By Gary Cokins, Founder of Analytics-Based Performance Management LLC
The confusion, ambiguity and lack of consensus about what enterprise performance management is will continue for a long time. Fortunately, many are realizing that enterprise performance management (EPM) is much broader than how it’s often narrowly perceived – as just a CFO initiative consisting of bunch of dashboard dials and some better reports.
In my last blog I shared some of my thoughts on what exactly is the broad topic of enterprise performance management. This time I’m going to take the narrow route and focus on an essential capability within enterprise performance management – modeling.
Managing performance requires a deep understanding of causality
I often use an analogy that compares EPM’s various integrated component methods to meshed gears in a machine operated by something akin to global positioning system (GPS) for navigating strategy. 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 the creation of long-term shareholder wealth through a deep understanding of the needs and preferences of customers. Great – but what does business analytics and modeling have to do with enterprise optimization?
A model is a representation of physical activities and their outcomes. Models are essential for improving decision making. In some models, such as weather forecasts, complex interdependencies between variables result in decreased accuracy. Hence, the model needs to be frequently re-calculated. 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 – and typically multiple and simultaneous ones. The better the relationships are understood, then the more reliable and longer lasting will be the model’s projections.
The understanding of the input-output relationships in a model requires analytics of all flavors, including segmentation, clustering 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.
When we can relate modeling in this way to sports or other interests we have we can begin to understand how it helps us interpret complex issues that confront us professionally. We can see it is not a big leap to see how scientific and engineering skills can be applied to the management of organizations.
In organizations, decisions abound – requiring marketing analysts to determine which types of customers to retain, grow, win back or acquire – and which types to not. More deeply, what is the optimal spending amount on deals, discounts and offers necessary 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 (since 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.
Strategy maps and companion balanced scorecards have long been popular for aligning the behavior and priorities of managers and teams to measureable strategic objectives for which they are accountable. Very simply, a strategy map is a model of an organization. They track your most vital key performance indicators (KPIs)
Optimization is about resources and outcomes
Some mistakenly think that enterprise resource planning (ERP) applications are the ultimate enterprise optimization solution. They are not. 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 is only accomplished by integrating the various methods of the enterprise performance management framework and embedding business analytics, especially predictive analytics, within each method.
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.
Keep an eye out for my next blog in which I talk about decision management.
About the Author: Gary Cokins, CPIM
Gary Cokins (Cornell University BS IE/OR, 1971; Northwestern University Kellogg MBA 1974) is an internationally recognized expert, speaker, and author in enterprise and corporate performance management (EPM/CPM) systems. He is the founder of Analytics-Based Performance Management LLC www.garycokins.com . He began his career in industry with a Fortune 100 company in CFO and operations roles. Then 15 years in consulting with Deloitte, KPMG, and EDS (now part of HP). From 1997 until 2013 Gary was a Principal Consultant with SAS, a business analytics software vendor. His most recent books are Performance Management: Integrating Strategy Execution, Methodologies, Risk, and Analytics and Predictive Business Analytics.
email@example.com; phone +919 720 2718
Hear Gary share some of his thoughts concerning EPM innovations and best practices at the SAP Conference for EPM in Chicago, October 13/14, 2014