This is the fourth in a four part series on the Unified Performance, Risk, and Compliance Model. Part I covered the strategize and prioritize phase, Part II covered the plan and execute phase
, and Part III covered the monitor and analyze phase. In the model and optimize phase of the Performance Management Lifecycle, we strive to assess the drivers of performance and risk at a deep level to understand the various alternatives we can pursue with the goal of making the best decision given a certain set of constraints. This phase is depicted graphically below:
Modeling falls into three categories.
Revenue, Cost, and Profitability Modeling. Modeling the costs, revenue, and profitability implications of performance management, risk management, and compliance management activities and their drivers can be achieved at a very detailed level using activity-based costing and associated methodologies.
Scenario Modeling. Scenario modeling can be applied to financial and operational modeling and focuses on creating different business scenarios. Simple scenario modeling can include creating a base case and then high and low cases based on changes made to input variables, such as market growth rates or inflation rates. This technique is often used in modeling market and business opportunities and creating business plans.
Simulation Modeling. More advanced modeling including Monte Carlo simulation supports creating a broad range of scenarios based on multiple iterations of input assumptions and combinations. With this technique, probabilities can be assigned to the various outcomes. These techniques allow the uncertainty associated with a given forecast to be estimated and to reduce risk by applying sensitivity analysis, correlation, and trend extrapolation. By simulating the effect of uncertainty, it becomes possible to answer questions such as, “How certain are we that a given project (or group of projects) will result in a minimum outcome of x?” Or, conversely, “What’s the minimum outcome that we can be, for example, 90% certain of achieving?” Simulation also makes it possible to identify and rank the various contributors to overall uncertainty.
The goal at this phase of the PM lifecycle is to determine the optimal way to achieve objectives by taking into account the entire context of the problem, including all relevant constraints and assessments (costs, benefits, risk, labor and time), as well as business strategies, objectives, risks, and compliance factors. Optimization can be done both through human evaluation as well as through advanced algorithmic techniques.
From a process unification perspective, risk and compliance management operating in tandem with performance management will become differentiating capabilities in the management of an organization. By effectively communicating and deploying strategy across the enterprise, proactively identifying and mitigating risks and integrating them with goals and plans, and doing so in a fashion compliant with external regulations and internal policies, the enterprise can be confident that it is maximizing performance in the context of its risks while adroitly responding to a dynamic market.
From a technology unification perspective, business intelligence can be conceptualized as the base of the pyramid upon which performance management and governance, risk, and compliance are built, since it provides the basic technology capabilities and infrastructure that serve as a foundation for the higher layers of the pyramid. Connecting governance, risk and compliance capabilities with performance management capabilities through a common business intelligence platform establishes a single, unified, cleansed repository of information and common semantics on top of that information, which is critical to enabling risk-aware performance management business processes. Without this common foundation, it is impossible to obtain any synergies that extend beyond deploying any one of these capabilities in isolation.
Excerpted from Driven to Perform: Risk-Aware Performance Management From Strategy Through Execution (Nenshad Bardoliwalla, Stephanie Buscemi, and Denise Broady, New York, NY, Evolved Technologist Press, 2009). Copyright © 2009 by Evolved Media, LLC
(Cross-posted @ Strategy-Driven Execution)