Florian Kache and Stefan Seuring
Despite the variety of supply chain management (SCM) research, little attention has been given to the use of Big Data Analytics for increased information exploitation in a supply…
Abstract
Purpose
Despite the variety of supply chain management (SCM) research, little attention has been given to the use of Big Data Analytics for increased information exploitation in a supply chain. The purpose of this paper is to contribute to theory development in SCM by investigating the potential impacts of Big Data Analytics on information usage in a corporate and supply chain context. As it is imperative for companies in the supply chain to have access to up-to-date, accurate, and meaningful information, the exploratory research will provide insights into the opportunities and challenges emerging from the adoption of Big Data Analytics in SCM.
Design/methodology/approach
Although Big Data Analytics is gaining increasing attention in management, empirical research on the topic is still scarce. Due to the limited availability of comparable material at the intersection of Big Data Analytics and SCM, the authors apply the Delphi research technique.
Findings
Portraying the emerging transition trend from a digital business environment, the presented Delphi study findings contribute to extant knowledge by identifying 43 opportunities and challenges linked to the emergence of Big Data Analytics from a corporate and supply chain perspective.
Research limitations/implications
These constructs equip the research community with a first collection of aspects, which could provide the basis to tailor further research at the nexus of Big Data Analytics and SCM.
Originality/value
The research adds to the existing knowledge base as no empirical research has been presented so far specifically assessing opportunities and challenges on corporate and supply chain level with a special focus on the implications imposed through Big Data Analytics.
Details
Keywords
Florian Kache and Stefan Seuring
This paper aims to assess the links among these supply chain constructs by conducting a full-scale systematic review of all supply chain management (SCM) literature reviews…
Abstract
Purpose
This paper aims to assess the links among these supply chain constructs by conducting a full-scale systematic review of all supply chain management (SCM) literature reviews published in ten leading logistics, SCM and operations management journals from 1989 to 2012. Collaboration and integration are as central to SCM as risk and performance management.
Design/methodology/approach
The authors apply content analysis to execute the systematic literature review on the sample of 103 articles, supplemented by contingency analysis. These approaches guarantee a replicable, rigorous and transparent research process and minimize researcher bias. The analytical categories required for the content analysis are defined along the constructs of collaboration/integration and risk/performance.
Findings
As can be expected, the review highlights the key role of the two constructs in SCM. In this light, the research claims to provide statistical evidence of a link between the constructs of collaboration/integration and risk/performance, most notably between collaboration and performance, information sharing and rewards sharing, as well as integration and supply chain performance.
Research limitations/implications
The study assesses the link between the constructs of collaboration/integration and risk/performance through research embedded in literature reviews, pinpointing research gaps and potential future research directions in the field. Contributing to SCM theory building, a thorough review provides statistical proof of the link between collaboration/integration and risk/performance.
Originality/value
Although numerous literature reviews have been conducted in the past on the SCM constructs of collaboration/integration and risk/performance, no full review of literature reviews aiming to test a theoretical link in the here presented form has yet been undertaken to the authors’ knowledge.
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Keywords
Ying Yang, Biao Yang, Hung Nguyen and George Onofrei
Data mining has been well-applied by maintenance service providers in identifying data patterns and supporting decision-making. However, when applying data mining for…
Abstract
Purpose
Data mining has been well-applied by maintenance service providers in identifying data patterns and supporting decision-making. However, when applying data mining for analytics-driven maintenance, maintenance service providers often adopt data mining with unstructured “trial-and-error” approaches. In response, we have followed design science to develop a comprehensive approach to diagnosing the problems with the existing data mining processes model for analytics-driven maintenance service.
Design/methodology/approach
This study conducted an in-depth case study with Siemens in the UK for data collection in order to apply a two-cycle build-and-evaluate design process. Based on the literature, the preliminary model is built. It is evaluated through the case company in the first cycle. In the second cycle, the model is refined based on the comments from the case company and then re-evaluated from both business management and information technology perspectives to ensure the applicability of the designed model in a real business environment.
Findings
Firstly, this study identifies three main shortcomings in the existing data mining process models for analytics-driven maintenance. Secondly, this study develops the “Gear-Wheel Model”, with a customer-oriented cycle, a project planning cycle and a machine comprehension cycle, to overcome all these shortcomings simultaneously and provide improvement solutions. Thirdly, this study highlighted that the data mining processes for analytics-driven maintenance service need interactions from different functional departments and supports of successive data collection.
Originality/value
The study expands data mining analysis beyond a single business function to include interactions with other internal functions and external customers. It contributes to existing knowledge by focusing on the managerial aspects of data mining and integrating maintenance service providers with their business customers.