Exploiting Big Data in Logistics Risk Assessment via Bayesian Nonparametrics

Shang, Yan; Dunson, David B.; Song, Jing-Sheng

doi:10.2139/ssrn.2539606

Cited by 15 publications

(16 citation statements)

References 32 publications

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“…Shang et al. () explore the cargo logistics risk (CLR), defined as the deviation of the actual arrival time from the planned arrival time, using Bayesian statistics. The authors focus on a flexible estimation of the conditional density function of the CLR by making use of big air cargo data.…”

Section: Big Data In Om Studiesmentioning

confidence: 99%

Big Data Analytics in Operations Management

Choi

Wallace

Wang

2018

Production and Operations Management

600

298

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Big data analytics is critical in modern operations management (OM). In this study, we first explore the existing big data‐related analytics techniques, and identify their strengths, weaknesses as well as major functionalities. We then discuss various big data analytics strategies to overcome the respective computational and data challenges. After that, we examine the literature and reveal how different types of big data methods (techniques, strategies, and architectures) can be applied to different OM topical areas, namely forecasting, inventory management, revenue management and marketing, transportation management, supply chain management, and risk analysis. We also investigate via case studies the real‐world applications of big data analytics in top branded enterprises. Finally, we conclude the study with a discussion of future research.

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Section: Big Data In Om Studiesmentioning

confidence: 99%

Big Data Analytics in Operations Management

Choi

Wallace

Wang

2018

Production and Operations Management

600

298

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“…These models are able to determine whether a firm is prone to supply, demand, product or external disruptions. Bayesian prediction is used in Shang, Dunson, and Song (2017) to assess transport time risks in air cargo supply chains. Li and Wang (2017) use big data collected by sensors in a food supply chain to dynamically predict the product time-temperature profile and adjust prices accordingly.…”

Section: Machine Learning and Big Datamentioning

confidence: 99%

Supply chain risk management and artificial intelligence: state of the art and future research directions

Baryannis

Validi

Dani

et al. 2018

International Journal of Production Research

571

284

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Supply chain risk management (SCRM) encompasses a wide variety of strategies aiming to identify, assess, mitigate and monitor unexpected events or conditions which might have an impact, mostly adverse, on any part of a supply chain. SCRM strategies often depend on rapid and adaptive decision-making based on potentially large, multidimensional data sources. These characteristics make SCRM a suitable application area for artificial intelligence (AI) techniques. The aim of this paper is to provide a comprehensive review of supply chain literature that addresses problems relevant to SCRM using approaches that fall within the AI spectrum. To that end, an investigation is conducted on the various definitions and classifications of supply chain risk and related notions such as uncertainty. Then, a mapping study is performed to categorise existing literature according to the AI methodology used, ranging from mathematical programming to Machine Learning and Big Data Analytics, and the specific SCRM task they address (identification, assessment or response). Finally, a comprehensive analysis of each category is provided to identify missing aspects and unexplored areas and propose directions for future research at the confluence of SCRM and AI.

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“…2014, Ryzhov et al., 2015, Shang et al. 2017), design data‐driven targeted promotions (Arora et al. 2008, Choudhary and Shivendu 2017, Fong et al.…”

Section: Literature Reviewmentioning

confidence: 99%

Is Investment in Data Analytics Always Profitable? The Case of Third‐Party‐Online‐Promotion Marketplace

Zhang

Shivendu

2021

Production and Operations Management

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Studies show that merchants are heterogeneous in profitability from offering promotions on third‐party‐online‐promotion marketplaces who often charge a single commission rate. Using a data analytics system, a marketplace can classify merchants according to their heterogeneous characteristics and offer merchant‐type specific commission rates. In this study, we construct a game‐theoretic model consisting a marketplace with two types of merchants who have heterogeneous proportion of consumers who are informed about their offering. The types are merchants’ private information, but the marketplace can invest in data analytics capability to classify merchants as per their types with a probability. We study a signal‐based strategy, where the marketplace invests in data analytics capability and offers a specific commission rate to individual merchant based on the merchant‐type classification and compare it with a single‐rate strategy of offering one commission rate to all merchants. We show that the relative strength and weakness of the signal‐based strategy depend on the merchant type distribution and the investment cost of improving the classification accuracy rate. Interestingly, the marketplace can be better off with the single‐rate strategy when a merchant type dominates the market. Moreover, we show that the signal‐based strategy, can lead to an increase in profit for merchants and an increase in consumer surplus. This is so because the marketplace’s signal‐based strategy has a cascade effect on consumers through the merchant’s optimal discount rate strategy. We conclude by identifying the conditions for a win–win–win situation wherein investment in data analytics capabilities by the marketplace also benefits merchants and consumers.

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Exploiting Big Data in Logistics Risk Assessment via Bayesian Nonparametrics

Cited by 15 publications

References 32 publications

Big Data Analytics in Operations Management

Big Data Analytics in Operations Management

Supply chain risk management and artificial intelligence: state of the art and future research directions

Is Investment in Data Analytics Always Profitable? The Case of Third‐Party‐Online‐Promotion Marketplace

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