by Işık Biçer, Murat Tarakci, and Ayhan Kuzu

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.

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