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Author: Gihan Samodha Edirisinghe
Publisher:
ISBN:
Category : Commerce
Languages : en
Pages : 163
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Book Description
This dissertation focuses on the general theme of data-driven decision making in online and brick-and-mortar retail.Chapter One proposes a dynamic shelf allocation-relocation scheme for rearranging storewide product allocations over time to maximize impulse buying behavior. The proposed method rearranges items based on customer behavior with the current arrangement. The method applies insights from association rule mining to group highly affine and profitable product pairs, optimize the assignment of departments to store aisles, and determine the optimal within-aisle space allocations for the products of each department. This strategic rearrangement technique consistently outperforms random shelf-space rearrangement and, in many instances, exceeds the profit potential of a more traditional static shelf space arrangement.In Chapter Two, we conduct a data-driven analysis of the profitability and customer effects of three free shipping policies commonly adopted by online retailers at present. Using a Python-based web crawler, we obtained a unique dataset containing data from more than 80,000 products listed on Amazon.com. We then analyzed the data using non-linear mixed integer programs under different free shipping policies to compare their profitability and effects on customers. We find that membership free shipping policies are more profitable than the other policies considered. We also present insights on setting optimal membership fee levels and free shipping thresholds under different conditions.Chapter Three develops a methodology that creates structural equations using regression techniques based on known optimal results. The procedure is useful during situations such as initial decision-making on network-wide safety stock levels where quick, robust answers are sufficient.In Chapter Four, we present workable strategies for rural colleges and instructors operating on limited budgets to hold highly interactive question and answer (Q&A) sessions with accomplished guest speakers from industry. We introduce an approach for the selection of high-quality guest speakers, which utilizes the detailed resumes of tens of thousands of college alumni listed on LinkedIn. We also illustrate an innovative Q&A format that uses free cloud-based services such as Google Forms and Google Sheets. These successfully implemented strategies can address many challenges faced by rural schools in attracting and accommodating quality guest speakers for engaging exchanges with students.
Author: Gihan Samodha Edirisinghe
Publisher:
ISBN:
Category : Commerce
Languages : en
Pages : 163
Get Book Here
Book Description
This dissertation focuses on the general theme of data-driven decision making in online and brick-and-mortar retail.Chapter One proposes a dynamic shelf allocation-relocation scheme for rearranging storewide product allocations over time to maximize impulse buying behavior. The proposed method rearranges items based on customer behavior with the current arrangement. The method applies insights from association rule mining to group highly affine and profitable product pairs, optimize the assignment of departments to store aisles, and determine the optimal within-aisle space allocations for the products of each department. This strategic rearrangement technique consistently outperforms random shelf-space rearrangement and, in many instances, exceeds the profit potential of a more traditional static shelf space arrangement.In Chapter Two, we conduct a data-driven analysis of the profitability and customer effects of three free shipping policies commonly adopted by online retailers at present. Using a Python-based web crawler, we obtained a unique dataset containing data from more than 80,000 products listed on Amazon.com. We then analyzed the data using non-linear mixed integer programs under different free shipping policies to compare their profitability and effects on customers. We find that membership free shipping policies are more profitable than the other policies considered. We also present insights on setting optimal membership fee levels and free shipping thresholds under different conditions.Chapter Three develops a methodology that creates structural equations using regression techniques based on known optimal results. The procedure is useful during situations such as initial decision-making on network-wide safety stock levels where quick, robust answers are sufficient.In Chapter Four, we present workable strategies for rural colleges and instructors operating on limited budgets to hold highly interactive question and answer (Q&A) sessions with accomplished guest speakers from industry. We introduce an approach for the selection of high-quality guest speakers, which utilizes the detailed resumes of tens of thousands of college alumni listed on LinkedIn. We also illustrate an innovative Q&A format that uses free cloud-based services such as Google Forms and Google Sheets. These successfully implemented strategies can address many challenges faced by rural schools in attracting and accommodating quality guest speakers for engaging exchanges with students.
Author: Santiago Gallino
Publisher: Springer Nature
ISBN: 3030201198
Category : Business & Economics
Languages : en
Pages : 353
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Book Description
The world of retailing has changed dramatically in the past decade. Sales originating at online channels have been steadily increasing, and even for sales transacted at brick-and-mortar channels, a much larger fraction of sales is affected by online channels in different touch points during the customer journey. Shopper behavior and expectations have been evolving along with the growth of digital channels, challenging retailers to redesign their fulfillment and execution processes, to better serve their customers. This edited book examines the challenges and opportunities arising from the shift towards omni- channel retail. We examine these issues through the lenses of operations management, emphasizing the supply chain transformations associated with fulfilling an omni-channel demand. The book is divided into three parts. In the first part, “Omni-channel business models”, we present four studies that explore how retailers are adjusting their fundamental business models to the new omni-channel landscape. The second part, “Data-driven decisions in an omni-channel world”, includes five chapters that study the evolving data opportunities enabled by omni-channel retail and present specific examples of data-driven analyses. Finally, in the third part, “Case studies in Omni-channel retailing”, we include four studies that provide a deep dive into how specific industries, companies and markets are navigating the omni-channel world. Ultimately, this book introduces the reader to the fundamentals of operations in an omni-channel context and highlights the different innovative research ideas on the topic using a variety of methodologies.
