Insights from big Data Analytics in supply chain management: an all-inclusive literature review using the SCOR model

Chehbi-Gamoura, Samia; Derrouiche, Ridha; Damand, David; Barth, Marc

doi:10.1080/09537287.2019.1639839

Cited by 106 publications

(55 citation statements)

References 189 publications

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“…First, the so-called predefined dimensions were formulated. For this purpose, the SCOR framework (APICS 2017) was found suitable as it is widely applied in both academia as well as practice and it has previously been used to structure systematic reviews (Kamble, Gunasekaran, and Gawankar 2020;Chehbi-Gamoura et al 2020). It includes five dimensions depicting the SC, namely 'Plan', 'Source', 'Make', 'Deliver', 'Return'.…”

Section: Definition Of Research Dimensionsmentioning

confidence: 99%

“…The selected methodology for the same is a systematic literature review organised according to the Supply Chain Operations Reference (SCOR) model (APICS 2017). This approach, that has previously been used in a different context by Chehbi-Gamoura et al (2020) as well as Kamble, Gunasekaran, and Gawankar (2020), offers researchers and practitioners easy guidance for specific issues at hand and allows the quick identification of potential challenges and benefits in the different areas of the SC related to AM. An industry-specific analysis is also included to allow a straightforward discussion in this regard.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Additive manufacturing and supply chains – a systematic review

Kunovjanek

Knofius

Reiner

2020

Production Planning & Control

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Section: Definition Of Research Dimensionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Additive manufacturing and supply chains – a systematic review

Kunovjanek

Knofius

Reiner

2020

Production Planning & Control

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“…SCOR is a reference model that describes the businesses activities related to meeting the demands of the customers in a SC environment. This model was previously used by researchers to ground SC analytics studies based upon to enhance the mapping of the concepts and SC activities [7], [18], [29]. The SCOR reference model is based on three underlying aspects: business processes, metrics and best practices and technology, linking them into a unified structure [30].…”

Section: Sc Operation Reference Modelmentioning

confidence: 99%

“…In our study, we focus on the SC activities, to specifically formulate the benefits of each of the VA tactics according to their use in the corresponding SC process. On the other hand, the study in [7] presented a literature review underlining the need for big data analytics in SC management, and the necessity for visualization is highlighted in the process of data discovery from different SC activities. We also investigate into literature on the use of data visualization, but in particular we look into identifying the analytical capabilities of visualization in SC processes.…”

Section: Introductionmentioning

confidence: 99%

Visual Analytics for Decision Support: A Supply Chain Perspective

2021

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Supply chain (SC) activities generate huge amount of data that can be used in decision making processes. However, proper data analytics techniques are required to combine, organize, and analyze data from different sources and produce required insights available for decision makers. These techniques promote analytical reasoning of the events and patterns hidden in the data using visualizations, so-called Visual Analytics (VA). Although there is a large number of VA systems to facilitate the process of analysis and decision making, there is a lack of an adequate overview of what already exists in this area for SC management. To address that need, we conducted a systematic literature review to analyze the state of the art in SC VA systems. Particularly, we focus on use cases, the type of the decisions that a VA system intended to support, the type of visualizations employed, the type of analytics used, and the data that has been used for analysis. The goal of this study is to provide SC and VA researchers with an overview of the works carried out in the field of SC VA, helping them to observe latest trends and to recognize existing gaps that need further investigation. Consequently, a mapping between decisions of various SC business processes and their reciprocal visualization techniques and tactics have been provided. Adding to that, VA applications and use cases in SC are identified based on the SC Operation Reference (SCOR) model and underlying decision areas are recognized.

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“…the speed of data generation and streaming, and Variety, i.e. data types and sources (Wamba et al 2020;Chehbi-Gamoura et al 2020). This phenomenon has resulted in the widely discussed term big data.…”

Section: Introductionmentioning

confidence: 99%

Value of data in multi-level supply chain decisions: a case study in the Dutch floriculture sector

Viet

Behdani

Bloemhof-Ruwaard

et al. 2020

International Journal of Production Research

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While many supply chain decisions could take advantage of big data, firms struggle with investments into supply chain analytics since they are not able to assess the application areas and benefits of these initiatives. In this paper, we provide a multi-level perspective to assess the value of supply chain data. We develop a framework that highlights the connections between data characteristics and supply chain decisions with different time horizons (i.e. short-or long-term) as well as different supply chain levels (i.e. individual-firm level or supply-chain level). As data gets more complex in one or more of the 4 V dimensions (i.e. volume, variety, velocity, veracity), firms must assess how to best take advantage of the opportunities offered. We use the Dutch floriculture sector as a case study for our framework in which we highlight four data analytics applications to improve logistics processes. In the applications, we demonstrate how the data is used to support the decisions at different time horizons and supply-chain levels. We find that each of the big data's Vs is required differently according to the decisions' characteristics. Based on the findings, applications in other industries and promising directions for future research are discussed.

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Insights from big Data Analytics in supply chain management: an all-inclusive literature review using the SCOR model

Cited by 106 publications

References 189 publications

Additive manufacturing and supply chains – a systematic review

Additive manufacturing and supply chains – a systematic review

Visual Analytics for Decision Support: A Supply Chain Perspective

Value of data in multi-level supply chain decisions: a case study in the Dutch floriculture sector

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