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Machine Learning And Artificial Intelligence In Demand Planning

Machine Learning And Artificial Intelligence In Demand Planning

A SoftBank Group Corp. Pepper robot. Photographer: Kiyoshi Ota/Bloomberg
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.
This is not surprising. 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.  It can truly be said that the machine is learning from experience.
The definition of artificial intelligence (AI) is broader. Any device that can perceive its environment and takes actions that maximize its chance of success at some goal is engaged in some form of AI. AI includes expert systems that read and interpret written language or natural language processing applications like the iPhone's Siri.
Before any demand management solution is implemented, the master data needs to be cleaned up. This process of cleaning and normalizing the data takes far longer, costs more, and requires more ongoing effort than almost all companies anticipate. AI is being used to help normalize the data. So, for example, demand planning applications can be used to forecast how much of a product will be shipped to a large customer. In the database, Procter & Gamble, Porter & Gambel (sp.), Procter and Gamble, and so forth all refer to the same customer.  But digital applications need precision; All these names must be spelled and punctuated identically. It is an easy task, but a time consuming one, for a human to do. With AI, the task can be greatly sped up. LLamasoft is one solution provider that is using AI in this manner.

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