Complex product manufacturing in the intelligence-connected era

Enterprise sustainability will be important for businesses in the intelligence-connected (Internet of Things, etc.) era, especially for high-variety low-volume businesses facing complex manufacturing problems for their products. We propose that many efficient management models are being developed to cope with these issues, applying highly flexible resource configurations to enable quick response to an ever-changing market environment. Supply chains inherently involve complexity, as multiple organizational supply chains have become predominant as opposed to the traditional vertically organized monopolies such as Standard Oil and US Steel. Turner, Aitken, and Bozarth (2018) examined the literature to describe supply chain complexity. The strategic role of information systems was noted by Turunen, Eloranta, and Hakanen (2018), while Zdravković et al. (2018) reviewed issues in the relationship of enterprise information systems infrastructure in the Internet-of-Things era. This complexity within supply chains involves many interactions across levels (Dittfeld, Scholten, and Van Donk 2018). Advances in technology have led to smarter factories that can be optimized in some sense (Yin et al. 2018). Intelligent planning enables more intelligent systems, enabling standardized complex physical entities and processes, accumulation of knowledge at different levels of complexity, thus providing decision makers with more timely and accurate information. This evolution expands beyond manufacturing to distributive trade (De Leeuw, Grotenhuis, and van Goor 2013), finance (Lam 2018), and services (Kreye 2019). In response to the call for papers, we received more than 50 submissions. After the review process, we have successfully ended up with 10 excellent papers presenting a variety of techniques to deal with special problems in a variety of topic areas. Complex product planning and resource configuration • Chansombat et al. (2019) provided experimental results to a problem of schedule delivery in the area of complex product planning and resource configuration. The developed complex heuristic optimization through use of a discrete bat algorithms with krill herd-based methodology. • Also in this topic area, Poeschl, Wirth, and Bauernhansl (2019) provided a methodology to optimize risks in the commissioning process. This was an application in mechanical engineering, specifically focused on machine production planning.