I have used Fisher’s (1997) supply chain–product match/mismatch framework (What Is the Right Supply Chain for Your Product?) in my teaching for years! Herein, the author argues that functional products require a physically efficient supply chain strategy, whereas innovative products require a market-responsive supply chain strategy. Fisher’s framework finds empirical support: Wagner et al. (2012) demonstrate that “the higher the supply chain fit, the higher the Return on Assets (ROA) of the firm”. Interestingly, a majority of the firms from their sample achieve a negative misfit, i.e. they target high responsiveness for their supply chain although their products are functional. Extensions of the framework exist, for example by Lee (2002), who adds a “supply” dimension, and more recently Gligor (2017), who argues that “benefits generated by perfect supply chain fit might be offset by the resources deployed to achieve that fit”. Research presented by Perez-Franco et al. (2016) helps to “capture, evaluate and re-formulate the supply chain strategy of a business unit”.
Fisher, M.L. (1997). What Is the Right Supply Chain for Your Product? Harvard Business Review, 75 (2), 105-116.
My guest post today comes from Kai Hoberg from the Kühne Logistics University (KLU) in Hamburg. Together with his co-authors, Alan McKinnon and Christoph Flöthmann, he has just published a new report, which is commissioned by the World Bank and analyzes the shortage of qualified logistics personnel.
Qualified logistics personnel is in short supply worldwide. This is the conclusion of our new report, titled Logistics Competencies, Skills, and Training: A Global Overview. While there are too few well-trained executives in the logistics sector in emerging countries, there is an acute shortage of qualified staff at the operational level in developed economies. We argue that this skills shortage is likely to worsen in the absence of new initiatives. There are two aspects that deserve further elaboration: First, physically, there are too few people available to cover vacant position in the logistics sector. Second, the currently employed workforce is partially lacking the skills demanded for their job. Based on an empirical analysis, we derive multiple recommendations for relevant stakeholders, i.e. companies, governmental institutions and logistics associations. The proposed measures include innovative training methods like logistics-related business games that can be employed without requiring high upfront investments or long implementation lead-times.
Kai Hoberg is Associate Professor of Supply Chain & Operations Strategy at KLU. In his academic career he was a visiting scholar at Cornell University, Israel Institute of Technology, University of Oxford and National University of Singapore. He is on the scientific advisory board of the German Logistics Association (BVL) and has been working with companies like Procter & Gamble, McKinsey & Company, Jungheinrich and Zalando on supply chain innovation projects.
A colleague recently recommended the following article to me: Mansfield (2003): Spatializing Globalization: A “Geography of Quality” in the Seafood Industry. Herein, the author takes a look at the quality of products in that industry. She challenges “recent perspectives that define quality as an alternative to global, industrial forms of production” and “finds that quality is also important for industrial food production and for the global geography of the surimi [a fish paste] seafood industry”. In general, the author takes an interpretive approach – an approach that is almost absent in SCM research, and that might be inspirational for our otherwise empiricist discipline. Particularly, she employs actor–network theory, which proposes that reality does not exist by nature but is rather constructed through socio-material networks. SCM researchers could learn from such a type of research that (1) theory could be mobilized in many different creative ways; (2) technical supply chain issues are embedded in larger social-political arrangements; (3) geography might inform SCM (theoretically as well as materially); and (4) “quality”, or other concepts, do not exist by nature but are stabilized through networks.
Today’s economy is a plastics economy, as most of our global supply chains contain plastics. A report, published by the Ellen MacArthur Foundation, is titled The New Plastics Economy: Rethinking the Future of Plastics. Herein it becomes evident that linear supply chains need to become circular: “The circular economy is gaining growing attention as a potential way for our society to increase prosperity, while reducing demands on finite raw materials and minimising negative externalities. Such a transition requires a systemic approach, which entails moving beyond incremental improvements to the existing model as well as developing new collaboration mechanisms.” The report “explores the intersection of these two themes, for plastics and plastic packaging in particular: how can collaboration along the extended global plastic packaging production and after-use value chain, as well as with governments and NGOs, achieve systemic change to overcome stalemates in today’s plastics economy in order to move to a more circular model?”
There seems to be a lot of confusion about what theory is. At least this is a recurring question I get from students. Let us first discuss what theory is not: Sutton & Staw (1995) show that “references, data, variables, diagrams, and hypotheses are not theory” and they “explain how each of these five elements can be confused with theory” (p. 371). But we should also be aware of the difference between facts and theory! In his essay, which is part of a collection of six essays, Pagell (in: Boer et al., 2015) paints the picture of an ideal research world where “most research will be building or testing facts, not theory”, while “theory building and testing [will be left] to a much smaller group of papers, where the theoretical argument would be critical” (p. 1244). So, what is theory? A definition I like comes from Suddaby (2015): “[T]heory is simply a way of imposing conceptual order on the empirical complexity of the phenomenal world” (p. 1).
“Scale purification” – the process of eliminating items from multi-item scales – is widespread in empirical research, but studies that critically examine the implications of this process are scarce. In our new article, titled Statistical and Judgmental Criteria for Scale Purification, we (1) discuss the methodological underpinning of scale purification, (2) critically analyze the current state of scale purification in supply chain management (SCM) research, and (3) provide suggestions for advancing the scale purification process. Our research highlights the need for rigorous scale purification decisions based on both statistical and judgmental criteria. We suggest several methodological improvements. Particularly, we present a framework to demonstrate that the justification for scale purification needs to be driven by reliability, validity and parsimony considerations, and that this justification needs to be based on both statistical and judgmental criteria. We believe that our framework and additional suggestions will help to advance the knowledge about scale purification in SCM and adjacent disciplines.
Wieland, A., Durach, C.F., Kembro, J. & Treiblmaier, H. (2017). Statistical and Judgmental Criteria for Scale Purification. Supply Chain Management: An International Journal, 22 (4). DOI: 10.1108/SCM-07-2016-0230
“Slicing and dicing the supply chain to service ever more diverse and demanding customers has become the core challenge for Chief Supply Chain Officers. But simply expanding the number of supply chain configurations and maintaining separate organizations to manage them—the approach followed by most organizations—is driving too much complexity and wasting potential synergies.” This is how a new report by Accenture starts. It is titled: Can Your Supply Chain Avoid Extinction? The authors recommend three strategies to move toward a differentiated supply chain: First, companies should focus on supply chain configurations that drive value, as this will serve customers best. Second, companies should choose the right digital technologies for each configuration, hereby applying only those capabilities that enable them to deliver the right supply chain response. Third, companies should find the right structure and governance; this includes embedding innovation thinking at the heart of the organization. Have a look at the full report.