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Forecasting systems for operations management
Two examples are evaluated in detail: ‘The International Travel and Tourism’ and the ‘Travel Analysis Model’ forecasts produced by Coopers and Lybrand. Witt and Witt conclude that the accuracy of both these published forecasts is no greater than that of the random walk model. The text is an excellent reference for use at either the undergraduate or graduate level. It is thoroughly researched and, in particular. provides a valuable source of literature about tourism forecasts. It is also a useful tool for practitioners interested in forecasting tourism demand. In addition, the book can be used as a special forecasting application for those investigating international applications of forecasting methodologies or for those teaching in an international setting. Witt and Witt provide a good, well-written addition to the forecasting literature. Essam
Mahmoud
American Graduate School oj International Management Arizona, U.S.A.
Stephen
A. Delurgio
Forecasting
(Business hardback,
Systems
One Irwin, US$49.95.
and Carl D. Bhame, for
Operations
Homewood,
1991,
Management
IL), pp. 648,
The book is intended for managers and analysts who use and prepare forecasts for decisionmaking in operations management. It is directed to all levels of management within an organization whether in manufacturing or service industries. Although it is written for readers at the college-level, perhaps at the first-year MBA level. there are no difficult statistical or mathematical discussions. For example. the presentation does not cover ARIMA methods. However, an introductory statistical background is helpful. It is a good reference book; it is very readable and the material is well referenced for readers interested in more detailed discussions. Both up-to-date and classical reference works are included. In addition, although it is not a text
book, it is intended for use in preparation for the master planning and the inventory management examinations in the APICS Certificate in Production and Inventory Management (CPIM) program. The purpose of the book is to the fill the gap between the hundreds of books on forecasting methods and the multitude of books in the general area of management information systems. The authors seek to address both ‘the art and science of forecasting’. Even though 40% of the book (276 of 648 pages) discusses forecasting methods, the authors focus on how to forecast rather than how to do statistics or mathematics. The book is based on the concept that a good forecasting system is much more than a good forecasting software package. The authors distinguish between a forecasting method (‘a mathematical or subjective technique for generating a forecast’) and a forecasting system (‘a computer-based system to collect demand data. produce forecasts, provide managerial interface and interaction, generate output reports, provide system control and maintenance, and maintain database records’). The authors outline and discuss all the parts of a forecasting system except for the database records. It is not clear why the topic is not covered in their book. even though they identify it as an important part of a forecasting system. The authors note that “forecasting systems are considerably more complex than forecasting methods”. To cover this complex subject as broadly as possible. they divide the book into six parts: ( 1) Introduction to Forecasting Systems - Forecasting & Operations Management; (2) Forecasting System Applications - Manufacturer. Manufacturer-Distributor, and Retail, Maintenance & Remanufdcturer Forecasting; Forecasting Methods (3) Introduction to Statistical Fundamentals and Comparing & Selecting Methods; (4) Forecasting Methods - Smoothing, Regression & Decomposition, Fourier Series, and Accuracy & System Control; System Design (5) Forecasting and
Book reviews
Application - What & Where, Management, and System Design tion; and (6) The Future of Forecasting System Systems & Artificial Intelligence, tronic Data Interchange.
