In business schools we routinely face classes that contain both engineers and "poets," and we must include materials in our courses of value to both groups (and everyone in between). From my experience in this environment, I have concluded that "O.R.: the science of better" has much to offer that could improve the lives of everyone, including people who will not or cannot understand mathematics. To reach everyone, however, we have to promote O.R. topics that remain valuable when taught without the math. Luckily, these topics are not hard to find.

Most business school faculty would agree that their courses prepare students to analyze complex business problems, but many of their courses contain little mathematics or statistics. How can "strategic analysis" or "market analysis" be taught without spreadsheets, statistics or mathematics? The answer lies in recognizing that "analysis" as understood and practiced by a very large number of very bright and highly successful people is often completely qualitative. Often a "rigorous analysis" of a complex problem involves a deep, thoughtful discussion centered on some form of framework that provides a common perspective and vocabulary to guide the participants' thinking. For example, the "SWOT" (strengths, weaknesses, opportunities, threats) framework represents a state-of-the-art approach for resolution of a complex decision problem for many people, although SWOT provides no algorithm to help you actually choose an alternative. Similarly, the Porter strategic model and the Balanced Scorecard provide qualitative frameworks that have been shown to help intelligent people resolve complex decision situations successfully, but neither includes any form of decision algorithm.

The O.R. body of knowledge is built upon a set of frameworks that enable O.R. people to first model and then resolve complex decision issues. We O.R. teachers are prone to spend most of our class time with modeling and algorithmic details, and consequently we may not spend enough time emphasizing the basic frameworks that allow us to build the models and algorithms in the first place. Knowledge of these frameworks can be highly beneficial to real world decision-makers, even if they don't build the model or run the algorithm.

Based on my experience teaching O.R. to both engineers and poets, here is my list of seven really useful O.R. frameworks that can be effectively used by anyone.

1. How to Make a Good Decision


The ability to make good decisions is one of life's most valuable skills and is a key success factor for both personal life and business. Managers become identified by their decision-making ability — those who make great decisions get promoted, while those who consistently make poor decisions struggle. O.R. includes a huge body of knowledge on how to make great decisions, but few people have been exposed to these ideas.

The O.R. decision-making framework includes many important concepts. For example, if you are going to make a choice, you need to think about what alternatives you have to select from, what criterion (or criteria) are affected by your choice and what value you attach to these, how risky are the outcomes and how much risk you are prepared to tolerate, and how to handle the ever present trade-off between return and risk. The O.R. decision-making framework also recognizes that not all decisions are simple "choose an action and live with it" situations. Many decision situations involve sequences of choices and uncertainties, where decision-making about future choices conditional on observed outcomes is critical to understanding current choices. In such situations, an understanding of contingency analysis is helpful. What do I do if this happens? When should I change my future conditional decision? It may be important to understanding the timing of sequential decisions and to delay making commitments until you have all the available data.

Most O.R. people will see this as pretty simple stuff and will want to start building the tree, estimating utility functions and computing expected values, but we forget that most of the world has not been exposed to these central ideas. For these people, building the tree may not be so important; the value of the analysis may come from using this framework to think more deeply about the alternatives, criteria, and the risks and potential returns. Once this is done, a great many decisions become pretty straightforward.

2. How to Tell a Good Decision from a Bad One


Most of the world believes that good decisions are ones that turn out well while decisions that turn out poorly were bad decisions. We O.R. people know that sometimes good decisions turn out badly, and bad decisions turn out well; there is both good luck and bad luck. Since we cannot assess the quality of a decision by its outcome, and yet we value people who make good decisions, we can be a step ahead if we have a framework that enables us to tell a good decision from a bad one, or a good decision-maker from a chump.

O.R. recognizes that good decisions — whether they turn out well or poorly — are made by following a sound decision-making process. O.R. has many decision-making process frameworks that enable us to critically review the process that led to a decision. Most of these include at least three important steps.

The first step has several names ("pathfinding," "thinking outside the box," etc.) but involves spending some time generating alternatives: What could we do? This step provides an opportunity to be creative without being shackled by the status quo, but it is also important to be exhaustive; it is hard to decide to do something that you did not think was a possibility.

The second step involves analysis of the alternatives dreamed up in the first step in order to arrive at a small set of decisions that are implementable. "Analysis" comes in many forms. "SWOT" is state-of-the-art for many people, but most operations researchers would see this as very light. A more satisfactory analysis would address the issues raised in "How to make a good decision" (above). Importantly "analysis" almost always involves making simplifying assumptions, and consequently we recognize that the outcome of "analysis" should not be a decision, but rather a set of recommendations for possible decisions.

