Advanced Analytics
Model Review & Validation



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Case Study

Case Study

A leading transportation company sought to evaluate and upgrade its already significant use of advanced analytics. The company retained Princeton Consultants to demonstrate how incorporating new data sources, improved algorithms, and a corresponding shift in business processes will result in better economics, service, nimbleness, consistency, and transparency.

Princeton Consultants evaluated the forecasting and optimization models used in operational decision making. The outputs of the forecasting models were used as inputs to a sequence of optimization models. The review and validation uncovered the following:

  • Forecasts were using small amounts of historical data and simplistic techniques for outlier removal, and were not tuned to account for the variability of the business in different geographies.
  • In one optimization model, two key issues were uncovered:
    • Multiple optimal solutions were possible, yielding a potential wide variability in the results that would drive future decisions.
    • There was an inconsistent understanding of the model’s objective function and its implementation, leading to results that did not reflect the intention of the person designing the model. 
  • In a second optimization model, two key issues were uncovered:
    • The model had additional variables that allowed answers to be computed that were not feasible in the business.
    • The data supplied to the model significantly overstated the business conditions.

The company’s analytics leaders found that the independent nature of Princeton Consultants’ validation and review was important in finding opportunities to improve the use of the models in their operational systems. As a result, they will further contribute to the company’s competitive advantage through analytics.