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and objectives can be quantified. However, even when these conditions are not met, the sophistication of analysis and the scale of computer power can generate a sense of false security especially where the relationships of local goals to systems goals are not understood or accounted for, or when local goals surface as “numerical” or binding constraints in the hard mathematical formulations—and do so without being questioned. Indeed, even when problems are not well defined, assumptions are often made to make the problem tractable or amenable to mathematical formulation, often without sufficient questioning of the appropriateness of such assumptions.

The value of the TOC TP as a comprehensive methodology is that they bring such issues to the fore, forcing a consideration of the broader problem situation, global and local goals, challenging the assumptions that underpin them, oftentimes setting the solution path toward a very different goal.

While it is often claimed that effective OR/MS practitioners do seek to achieve the global and systems goals, the reality is often a suboptimization of a technical subsystem (in the material world of the M-B grid) that can be modeled, undertaken without any mandate or ability to place the problem in a wider context or to consider broader issues and ramifications.

In brief, most OR/MS methods have strengths in evaluating the relative effectiveness of alternative choices and decisions, and identifying the best among them, according to prescribed quantitative criteria and objectives, or in some cases, from a prescribed or readily imputed list of choices (for example, optimization by linear programming (LP). These methods stop short of guiding decisions on value systems, strategic direction, or other matters of identifying strategic choice for a variety of stakeholders. Furthermore, if methods of constrained or numerical optimization are examples of such methods, then by contrast, it is the methods of soft OR, along with TOC, that have been designed to grapple with the wicked problems or messes that are beyond the scope of the traditional mathematical modeling methods of OR/MS (Mingers, 2009a). We explore these matters further in the next section.

Soft OR

The concern of some OR writers over problem identification and problem definition—rather than merely problem solution—is shared by the developers of various soft OR methods or PSMs. These were showcased in the book Rational Analysis for a Problematic World (Rosenhead, 1989), which became the most referenced book in the Journal of the Operational Research Society in the next decade (Rosenhead, 2009). Soft OR is suited to messy situations, where the first issue is that of not knowing what the problem is. Soft OR methods have been designed and developed to grapple with wicked problems or messes by seeking to gain understanding of what would be desirable and appropriate goals of the organizational system and subsystems, and by seeking a broader, often predominantly qualitative, understanding of the problem domain or wider system within which it sits.

Soft OR or PSMs aim to:

structure complexity of content and represent it in a transparent manner

be deployed in a facilitated group environment

develop model structure interactively

incorporate tools to encourage participation and generate commitment to action.

However, they do so in a manner that bears the scrutiny of rigor expected of any science-based approach. Virtually none of the attributes of soft OR apply to traditional OR/MS approaches, the latter being increasingly known as hard OR (Rosenhead, 2009, S10.) The field of soft OR now includes a wide variety of approaches, developed for a range of purposes and applications, some of which could be considered systems approaches. They include:

Strategic Choice Approach (SCA)

Strategic Assumption Surfacing and Testing (SAST)

Soft Systems Methodology (SSM)

Critical Systems Heuristics (CSH)

Cognitive Mapping (CM)

Strategic Options Development and Analysis (SODA)

Robustness Analysis

Interactive Planning

Soft Game Theory