Improving demand forecasting with artificial intelligence is one of the most promising applications for supply chains, but how do the non-traditional methods compare in performance with established forecasting practices?
Improving Demand Forecasting with Artificial Intelligence
Using artificial intelligence (AI) and machine learning to improve demand forecasting is one of the most promising applications of AI for supply chains.
The technology “learns” from past experience and can analyze the multitude of complex relationships and factors that influence product demand.
However, AI-enabled demand forecasting is still at a relatively early stage of development.
A key question for supply chain professionals is: How do the non-traditional methods compare in performance with established forecasting practices?
And, to what extent does it affect supply chain efficiency?
A thesis research project at the Malaysia Institute of Supply Chain Innovation (MISI) made such a comparison.
The project affirms the value of AI in demand forecasting for certain product types and highlights areas where more research is needed.
Accuracy in Demand
The subject of the research is a steel-making enterprise that operates globally.
Steel products are “functional” in that they typically have long life cycles, fewer variants (compared, to say, “innovative” products), and modest margins.
Demand for these products is generally stable and predictable.
However, a product that benefits from stable demand invites competition. To create and sustain competitive advantage for functional products, companies must keep inventory, storage, and transportation costs to a minimum.
Reducing inventory costs also improves working capital performance; an important benefit where capital is constrained.
The steel manufacturer for this research project has been operating for some 50 years. Historically, the company focused on supplying construction markets with customized products.
Orders are received in advance, and the competitive strategy for this segment lies in providing attractive lead times to customers. In response to economic growth, the company has expanded into the retail market segment. Retail has become its fastest-growing segment, with a year-over-year growth rate of 5% by volume.
In this market, the company offers a wide range of products such as coated and painted steel coils for roofing applications in a make-to-stock (MTS) manufacturing environment. In this operating mode, the planning horizon is short-term – one month to three months – and demand forecasting has to be accurate at both the product family and individual SKU levels.
Knowing how much to manufacture as precisely as possible is a key requirement that will gain in importance as the manufacturer continues on its current growth path – hence its interest in improving the demand forecasts that drive production. Read more here
November 20, 2018 · By Javad Feizabadi and Apurv Shrivastava