Inventory & Selec- Expert and Elec-
Each of the six parts begins with an overview of its purpose and use which provides the reader with a helpful guide through the subsequent chapters. The authors begin by stating that “customer service begins with having the right product or service, in the right quantity, at the right time, in the right location. This is only achievable through excellence in operational forecasting systems”. They observe that “a good operational forecasting system is an essential part of a good strategic planning system”. Consequently, they assert that forecasting skills are important managerial skills since “all managers are forecasters”. However, the most basic tenet of the book is that “the benefits from improved forecasting are truly extraordinary”. It is obvious that the authors believe this and that they are true disciples on an evangelistic crusade to gain converts. To do this they want to “share the basics of successful forecasting”. The book is sprinkled throughout with ‘forecasting system principles’ which are actually easy-to-remember adages summarizing forecasting concepts. These concise and memorable summary statements are nuggets of knowledge which the authors have mined out for the reader’s easy access and mastery. However, there may be too many for the reader to remember them all. As an example of one of the principles, the authors give the sage advice: “No one forecasting method is best for all series and forecast horizons. While best forecasting methods do not exist, better methods do”. However, some caution is needed in using the ‘principles’. The authors also state: “The best forecasting model is the one with the smallest standard deviation of the forecast errors”. The reader should be careful here; a forecasting method can produce the minimum mean squared error and still be operationally unacceptable if it produces negative profits. Maximum profit can be a preferable
645
measure of ‘better/best’ in operations management . The only statistical weakness in the book concerns confidence intervals. The authors only discuss point estimate forecasts; this simplification leads to some ambiguity. For example, although they refer to ‘probability statements’ when discussing statistical fundamentals, the concept is not expanded further to develop confidence intervals for point estimates. On the whole, though, the presentations and discussions throughout the book are clear and accurate with many useful charts, tables, and graphs. In addition, when discussing forecasting methods the authors use six data series as illustrations. The six data series are included in the appendix, and analyses of these series should be instructive for the beginning forecaster to cut his or her forecasting teeth. The best feature of the authors’ discussion of forecasting methods is that for each method the authors provide a brief statement of its advantages and disadvantages. This helps the manager become an informed user of forecasts. The authors’ discussion of forecasting methods is not comprehensive, however, since they only discuss time-series analysis. The authors do provide a forecasting system checklist and ranking criteria for the reader to use in evaluating his or her organization’s forecasting system needs. This can be a valuable guide to one of the most difficult aspects of forecasting: applying general methods and techniques to a specific application. Further, the authors provide excellent insight into system developments and implementation. Finally, one last attractive feature of the book is an extensive glossary of words and concepts in forecasting. Unfortunately, some terms are not expanded upon or even discussed in the text, e.g. ARIMA. Some of the definitions are somewhat oversimplified and vague, but, on the whole, they amply fill their intended purpose of enlightening managers about forecasting. The book’s main contribution is that it does fill the gap between books on forecasting methods and books on management information systems. The authors note that one learns how to read by reading and how to write by writing. They imply that one learns how to forecast by forecasting. This book provides a good guide for
managers reap the casting.
to begin substantial
the learning process and to rewards of improved fore-
Paul J. Fields The Pennsylvania State University University Park, PA USA
F.
Morrison,
namic
Systems:
The Art of Modeling DyForecasting for Chaos, Random-
1991,
ness,
and Determinism ( Wiley-Interscience, John Wiley & Sons, Inc., New York), pp. 387, ISBN O-471-52004-7, $54.95.
To appreciate Foster Morrison’s book, one must be aware of the increasing popularity and potential on non-linear approaches in business. Instead of statistically forecasting from past performance, non-linear approaches seek to mode1 the dynamical behavior of a system and anticipate future performance as a deterministic result of the system. Non-linear approaches, which are based on what is often called ‘Chaos Theory’, are increasingly prevalent in business research because organizations and their varied subsystems can be viewed as non-linear systems. Nonlinear techniques stand in stark contrast to traditional statistical approaches with their own set of terms, different mathematics, and different assumptions. Foster Morrison’s book can best be described as a toolbox for modeling dynamic systems. It describes the tools of dynamical modeling independent of their traditional engineering applications, although it stops short of making very many specific business applications. There is little room for applications because of the thorough treatment of dynamical modeling theory and the associated mathematical techniques. This book is to non-linear mathematics what your graduate text in linear models is to traditional statistics; it is an essential reference for anyone seriously interested in constructing dynamic models. The potential of more accurate forecasting with these techniques depends upon one’s willingness to learn the tools in this toolbox. Morrison describes the book as “a sort of
field guide” for those interested in dynamic modeling. After two introductory chapters, the book provides a five-chapter section titled “A Thumbnail Sketch of Applied Mathematics”. This section summarizes varied approaches to modeling such as ‘Classical Analysis’ (including calculus, complex numbers, and vectors), ‘Statistical Analysis’ (including curve fitting and spectral analysis), and Classical Modeling Techniques’ (which includes ordinary and partial differential equations). Chapters 8 through 11 provide an introduction to dynamical concepts. The discussion moves from analysis of dynamical systems without calculus to a discussion of oscillators, cycles, equilibrium points, and Liapunov Functions. Chapters 12 through 14 address the progressive disorder found in dynamic systems ranging from Static Systems to solvable systems, through perturbation theory, Chaotic Systems, and Stochastic Systems. Chapters 15 through 20 address issues of model-making dealing with qualitative. quantitative, and validation issues. Morrison includes a unique and exhaustive set of references in a section titled “The Modeler’s Library”. Those in the forecasting business may find much of this material quite removed from their current practice. It is not a particularly good introduction to dynamical or non-linear systems as the potential of these increasingly popular approaches is somehow lost in the technical discussion. It is, however, more accessible than an advanced engineering text on the same topic and, to Morrison’s credit, he has addressed the difficult mathematics of dynamic modeling with some attention to its potential in business. This is not a book for those interested in an initial exposure to chaos theory; rather, it is an essential book for those with strong quantitative abilities who find themselves already immersed in the challenging effort of modeling business functions as dynamical systems. H. Richard
Priesmeyer
Department of Management St. Mary’s University San Antonio, TX, USA
Mahmoud
American Graduate School oj International Management Arizona, U.S.A.
