Demand Prediction in Retail

2022-01-01 Business & Economics

Author : Maxime C. Cohen
Release : 2022-01-01
Genre : Business & Economics
Kind : eBook
Book Rating : 553/5 ( reviews)

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Book Synopsis Demand Prediction in Retail by : Maxime C. Cohen

Download or read book Demand Prediction in Retail written by Maxime C. Cohen. This book was released on 2022-01-01. Available in PDF, EPUB and Kindle. Book excerpt: From data collection to evaluation and visualization of prediction results, this book provides a comprehensive overview of the process of predicting demand for retailers. Each step is illustrated with the relevant code and implementation details to demystify how historical data can be leveraged to predict future demand. The tools and methods presented can be applied to most retail settings, both online and brick-and-mortar, such as fashion, electronics, groceries, and furniture. This book is intended to help students in business analytics and data scientists better master how to leverage data for predicting demand in retail applications. It can also be used as a guide for supply chain practitioners who are interested in predicting demand. It enables readers to understand how to leverage data to predict future demand, how to clean and pre-process the data to make it suitable for predictive analytics, what the common caveats are in terms of implementation and how to assess prediction accuracy.

Domain Adaptation for Retail Demand Prediction

2022

Author : Niloofar Tarighat
Release : 2022
Genre :
Kind : eBook
Book Rating : /5 ( reviews)

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Book Synopsis Domain Adaptation for Retail Demand Prediction by : Niloofar Tarighat

Download or read book Domain Adaptation for Retail Demand Prediction written by Niloofar Tarighat. This book was released on 2022. Available in PDF, EPUB and Kindle. Book excerpt: "Demand Forecasting is an important tool in many industries including retail. Althoughmany approaches have been developed to accurately predict the demand of productsbased on their historical sales data, demand prediction is still a complex issue especiallywhen there is a domain shift between training and testing data.In this work, we study three examples of domain shifts in the context of retail: outbreak ofthe COVID-19 pandemic, opening a new store, and introducing a new product. We firstshow that the accuracy of demand prediction models suffers after each sudden change.Then, we use domain adaptation methods, such as Frustratingly Easy (FE) and KernelMean Matching (KMM) to help improve the demand prediction accuracy by leveragingthe available data from the period before the shift (source domain) and adapting it to thedata after the shift (target domain). Additionally, we show that using a pairing techniquefurther helps improve the prediction accuracy.We use two methods as our base forecasting model: XGBoost and Transformers, and weshow that in the context of our data, it is better to use XGBoost.Our dataset comprises of point-of-sales data from 89 locations of Alimentation Couche-Tard convenient stores in the island of Montreal gathered between 2019-07 and 2021-02.We use product price information in addition to sales information to predict the demandof products in each store. In this study, we focus our attention on the two high-sellingcategories of coffee and energy drinks"--

Intermittent Demand Forecasting

2021-06-02 Medical

Author : John E. Boylan
Release : 2021-06-02
Genre : Medical
Kind : eBook
Book Rating : 303/5 ( reviews)

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Book Synopsis Intermittent Demand Forecasting by : John E. Boylan

