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Kategorie szczegółowe BISAC

Retail Space Analytics

ISBN-13: 9783031270574 / Angielski

Ahmed Ghoniem; Bacel Maddah
Retail Space Analytics Ahmed Ghoniem Bacel Maddah 9783031270574 Springer - książkaWidoczna okładka, to zdjęcie poglądowe, a rzeczywista szata graficzna może różnić się od prezentowanej.

Retail Space Analytics

ISBN-13: 9783031270574 / Angielski

Ahmed Ghoniem; Bacel Maddah
cena 685,93
(netto: 653,27 VAT:  5%)

Najniższa cena z 30 dni: 616,85
Termin realizacji zamówienia:
ok. 22 dni roboczych.

Darmowa dostawa!
inne wydania

This edited volume presents state-of-the-art research that can leverage large-scale sensory data collected in grocery/retail stores where a single customer visit may generate nearly 10,000 data points. For decades, retail shelf space optimization has been confined to the analysis of product allocation decisions over a limited number of shelves, often taken in isolation. Such models incorporated interesting concepts relating to space and cross-space elasticity in the design of planograms. Although useful, these models have not addressed the bigger picture of planning store shelf space in a more holistic manner.  It is important to note that the space planning analytics in the book are particularly important in an era where e-commerce is on the rise and brick-and-mortar retailing is declining and experiencing severe crises (the retail apocalypse).This is the first research-oriented book that examines novel problems in store space analytics, triggered by modern-day sensory technologies, customer trackers, and transactional tools (point-of-sales, etc.). In fact, such transformative technologies have prompted the development of new and exciting business practices, accompanied by the need for powerful data-driven models and analyses in retail shelf space and layout planning. The book will facilitate developing algorithms and decision tools that allow a better leverage of the data collected from these mediums.

This edited volume presents state-of-the-art research that can leverage large-scale sensory data collected in grocery/retail stores where a single customer visit may generate nearly 10,000 data points. For decades, retail shelf space optimization has been confined to the analysis of product allocation decisions over a limited number of shelves, often taken in isolation. Such models incorporated interesting concepts relating to space and cross-space elasticity in the design of planograms. Although useful, these models have not addressed the bigger picture of planning store shelf space in a more holistic manner.  It is important to note that the space planning analytics in the book are particularly important in an era where e-commerce is on the rise and brick-and-mortar retailing is declining and experiencing severe crises (the retail apocalypse).

This is the first research-oriented book that examines novel problems in store space analytics, triggered by modern-day sensory technologies, customer trackers, and transactional tools (point-of-sales, etc.). In fact, such transformative technologies have prompted the development of new and exciting business practices, accompanied by the need for powerful data-driven models and analyses in retail shelf space and layout planning. The book will facilitate developing algorithms and decision tools that allow a better leverage of the data collected from these mediums.

Kategorie:
Nauka, Ekonomia i biznes
Kategorie BISAC:
Business & Economics > Operations Research
Business & Economics > Sales & Selling - General
Business & Economics > Consumer Behavior - General
Wydawca:
Springer
Seria wydawnicza:
International Operations Research & Management Science
Język:
Angielski
ISBN-13:
9783031270574

Effect of Customer Travel Behavior on Grid Layout and Shelf.- A Solver-Free Heuristic for Store-wide Shelf Space Allocation.- In-Store Tra c Density Estimation.- A Simulation Based Tool to Guide Periodic Changes in a Supermarket Layout.- Data-Driven Analytical Grocery Store Design.- Optimizing Stock-Keeping Unit Selection for Promotional Display Space at Grocery Retailers.- Merchandise Placement Optimization.- Problems and Opportunities of Applied Optimization Models in Retail Space Planning


Ahmed Ghoniem is an Associate Professor of Operations and Information Management at the Isenberg School of Management, University of Massachusetts, Amherst, USA. His research interests include Retail Analytics, Supply Chain Management, Manufacturing & Service Operations Management, and Routing & Scheduling.


Bacel Maddah is Professor and Chair, Department of Industrial Engineering and Management, Faculty of Engineering and Architecture, American University of Beirut, Lebanon. His research interests include Retail Operations Management, Supply Chain Management and Stochastic Processes.

This edited volume presents state-of-the-art research that can leverage large-scale sensory data collected in grocery/retail stores where a single customer visit may generate nearly 10,000 data points. For decades, retail shelf space optimization has been confined to the analysis of product allocation decisions over a limited number of shelves, often taken in isolation. Such models incorporated interesting concepts relating to space and cross-space elasticity in the design of planograms. Although useful, these models have not addressed the bigger picture of planning store shelf space in a more holistic manner.  It is important to note that the space planning analytics in the book are particularly important in an era where e-commerce is on the rise and brick-and-mortar retailing is declining and experiencing severe crises (the retail apocalypse).

This is the first research-oriented book that examines novel problems in store space analytics, triggered by modern-day sensory technologies, customer trackers, and transactional tools (point-of-sales, etc.). In fact, such transformative technologies have prompted the development of new and exciting business practices, accompanied by the need for powerful data-driven models and analyses in retail shelf space and layout planning. The book will facilitate developing algorithms and decision tools that allow a better leverage of the data collected from these mediums.



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