Stephen
A. Delurgio
Forecasting
(Business hardback,
Systems
One Irwin, US$49.95.
and Carl D. Bhame, for
Operations
Homewood,
1991,
Management
IL), pp. 648,
The book is intended for managers and analysts who use and prepare forecasts for decisionmaking in operations management. It is directed to all levels of management within an organization whether in manufacturing or service industries. Although it is written for readers at the college-level, perhaps at the first-year MBA level. there are no difficult statistical or mathematical discussions. For example. the presentation does not cover ARIMA methods. However, an introductory statistical background is helpful. It is a good reference book; it is very readable and the material is well referenced for readers interested in more detailed discussions. Both up-to-date and classical reference works are included. In addition, although it is not a text
book, it is intended for use in preparation for the master planning and the inventory management examinations in the APICS Certificate in Production and Inventory Management (CPIM) program. The purpose of the book is to the fill the gap between the hundreds of books on forecasting methods and the multitude of books in the general area of management information systems. The authors seek to address both ‘the art and science of forecasting’. Even though 40% of the book (276 of 648 pages) discusses forecasting methods, the authors focus on how to forecast rather than how to do statistics or mathematics. The book is based on the concept that a good forecasting system is much more than a good forecasting software package. The authors distinguish between a forecasting method (‘a mathematical or subjective technique for generating a forecast’) and a forecasting system (‘a computer-based system to collect demand data. produce forecasts, provide managerial interface and interaction, generate output reports, provide system control and maintenance, and maintain database records’). The authors outline and discuss all the parts of a forecasting system except for the database records. It is not clear why the topic is not covered in their book. even though they identify it as an important part of a forecasting system. The authors note that “forecasting systems are considerably more complex than forecasting methods”. To cover this complex subject as broadly as possible. they divide the book into six parts: ( 1) Introduction to Forecasting Systems - Forecasting & Operations Management; (2) Forecasting System Applications - Manufacturer. Manufacturer-Distributor, and Retail, Maintenance & Remanufdcturer Forecasting; Forecasting Methods (3) Introduction to Statistical Fundamentals and Comparing & Selecting Methods; (4) Forecasting Methods - Smoothing, Regression & Decomposition, Fourier Series, and Accuracy & System Control; System Design (5) Forecasting and
Book reviews
Application - What & Where, Management, and System Design tion; and (6) The Future of Forecasting System Systems & Artificial Intelligence, tronic Data Interchange.
Inventory & Selec- Expert and Elec-
Each of the six parts begins with an overview of its purpose and use which provides the reader with a helpful guide through the subsequent chapters. The authors begin by stating that “customer service begins with having the right product or service, in the right quantity, at the right time, in the right location. This is only achievable through excellence in operational forecasting systems”. They observe that “a good operational forecasting system is an essential part of a good strategic planning system”. Consequently, they assert that forecasting skills are important managerial skills since “all managers are forecasters”. However, the most basic tenet of the book is that “the benefits from improved forecasting are truly extraordinary”. It is obvious that the authors believe this and that they are true disciples on an evangelistic crusade to gain converts. To do this they want to “share the basics of successful forecasting”. The book is sprinkled throughout with ‘forecasting system principles’ which are actually easy-to-remember adages summarizing forecasting concepts. These concise and memorable summary statements are nuggets of knowledge which the authors have mined out for the reader’s easy access and mastery. However, there may be too many for the reader to remember them all. As an example of one of the principles, the authors give the sage advice: “No one forecasting method is best for all series and forecast horizons. While best forecasting methods do not exist, better methods do”. However, some caution is needed in using the ‘principles’. The authors also state: “The best forecasting model is the one with the smallest standard deviation of the forecast errors”. The reader should be careful here; a forecasting method can produce the minimum mean squared error and still be operationally unacceptable if it produces negative profits. Maximum profit can be a preferable
645