Author: Divya Singhvi
Publisher:
ISBN:
Category :
Languages : en
Pages : 238
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Book Description
.Retail operations have experienced a transformational change in the past decade with the advent and adoption of data-driven approaches to drive decision making. Granular data collection has enabled firms to make personalized decisions that improve customer experience and maintain long-term engagement. In this thesis we discuss important problems that retailers face in practice, before, while and after a product is introduced in the market. In Chapter 2, we consider the problem of estimating sales for a new product before retailers release the product to the customer. We introduce a joint clustering and regression method that jointly clusters existing products based on their features as well as their sales patterns while estimating their demand. Further, we use this information to predict demand for new products. Analytically, we show an out-of-sample prediction error bound. Numerically, we perform an extensive study on real world data sets from Johnson & Johnson and a large fashion retailer and find that the proposed method outperforms state-of-the-art prediction methods and improves the WMAPE forecasting metric between 5%-15%. Even after the product is released in the market, a customer's decision of purchasing the product depends on the right recommendation personalized for her. In Chapter 3, we consider the problem of personalized product recommendations when customer preferences are unknown and the retailer risks losing customers because of irrelevant recommendations. We present empirical evidence of customer disengagement through real-world data. We formulate this problem as a user preference learning problem. We show that customer disengagement can cause almost all state-of-the-art learning algorithms to fail in this setting. We propose modifying bandit learning strategies by constraining the action space upfront using an integer optimization model. We prove that this modification can keep significantly more customers engaged on the platform. Numerical experiments demonstrate that our algorithm can improve customer engagement with the platform by up to 80%. Another important decision a retailer needs to make for a new product, is its pricing. In Chapter 4, we consider the dynamic pricing problem of a retailer who does not have any information on the underlying demand for the product. An important feature we incorporate is the fact that the retailer also seeks to reduce the amount of price experimentation. We consider the pricing problem when demand is non-parametric and construct a pricing algorithm that uses piecewise linear approximations of the unknown demand function and establish when the proposed policy achieves a near-optimal rate of regret ( O)( [square root of] T), while making O(log log T) price changes. Our algorithm allows for a considerable reduction in price changes from the previously known O(log T) rate of price change guarantee found in the literature. Finally, once a purchase is made, a customer's decision to return to the same retailer depends on the product return polices and after-sales services of the retailer. As a result, in Chapter 5, we focus on the problem of reducing product returns. Closely working with one of India's largest online fashion retailers, we focus on identifying the effect of delivery gaps (total time that customers have to wait for the product they ordered to arrive) and customer promise dates on product returns. We perform an extensive empirical analysis and run a large scale Randomized Control Trial (RCT) to estimate these effects. Based on the insights from this empirical analysis, we then develop an integer optimization model to optimize delivery speed targets.
Author: Constant Berkhout
Publisher: Springer
ISBN: 3030111636
Category : Business & Economics
Languages : en
Pages : 224
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Book Description
Demonstrating how retailers can tap into shoppers’ needs for variety without increasing complexity and stress, this innovative book combines cutting-edge research with hands-on, practical frameworks. Experts in the retail sector have long been convinced that small assortments are more appealing to shoppers than large selections of products; in other words, less is more. However, the human brain has an innate need for variety. Addressing this challenge Constant Berkhout offers practical merchandising guidelines both for stores and online retailers. Indeed, studies show that it is not the actual size of assortment that drives traffic to online stores, but the perception of assortment variety. The author illustrates how decisions around assortment and visual merchandising must be made in conjunction with each other, rather than separately, and provides a step-by-step plan to do so. Grounded on shopper needs, emotions and behaviours that apply to both online and brick-and-mortar stores, this book integrates assortment and merchandise thinking and takes a human and shopper perspective. With practical frameworks that can easily be implemented in real-life situations along with examples from a number of retail sectors, Assortment and Merchandising Strategy provides a deeper and much-needed understanding of how shoppers process information, and the strategies that retailers must adopt in order to satisfy and retain their customers.