The final step in a sound decision-making process is to step back and consider the recommendations of the analysis under a real world lens. In the business this would involve management review and then action. This review may involve reviewing tradeoffs among criteria (including assessing the risk-return tradeoff among the various recommendations), or assumptions about uncertainties, or deciding which recommendation best aligns with management's objectives or corporate strategy.

This simple decision-making process framework provides a whole set of intelligent questions anytime someone proposes a course of action. What alternatives did you consider? What analysis did you do that leads you to conclude that this is a sound course of action? What assumptions were made in the analysis? How does your recommendation align with our real-world objectives? It also provides fodder to challenge some of the popular "buzz" of the day; for example, does "thinking outside the box" imply "acting outside the box"?

3. How to Cope with an Uncertain Future


Whether we like it or not, most decisions are made about the future, so decision-makers must almost always cope with uncertainties. O.R. includes several frameworks that enable us to model uncertainty, which also enable us to examine how effectively other people cope with an uncertain future. We can separate people who ignore uncertainty by pretending that averages will happen (exposing themselves to Sam Savage's "Flaw of Averages") from others who construct "best case" and "worst case" scenarios by combining the optimistic or pessimistic outcomes for all the stochastic events (and thereby often make their decisions based on scenarios that could well have a vanishingly small probability of actually occurring).

Stepping up from these naïve approaches requires a more detailed framework for thinking about uncertainty, and the O.R. body of knowledge provides a valuable set of concepts and tools that add value to any decision situation. We start with uncertain "events," which we can list to identify the sources of uncertainty. Some events are simple: Will it rain tomorrow? Others events sound complex: Will our product pass our market research trial and how many will we sell over a five-year period? These can usually be broken down into sequences of simpler events that are easier to understand. Probabilities provide a way of representing what we think will happen at each event and can be accessed without doing any math. We can use our skill and knowledge to assign subjective probabilities or we can ask someone more knowledgeable about the event than us to provide probabilities. If we really want to know the probability of rain tomorrow it's not a bad idea to ask a weather forecaster.

Many people do not like the idea of subjective probabilities, and there is an alternative. You can collect some data and extract probabilities from this data. Extracting rough probabilities from data does not always require a degree in statistics or operations research. A jewelry store here in town offered a 100 percent refund on all pre-Christmas purchases if there was more than 15 centimeters of snow on Jan. 7. Everyone wanted to know whether this offer was worth anything. Daily snowfall data is available on the Web, and some counting is all that is required to come up with pretty good probabilities.

Listing relevant events with their probabilities provides the basis for a deep and rich discussion of the uncertainties in the future. Such a discussion will produce an appreciation of the possible risks and rewards, will identify sources of disagreement among multiple parties to the decision, and will improve most people's decision-making.

4. How to Prosper in Risky Situations


Recognizing the opportunities that risk provides is a way of achieving personal and professional goals, yet when we expose students to risky situations in the classroom, they overwhelmingly decide to avoid taking on risk, even in situations where the expected value is quite positive and even when playing with corporate funds. Corporations value risk takers who can prosper by recognizing the upside of risky situations, and O.R. provides tools to help people overcome their aversion to downside risk.

The "three Ms" of risk provide a framework that helps people to understand how to take on risk and capture the upside. First, risk must be measured so we understand what we are dealing with. A risk profile that lists all the possible outcomes with their probabilities provides the most complete specification of the riskiness of a situation, although other summaries (such as the mean and standard deviation of returns) are also useful. A risk profile can be put together subjectively as a discussion item, although we would like to see more people using a spreadsheet with random numbers to move from their knowledge of elementary events to a risk profile for a more complex outcome.

Second, risk can usually be mitigated; if we like the upside but not the downside, there are usually steps that we can take to reduce our downside probabilities. These might include selling off the risk to someone else (for example, by buying insurance), taking on a partner to share the risks and rewards, designing a trial so that preliminary results are obtained for a much reduced cost or diversifying. Diversification is a powerful risk mitigation tool and not just in putting together a financial portfolio. The firm that takes on only a single research-and-development project might be exposed to high downside risk, but the firm that takes on 20 such projects is going to come away pretty close to the expected return.

