“In God we trust. All others must bring data.” – W. Edwards Deming, engineer, statistician, professor, author, lecturer, and management consultant
Data collection has grown at an enormous pace. Hidden within the expanse of data are treasures that can help run the business in an efficient way as well as transform its long-term potential. Harnessing the power of big data requires clever data strategy and transformation of existing business processes. There are many businesses in different industries that have been successful institutionalizing data driven decision making, leveraging both predictive analytics and mathematical models within their organizations. In this blog post we will explore the industries and the mathematical models that can help to provide a competitive advantage.
Lodging and Consumer Goods companies with dedicated Operations Research personnel and departments have made significant progress in guiding business decisions with predictive analytics. While companies differ in their level of success, most have created an end-to-end process that begins with understanding demand signals and predicting demand potential. Tens of millions of forecasts are generated by companies for user review and/or feedback. Pricing and inventory allocation decisions are actively managed by optimization models that balance available supply and forecasted demand across different business segments. Business teams are encouraged to interact with the system generating pricing/inventory decisions. In the past, companies have focused on performance measurement using optimal models to provide feedback to business users on their actions. The following diagram illustrates this end-to-end process institutionalized over the years.
Demand forecast (d) is often modeled with price (p), time (t) and other (x) key demand signals.
Integrating user established available demand (d) and optimizing profit (Z) (or) revenue across all business segments (s) and time period (t), with other key variables like accepted demand (x), available and capacity (C) can yield valuable insights to pricing actions (p) and inventory allocated by business segment.
Business performance is often tracked using a scaled Opportunity Scoring (S) model that can act as valuable method to compare business decisions and performance over time.
Original Equipment Managers (OEMs)/ Retail dealership networks can leverage the above process and model, institutionalize analytical sophistication; and guide business decisions with predictive analytic insights. An essential implementation requires data integration across functional silos. Going the extra mile and integrating across external data sources can yield valuable insights to industry and segment level performance. Such an approach can help Original Equipment Managers (OEMs) focus and understand relative strength of their key products and produce better forecast as well as analyze different scenarios. Forecasts generated at national and regional levels can then be used to allocate inventory and incentive budget better. Performance measurement processes that establish optimal metrics provide feedback to regional allocation and incentive budgeting teams and help drive long-term transformation using predictive analytics to guide future business decisions.