Tools for Thinking: Modelling in Management Science

by Michael Pidd

This update of the successful first edition offers tools and methods for effectively thinking through the consequences of a given action before making major decisions. Managers will not only gain an understanding of effective tools and methodologies for modelling in decision making, but also come to understand how modelling functions alongside intuition,vision, and leadership. This is an invaluable guide to using modelling to explore potential scenarios and make the right business decision.

Genres: Business

332 pages, Paperback

First published January 24, 1997

Reviews

[Sicofonia · Rating 4 out of 5]

Tools for Thinking is an introduction to a variety of methods used for modelling in Management Science. The book consists of four parts: theoretical foundations, soft (or qualitative) modelling methods, hard (or quantitative) modelling methods and model validation.

The first part makes for a dry read, and it's a bit philosophical. Pidd stresses that before we can set out on the task of creating a model, we need to understand the problem we're trying to solve or alleviate first. To do so, we have to understand the meaning of the word “problem” itself. The highlight for me was the section where it explains the three categories for problems: puzzles, problems and messes. This is the key to choosing the right modelling method for our real-world situation.

The second part of the book presents us with three popular methods for soft (qualitative) modelling; namely, Soft-Systems Methodology (SSM), Cognitive Maps (SODA) and System Dynamics. The latter being something that can be used for quantitative purposes. These types of models are best used as interpretative, the intent for them is not so much to represent the real world but to be objects of discussion that can enable groups of people to make decisions. In my opinion, the details given here about each method are high level; therefore, the reader would be better to pick from the selected bibliography to find other texts that explain those methods fully.

The third part deals with hard or quantitative methods; that is linear regression and heuristic algorithms (tabu search, simulated annealing and the analogy of genetics). Linear regression is the method used for puzzles, that is, a well-defined problem for which a single best solution exits. Whereas, heuristics methods are employed when the complexity of the problem makes it, so it is not economical nor possible to find the best solution. So, heuristics help us find a global optimum by searching the solution space iteratively.

Lastly, there's the model validation part, where Pidd talks about the statistics applied for validating quantitative methods and also explores the highly controversial topic of validating soft models. As it is the general theme of the book, this part is also an introductory text to the subject.

All in all, this was a good book to get a 360-degree view of tools available to Management Science practitioners; covering all aspects ranging from the problem framing end to the modelling and validation side of things. Unfortunately, the text is a dated by now, as some websites or simulation software packages do not exist any more or there have been new additions to the market. Nevertheless, I have come across few books that give this high-level, all-encompassing view of modelling in Management Science. So I would say this is a good starting point for anybody new to the subject.

[Anelle Faure · Rating 2 out of 5]

Not the easiest book to read and understand. The examples aren't always that logic and easy to understand. Thank goodness for an excellent lecturer that helped me to better understand the concepts.

[Jaszmina · Rating 4 out of 5]

Beside hard methods a good summary of soft methods and problem structuring. Pity that out of the two heuristics only search heuristics are described and reasoning heuristics are neglected. Must read for OR undergraduates.