Microsimulation modeling - an analytic method pioneered in the public sector - offers an innovative solution to the challenges surrounding enterprise decision management. First developed in the 1960s, microsimulation models were originally maintained on government supercomputers, where they spun through large data files to simulate the impact of proposed changes to tax and transfer programs. The ambitious scope and granularity of these models led a panel of the National Research Council to conclude in 1991, "No other type of model can match microsimulation in its potential for flexible, fine-grained analysis of proposed policy changes."
Until recently, the applicability of microsimulation models was limited to a handful of government programs due to the enormous cost of computing resources and the lack of appropriate microdata. Now, with the power of supercomputers on every desktop and with enterprise-wide information sharing enabled by mature data warehouses, an opportunity exists to repurpose microsimulation modeling for private-sector use.
One of the virtues of microsimulation models is that they establish a stable and consistent framework for conducting analysis of structured data. This quality makes microsimulation an attractive tool for bridging the gap between facts and truth, offering a platform for the comparison of alternative business strategies. This article describes the key characteristics of a microsimulation model and presents a brief description of the steps required to build and use such a model. In the future, we expect to see more data warehouse-enabled organizations using microsimulation to accelerate enterprise decision management.
Key Characteristics of a Microsimulation Model
At its core, microsimulation is a computational technique used to predict the behavior of a system by predicting the behavior of microlevel units that make up the system. Microsimulation models operate by taking a representative sample of units (e.g., individuals, households, transactions) and applying parameterized algorithms to simulate processes, behaviors and outcomes. The parameters that govern the model are then varied to simulate the impact of changes to a process, policy or procedure at the individual case level, and overall results are aggregated to address a broad range of management questions.
Microsimulation is best suited for the analysis of systems where decision-making occurs at the transaction or unit level, where interactions are numerous and complex and where it is important to understand both aggregate and distributional impacts. All of these traits are evident in enterprise management, particularly for organizations that handle large volumes of customer interactions.
Microsimulation models present a view of the business based on a detailed representation of the process used to perform a particular business function. This process is described down to a level of detail where outcomes for particular cases are determined. A case could be an individual order, customer interaction or manufactured part. Whatever the modeled process, the attributes of each observed case are measured, and the path each takes through the production process is tracked.
The process is then represented in a modular fashion, with all key process steps considered in isolation. After individual models are produced for each process step, the process steps are combined into a larger model, with the outputs of upstream process steps feeding into the downstream steps that occur later. It is important to recognize that these steps are constructed somewhat differently than a simple process simulation, which can be similar in appearance to the modular structure of a microsimulation model. In a typical process simulation, where the units of observation are often identical, variation in outcomes is caused solely by randomness in the process. In contrast, microsimulation modules typically use business rules or mathematical probabilities to reflect the correlation between characteristics of each distinct unit and the outcomes that it experiences. Note that this formulation may also include random effects, as in the case of a simple process simulation.
The completed microsimulation model is a reusable analytic resource targeted toward the forecasting of future results and the improvement of a specific process. In order to exploit this resource for maximum gain, the modeling team builds scenarios that introduce changes in the population, process parameters or environment. Simulation of alternative scenarios - structured to capture key features of proposed business strategies and tactics - facilitates management understanding of the drivers of organizational performance.
Building and Using a Microsimulation Model
The flowchart in Figure 1 shows the typical sequence of steps involved in the design, development and use of a microsimulation model.
Figure 1: Steps to Building a Microsimulation Model
Establish Foundational Data
Microsimulation modeling requires high-quality data that is complete and clean. As a result, the data warehouse is the foundation of the microsimulation modeling effort. First, the data warehouse should be tapped to create a data set containing records for past transactions. These transactions should be paired with customer characteristics and other information in the warehouse that might be correlated with the outcome of the transaction.
Second, the business rules and parameters that describe the underlying business process must be assembled. These might include standard policies such as shipping charges, allowable wait times for telephone orders or shipping consolidation policies.
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