A perspective on applications of in-memory analytics in supply chain management

Hahn, Gerd J.; Packowski, Josef

doi:10.1016/j.dss.2015.01.003

Cited by 61 publications

(38 citation statements)

References 25 publications

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“…This observation is supported by and builds on the arguments of Kwon et al (2014), Shin (2014Shin ( , 2015, Hazen (2014), Demirkan and Delen (2013) and Esteves and Curto (2013) stating quality of internal and external data. Regarding benefits of big data services, this observation agrees with Hahn and Packowski (2015), Rozados and Tjahjono (2014), Fairhurst (2014), Varian (2014. Regarding absorptive capacity to manage big data, it is in line with Waller and Fawcett (2014), Bersin 2012), Tambe (2014 and Davenport and Dyché (2013).…”

Section: Discussionsupporting

confidence: 78%

The adoption of Big Data Services by Manufacturing firms: An empirical investigation in India

Verma

2017

JISTEM

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Although some leading companies are actively adopting Big data services (BDS) to strengthen market competition , many manufacturing firms are still in the early stage of the adoption curve due to lack of understanding of and experience with BDS. Hence, it is interesting and timely to understand issues relevant to BDS adoption. The empirical investigation reveals that a firm's intention to adopt BDS can be positively affected by the quality and benefits of BDS. Surprisingly, a firm's absorptive capacity in utilizing big data and risks and costs associated with implementation and maintenance does not impact the adoption intention of BDS.

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Section: Discussionsupporting

confidence: 78%

The adoption of Big Data Services by Manufacturing firms: An empirical investigation in India

Verma

2017

JISTEM

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“…The research has generally argued that BI Investments induce better business performance [e.g., 45,51,52,54]. Some studies are more specialised, and analysed the impacts of BI Investments on specific parts of organizations such as relationship-based marketing [42,43], supply chain management [44,45], risk management [41], and knowledge discovery and management [46,47,50]. However, studies on investment in BI hardware, and management capabilities are scarce.…”

Section: Bi Investmentsmentioning

confidence: 99%

Getting value from Business Intelligence systems: A review and research agenda

Trieu

2017

Decision Support Systems

218

155

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Much of the research on Business Intelligence (BI) has examined the ability of BI systems to help organizations address challenges and opportunities. However, the literature is fragmented and lacks an overarching framework to integrate findings and systematically guide research. Moreover, researchers and practitioners continue to question the value of BI systems. This study reviews and synthesizes empirical Information System (IS) studies to learn what we know, how well we know, and what we need to know about the processes of organizations obtaining business value from BI systems. The study aims to identify which parts of the BI business value process have been studied and are still most in need of research, and to propose specific research questions for the future. The findings show that organizations appear to obtain value from BI systems according to the process suggested by Soh and Markus (1995), as a chain of necessary conditions from BI investments to BI assets to BI impacts to organizational performance; however, researchers have not sufficiently studied the probabilistic processes that link the necessary conditions together. Moreover, the research has not sufficiently covered all relevant levels of analysis, nor examined how the levels link up. Overall, the paper identified many opportunities for researchers to provide a more complete picture of how organizations can and do obtain value from BI.

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“…The meaning of descriptive analytics is twofold. On the one hand, it presents the summary of data to report and monitor [23]. On the other hand, it describes root cause analysis used to gain insights about the underlying phenomenon or process [30,31].…”

Section: Analyticsmentioning

confidence: 99%

“…The models and software used in Analytics are commonly distinguished as being descriptive, predictive, or prescriptive (e.g., [9,23,29]). The meaning of descriptive analytics is twofold.…”

Section: Analyticsmentioning

confidence: 99%

“…In our view, it is too wide of a field to assume that all reported effects can equally be applied on different Supply Chain Analytics initiatives, all initiatives having the same potential of providing value for an organization or all barriers appearing could be overcome in a single manner. The sole attempt to subdivide Supply Chain Analytics in extant research can be found in the framework for Analytics applications in LSCM [23], which focuses on off-the-shelf IT Systems and therefore ignores important aspects of Analytics as well as initiatives which are not system-specific. To derive more sophisticated and reliable research conclusions on the effects of Analytics on LSCM, a distinction of Supply Chain Analytics approaches is needed.…”

Section: Introductionmentioning

confidence: 99%

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Archetypes of Supply Chain Analytics Initiatives—An Exploratory Study

Herden

Bunzel

2018

Logistics

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While Big Data and Analytics are arguably rising stars of competitive advantage, their application is often presented and investigated as an overall approach. A plethora of methods and technologies combined with a variety of objectives creates a barrier for managers to decide how to act, while researchers investigating the impact of Analytics oftentimes neglect this complexity when generalizing their results. Based on a cluster analysis applied to 46 case studies of Supply Chain Analytics (SCA) we propose 6 archetypes of initiatives in SCA to provide orientation for managers as means to overcome barriers and build competitive advantage. Further, the derived archetypes present a distinction of SCA for researchers seeking to investigate the effects of SCA on organizational performance.

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A perspective on applications of in-memory analytics in supply chain management

Cited by 61 publications

References 25 publications

The adoption of Big Data Services by Manufacturing firms: An empirical investigation in India

The adoption of Big Data Services by Manufacturing firms: An empirical investigation in India

Getting value from Business Intelligence systems: A review and research agenda

Archetypes of Supply Chain Analytics Initiatives—An Exploratory Study

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