Guest editorial: State-of-the-art for manufacturing management: advancing the research agenda and practice through literature reviews

摘要

When we put out the call for papers for this special issue, our aim was to develop a comprehensive collection of scholarly knowledge on manufacturing management and its associated topics. By doing so, we hoped to provide both academics and practitioners with valuable insights gained from previous research. Through this consolidation of knowledge, researchers can establish a solid foundation for future studies, identify new avenues for research and inform practitioners about the effectiveness of concepts and interventions, as well as any trade-offs they might need to consider.For the call, we identified four topics that present challenges to both academics conducting studies and managers, particularly those involved in manufacturing and supply chain management, regarding the effectiveness and implementation of concepts, methods and tools. Let us look at these challenges first.The first challenge is the need for manufacturing and its supply networks to cope with adverse events. This has become more prominent with the pandemic, but not limited to; further examples are geopolitical tensions tied to new nationalism, natural disasters and economic cycles. With Bhamra et al. (2011) providing a review on resilience, it could be expected that further literature reviews would go beyond being conceptual in nature. A case in point of a conceptual model is Kusiak (2020), who presents an approach that combines the cluster algorithm, known from group technology, and modular design to improve resilience. In this spirit of dealing with the impact of unexpected events, Bhamra et al. (2011, p. 5380) observe three dimensions in studies looking at resilience (slightly reformulated here for consistency):• Readiness and preparedness;• Responsiveness and capability for adaption;• Recoverability and accommodation.Another take is presented by Zhang and van Luttervelt (2011, p. 471) when they succinctly describe resilience as insensitivity to perturbations of a manufacturing system (a reformulation here better reflecting their intent). Perturbations may be caused by abrupt disruptions in markets, availability of resources, geopolitical tensions and disruptions of supply. They can be categorised as black swans and grey swans (see Akkermans and Van Wassenhove, 2013, p. 6747 ff.), events that have highly unlikely probability and huge impact, respectively, unlikely probability and severe impact on manufacturing systems. Hence, this challenge requires both evaluation of conceptual developments and empirical data, particularly through literature reviews.A second challenge for manufacturing firms is becoming more sustainable. There is a contradiction here, as any manufacturing process calls on the use of systems of resources and requires input of materials in one form or another. Logically, this implies that only both the impact of materials and systems of resources on the environment can be reduced or mitigated (with the latter calling on other systems of resources). Implicitly, this is acknowledged by Gunasekaran and Spalanzani (2012, pp. 40–41, 44) when they review other studies, and declare that business process reengineering, just-in-time production, computer-integrated manufacturing, quality management, lean production, virtual enterprise, supply chain management and agile production are among concepts that make significant contributions to sustainability; however, all of these fall into the category for reduction of impact. Following on from this, Herrmann et al. (2014, pp. 286–288) propose the holistic factory as a concept for sustainability in manufacturing, albeit that it implicitly relies on traditional concepts from systems theories such as the distinction of aspects, process modelling, control processes and steady-state model (Dekkers, 2017, pp. 29–32, 117–119, 164, 188). However, Hoekstra (2015, p. 83) seems to imply that some caution may be necessary since a holistic approach to the sustainability of a manufacturing system does not necessarily m