Mining Analog Business Information

Copyright 1995, Mustafa Uzumeri and David Nembhard

Many business processes are inherently analog in nature. The key measurements from these processes (e.g., output rates, productivity, and defect rates) can be seen as "signals" that are generated by the internal activities of each process. A product's sales history, for example, signals the marketplace's satisfaction with the product. Similarly, a worker's hourly production is a signal of that worker's ability and motivation under current working conditions.

This view differs from the implicit perspective of most current MIS systems. While they are typically very good at collecting and processing real-time transaction data, their reliance on tabular data tends to obscure the dynamic behavior of the process.

These shortcomings have encouraged system designers to look for better ways to understand and visualize dynamic process behavior. To our way of thinking, the options really fall into three categories:

By taking an analog perspective, we are led to the third option. The analog approach uses a mathematical function to describe the observed pattern. Some of the variables in the mathematical equation are adjusted to produce a curve that captures the essential shape of the underlying "signal". This produces a "filtered" summary of the process that is much more compact and offers two important benefits over traditional representations and analysis:

We believe that these advantages, combined with the growing need to understand process dynamics, justify the extra effort required to develop and apply an analog approach. Every large organization will have some strategic measures or operations that merit fuller description and closer analysis.

We expect that the analog approach will be particularly relevant when managers need to understand the behavior of populations of individuals or critical processes. Possible examples would include the sales rates for products, delivery times for shipments, market prices for stocks, defect rates from processes, cycle times through production cells, and productivity by operators.

By characterizing the shapes of these signals across a meaningful population, the analog approach can provide decision-makers with a powerful view of dynamic operations. Moreover, our initial explorations show that this view can be achieved with current hardware and software tools. To illustrate this, we offer an example that applies this approach with a specifically chosen function.


By viewing operational data as an "analog signal", it is possible to create mathematical descriptions of the patterns in the data. Since these descriptions are much simpler than the raw data they describe, they can be visualized in fewer dimensions. This, combined with the inherent ease of mathematical analysis, makes the analog approach capabilities.

Some Questions and Answers regarding mining analog business data

This research is being conducted jointly with David Nembhard, a colleague at Auburn's College of Business.

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