Decision Making in Complex Times

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Managers are paid to make good decisions, and most of us have our own methods for decision making. Some are very formal, and others are shoot-from-the-hip, depending on a myriad of factors, many of which may be subconscious. Indeed, our individual decision making process may be directly correlated with our personalities. Often in business, our decision making style depends on the subjective magnitude of the decision and the potential outcomes. In this era of fake news, Technology-speaks and designed misdirection, one of the main decision making factors is prioritizing whose agenda is at the center of the problem or solution. Secondly, there is a risk-benefit analysis that constantly underpins the process.

Decision Making in Complex Times

It’s impossible to eliminate risk from strategic decision making. The unexpected is always out there. That said, we believe that it is possible for executives—and companies—to significantly improve their chances of success by way of good decision making. According to an article in the November 2013 Harvard Business Review, “most companies overly depend on basic tools like discounted cash flow analysis or very simple quantitative scenario testing, even when they’re facing highly complex, uncertain contexts. The conventional tools we all learned in business school are terrific when you’re working in a stable environment, with a business model you understand and access to sound information. They’re far less useful if you’re on unfamiliar terrain—if you’re in a fast-changing industry, launching a new kind of product, or shifting to a new business model.”

“Surveys have demonstrated that many decisions are made with incomplete or suspect data.”

Also, when it comes to any statistical or quantitative analysis, it is very important to understand fully the assumptions that the data describes. For example, we all use correlation and Rregression to help identify relationships, but we must consider how each variable is defined. Indeed, how we collect and clean the data can have a real impact on the validity of the metrics produced. Too often, market studies depend on the findings of other studies without verifying the data or considering how, when and where the data was collected. As the article also mentions, “conventional tools assume that decision-makers have access to remarkably complete and reliable information.” In fact, surveys have demonstrated that many decisions are made with incomplete or suspect data. While quantitative input is helpful, the quality of that input should always be challenged, and the input weighted accordingly.

While there is a wide variety of decision making tools, including case-based decision analysis, qualitative scenario analysis, and information markets, most decisions include the uncertainty of what the future may bring. Entropy and chaos are always lurking out there, ready to dismiss even the most rigorous of decision making processes. Indeed, linear thinking can be a real trap.

A Method to Help Select a Decision Making Methodology

The article proposes that the best way to identify a best-fit model for matching the decision making tools can be based on three factors: 1) how well you understand the variables that will determine success; 2) how well you can predict the range of possible outcomes; and 3) how centralized the relevant information is. For cases that involve a lot of uncertainty as to outcomes, the authors (Courtney, Lovallo, and Clarke) suggest that case-based decision analysis (which relies on multiple analogies) and qualitative scenario analysis be considered under conditions of uncertainty.

Common Complications in Decision Making

  • Most executives underestimate the uncertainty they face.
  • Organizational protocols can hinder decision making, and managers have little understanding of when it’s ideal to use several different tools to analyze a decision.
  • A reluctance to delay a decision until they can frame it better.

The Question to Ask: Do I know What it Will Take to Succeed?

According to the authors of the article, “You need to know whether you have a causal model—that is, a strong understanding of what critical success factors and economic conditions, and in what combination, will lead to a successful outcome. Companies that repeatedly make similar decisions often have strong causal models. Consider a retailer that has launched outlets for years in one country or one that has made many small acquisitions of adjacent competitors. One simple test of the strength of your causal model is whether you can specify with confidence a set of “if-then” statements about the decision. (“If our proposed new process technology lowers costs by X% and we are able to achieve Y% market share by passing those savings on to our customers, then we should invest in this technology.”) You should also be able to specify a financial model into which you can plug different assumptions (such as how much the technology lowers costs and how much market share you are able to capture).”

In summary, the authors suggest that decision-makers ask these simple questions:

Ask Yourself:

  • Do you understand what combination of critical success factors will determine whether your decision leads to a successful outcome?
  • Do you know what metrics need to be met to ensure success?
  • Do you have a precise understanding of—almost a recipe for—achieving success?

It goes without saying that decision making inherently contains risk. Moreover, it is the magnitude of the worst-case scenario that defines the depth and complexity of making the best decision, as well as risk mitigation.


Additional Reading

Staffing for Operational Excellence

What is the Optimal Group Size for Decision Making

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