The third "M" of risk is management. Managers exposed to serious downside risk don't just sit there and await fate. They monitor the situation very closely and do everything they can to change the probabilities or the payoffs in their favor as events unfold. This might involve spending additional funds to ensure a desired outcome occurs or cutting a trial short if preliminary results suggest it is not going to work out as expected.

When facing a risky decision, everyone should ask three questions: What are the risks? How can I mitigate the risks? How can I manage the risks? Thinking through the answers to these questions in advance will improve everyone's decision-making.

5. Recognizing and Exploiting Simultaneous Decision Situations


Most people outside the O.R. community believe that all complex decisions can be broken down into a series of simple decisions that can be taken sequentially to achieve a good solution. The O.R. body of knowledge recognizes that this approach can be expensively sub-optimal, even if you take the additional step of iterating a few times to ensure that the early decisions remain consistent with the later ones.

The O.R. framework for identifying and solving simultaneous decision problems, which we usually call "optimization," involves identifying the decisions to be made, finding an objective that enables alternate solutions to be compared, and recognizing the constraints that limit the range of implementable solutions. This is one case, however, where the framework also includes recognition of the fact that we humans are very poor at solving complex simultaneous problems intuitively. There is certainly value in laying out any decision problem using this framework, but we recognize that the big advantage in the case of simultaneous decision problems comes from using a tool (such as Excel Solver) to actually "do the math" and solve the problem.

Those who understand the simultaneous framework and can recognize the kinds of problems where a sequential decision approach fails, and who know a better approach, have a competitive advantage in the marketplace. A great many people, including students of mine, have used the standard Excel Solver to save thousands of dollars for their employers.

6. Revenue Management


Revenue management (RM) has had and is having a dramatic effect on the way firms price their products and make these products available to the marketplace. However, the human impact is even greater; most people who have been exposed to highly variable pricing or restricted supply are frustrated because they do not understand the revenue managed marketplace. An understanding of the basics of RM pricing and a framework that ties the various tools (such as overbooking, trading-up, discount allocation, short-selling and reservation levels) together is a professional and social winner and will also help with personal shopping. Knowing how to maximize revenues while selling the same quantity of product is also a business winner. [More on the concepts of RM can be found at "Revenue Management for MBAs," OR/MS Today, August 2004, pp. 22-27. (http://lionhrtpub.com/orms/orms-8-04/frbell.html)]

7. How to Link O.R. to Corporate Strategy


The ascendancy of O.R. to an important role in the business world will require that highly paid and highly intelligent managers come to understand the strategic value of O.R. to their organizations. Many of our senior "C-level" executives are not quantitatively trained and see management as an art rather than a science. Convincing these executives of the strategic value of O.R. so that they will invest in O.R. work requires a framework that links O.R. to strategy.

A basic framework describes four ways to link O.R. and strategy. First, since the primary impact of a successful business strategy is that it creates a competitive advantage that is sustainable over a period of time, O.R. work that creates and maintains a competitive advantage is strategic to the corporation. There are many well-documented examples of such "strategic O.R." in the literature (see Bell, Anderson, and Kaiser, Operations Research, Vol. 51, No. 1, 2003). Second, O.R. can be linked to strategy by providing assistance with the resolution of decisions that are strategic to the organization. Third, a number of organizations have had an O.R. group that for some time was comprehensively involved in their organization's decision-making. Examples include the O.R. groups at FedEx and San Miguel corporation, the decision technologies group at American Airlines before the spin-out of Sabre, and the "Global Analytics" group at Procter and Gamble. Finally, a number of firms market O.R. products, and for them, nurturing their O.R. capability is a critical part of their business strategy. Examples include firms that market O.R. tools (e.g.: ILOG, Frontline Systems), firms that market solutions that include serious O.R. algorithms (Giro, Aspen Technologies, Visual8) and firms that provide O.R. consulting services.

Much of the real-life impact of O.R. arises from the application of our basic frameworks in a thoughtful way, rather than from building sophisticated models or performing complex calculations. Everyone, including the poets, can achieve significant benefits by using these frameworks to help them make decisions and to contribute meaningfully in an environment where complex decisions are made.

The business world believes that SWOT or the Porter model represent the leading edge of business analysis, but people armed with these seven basic frameworks (in addition to a familiarity with SWOT and Porter) ought to be able to do better. We should seize the opportunity to put our basic frameworks out there. If we are successful, we will improve people's lives, enhance our students' promotion prospects and help further develop "O.R.: the science of better."



Peter C. Bell is a professor at the Ivey School of Business, University of Western Ontario.

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