Innovation as the strategic driver of sustainability: big data knowledge for profit and survival

Jelinek, Mariann; Bergey, Paul K.

doi:10.1109/emr.2013.2259978

Cited by 25 publications

(25 citation statements)

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“…Nevertheless, large corporations perceive sustainability efforts as long-term investments aimed toward building strategic resources [48]. Corporations are increasingly interested in using big data analytics in their sustainable efforts, which in turn give them a strategic edge [21] According to a Mckinsey survey report, companies using BDA are able to predict the 65% of customers that make repeated purchases through shop alerts and 75% of those customers reported that they are likely to use the service again [22].…”

Section: Big Data Analytics and Sustainable Supply Chain Managementmentioning

confidence: 99%

“…However, literature on the application of BDA for supply chain sustainability has been much less explored. Thus, scholars acknowledge the need for further exploration in this domain [21,29,30,44]. Furthermore, for the supply chain to be sustainable, the potential risks disrupting operations must be identified and predicted.…”

Section: Big Data Analytics and Sustainable Supply Chain Managementmentioning

confidence: 99%

“…The knowledge-based view can be traced from Simon's [16] seminal work with significant extensions by Grant [17,18], Huber [19] and Levitt and March [20]. Further, the knowledge-based perspective suggests how big data can fuel the purposive search for market and resource innovation opportunities [21]; such resources can provide corporations a competitive advantage [22].While building knowledge-based resources, it is also imperative to predict and avoid risks causing potential disruptions [23]. Recently, supply chain risk management (SCRM) has become a priority because of its capability to avert potential disruption and recover more quickly from disruptions that cannot be averted [24].…”

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Mitigating Supply Chain Risk via Sustainability Using Big Data Analytics: Evidence from the Manufacturing Supply Chain

et al. 2017

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The use of big data analytics for forecasting business trends is gaining momentum among professionals. At the same time, supply chain risk management is important for practitioners to consider because it outlines ways through which firms can allay internal and external threats. Predicting and addressing the risks that social issues cause in the supply chain is of paramount importance to the sustainable enterprise. The aim of this research is to explore the application of big data analytics in mitigating supply chain social risk and to demonstrate how such mitigation can help in achieving environmental, economic, and social sustainability. The method involves an expert panel and survey identifying and validating social issues in the supply chain. A case study was used to illustrate the application of big data analytics in identifying and mitigating social issues in the supply chain. Our results show that companies can predict various social problems including workforce safety, fuel consumptions monitoring, workforce health, security, physical condition of vehicles, unethical behavior, theft, speeding and traffic violations through big data analytics, thereby demonstrating how information management actions can mitigate social risks. This paper contributes to the literature by integrating big data analytics with sustainability to explain how to mitigate supply chain risk.In reviewing literature on big data analytics (BDA) and sustainability, Keeso [12] argues that the application of big data in sustainable operations is often slow to achieve desired outcomes. Although sustainable supply chain management has little history in academic literature, it has become a mainstream area for practitioners [13][14][15]. On the other hand, building on the knowledge-based perspective some suggest the need for BDA as a knowledge resource that can be harvested and retained. The knowledge-based view can be traced from Simon's [16] seminal work with significant extensions by Grant [17,18], Huber [19] and Levitt and March [20]. Further, the knowledge-based perspective suggests how big data can fuel the purposive search for market and resource innovation opportunities [21]; such resources can provide corporations a competitive advantage [22].While building knowledge-based resources, it is also imperative to predict and avoid risks causing potential disruptions [23]. Recently, supply chain risk management (SCRM) has become a priority because of its capability to avert potential disruption and recover more quickly from disruptions that cannot be averted [24]. There are various risks including material flow risk, information flow risk and financial flow risk that cause supply chain disruptions [25]. Among these risks, supply chain social issues can impose significant operational risks on the supply chain [26]. In the literature the linkage between supply chain social issues, and risk management has been established [26]. Further, Klassen and Vreecke [26] show that social issues can be managed through effective monitoring, innovation cap...

