 | From the Editor's Desk
Using Uncertainty Modeling to Better Predict Demand In the effort to reduce waste and eliminate redundancy, many companies have exposed themselves to greater risks of supply chain disruption, despite heavy investment in data analytics around demand prediction that should, in principle, drive out uncertainty. This article argues that the failure of demand prediction models is rooted in the fact that they do not take into account how data is generated, but simply explore apparent relationships in aggregated data that has been transferred from other functions in the organization. By unpacking the aggregation through a process the authors call uncertainty modeling, data scientists can identify new parameters to plug into the prediction models, which brings more information into the predictions and makes them more accurate.
The Covid-19 pandemic has triggered widespread supply chain disruptions across the globe: chip shortages are forcing automobile and medical equipment manufacturers to cut back production, while the blockage of the Suez Canal and the lack of shipping containers have inflated delivery lead times and shipping prices. Their effects have been exacerbated by management practices such as just-in-time manufacturing that are aimed at reducing redundancies in operations: with the redundancies have gone the safety buffers previously available to business supply chains.
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