This article originally published at https://www.linkedin.com/pulse/ai-supply-chain-forecasting-cas-milner/ on January 27, 2021. It talks about one of the CFO pain points, which is planning.
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.
Complete Intelligence has built a comprehensive platform for making very accurate supply chain ingredient forecasts. The forecasting Saas have 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 Tony Nash , 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.
Many companies have antiquated, inaccurate processes for forecasting costs in their supply chain. Their standard statistical forecasting is usually done with linear regression – a straight-line projection of historical costs, into the future. But the price behavior of most commodities is not linear, it is non-linear. Artificial intelligence algorithms are especially suited to making accurate forecasts using non-linear data, which is why they are increasingly applied to dynamic financial forecasting.
Many industries are especially sensitive to supply costs:
- Manufacturing (electronics, energy equipment, automotive, health supplies, pharmaceuticals, metals, plastics, papers)
- Extraction operations (oil and gas, forestry, mining)
- Services (transportation, shipping, hospitality, food and beverage)
Supply chain cost planning is a core process, and AI tools are destined to become key ingredients, deeply embedded in operations. They enable automation of proactive planning and monitoring to digitally transform the organization. The licensing cost for these financial forecasting tools or financial projection software is a small fraction of the operations cost – and potential savings. It is also worth noting that having reliable forecasts of future price trends can create a rational basis for supplier negotiations. Simplify financial planning with AI and machine learning.
I’m excited about the AI-driven digital transformation of micro-economic forecasting, and would eagerly discuss the benefits with you.
#SupplyChain #AI #EconomicForecasting