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Section: Big Data Analytics and Sustainable Supply Chain Managementmentioning

confidence: 99%

Section: Big Data Analytics and Sustainable Supply Chain Managementmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Mitigating Supply Chain Risk via Sustainability Using Big Data Analytics: Evidence from the Manufacturing Supply Chain

et al. 2017

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“…The incorporation of our regime model in real‐life decision support systems, as modeled by Sprague and Carlson (). This visualization has been adapted from Jelinek and Bergey (). Solid arrows represent strategic decisions, whereas dashed arrows represent tactical decision making.…”

Section: Managerial Implicationsmentioning

confidence: 99%

Adaptive Tactical Pricing in Multi‐Agent Supply Chain Markets Using Economic Regimes

et al. 2015

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In today's complex and dynamic supply chain markets, information systems are essential for effective supply chain management. Complex decision making processes on strategic, tactical, and operational levels require substantial timely support in order to contribute to organizations' agility. Consequently, there is a need for sophisticated dynamic product pricing mechanisms that can adapt quickly to changing market conditions and competitors' strategies. We propose a two-layered machine learning approach to compute tactical pricing decisions in real time. The first layer estimates prevailing economic conditions-economic regimes-identifying and predicting current and future market conditions. In the second layer, we train a neural network for each regime to estimate price distributions in real time using available information. The neural networks compute offer acceptance probabilities from a tactical perspective to meet desired sales quotas. We validate our approach in the trading agent competition for supply chain management. When competing against the world's leading agents, the performance of our system significantly improves compared to using only economic regimes to predict † Corresponding author 791 792 Adaptive Tactical Pricing Using Economic Regimes prices. Profits increase significantly even though the prices and sales volume do not change significantly. Instead, tactical pricing results in a more efficient sales strategy by reducing both finished goods and components inventory costs.

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Big data analytics capabilities: a systematic literature review and research agenda

Mikalef

Pappas

Krogstie

et al. 2017

Inf Syst E-Bus Manage

584

487

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With big data growing rapidly in importance over the past few years', academics and practitioners have been considering the means through which they can incorporate the shifts these technologies bring into their competitive strategies. To date, there has been an emphasis on the technical aspects of big data with limited attention on the organizational changes they entail and how they should be leveraged strategically. As with any novel technology, it is important to understand the mechanisms and processes through which big data can add business value to companies and have a clear picture of the different elements and their interdependencies. To this end, the present paper aims to provide a theoretical discussion leading up to a research framework that can help explain the mechanisms through which big data lead to competitive performance gains. The research framework is grounded on past empirical work on IT-business, and builds on the resource-based view (RBV) and dynamic capabilities view (DCV) of the firm. By identifying the main areas of focus for big data and explaining the mechanisms through which they should be leveraged, this paper attempts to add to literature on how big data should be examined as a source of a competitive advantage. Response to Reviewers:The authors would like to thank the two anonymous reviewers for their constructive comments and feedback. In the new version of the manuscript we have incorporated the best we could the suggestion put forward. Specifically, these include:# The manuscript has been professionally copy-edited so the clarity and meaning are more clearly conveyed # The second and third paragraph of the introduction have been revised# We have more clearly defined the significance for IT/management research

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Innovation as the strategic driver of sustainability: big data knowledge for profit and survival

Cited by 25 publications

References 0 publications

Mitigating Supply Chain Risk via Sustainability Using Big Data Analytics: Evidence from the Manufacturing Supply Chain

Mitigating Supply Chain Risk via Sustainability Using Big Data Analytics: Evidence from the Manufacturing Supply Chain

Adaptive Tactical Pricing in Multi‐Agent Supply Chain Markets Using Economic Regimes

Big data analytics capabilities: a systematic literature review and research agenda

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