Rapid advancements in an array of digital technologies and applications promote the transformation of industrial production into cyber-physical systems (CPS). This process is projected to lead to a completely new level of process automation, thereby redefining the role of humans and altering current work designs in yet unknown ways. However, existing literature is rather ambiguous and not explicit on how the transformation towards CPS affects work design. In this study, we therefore consider this transformation at a much more detailed level. Our main contribution is the development of a framework to assess work design changes in the transformation towards CPS, and the consideration of the role of management choice therein. The framework relates (future) capabilities of CPS on the machine, production line, factory and supply chain scope to functions of human information processing. We then evaluate how the potential automation or augmentation of those functions by CPS affects job characteristics. Automation in this context is defined as the transfer of control and decisionmaking from humans to CPS, while augmentation means that technology is used to enhance human productivity or capability. We expect that the transformation towards CPS and the resulting automation and augmentation of tasks will shift the majority of human work to jobs characterized by high levels of job complexity, job autonomy and skill variety. This effect will become more severe when tasks are increasingly automated in the transformation towards CPS. During this development, human skills and knowledge will presumably remain critical in near future industrial production. Nevertheless, the ultimate implications for work design are strongly dependent on management choice. Strategic decisions are required on (1) which functions to automate across different scopes of operations and (2) how to group the resulting pool of tasks into jobs. This may result in various work designs. However, this choice is to a certain degree limited, and the role of technology is to restrict, rather than determine management choice.what exactly leads to variations in these work designs (Parker, Morgeson, & Johns, 2017). One source of variation stems from the implementation of innovative digital technologies in industrial production, linked to the concept of Industry 4.0 (Hirsch-Kreinsen, 2016;Hirsch-Kreinsen & Ten Hompel, 2015). Industry 4.0 is an umbrella term comprising an array of different high-tech technologies and is characterized by Cyber-Physical Systems (CPS) in the context of industrial production (Colombo, Karnouskos, Kaynak, Shi, & Yin, 2017;Hermann, Pentek, & Otto, 2016).In essence, CPS are systems of interconnected physical and computational objects, resulting in a close coupling of the cyber and physical contexts (Lee, Bagheri, & Kao, 2015). In industrial production, this means that physical objects, such as machines or products, are integrated with computational components. As a consequence, CPS are

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Due to the success of lean manufacturing, many companies are interested in implementing a lean production control system. Lean production control principles include the levelling of production, the use of pull mechanisms and takt time control. These principles have mainly been applied in high volume flow shop environments where orders move through the production system in one direction in a limited number of identifiable routing sequences. This article investigates how lean production control principles can be used in a make-to-order job shop, where volume is typically low and there is high variety. We show how production levelling, constant work in process, first in first out and takt time can be integrated in a lean production control system. A case study is presented to illustrate the design and phased implementation of the system in a typical dual resource constrained production environment. The case study demonstrates that lean production control principles can be successfully implemented in a high-variety/low-volume context. Implementation led to a reduction in flow times and an increase in the service level achieved, with on-time delivery performance improving from 55 to 80%.

There is a substantive amount of literature showing that demand information sharing can lead to considerable reduction of the bullwhip effect/inventory costs. The core argument/analysis underlying these results is that the downstream supply-chain member (the retailer) quickly adapts its inventory position to an updated end-customer demand forecast. However, in many real-life situations, retailers adapt slowly rather than quickly to changes in customer demand as they cannot be sure that any change is structural. In this paper, we show that the adaption speed and underlying (unknown) demand process crucially effect the value of information sharing. For the situation with a single upstream supply-chain member (manufacturer) and a single retailer, we consider two demand processes: stationary or random walk. These represent two extremes where a change in customer demand is never or always structural, respectively. The retailer and manufacturer both forecast demand using a moving average, where the manufacturer bases its forecast on retailer demand without information sharing, but on end-customer demand with information sharing. In line with existing results, the value of information turns out to be positive under stationary demand. One contribution, though, is showing that some of the existing papers have overestimated this value by making an unfair comparison. Our most striking and insightful finding is that the value of information is negative when demand follows a random walk and the retailer is slow to react. Slow adaptation is the norm in real-life situations and deserves more attention in future researchexploring when information sharing indeed pays off.