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Supply Chain Predictive Analytics

As the name suggests, Supply Chain Predictive Analytics (SCPA), deals with predictions on the operations side of the business and what impact it can have on the financials of the company as well as customer service. Typically, predictive analytics engines look into the past data and try to predict the future. The main distinction of SCPA is that it has a model of the future and that is how it can predict expected events both operationally and financially. An interesting analogy is a typical GPS system and how it operates compared to past data. If you were to go to airport to catch a flight, based on your past experience, you would estimate the time it takes to be (say) 45 minutes at a given time of the day. In contrast, the GPS system looks at the current conditions and potential issues as well as all the alternatives and comes up with the best alternative and provides an accurate estimate without having to look at the past data. Supply chain models work in the same way and predict the expected events in a realistic way based on current and expected future conditions and all the possible alternatives. In addition, Adexa’s planning engines monitor the occurrences of future events and if they see a trend then that information is used to recommend an alternative to the end user based on expected trends. In our GPS analogy, the system may recommend use of a different airport because of faster commute time or cheaper fares or more frequent flights to the desired destinations. Interested reader may refer to the section on Self-Improving Supply Chains for additional detail on this subject.

Adexa Predcitive Analytics

Adexa Predictive Analytics solution provides supply chain leaders with hundreds of ready-to-go Key Predictive Analytics (KPIs), complete with formulas and calculations. It is an umbrella layer in the overall IBP solutions of Adexa deployed with any of the Adexa planning solutions on the demand or supply side including all the financial and operational KPIs and risk management measures. The system can be extended by adding customized KPI’s, data, or measures. Each stakeholder can personalize their view of the “future” by bringing together reports, metrics, analytics, and scorecards based on their role and authority, whether operational or financial. Amongst other measures, the robust library of data and metrics includes:

  • Financial (Revenue, Operations cost, Inventory costs)
  • Demand (Forecast Accuracy, Fill Rates, Order lead-times)
  • Production and Distribution (Plans and Conformance)
  • Inventory (Days-on-hand, Slow-moving, Inventory Turns)
  • Resource (Capacity, Utilization, Loads)
  • Purchasing (Quantities, On-time performance, Quality)

All for what to expect and the potential impact of any risks that the company might be exposed to. In addition, the users can perform what-if analysis to examine sensitivity analysis and or simulate various events and examine how robust the supply chain is when (say) the demand increases or a transportation route is cut off or a supplier cannot deliver.

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