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Is demand planning ready for AI? – Technology – CSCMP's Supply Chain Quarterly

#artificialintelligence

Artificial intelligence (AI) continues to draw a lot of attention as companies and technology vendors look at how machine learning could improve supply chain operations. In particular demand planning, understood here as the process of developing forecasts that will drive operational supply chain decisions, is being touted as the next potential field for innovation. Technology giants like Amazon and Microsoft have announced AI tools for improving demand planning, and several consulting companies are promoting their skills to bring AI to companies' demand planning processes. In fact, a recent survey by the Institute of Business Forecasting and Planning (IBF) identified AI as the technology that will have the largest impact on demand planning in the next seven years.1 It's not hard to see the fit between AI and demand planning. Demand planning involves lots of number crunching and data analytics, and it is repeated cycle after cycle.


'Bold And The Beautiful' Spoilers: Brooke Demands Answers From Bill

International Business Times

She's determined to prove her husband's innocence and knows the only way to get it is to convince Bill to take back his assertions that Ridge was the one who tried to shoot him. Now, after paying him another visit in his hospital room, Brooke will continue trying to get through to her ex on the Friday, March 30 episode of "The Bold and the Beautiful." Brooke (Katherine Kelly Lang) is positive that Bill (Don Diamont) only named Ridge (Thorsten Kaye) as his shooter because of their long-standing relationship as mortal enemies. The situation between them has only grown more contemptuous over the years on the CBS soap, especially as they battled over her. After he woke up from his coma, Bill named Ridge as the one who tried to kill him, resulting in the other man's arrest.


Machine Learning And Artificial Intelligence In Demand Planning

#artificialintelligence

While machine learning and artificial intelligence (AI) have been used in supply chain applications for some time, there is an ongoing arms race to more effectively leverage both machine learning and artificial intelligence in demand planning solutions in new ways. Demand planning is one of the key applications in supply chain planning (SCP) suites. In ARC's recent global market study on this market, demand applications account for just under a third of a $2 billion plus market. And these applications are often the wedge purchase; the SCP solution that is first implemented by a company that then goes on to purchase other solutions in the suite. Machine learning works by taking the output of an application (for example, a forecast), examining that output against some measure of the truth, and then adjusting the parameters or math involved in generating the output (forecast), and seeing if the adjustments lead to more accurate outputs.


Machine Learning And Artificial Intelligence In Demand Planning

#artificialintelligence

While machine learning and artificial intelligence (AI) have been used in supply chain applications for some time, there is an ongoing arms race to more effectively leverage both machine learning and artificial intelligence in demand planning solutions in new ways. Demand planning is one of the key applications in supply chain planning (SCP) suites. In ARC's recent global market study on this market, demand applications account for just under a third of a $2 billion plus market. And these applications are often the wedge purchase; the SCP solution that is first implemented by a company that then goes on to purchase other solutions in the suite. Machine learning works by taking the output of an application (for example, a forecast), examining that output against some measure of the truth, and then adjusting the parameters or math involved in generating the output (forecast), and seeing if the adjustments lead to more accurate outputs.


Automated Statistical Forecasting At Nespresso Demand-Planning.com

@machinelearnbot

Having completed the implementation and roll-out, we are now working with teams in individual markets to build trust and increase forecast adoption. This is where the expertise of the HQ Demand Planners is coming into play. Each month, the team is reviewing, analyzing and sharing forecasts with the markets. At this point, we see the limitations of what the tool can do. The forecasting tool is perfect to find models that minimize forecast error but several proposed models have been found to be unusable and unrealistic.