lean production, mass customization, and more. With the increasing globalization of manufacturing, the field will continue to expand. This encyclopedia's audience includes anyone concerned with manufacturing techniques, methods, and manufacturing decisions. |
Forecasting - Wikipedia
Forecasting is the process of making predictions based on past and present data. Later these can be compared with what actually happens. For example, a company might estimate their …
What Is Forecasting? - IBM
Jul 22, 2024 · Forecasting is a method of predicting a future event or condition by analyzing patterns and uncovering trends in previous and current data. It employs mathematical …
Forecasting - Overview, Methods and Features, Steps
Forecasting refers to the practice of predicting what will happen in the future by taking into consideration events in the past and present. Basically, it is a decision-making tool that helps …
Six Rules for Effective Forecasting - Harvard Business Review
In describing what forecasters are trying to achieve, Saffo outlines six simple, commonsense rules that smart managers should observe as they embark on a voyage of discovery with …
Forecasting: Meaning, Nature, Planning and Forecasting, …
Jun 5, 2024 · What is Forecasting? Forecasting involves making educated guesses about future events that could affect a company. Businesses can predict sales, finances, customer …
Forecasting | Definition, Methods, Steps, & Limitations
Sep 7, 2023 · Financial forecasting is the act of estimating future financial outcomes for a business or an investment. It is a critical process in financial planning and decision-making. It …
Q&A: What Is Forecasting? Definition, Methods and Examples
Jun 6, 2025 · Forecasting is a method of making informed predictions by using historical data as the main input for determining the course of future trends. Companies use forecasting for …
Top 6 Types of Forecasting Models (+ Examples) - 10XSheets
Jul 12, 2023 · Forecasting models provide valuable insights into future trends and patterns, enabling organizations to allocate resources effectively, optimize inventory levels, manage …
What is Forecasting? Modern Techniques & AI Solutions | ketteQ
Feb 12, 2025 · Forecasting has come a long way in the last few decades, with gut feelings and educated guesses giving way to data-driven insights based on complex algorithms. …
What is a Forecast? - Forecasting Models Explained - AWS
Forecasting acts as a planning tool to help enterprises prepare for the uncertainty that can occur in the future. It helps managers respond confidently to changes, control business operations, …
Forecasting - Wikipedia
Forecasting is the process of making predictions based on past and present data. Later these can be compared with what actually happens. For example, a company might estimate their …
What Is Forecasting? - IBM
Jul 22, 2024 · Forecasting is a method of predicting a future event or condition by analyzing patterns and uncovering trends in previous and current data. It employs mathematical …
Forecasting - Overview, Methods and Features, Steps
Forecasting refers to the practice of predicting what will happen in the future by taking into consideration events in the past and present. Basically, it is a decision-making tool that helps …
Six Rules for Effective Forecasting - Harvard Business Review
In describing what forecasters are trying to achieve, Saffo outlines six simple, commonsense rules that smart managers should observe as they embark on a voyage of discovery with …
Forecasting: Meaning, Nature, Planning and Forecasting, …
Jun 5, 2024 · What is Forecasting? Forecasting involves making educated guesses about future events that could affect a company. Businesses can predict sales, finances, customer …
Forecasting | Definition, Methods, Steps, & Limitations
Sep 7, 2023 · Financial forecasting is the act of estimating future financial outcomes for a business or an investment. It is a critical process in financial planning and decision-making. It …
Q&A: What Is Forecasting? Definition, Methods and Examples
Jun 6, 2025 · Forecasting is a method of making informed predictions by using historical data as the main input for determining the course of future trends. Companies use forecasting for …
Top 6 Types of Forecasting Models (+ Examples) - 10XSheets
Jul 12, 2023 · Forecasting models provide valuable insights into future trends and patterns, enabling organizations to allocate resources effectively, optimize inventory levels, manage …
What is Forecasting? Modern Techniques & AI Solutions | ketteQ
Feb 12, 2025 · Forecasting has come a long way in the last few decades, with gut feelings and educated guesses giving way to data-driven insights based on complex algorithms. …
What is a Forecast? - Forecasting Models Explained - AWS
Forecasting acts as a planning tool to help enterprises prepare for the uncertainty that can occur in the future. It helps managers respond confidently to changes, control business operations, …
