Data-driven operations and supply chain management: established research clusters from 2000 to early 2020

PurposeData-driven quality management systems, brought about by the implementation of digitisation and digital technologies, is an integral part of improving supply chain management performance. The purpose of this study is to determine a methodology to aid the implementation of digital technologies and digitisation of the supply chain to enable data-driven quality management and the reduction of waste from manufacturing processes.Design/methodology/approachMethodologies from both the quality management and data science disciplines were implemented together to test their effectiveness in digitalising a manufacturing process to improve supply chain management performance. The hybrid digitisation approach to process improvement (HyDAPI) methodology was developed using findings from the industrial use case.FindingsUpon assessment of the existing methodologies, Six Sigma and CRISP-DM were found to be the most suitable process improvement and data mining methodologies, respectively. The case study revealed gaps in the implementation of both the Six Sigma and CRISP-DM methodologies in relation to digitisation of the manufacturing process.Practical implicationsValuable practical learnings borne out of the implementation of these methodologies were used to develop the HyDAPI methodology. This methodology offers a pragmatic step by step approach for industrial practitioners to digitally transform their traditional manufacturing processes to enable data-driven quality management and improved supply chain management performance.Originality/valueThis study proposes the HyDAPI methodology that utilises key elements of the Six Sigma DMAIC and the CRISP-DM methodologies along with additions proposed by the author, to aid with the digitisation of manufacturing processes leading to data-driven quality management of operations within the supply chain.

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Competence-Oriented, Data-Driven Approach for Sustainable Development in University-Level Education

Sustainability ◽

10.3390/su13179977 ◽

2021 ◽

Vol 13 (17) ◽

pp. 9977

Author(s):

Szabina Fodor ◽

Ildikó Szabó ◽

Katalin Ternai

Keyword(s):

Supply Chain ◽

Labor Market ◽

Supply Chain Management ◽

Data Driven ◽

Green Supply Chain ◽

Chain Management ◽

Job Advertisements ◽

Data Driven Approach ◽

Market Needs ◽

The Given

Higher education has a number of key roles to play in accelerating progress toward sustainability goals. It has a responsibility to provide and teach curricula that are tailored to labor market needs, to help change people’s attitudes and motivation toward sustainability, and to reduce inequalities between different students. Course leaders and developers of curricula should monitor and assess these needs in order to improve their curricula from time to time. In the present work, we describe a data-driven approach based on text-mining techniques to identify the competences required for a given position based on job advertisements. To demonstrate the usefulness of our suggested method, the supply chain management occupation was selected as the supply chain is a constantly changing domain that is highly affected by green activities and initiatives, and the COVID-19 pandemic strongly influenced this sector, as well. This data-driven process allowed the identification of required soft and hard skills contained in job descriptions. However, it was found that some important concepts of green supply chain management, such as repair and refurbishment, were only marginally mentioned in the job advertisements. Therefore, in addition to labor market expectations, a business process model from relevant green supply chain management literature was developed to complement the required competences. The given new techniques can support the paradigm shift toward sustainable development and help curriculum developers and decision makers assess labor market needs in the area of sustainability skills and competences. The given result can serve as an input of outcome-based training development to design learning objective-based teaching materials.

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Explaining the competitive advantage generated from Analytics with the knowledge-based view: the example of Logistics and Supply Chain Management

BuR - Business Research ◽

10.1007/s40685-019-00104-x ◽

2019 ◽

Vol 13 (1) ◽

pp. 163-214 ◽

Cited By ~ 2

Author(s):

Tino T. Herden

Keyword(s):

Supply Chain ◽

Supply Chain Management ◽

Competitive Advantage ◽

Case Studies ◽

Data Driven ◽

Sustainable Competitive Advantage ◽

Chain Management ◽

Knowledge Based ◽

Data Scientist ◽

Functional Teams

AbstractThe purpose of this paper is to provide a theory-based explanation for the generation of competitive advantage from Analytics and to examine this explanation with evidence from confirmatory case studies. A theoretical argumentation for achieving sustainable competitive advantage from knowledge unfolding in the knowledge-based view forms the foundation for this explanation. Literature about the process of Analytics initiatives, surrounding factors, and conditions, and benefits from Analytics are mapped onto the knowledge-based view to derive propositions. Eight confirmatory case studies of organizations mature in Analytics were collected, focused on Logistics and Supply Chain Management. A theoretical framework explaining the creation of competitive advantage from Analytics is derived and presented with an extensive description and rationale. This highlights various aspects outside of the analytical methods contributing to impactful and successful Analytics initiatives. Thereby, the relevance of a problem focus and iterative solving of the problem, especially with incorporation of user feedback, is justified and compared to other approaches. Regarding expertise, the advantage of cross-functional teams over data scientist centric initiatives is discussed, as well as modes and reasons of incorporating external expertise. Regarding the deployment of Analytics solutions, the importance of consumability, users assuming responsibility of incorporating solutions into their processes, and an innovation promoting culture (as opposed to a data-driven culture) are described and rationalized. Further, this study presents a practical manifestation of the knowledge-based view.

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