Back in business: operations research in support of big data analytics for operations and supply chain management

Hazen, Benjamin T.; Skipper, Joseph B.; Boone, Christopher A.; Hill, Raymond R.

doi:10.1007/s10479-016-2226-0

Cited by 138 publications

(71 citation statements)

References 36 publications

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“…For instance, O'Donovan et al (2015), Dutta and Bose (2015), and Babiceanu and Seker (2016) conducted literature reviews on material flow in manufacturing operations while focused on logistics applications. A literature review that takes a broad perspective of SC as a whole and cross-maps with BDA techniques in SCM is yet scarce (Olson, 2015;Addo-Tenkorang and Helo, 2016;Hazen et al, 2016;Mishra et al, 2016). Our literature review develops a classification framework, which identifies and connects SC functions with levels of analytics, BDA models and techniques.…”

Section: Introductionmentioning

confidence: 99%

Big data analytics in supply chain management: A state-of-the-art literature review

Nguyen

Zhou

Spiegler

et al. 2018

Computers & Operations Research

414

238

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Abstract:The rapidly growing interest from both academics and practitioners in the application of big data analytics (BDA) in supply chain management (SCM) has urged the need for review of up-to-date research development in order to develop a new agenda. This review responds to the call by proposing a novel classification framework that provides a full picture of current literature on where and how BDA has been applied within the SCM context. The classification framework is structurally based on the content analysis method of Mayring (2008) techniques are employed to develop these models? The discussion tackling these four questions reveals a number of research gaps, which leads to future research directions.

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Section: Introductionmentioning

confidence: 99%

Big data analytics in supply chain management: A state-of-the-art literature review

Nguyen

Zhou

Spiegler

et al. 2018

Computers & Operations Research

414

238

View full text Add to dashboard Cite

show abstract

“…Chae (2015) and Hazen et al (2016) have suggested a mechanism of twitter analytics for analysis of tweets in the domain of supply chain management. They have attempted to develop an understanding of prospective role of Twitter in the practice of supply chain management and future research.…”

Section: Literature Reviewmentioning

confidence: 99%

Use of twitter data for waste minimisation in beef supply chain

2016

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Approximately one third of the food produced is discarded or lost, which accounts for 1.3 billion tons per annum. The waste is being generated throughout the supply chain viz. farmers, wholesalers/processors, logistics, retailers and consumers. The majority of waste occurs at the interface of retailers and consumers. Many global retailers are making efforts to extract intelligence from customer's complaints left at retail store to backtrack their supply chain to mitigate the waste. However, majority of the customers don't leave the complaints in the store because of various reasons like inconvenience, lack of time, distance, ignorance etc. In current digital world, consumers are active on social media and express their sentiments, thoughts, and opinions about a particular product freely. For example, on an average, 45,000 tweets are tweeted daily related to beef products to express their likes and dislikes. These tweets are large in volume, scattered and unstructured in nature. In this study, twitter data is utilised to develop waste minimization strategies by backtracking the supply chain. The execution process of proposed framework is demonstrated for beef supply chain. The proposed model is generic enough and can be applied to other domains as well.

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“…Hazen et al (2016) suggested the need to bridge the gap between operations research/supply chain management and big data analytics by synergizing decision-making with quantitative results, transitioning to business analytics, enhancing data quality, diversifying team structure, and defining a structured plan for alternative selection. Recent research on big data sets in the operations management domain includes the studies by Wang et al (2016) which focused on developing a capacitated network design to locate distribution centers for scattered demand points and Tail and Singh (2016) which focused on the facility layout problem.…”

Section: Summary Of Research Gapsmentioning

confidence: 99%

Cold chain configuration design: location-allocation decision-making using coordination, value deterioration, and big data approximation

et al. 2016

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The study proposes a cold chain location-allocation configuration decision model for shippers and customers by considering value deterioration and coordination by using big data approximation.Value deterioration is assessed in terms of limited shelf life, opportunity cost, and units of product transportation. In this study, a customer can be defined as a member of any cold chain, such as cold warehouse stores, retailers, and last mile service providers. Each customer only manages products that are in a certain stage of the product life cycle, which is referred to as the expected shelf life. Because of the geographical dispersion of customers and their unpredictable demands as well as the varying shelf life of products, complexity is another challenge in a cold chain.Improved coordination between shippers and customers is expected to reduce this complexity, and this is introduced in the model as a longitudinal factor for service distance requirement. We use big data information that reflects geospatial attributes of location to derive the real feasible distance between shippers and customers. We formulate the cold chain location-allocation decision problem as a mixed integer linear programming problem, which is solved using the CPLEX solver.The proposed decision model increases efficiency, adequately equates supply and demand, and reduces wastage. Our study encourages managers to ship full truck load consignments, to be aware of uneven allocation based on proximity, and to supervise heterogeneous product allocation according to storage requirements.

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Back in business: operations research in support of big data analytics for operations and supply chain management

Cited by 138 publications

References 36 publications

Big data analytics in supply chain management: A state-of-the-art literature review

Big data analytics in supply chain management: A state-of-the-art literature review

Use of twitter data for waste minimisation in beef supply chain

Cold chain configuration design: location-allocation decision-making using coordination, value deterioration, and big data approximation

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