measure of ‘better/best’ in operations management . The only statistical weakness in the book concerns confidence intervals. The authors only discuss point estimate forecasts; this simplification leads to some ambiguity. For example, although they refer to ‘probability statements’ when discussing statistical fundamentals, the concept is not expanded further to develop confidence intervals for point estimates. On the whole, though, the presentations and discussions throughout the book are clear and accurate with many useful charts, tables, and graphs. In addition, when discussing forecasting methods the authors use six data series as illustrations. The six data series are included in the appendix, and analyses of these series should be instructive for the beginning forecaster to cut his or her forecasting teeth. The best feature of the authors’ discussion of forecasting methods is that for each method the authors provide a brief statement of its advantages and disadvantages. This helps the manager become an informed user of forecasts. The authors’ discussion of forecasting methods is not comprehensive, however, since they only discuss time-series analysis. The authors do provide a forecasting system checklist and ranking criteria for the reader to use in evaluating his or her organization’s forecasting system needs. This can be a valuable guide to one of the most difficult aspects of forecasting: applying general methods and techniques to a specific application. Further, the authors provide excellent insight into system developments and implementation. Finally, one last attractive feature of the book is an extensive glossary of words and concepts in forecasting. Unfortunately, some terms are not expanded upon or even discussed in the text, e.g. ARIMA. Some of the definitions are somewhat oversimplified and vague, but, on the whole, they amply fill their intended purpose of enlightening managers about forecasting. The book’s main contribution is that it does fill the gap between books on forecasting methods and books on management information systems. The authors note that one learns how to read by reading and how to write by writing. They imply that one learns how to forecast by forecasting. This book provides a good guide for
managers reap the casting.
to begin substantial
the learning process and to rewards of improved fore-
Paul J. Fields The Pennsylvania State University University Park, PA USA
F.
Morrison,
namic
Systems:
The Art of Modeling DyForecasting for Chaos, Random-
1991,
ness,
and Determinism ( Wiley-Interscience, John Wiley & Sons, Inc., New York), pp. 387, ISBN O-471-52004-7, $54.95.
To appreciate Foster Morrison’s book, one must be aware of the increasing popularity and potential on non-linear approaches in business. Instead of statistically forecasting from past performance, non-linear approaches seek to mode1 the dynamical behavior of a system and anticipate future performance as a deterministic result of the system. Non-linear approaches, which are based on what is often called ‘Chaos Theory’, are increasingly prevalent in business research because organizations and their varied subsystems can be viewed as non-linear systems. Nonlinear techniques stand in stark contrast to traditional statistical approaches with their own set of terms, different mathematics, and different assumptions. Foster Morrison’s book can best be described as a toolbox for modeling dynamic systems. It describes the tools of dynamical modeling independent of their traditional engineering applications, although it stops short of making very many specific business applications. There is little room for applications because of the thorough treatment of dynamical modeling theory and the associated mathematical techniques. This book is to non-linear mathematics what your graduate text in linear models is to traditional statistics; it is an essential reference for anyone seriously interested in constructing dynamic models. The potential of more accurate forecasting with these techniques depends upon one’s willingness to learn the tools in this toolbox. Morrison describes the book as “a sort of
field guide” for those interested in dynamic modeling. After two introductory chapters, the book provides a five-chapter section titled “A Thumbnail Sketch of Applied Mathematics”. This section summarizes varied approaches to modeling such as ‘Classical Analysis’ (including calculus, complex numbers, and vectors), ‘Statistical Analysis’ (including curve fitting and spectral analysis), and Classical Modeling Techniques’ (which includes ordinary and partial differential equations). Chapters 8 through 11 provide an introduction to dynamical concepts. The discussion moves from analysis of dynamical systems without calculus to a discussion of oscillators, cycles, equilibrium points, and Liapunov Functions. Chapters 12 through 14 address the progressive disorder found in dynamic systems ranging from Static Systems to solvable systems, through perturbation theory, Chaotic Systems, and Stochastic Systems. Chapters 15 through 20 address issues of model-making dealing with qualitative. quantitative, and validation issues. Morrison includes a unique and exhaustive set of references in a section titled “The Modeler’s Library”. Those in the forecasting business may find much of this material quite removed from their current practice. It is not a particularly good introduction to dynamical or non-linear systems as the potential of these increasingly popular approaches is somehow lost in the technical discussion. It is, however, more accessible than an advanced engineering text on the same topic and, to Morrison’s credit, he has addressed the difficult mathematics of dynamic modeling with some attention to its potential in business. This is not a book for those interested in an initial exposure to chaos theory; rather, it is an essential book for those with strong quantitative abilities who find themselves already immersed in the challenging effort of modeling business functions as dynamical systems. H. Richard
Priesmeyer
Department of Management St. Mary’s University San Antonio, TX, USA