{"id":19554,"date":"2026-03-11T16:41:39","date_gmt":"2026-03-11T21:41:39","guid":{"rendered":"https:\/\/uwm.edu\/business\/?p=19554"},"modified":"2026-04-06T14:56:11","modified_gmt":"2026-04-06T19:56:11","slug":"what-beer-diapers-and-ai-reveal-about-the-future-of-sales-forecasting","status":"publish","type":"post","link":"https:\/\/uwm.edu\/business\/what-beer-diapers-and-ai-reveal-about-the-future-of-sales-forecasting\/","title":{"rendered":"What Beer, Diapers, and AI Reveal About the Future of Sales Forecasting"},"content":{"rendered":"\n

When companies try to predict product sales, they often look at one product at a time. But according to new research co-authored by Huimin Zhao1<\/a><\/sup>, that approach can miss a much bigger story about how customers actually shop.<\/p>\n\n\n\n

Zhao, a professor of Information Technology Management in the Lubar College of Business, studies how relationships among products influence sales. His recent research shows that forecasting improves when businesses move beyond the traditional idea that products are either \u201ccomplements\u201d or \u201csubstitutes.\u201d<\/p>\n\n\n\n

\u201cMost models assume products only matter to each other in very limited ways,\u201d Zhao said. \u201cBut in real life, customers make choices based on many signals at once.\u201d<\/p>\n\n\n\n

Classic examples of complements include phones and phone cases, while substitutes might be two similar smartphone brands. Zhao\u2019s research shows there are many other connections that matter. Products can be linked because they are displayed near each other, share a brand reputation, or follow similar buying patterns over time. Some relationships are indirect or one-sided, and others change as technology and consumer habits evolve.<\/p>\n\n\n\n

\u201cProduct relationships are not fixed,\u201d Zhao said. \u201cThey can grow stronger, weaker, or even flip from complements to substitutes as markets change.\u201d<\/p>\n\n\n\n

To capture this complexity, Zhao and his co-authors developed advanced artificial intelligence models that analyze sales data across many products at once. These models learn how products influence each other over time and use that insight to make more accurate forecasts.<\/p>\n\n\n\n

The results were striking. In tests using real retail data, the new approach consistently outperformed existing forecasting methods. It also uncovered surprising connections. In one case, sales of a respiratory drug and a cardiovascular drug showed a negative relationship.<\/p>\n\n\n\n

\u201cAt first, that looks strange,\u201d Zhao said. \u201cBut once you think about seasonal health patterns, it starts to make sense.\u201d<\/p>\n\n\n\n

Cold weather can increase cardiovascular risks, while respiratory issues are often more common in warmer or drier conditions. Understanding patterns like these helps retailers plan inventory, staffing, and promotions more effectively.<\/p>\n\n\n\n

For business leaders, the takeaway is clear. Better data and smarter models lead to better decisions.<\/p>\n\n\n\n

\u201cSales forecasting isn\u2019t just about numbers,\u201d Zhao said. \u201cIt\u2019s about understanding customer behavior in context.\u201d<\/p>\n\n\n\n

That insight is increasingly important as companies face faster product cycles, tighter margins, and more complex supply chains. Zhao\u2019s research shows that looking beyond individual products can give organizations a clearer view of what\u2019s coming next, and a competitive edge in preparing for it.<\/p>\n\n\n\n

Research@Lubar<\/strong> Faculty scholarship in the Lubar College of Business spans the business fields and beyond through both theoretical and applied research that is published in leading journals.  Here are some of our faculty\u2019s most recent publications:<\/em><\/td><\/tr>
Consumer-driven Class Pricing<\/a>
Marketing Science<\/em>
Author: Zuhui Xiao<\/a><\/td><\/tr>
Does Gibrat\u2019s Law Apply to Chain Organizations? The Case of Franchising<\/a>
Applied Economics<\/em>
Authors: Steve Michael<\/a> and Antonio C. J. Porto<\/td><\/tr>
How Do Technology Paradigms Influence Configurations of Contract Characteristics for Success of Inter-Organizational Outsourcing Projects, 1991\u20132009?<\/a>
Journal of Operations Management<\/em>
Authors: Onkar S. Malgonde, Moez Farokhnia Hamedani, Sunil Mithas, Manish Agrawal, Kaushal Chari<\/a><\/td><\/tr>
Click here<\/a><\/strong> to see more faculty research<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n
  1. Beyond Complements and Substitutes: A Graph Neural Network Approach for Collaborative Retail Sales Forecasting<\/a>
    Information Systems Research<\/em>
    Authors: Jing Liu, Gang Wang,\u00a0    Huimin Zhao<\/a>, Mingfeng Lu, Lihua Huang,\u00a0and Gang Chen\u00a0 \u21a9\ufe0e<\/a><\/li><\/ol>\n\n\n

    <\/p>\n","protected":false},"excerpt":{"rendered":"

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    Authors: Jing Liu, Gang Wang,\\u00a0    Huimin Zhao<\\\/a>, Mingfeng Lu, Lihua Huang,\\u00a0and Gang Chen\\u00a0\"}]","uwm_wg_additional_authors":[]},"categories":[39,62,22],"tags":[],"class_list":["post-19554","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news","category-research","category-spotlight"],"yoast_head":"\nLubar College of Business<\/title>\n<meta name=\"description\" content=\"When companies try to predict product sales, they often look at one product at a time. 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