A common phrase in SCM (Supply Chain Management) is Just-In-Time (JIT) inventory. JIT refers to a management strategy in which raw materials, products, or services are delivered to the right place, at the right time, as demand requires. This has always been an excellent business goal, but the power to excel at JIT inventory management is now improving dramatically with the increased use of data analytics across the supply chain.
In the article “Operational Analytics and Droning About Big Data“, we discussed two examples of JIT: (1) a just-in-time supply replenishment system for human bases on the Moon, and (2) the proposal by Amazon to use drones to deliver products to your front door “just in time”! The Internet of Things will almost certainly generate similar use cases and benefits.
Descriptive analytics (hindsight) tells you what has already happened in your supply chain. If there was a deficiency or problem somewhere, then you can react to that event. But, that is “old school” supply chain management. Modern analytics is predictive (foresight), allowing you to predict where the need will occur (in advance) so that you can proactively deliver products and services at the point of need, just in time.
The next advance in analytics is prescriptive (insight), which uses optimization techniques (from operations research) in combination with insights and knowledge of your business (systems, processes, and resources) in order to optimize your delivery systems, for the best possible outcome (greater sales, fewer losses, reduced inventory, etc.). Just-in-time supply chain management then becomes something more than a reality — it now becomes an enabler of increased efficiency and productivity.
Many more examples of use cases in the manufacturing and retail industries (and elsewhere) where just-in-time analytics is important (and what you can do about it) have been enumerated by the fast Automatic Modeling folks from Soft10, Inc. Check out their fast predictive analytics products at http://soft10ware.com/.
(Read more about these ideas at: https://www.linkedin.com/pulse/supply-chain-data-analytics-jit-legit-kirk-borne)
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