{"id":41069,"date":"2021-01-31T18:30:57","date_gmt":"2021-02-01T00:30:57","guid":{"rendered":"https:\/\/completeintel.com\/?p=41069"},"modified":"2021-01-31T18:30:57","modified_gmt":"2021-02-01T00:30:57","slug":"ai-for-supply-chain-forecasting","status":"publish","type":"post","link":"https:\/\/completeintel.com\/ci-markets-weekly\/ai-for-supply-chain-forecasting\/","title":{"rendered":"AI for Supply Chain Forecasting and Proactive Planning"},"content":{"rendered":"

This article originally published at https:\/\/www.linkedin.com\/pulse\/ai-supply-chain-forecasting-cas-milner\/\u00a0<\/a>on January 27, 2021. It talks about one of the CFO\u00a0pain points, which is planning.<\/span><\/em><\/p>\n

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How much confidence do you have in traditional price forecasts for the components of your supply chain? Your answer is probably “not much”, if you have been in business for over a decade — or even just during 2020! But AI can do better — much better — at price forecasting than the standard statistical technique of linear regression most of us learned in college.<\/p>\n

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Complete Intelligence<\/a>\u00a0has built a comprehensive platform for making very accurate supply chain ingredient forecasts. The forecasting Saas<\/span>\u00a0have done the hard work of aggregating (and cleaning!) billions of data points from many high-quality sources, including import\/export trade data, all feeding the AI algorithm engines to produce amazingly accurate predictions. You should follow the postings of\u00a0Tony Nash<\/a>\u00a0, for his economic commentary based on many forecasts for exchange rates, basic commodities, and supply chain components important for world economies and local business operations.<\/p>\n

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Many companies have antiquated, inaccurate processes for forecasting costs in their supply chain. Their standard statistical forecasting is usually done with linear regression \u2013 a straight-line projection of historical costs, into the future.\u00a0But the price behavior of most commodities is not linear, it is non-linear.\u00a0Artificial intelligence algorithms are especially suited to making accurate forecasts using non-linear data, which is why they are increasingly applied to dynamic financial forecasting<\/span>.<\/p>\n

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Many industries are especially sensitive to supply costs:<\/p>\n

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