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Past Krenicki Center Projects

Optimal Clustering of Products for Regression-Type and Classification-Type Predictive Modeling for Assortment Planning

In collaboration with a national retailer, this study focused on assessing the impact of sales prediction accuracy when clustering sparse demand products in various ways. While also trying to identify scenarios when framing the problem as a regression-problem or classification-problem would lead to the best demand decision-support. This problem was motivated because modeling very sparse demand products can be extremely difficult. Some retailers frame the prediction problem as a classification problem, where they obtain the propensity that a product will sell or not sell within a specified planning horizon. Likewise they might model it in a regression setting that is plagued by many zeros in the response. In our study, we clustered products using k-means, SOMs, and HDBSCAN algorithms using lifecycles, failure rates, product usability, and market-type features. We found there was a consistent story behind the clusters generated, which was primarily distinguished by particular demand patterns. Next, we aggregated the clustering results into a single input feature, which led to improved prediction accuracy of the models we examined. When forecasting sales, we investigated a variety of different regression and classification type models, and reported a short list of those models that performed the best in each case. Lastly, we identified certain scenarios we observed when modeling the problem, a classification problem versus a regression problem, so that our partner could more strategically forecast their assortment decision.

Effect of Forecast Accuracy on Inventory Optimization Model

This study described an optimization solution to minimize costs at the inventory system by the retailer. Previously, all demands were forecasted yearly and information regarding item distribution was not used. The retailer used weekly and monthly demand forecasts by using yearly forecasts. As a result, the retailer purchased items in bulk to prepare for unexpected demand from vendors, which generated huge holding costs. By approaching the distribution of each item, then a dynamic economic order quantity model would be possible. We solved this problem by using diverse distributions for each item. We then built formulas to calculate costs and service levels, and optimized our model to minimize the cost. While also meeting several business requirements, such as minimum service level, for each item. We demonstrated the impact that the quality of the demand forecast had on the client’s business.

A Comparative Study of Machine Learning Frameworks for Demand Forecasting

While working with a national consulting company our study had a two pronged approach with objectives. Firstly we asked which machine learning approaches perform the best at predicting demand for grocery items? Secondly we asked what is the performance one could expect to achieve using an open-source workflow versus using proprietary in-house machine learning software? Our main motivation behind this research was that consulting companies regularly assist their retail clients to try to understand demand as accurately as possible. Efficient and accurate demand forecasts enable retailers to anticipate demand and plan better. In addition to delivering accurate results, data science teams must also continue to develop and improve their workflow so that experiments can be performed with greater ease and speed. We found that with using open-source technologies such as scikit-learn, postgreSQL, and R, a decent performing workflow could be developed. This could help train and score forecasts for thousands of products and stores accurately at various aggregated levels (e.g. day/week/month) level using deep-learning algorithms. While the performance of our solution is yet to be compared to the data science team’s commercial platform, we will add that data soon. We have learned how they are able to achieve performance gains through model accuracy and runtime. Making this collaboration a great learning experience.

A Retrospective Investigation of Test & Learn Business Experiments & Lift Analysis

This study provided an analysis to retrospectively investigate how various promotional activities (e.g. discount rates and bundling) affect a firm’s KPIs such as sales, traffic, and margins. The motivation for this study is that in the retail industry, a small change in price has significant business implications. The Fortune 500 retailer we collaborated with thrives on low price margins and had historically run many promotions. However, until this study they had limited ability to estimate the impact of these promotions on the business. The solution given employs a traditional log-log model of demand versus price to obtain a baseline measure of price sensitivity, followed by an efficient dynamic time-series intermittent forecast to estimate the promotional lift. We believe our approach was both a novel and practical solution to retrospectively understand promotional effects of test-and learn type experiments that all retailers could implement to help improve their revenue management.

An Analytical Approach for Understanding Promotion Effects on Demand and Improving Profits

The objective of this study was to design and develop a better revenue management system that focused on leveraging and understanding of price elasticity and promotional effects to predict demand for grocery items. This study was important because the use of sales promotions in grocery retailing has intensified over the last decade where competition between retailers has increased. Category managers constantly face the challenge of maximizing sales and profits for each category. Price elasticities of demand play a major role in the selection of products for promotions, and are a major lever retailers will use to push not only the products on sale. We modeled price sensitivity and developed highly accurate predictive demand models based on the product, discount, and other promotional attributes, using machine learning approaches, and compared performance of those models against time-series forecasts.