I have frequently described this much broader view of the performance management framework. Read my column, “Why the High Interest in Performance Management Now?” if you’d like to learn more about that topic. This column discusses an essential capability of enterprise performance management - modeling.
Managing Performance Requires a Deep Understanding of Causality
In my articles and books, I use an analogy that compares performance management components to meshed gears in a machine with a global positioning system for navigating strategy execution. I think of this as enterprise optimization. I really like the term “optimization” even though it can be dismissed by some managers as theoretical or impractical to achieve. Enterprise optimization can be described as the pursuit and realization of an organization’s strategic objectives with the least amount of total resources in an ever-changing environment. This pursuit maximizes long-term shareholder wealth creation through a deep understanding of customers. But this description of optimization is only shallow rhetoric unless we dig deeper. What roles do business analytics and modeling have in enterprise optimization?
Modeling is essential to improving decisions. A model is a representation of physical activities and their outcomes. For some models, such as weather forecasts, the complex interdependencies of all the variables make the accuracy quickly decline. Hence, frequent recalculation of the model is needed. For example, reliable weather forecasts, at best, project a week or two into the future. However, at its core a model is based on understanding cause-and-effect relationships - typically multiple and simultaneous ones. The better the relationships are understood, then the more reliable and longer lasting the model’s projections will be.
Where does understanding the input/output relationships for a model come from? From analytics. That is, understanding the behavior of how anything works is based analytics of all flavors, including segmentation and statistical correlation analysis.
The Emergence of Business Analytics
Modeling is prominent in fields such as skyscraper construction and oil and gas exploration. Biologists model cell behavior. Geneticists model DNA to understand diseases. Baseball executives model batter and pitcher outcomes to determine who to trade or pay higher salaries. When I was a junior at Cornell University in 1970, I wrote a computer baseball game with a classmate, based on a dice baseball game I played when I was a kid. The computer game simulated the 1969 National League season by calibrating the batters and hitters to their records, and the computer’s team rankings and win-loss records nearly matched the actual results. My program was accepted by the National Baseball Hall of Fame as the oldest computer baseball game. Now that’s modeling.
It is not a big leap to apply the analytical methods and skills of engineers and scientists to managing and transforming an organization.
At the heart of modeling is decision-making. And in any organization, decisions abound -requiring marketing analysts to determine which types of customers to retain, grow, win back or acquire and which types not to. More deeply, what is the optimal spending amount on deals, discounts and offers to optimize future customer net revenues (profits)? How should an organization’s risk appetite be balanced against its risk exposure? How should the CFO report reliable rolling financial forecasts (because the budget is so quickly obsolete due to unexpected changes)? How should a personnel department identify the next employees who are likely to voluntarily quit or who to hire next? These questions can all be answered by using business analytics.
A strategy map and its companion (the balanced scorecard) are becoming popular for aligning the behavior of managers and employee teams with measureable strategic objectives they can be held accountable for. When you think of it, a strategy map is a model of an organization. What are your most vital key performance indicators? Use business analytics to test their correlations.
Optimization is About Resources and Outcomes
Some mistakenly think that enterprise resource planning software systems are the ultimate solution to enterprise optimization. They are not. ERP systems can be used as technology-based tools that contribute transactional data. However, managerial tasks - such as planning, simulating, defining and analyzing alternatives, and then selecting the optimum outcome - require far more input than transactional data from an ERP system. Getting all of the information needed for optimization can only be accomplished by integrating the various methodologies of the performance management framework and embedding business analytics, especially predictive analytics, within each methodology.
Optimization is about determining the best level of resources (i.e., human capital or equipment) to produce the highest yield and desired outcomes. Optimization includes managing that same “best level” of resources and aligning their behavior and priorities with the strategic objectives of the executive team. Optimization cannot be realized without business analytics. Modeling is foundational to achieving effective enterprise performance management, and business analytics is at the heart of modeling.
Gary Cokins is the founder of Analytics-Based Performance Management LLC, an advisory firm. He is an internationally recognized expert, speaker and author in advanced cost management and performance improvement systems previously a principal consultant with SAS. You can contact him at gcokins@garycokins.com. For more of Cokins' unique look at the world, visit his website at www.garycokins.com.
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