Predictive Analytics in the Supply ChainSummary:  Predictive analytics are increasingly important to Supply Chain Management making the process more accurate, reliable, and at reduced cost.  To be at the top of your game as a supply chain manager you need to understand and utilize advanced predictive analytics.

As a large continuous process the Supply Chain has been extensively studied and is pretty well understood. It goes in well recognized steps from:

  1. Procurement
  2. In-Bound Logistics
  3. Parts Inventory
  4. Manufacturing
  5. Finished Goods Inventory
  6. Fulfillment (customer’s order to delivery)
  7. Out-Bound Logistics

As you can see these are not completely unique processes.  Fulfillment for example could easily be understood to encompass all of finished goods inventory, order-to-deliver, and out-bound logistics. Manufacturing is understood by some to include all the process steps leading up to that point.  But however you divide it, there is agreement that it is one continuous process and that a delay or failure at any point will ripple through the system and prevent efficient execution.

While these seven elements of the supply chain are each the focus of separate management activity, visibility over the entire supply chain is also a requirement.  Particularly visibility into exceptions to the plan that might mean failure or delay.

Historically ‘visibility’ has been the key word, along with integration.  In the past this meant actions taken based on observed events being closely linked with mitigating strategies up and down the chain.

 

Increasingly though, a requirement of high maturity is the ability to better foresee the future, anticipate future events, and make optimal tradeoffs based on intentional strategic choices of top management.  In short, to be at the top of the game in Supply Chain Management now requires including advanced predictive analytics.

 

This article originally appeared in datasciencecentral.com.  To read the full article, click here