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Business intelligence is all about employing processes and tools to achieve efficiency in commerce. These technological approaches include all business functions that occur across the enterprise, including the supply chain. Assumed within this quest for efficiency is the ultimate goal of maximizing profit through minimizing operating costs. In order for this to become reality, the performance management-savvy organization must frequently rely on the application of analysis techniques like maximizing gross margin through measuring inventory return on investment or minimizing total operating expenses. Accordingly, supply chain optimization must look at the general problem of putting goods into the hands of one's customers at the best cost and highest yield.
The classic approach to solving this optimization problem has involved traditional forecasting based on historic demand and prediction of future events. This approach is applied to aggregate data resident in smaller data repositories that require little manipulation. The analyst then manually manages any variability in the metrics. Using this forecasted demand, a plan is crafted that addresses the salient supply-related objectives, such as manufacturing rate, plant resource scheduling, re-stock strategy, or transportation modes.
The BI supply chain forecasting and reporting ability resident in today's software solutions provides the technical ability to access and manipulate larger databases more swiftly. The metrics are gathered and manipulated using automated processes and key performance indicators using advanced capabilities, like scorecards and dashboards. Some vendors have integrated these features into their BI products and applying multiple modeling and analytic techniques to supply chain data to enhance the optimization problem.