Capacity and Inventory Management: Review, Trends, and Projections

Song, Jing‐Sheng; Houtum, Geert‐Jan van; Mieghem, Jan A. Van

doi:10.1287/msom.2019.0798

Cited by 71 publications

(43 citation statements)

References 47 publications

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“…In the U.K., to maximize the number of first-dose inoculations, the recommended lead time between two doses has been stretched from three or four weeks (as recommended by the manufacturers) to 12 weeks. Despite the large body of inventory management literature (see [ 35 ] for a comprehensive review), this type of problem has not been examined and offers new research opportunities. Mak et al [ 36 ] model and compared different dose inventory policies and show that releasing all the second doses leads to scheduling difficulties and would only have a modest benefit in reducing infections; stretching the lead time has significant short- and mid-term benefits in reducing infections but may lead to more infections in the long-term.…”

Section: Matching Supply With Demandmentioning

confidence: 99%

Transforming COVID-19 vaccines into vaccination

Dai

Song

2021

Health Care Manag Sci

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Amid the prolonged COVID-19 pandemic, the miraculous breakthroughs of multiple effective and safe COVID-19 vaccines offer hopeful prospects. Yet, the endgame of the pandemic is not vaccines; it is vaccination. The daunting challenge of vaccinating the world offers ample investigative opportunities for management scientists who are interested in improving the efficiency and equity of vaccine supply chains. In this article, we provide a brief overview of these opportunities through three constituent parts: (1) supply, (2) demand, and (3) matching supply with demand.

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Section: Matching Supply With Demandmentioning

confidence: 99%

Transforming COVID-19 vaccines into vaccination

Dai

Song

2021

Health Care Manag Sci

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“…Inventory management is well-established in the operations management literature. The existing studies are mostly focused on quantitative models, for example, inventory lot-sizing problems [13], capacity and inventory management [14], inventory control models [15], and inventory systems with deterioration [16]. Limited attention has been given to the issue of excess (over) inventory.…”

Section: Introductionmentioning

confidence: 99%

Destruction Decisions for Managing Excess Inventory in E-Commerce Logistics

Pourhejazy

2020

Sustainability

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The Internet has brought about new possibilities for innovation and radically changed business activities. Internet shopping is a prime example of increasing popularity, which is exacerbated due to the recent pandemic. It is expected that e-commerce will accommodate more than a quarter of the total retail sales worldwide in the next few years. Given the characteristics of e-commerce, inventory management is of paramount importance for an effective and timely response to the online customers’ demand. Despite its relevance, the issue of warehouse excess inventory is not sufficiently studied in the operations management literature. This study explores the factors, including sustainability and strategic considerations, that influence the inventory destruction decisions as one of the alternatives for managing excess inventory. Applying the Fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) method, the interrelationships between the decision factors are investigated and the decisive considerations are identified. Overall, the outcomes provide insights for the e-commerce practitioners and offer directions for modeling and managing inventory destruction decisions.

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“…For example, Ferreira, Lee, and Simchi-Levi (2015) predict demand for a limited-time discount online retailer, Glaeser, Fisher, and Xuanming (2019) for a "Buy-Online-Pick-Up-In-Store" retailer, and Cui, Gallino, Moreno, and Zhang (2018) for an online apparel retailer. Outside retailing, how machine learning methods could be applied to advance OM research remains less clear (Hopp, Li, & Wang, 2018;Song, van Houtum, & Van Mieghem, 2020). Our research extends the understanding of how machine learning methods can be useful in a heavily OM, yet rarely applied, domain-customer contact centers.…”

Section: Introductionmentioning

confidence: 86%

Improving customer routing in contact centers: An automated triage design based on text analytics

Ilk

Shang

Góes

2020

J of Ops Management

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We propose an automated triage design for intelligent customer routing in live-chat contact centers and demonstrate its implementation using a realworld data set from an S&P 500 firm. The proposed design emerges as a synthesis of text analytics and predictive machine learning methods. Using numerical experiments based on the simulation of the firm's contact center, we demonstrate the service level, time, and labor cost benefits of the automated design over two other triage designs (i.e., customer choice triage and human expert triage) that are commonly employed in the real world. Through additional analyses, we explore the generalizability of the automated design for creating solutions for different types of communication channels. Our work has implications for managing customer relations under emerging communication technologies (e.g., live-chat, e-mail, and social media) and more broadly for demonstrating the use of text analytics and machine learning to improve Operations Management practice.

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Capacity and Inventory Management: Review, Trends, and Projections

Cited by 71 publications

References 47 publications

Transforming COVID-19 vaccines into vaccination

Transforming COVID-19 vaccines into vaccination

Destruction Decisions for Managing Excess Inventory in E-Commerce Logistics

Improving customer routing in contact centers: An automated triage design based on text analytics

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