Data Analytics in Operations Management: A Review

Mišić, Velibor V.; Perakis, Georgia

doi:10.1287/msom.2019.0805

Cited by 126 publications

(51 citation statements)

References 55 publications

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“…There are also hybrids to this approach, where the algorithms are given some guidance but not the entire model (see, for example, the dual-sourcing application in Gijsbrechts et al 2018). Because machine learning in OM is the focus of the article by Mišić and Perakis (2019) in this issue (and also discussed somewhat by in this issue), we do not elaborate further here and refer the interested reader to those articles.…”

Section: Artificial Intelligencementioning

confidence: 99%

Industry 4.0: Opportunities and Challenges for Operations Management

Olsen

Tomlin

2020

M&SOM

422

212

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Industry 4.0 connotes a new industrial revolution centered around cyber-physical systems. It posits that the real-time connection of physical and digital systems, along with new enabling technologies, will change the way that work is done and therefore, how work should be managed. It has the potential to break, or at least change, the traditional operations trade-offs among the competitive priorities of cost, flexibility, speed, and quality. This article describes the technologies inherent in Industry 4.0 and the opportunities and challenges for research in this area. The focus is on goods-producing industries, which includes both the manufacturing and agricultural sectors. Specific technologies discussed include additive manufacturing, the internet of things, blockchain, advanced robotics, and artificial intelligence.

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Section: Artificial Intelligencementioning

confidence: 99%

Industry 4.0: Opportunities and Challenges for Operations Management

Olsen

Tomlin

2020

M&SOM

422

212

View full text Add to dashboard Cite

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“…For instance, Qi et al (2020) provide an example of using domain knowledge to develop a better deep learning solution in a scenario where it is not apparent how a supervised learning framework can be applied. Besides, as pointed out by Mišić and Perakis (2020), it is an important future direction to develop interpretable data‐driven models. Black‐box machine learning models, although they may achieve favorable numerical performance, often fail to provide intuitive insight that helps real‐world practitioners to understand this model.…”

Section: Discussionmentioning

confidence: 97%

Data‐driven research in retail operations—A review

Meng

Mak

Shen

2020

Naval Research Logistics

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We review the operations research/management science literature on data‐driven methods in retail operations. This line of work has grown rapidly in recent years, thanks to the availability of high‐quality data, improvements in computing hardware, and parallel developments in machine learning methodologies. We survey state‐of‐the‐art studies in three core aspects of retail operations—assortment optimization, order fulfillment, and inventory management. We then conclude the paper by pointing out some interesting future research possibilities for our community.

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“…There is certainly no shortage of applications that motivate the investigation of tensor completion (e.g., personalized medicine (Soroushmehr and Najarian 2016, Pawlowski 2019), medical imaging (Gandy et al 2011, Semerci et al 2014, seismic data analysis (Kreimer et al 2013, Ely et al 2013, and multidimensional harmonic retrieval Chi 2014, Ying et al 2017)). One concrete example in operations research arises when learning the preference of individual customers for a collection of products on the basis of historical transactions (Farias andLi 2019, Mišić andPerakis 2020). Given the limited availability of transaction data (e.g., each customer might only have purchased very few products before), it is crucial to exploit multiway customer-product interactions (e.g., users' browsing and searching histories) in order to better predict the likelihood of a customer purchasing a new product.…”

Section: Tensor Completion From Noisy Entriesmentioning

confidence: 99%

Nonconvex Low-Rank Tensor Completion from Noisy Data

Cai

Poor

et al. 2022

Operations Research

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This paper investigates a problem of broad practical interest, namely, the reconstruction of a large-dimensional low-rank tensor from highly incomplete and randomly corrupted observations of its entries. Although a number of papers have been dedicated to this tensor completion problem, prior algorithms either are computationally too expensive for large-scale applications or come with suboptimal statistical performance. Motivated by this, we propose a fast two-stage nonconvex algorithm—a gradient method following a rough initialization—that achieves the best of both worlds: optimal statistical accuracy and computational efficiency. Specifically, the proposed algorithm provably completes the tensor and retrieves all low-rank factors within nearly linear time, while at the same time enjoying near-optimal statistical guarantees (i.e., minimal sample complexity and optimal estimation accuracy). The insights conveyed through our analysis of nonconvex optimization might have implications for a broader family of tensor reconstruction problems beyond tensor completion.

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Data Analytics in Operations Management: A Review

Cited by 126 publications

References 55 publications

Industry 4.0: Opportunities and Challenges for Operations Management

Industry 4.0: Opportunities and Challenges for Operations Management

Data‐driven research in retail operations—A review

Nonconvex Low-Rank Tensor Completion from Noisy Data

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