The best of times and the worst of times: empirical operations and supply chain management research

Abstract

We assess the current state of empirical research in operations and supply chain management (OSM), using Dickens' contrast between the best of times and the worst of times as a frame. The best of times refers to the future that empirical OSM research is now entering, with exciting opportunities available using big data and other new data sources, new empirical approaches and analytical techniques, and innovative tools for developing theory. These are well aligned with new research questions related to the digital economy, Industry 4.0, the impact of the millennial generation as consumers, social media, 3D printing, etc. However, we also explore how it is the worst of times, focusing on the challenges and problems that plague empirical OSM research. Our goal is to show how OSM researchers can learn from the worst of times, in order to be poised to take advantage of the best of times. We introduce the research diamond as a vehicle for emphasizing the importance of a balanced research perspective that treats the research problem, theory, data collection and data analysis as equally important, requiring alignment between them. By learning and addressing the issues in this period of the best of times and the worst of times, we can take advantage of the opportunities facing our field to generate research that is balanced, insightful, rigorous, relevant, impactful and interesting.

Digitalizing supply chains potential benefits and impact on lean operations

“…The least two ranked potential impacts were preventing defects from moving to the next process (mean = 3.57) and identifying causes of any errors (mean = 3.53). The use of certain smart technologies such as data-mining, Machine Learning and Big Data (Baryannis et al , 2018; Lade et al , 2017; Melnyk et al , 2018) enabled businesses to identify root-causes of certain disruptions in their operations and processes either within supply chains or other parts of the business. However, it is important to note that the use of such techniques is still developing and there is no wide-spread use of such techniques yet within different sectors.…”

Decision-making trends in quality management: a literature review about Industry 4.0

“…Yadegaridehkordi et al (2018) and Irani et al (2018) were also classified as addressing microlevel engagement because they used computational simulation methodologies to aid decision-making in companies. The article developed by Melnyk et al (2018), who approached and dealt with tools of low technological level, was classified as addressing macrolevel engagement. In the same group, Müller et al (2018) also used mechanisms of low technological complexity and human involvement.…”

Supply chain digitalization: past, present and future

“…Theory provides a way to 'make sense of what would otherwise be inscrutable or unmeaning empirical findings' (Gaile, Clarke, and Huff 2009, 286). According to Melnyk, Flynn, and Awaysheh (2018) it is that which makes a field like operations and supply chain management a science, rather than a set of practices or an art, providing a roadmap for investigating the research problem, elucidating relevant constructs and expected relationships between them, and avoiding extraneous constructs and relationships. As can be observed in Appendix 1 the most dominant theory underpinning empirical work is the resource-based view it is therefore not surprising that the empirical works tend to focus on optimization of the supply chain by leveraging technological resources be it Big Data or 3DP.…”