Theory of Constraints Handbook - James Cox Iii [442]
OR/MS Structured Approaches
OR/MS has adopted the phrase, The Science of Better, describing itself as the scientific approach to solving business problems. Similar terms have been used to describe the TP. Hence, a comparison between OR/MS and TOC would appear to be appropriate. Despite its origins as a problem-focused, multidisciplinary activity employing top scientists to attack operational problems, OR/MS has become very focused on techniques. In the United States, these techniques are almost exclusively quantitative in nature, and most modern-day OR/MS textbooks are rooted in the language of mathematics: mathematical modeling in its various forms such as mathematical programming (including linear and integer programming), simulation, heuristics, scheduling, decision analysis, data envelopment analysis, inventory control, and project scheduling. In these areas, OR/MS has achieved notable successes, largely through the use of powerful mathematical and computer modeling techniques to crack large problems. As such, OR/MS tools and techniques have predominantly contributed to the analysis and assessment phases of the problem intervention process, set out in the M-B framework. Indeed, such emphasis on mathematics and its use in this way is well recognized and even reinforced by the publication regimes of the top American OR/MS journals, which restrict their scope to those papers containing mathematically rigorous treatment (Simchi-Levi, 2009).
Some leading OR/MS authors, however, view this narrow definition of OR/MS—a collection of powerful mathematical tools—as unhelpful, even detrimental to achieving the full potential of OR/MS. As Daellenbach (1994, 112) puts it:
When reading about how to do a problem formulation, the tyro management scientist is often somewhat impatient: “This seems to be all obvious—let’s get down to the really interesting mathematical modelling phase! That is real OR/MS!” Unfortunately unless the groundwork for the modelling phase is properly done in the formulation, the risk is great that, although challenging, the modelling may address the wrong problem. Not only can this have serious consequences for the analyst, it also puts OR/MS into disrepute.
Refreshingly, perhaps pointedly, Daellenbach devotes the early chapters of his text to systems thinking, systems concepts, systems modeling, and problem formulation before introducing mathematical modeling.
Many OR/MS writers have made similar points about the tendency to solve the wrong problem; for example, Gass (1989), Zeleny (1981), Rosenhead (1989), and Mabin and Gibson (1998), and offered alternatives (e.g. Pidd, 1996). The debate that raged in OR/MS circles in the 1970s, led by Ackoff (1977; 1978; 1979), was largely due to this concern that the obsession OR/MS had with mathematical modeling led the OR profession astray. TOC writers have added their voices: Jackson et al. (1994) provided a powerful case comparing the standard OR-derived Economic Order Quantity (EOQ) with the EC approach for inventory control, following Goldratt’s own treatment of batch sizing decisions (Goldratt, 1990b, 43); Mabin et al. (2009) compared OR’s math programming approach with an EC approach to a warehouse/distribution problem. The concern of these authors over problem definition—rather than merely problem solution—is shared by the developers of the various soft OR methods, also known as Problem-Structuring Methods (PSMs), which are discussed in the next section.
Present-day OR/MS tools and methods have much to offer in addressing complexities relating to scale, time, and computation. They have much to offer when the problem is well defined and when goals, local or global, are known, understood, and accepted by stakeholders with common perspectives; when desired outcomes can be guaranteed by action; when the successful accommodation of multiple objectives is unambiguous;