Quantitative Analysis for Management (9th ed.)

Citation

Bischoff, E.E. (2006), "Quantitative Analysis for Management (9th ed.)", Journal of Modelling in Management, Vol. 1 No. 2, pp. 184-185. https://doi.org/10.1108/17465660610703503

The latest edition of this standard student textbook comprises 17 chapters plus seven appendices and a CD‐ROM. It is intended as an introduction to Management Science techniques and covers the main topics in this field, including, amongst others, decision analysis, forecasting, inventory control, linear/integer/nonlinear programming, network models, simulation, and statistical quality control. Each chapter starts with a list of learning objectives and concludes with a brief summary. There are also separate glossaries for each topic and extensive sections containing solved problems, self‐test questions, as well as discussion questions and exercises. Short answers to the latter are provided in some – but by no means all – cases. Whilst I appreciate that some instructors might prefer not to have a complete set of solutions included, many readers may find this somewhat annoying.

So what are the main improvements in the new edition? The preface lists a number of updated and new features, but only four are referred to as major changes. The most significant is probably the introduction of a separate chapter on regression models, which provides a good basic overview of this topic – although the section on associated pitfalls would perhaps benefit from additional concrete examples. Other modifications include the grouping together under a single heading of various topics in the area of decision theory and additions to the chapters on inventory control and forecasting. Some of this material had previously been on the accompanying CD‐ROM. The six modules now provided on it include game theory, dynamic programming and calculus‐based optimisation.

The new edition, therefore, is essentially little more than a slightly updated version of the previous one and this means that the text has retained not only its main advantages, but also its key shortcomings. Amongst the positive features are an excellent chapter on applications of linear programming and a pretty good one on transportation and assignment models. The lucid writing style makes the material easily accessible, although for my liking it is a bit too dry in places. The descriptions of the various approaches presented are generally well supported and illustrated through numerical examples. These can be tackled through the software also supplied on the CD‐ROM (Version 2.2 of “QM for Windows” and “ExcelQM 2”, an add‐in for Microsoft Excel). Moreover, there is a useful companion web site containing additional tests and exercises as well as PowerPoint presentation files for each of the different topics. There are two main shortcomings as far as I am concerned. The most serious one is that the book focuses very firmly on explaining how to apply the techniques described and puts little effort into engendering a deeper understanding of the underlying concepts. I could cite many examples. A simple one is the description of exponential smoothing, where the reader is given absolutely no guidance for selecting a value for the first forecast and only a rather cryptic footnote hints at the reason why this value might not be important. In my opinion anybody using this technique should be aware of the (lack of) sensitivity of the results to the starting value used. My second quibble is with the presentation. The colour scheme employed merely consists of various shades of grey and blue – and the book looks rather boring as a result. More importantly, some illustrations would probably have a much greater impact if additional colours were used.

Overall, I would describe this text as an adequate – but unexciting – offering, and considering the price tag involved it is not likely to be cited outside the “additional useful texts” category on my reading lists.