Download or read book Intermittent Demand Forecasting written by John E. Boylan. This book was released on 2021-06-02. Available in PDF, EPUB and Kindle. Book excerpt: INTERMITTENT DEMAND FORECASTING The first text to focus on the methods and approaches of intermittent, rather than fast, demand forecasting Intermittent Demand Forecasting is for anyone who is interested in improving forecasts of intermittent demand products, and enhancing the management of inventories. Whether you are a practitioner, at the sharp end of demand planning, a software designer, a student, an academic teaching operational research or operations management courses, or a researcher in this field, we hope that the book will inspire you to rethink demand forecasting. If you do so, then you can contribute towards significant economic and environmental benefits. No prior knowledge of intermittent demand forecasting or inventory management is assumed in this book. The key formulae are accompanied by worked examples to show how they can be implemented in practice. For those wishing to understand the theory in more depth, technical notes are provided at the end of each chapter, as well as an extensive and up-to-date collection of references for further study. Software developments are reviewed, to give an appreciation of the current state of the art in commercial and open source software. “Intermittent demand forecasting may seem like a specialized area but actually is at the center of sustainability efforts to consume less and to waste less. Boylan and Syntetos have done a superb job in showing how improvements in inventory management are pivotal in achieving this. Their book covers both the theory and practice of intermittent demand forecasting and my prediction is that it will fast become the bible of the field.” —Spyros Makridakis, Professor, University of Nicosia, and Director, Institute for the Future and the Makridakis Open Forecasting Center (MOFC). “We have been able to support our clients by adopting many of the ideas discussed in this excellent book, and implementing them in our software. I am sure that these ideas will be equally helpful for other supply chain software vendors and for companies wanting to update and upgrade their capabilities in forecasting and inventory management.” —Suresh Acharya, VP, Research and Development, Blue Yonder. “As product variants proliferate and the pace of business quickens, more and more items have intermittent demand. Boylan and Syntetos have long been leaders in extending forecasting and inventory methods to accommodate this new reality. Their book gathers and clarifies decades of research in this area, and explains how practitioners can exploit this knowledge to make their operations more efficient and effective.” —Thomas R. Willemain, Professor Emeritus, Rensselaer Polytechnic Institute.

Pooling and Boosting for Demand Prediction in Retail

2022

Author : Dazhou Lei
Release : 2022
Genre :
Kind : eBook
Book Rating : /5 ( reviews)

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Book Synopsis Pooling and Boosting for Demand Prediction in Retail by : Dazhou Lei

Download or read book Pooling and Boosting for Demand Prediction in Retail written by Dazhou Lei. This book was released on 2022. Available in PDF, EPUB and Kindle. Book excerpt: How should retailers leverage aggregate (category) sales information for individual product demand prediction? Motivated by inventory risk pooling, we develop a new prediction framework that integrates category-product sales information to exploit the benefit of pooling. We propose to combine data from different aggregation levels in a transfer learning framework. Our approach treats the top-level sales information as a regularization for fitting the bottom-level prediction model. We characterize the error performance of our model in linear cases and demonstrate the benefit of pooling. Moreover, our approach exploits a natural connection to regularized gradient boosting trees that enable a scalable implementation for large-scale applications. Based on an internal study with JD.com on more than 6,000 weekly observations between 2020 and 2021, we evaluate the out-of-sample forecasting performance of our approach against state-of-the-art benchmarks. The result shows that our approach delivers superior forecasting performance consistently with more than 9% improvement over the benchmark method of JD.com. We further validate its generalizability on a standard public data set. Our result highlights the value of transfer learning to demand prediction in retail with both theoretical and empirical support. Based on a conservative estimate of JD.com, the improved forecasts can reduce the operating cost by 0.01-0.34 RMB per sold unit on its platform, which implies significant cost savings for the low-margin e-retail business.

Data Science for Supply Chain Forecasting

2021-03-22 Business & Economics

Author : Nicolas Vandeput
Release : 2021-03-22
Genre : Business & Economics
Kind : eBook
Book Rating : 123/5 ( reviews)

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Book Synopsis Data Science for Supply Chain Forecasting by : Nicolas Vandeput

Download or read book Data Science for Supply Chain Forecasting written by Nicolas Vandeput. This book was released on 2021-03-22. Available in PDF, EPUB and Kindle. Book excerpt: Using data science in order to solve a problem requires a scientific mindset more than coding skills. Data Science for Supply Chain Forecasting, Second Edition contends that a true scientific method which includes experimentation, observation, and constant questioning must be applied to supply chains to achieve excellence in demand forecasting. This second edition adds more than 45 percent extra content with four new chapters including an introduction to neural networks and the forecast value added framework. Part I focuses on statistical "traditional" models, Part II, on machine learning, and the all-new Part III discusses demand forecasting process management. The various chapters focus on both forecast models and new concepts such as metrics, underfitting, overfitting, outliers, feature optimization, and external demand drivers. The book is replete with do-it-yourself sections with implementations provided in Python (and Excel for the statistical models) to show the readers how to apply these models themselves. This hands-on book, covering the entire range of forecasting—from the basics all the way to leading-edge models—will benefit supply chain practitioners, forecasters, and analysts looking to go the extra mile with demand forecasting.

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