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Data Analytics in Supply Chain

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upply chain analytics is the analysis of information companies draw from procurement, inventory management, order management, warehouse management, fulfillment, and transportation management. This information helps business leaders improve supply chain logistics to resolve inefficiencies and give businesses a competitive edge. 

Supply chain analytics uses data produced by the various parts of the supply chain,  such as procurement, manufacturing, and fulfillment--to help business leaders improve supply chain logistics. There are four types of analytics that are most commonly used:  

Descriptive analytics provides metrics into what’s currently happening with the business,  such as safety stock levels, fill rate, or average lead times. 

Predictive analytics focuses on the future, helping you forecast demand and mitigate potential risks. 

Prescriptive analytics combines the results of descriptive and predictive analytics to suggest what actions a business should take right now in order to reach its desired goals in the future.  

And finally, 

cognitive analytics helps solve complex problems using machine learning and AI.  

With these valuable insights, supply chain analytics resolves hidden inefficiencies for near-term cost savings and helps leaders put long-term strategic changes into effect-- all of which ultimately gives the business a competitive advantage.

 

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