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AI-Enabled Demand Forecasting Streamlines Supply Chain

By Logistics Tech Outlook | Tuesday, August 13, 2019

The ability of ML algorithms to learn from past experience and data sets is benefitting the supply chain processes significantly.

FREMONT, CA: Supply chain spans across complicated processes in procurement, manufacturing, and distribution with effective inventory management as a critical element. Thus the firms are leveraging technologies such as artificial intelligence (AI) to gain better insights into demand forecasting concerning their supply chain processes. The ability of machine learning (ML) algorithms to learn from past experience and data sets is enabling them to analyze multitudes of complex relationships and factors that impact product demand. Despite such advancements, AI-enabled demand forecasting is still at a raw stage of development. Another critical question regarding AI is the extent to which it affects supply chain efficiency against conventional methods.

As per a thesis research report at the Malaysia Institute of Supply Chain Innovation (MISI), the above-mentioned comparison has been made. The project tries to assess the value of demand forecasting for certain products while highlighting the area that requires more research.

Accuracy in Demand

A product that gains from stable demand invites the competition. Companies are required to keep storage, inventory, and transportation costs to a minimum in order to create and maintain a competitive advantage. Offsetting inventory costs also enhances working capital performance providing a vital benefit where the capital is constrained. Accurate estimation of how much to manufacture is a primary requirement that will gain in importance in the near future.

Drawbacks of Traditional Solutions

Traditional solutions account for a relatively narrow area of demand-impacting factors such as seasonality. In reality, demand is affected in response to various market and macroeconomic conditions, and traditional solutions often fail to capture these factors. As a result, conventional solutions produce less accurate forecasts, which are reflected in a company’s prediction performance.

Knowledge Gaps

ML-based forecasting can assist the companies in addressing the above challenges and enhancing their supply chain as well as engagements with customers. The latest solutions incorporate AI learning algorithms with Big Data to assess an unlimited number of factors simultaneously. The algorithm learns from the past and current insights while regularly improving and refining the demand forecasting process.

AI-Based Solutions vs. Traditional Solutions

However, these applications are yet to mature. More information on how they behave in practice is required. For instance, despite the technology being deployed in various areas of the supply chain such as sourcing and demand forecasting and supply chain, the results are relatively inconsistent. Some experts have even argued that traditional demand forecasting methods can yield comparable or even superior to those achieved via ML. The increasing doubts concerning AI-based solutions and traditional solutions have led researchers onto hybrid alternatives that combine conventional and non-conventional methods. However, in general, the ML approach to demands forecasting captures a complex mix of market variables and historical data which perform better than traditional time series and linear models.

Applying technology can counter the bullwhip effect to a degree, particularly in industries such as steel manufacturing where demand does not remain seasonal. However, a better approach to this issue is to foster communication and collaboration across the supply chain resulting in a more transparent system.

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