Author: P. Kaliraj
Publisher: CRC Press
ISBN: 1000537668
Category : Computers
Languages : en
Pages : 446
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Book Description
Industry 4.0 is the latest technological innovation in manufacturing with the goal to increase productivity in a flexible and efficient manner. Changing the way in which manufacturers operate, this revolutionary transformation is powered by various technology advances including Big Data analytics, Internet of Things (IoT), Artificial Intelligence (AI), and cloud computing. Big Data analytics has been identified as one of the significant components of Industry 4.0, as it provides valuable insights for smart factory management. Big Data and Industry 4.0 have the potential to reduce resource consumption and optimize processes, thereby playing a key role in achieving sustainable development. Big Data Applications in Industry 4.0 covers the recent advancements that have emerged in the field of Big Data and its applications. The book introduces the concepts and advanced tools and technologies for representing and processing Big Data. It also covers applications of Big Data in such domains as financial services, education, healthcare, biomedical research, logistics, and warehouse management. Researchers, students, scientists, engineers, and statisticians can turn to this book to learn about concepts, technologies, and applications that solve real-world problems. Features An introduction to data science and the types of data analytics methods accessible today An overview of data integration concepts, methodologies, and solutions A general framework of forecasting principles and applications, as well as basic forecasting models including naïve, moving average, and exponential smoothing models A detailed roadmap of the Big Data evolution and its related technological transformation in computing, along with a brief description of related terminologies The application of Industry 4.0 and Big Data in the field of education The features, prospects, and significant role of Big Data in the banking industry, as well as various use cases of Big Data in banking, finance services, and insurance Implementing a Data Lake (DL) in the cloud and the significance of a data lake in decision making
Author: T P Singh
Publisher: Springer Nature
ISBN: 9811598738
Category : Technology & Engineering
Languages : en
Pages : 597
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Book Description
This book is a compilation of peer-reviewed papers presented at the International Conference on Machine Intelligence and Data Science Applications, organized by the School of Computer Science, University of Petroleum & Energy Studies, Dehradun, India, during 4–5 September 2020. The book addresses the algorithmic aspect of machine intelligence which includes the framework and optimization of various states of algorithms. Variety of papers related to wide applications in various fields like data-driven industrial IoT, bioinformatics, network and security, autonomous computing and various other aligned areas. The book concludes with interdisciplinary applications like legal, health care, smart society, cyber-physical system and smart agriculture. All papers have been carefully reviewed. The book is of interest to computer science engineers, lecturers/researchers in machine intelligence discipline and engineering graduates.
Author: Cybellium
Publisher: Cybellium
ISBN: 1836794037
Category : Study Aids
Languages : en
Pages : 269
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Book Description
Welcome to the forefront of knowledge with Cybellium, your trusted partner in mastering the cuttign-edge fields of IT, Artificial Intelligence, Cyber Security, Business, Economics and Science. Designed for professionals, students, and enthusiasts alike, our comprehensive books empower you to stay ahead in a rapidly evolving digital world. * Expert Insights: Our books provide deep, actionable insights that bridge the gap between theory and practical application. * Up-to-Date Content: Stay current with the latest advancements, trends, and best practices in IT, Al, Cybersecurity, Business, Economics and Science. Each guide is regularly updated to reflect the newest developments and challenges. * Comprehensive Coverage: Whether you're a beginner or an advanced learner, Cybellium books cover a wide range of topics, from foundational principles to specialized knowledge, tailored to your level of expertise. Become part of a global network of learners and professionals who trust Cybellium to guide their educational journey. www.cybellium.com
Author: Priya Shahi
Publisher: Abhishek Publications
ISBN: 9356529620
Category : Business & Economics
Languages : en
Pages : 204
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Book Description
Fashion Forward: Merchandising in the Digital Era" delves into the transformative impact of digital technology on the fashion industry, particularly in merchandising. This book explores how e-commerce, social media, and advanced analytics have revolutionized traditional practices, enabling brands to reach global audiences, personalize shopping experiences, and optimize their supply chains. Through a blend of expert insights, case studies, and practical strategies, "Fashion Forward" provides a comprehensive guide for fashion professionals looking to navigate and thrive in the fast-evolving digital landscape.
Author: Dr. Mahavir M. Shetiya
Publisher: Thakur Publication Private Limited
ISBN: 9357551441
Category : Education
Languages : en
Pages : 207
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Book Description
Buy BASIC BUSINESS ANALYTICS USING R e-Book for Mba 2nd Semester in English language specially designed for SPPU ( Savitribai Phule Pune University ,Maharashtra) By Thakur publication.
Author: Maxime C. Cohen
Publisher: Springer Nature
ISBN: 3030858553
Category : Business & Economics
Languages : en
Pages : 166
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Book Description
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.
