Abstract
The paradigm shift toward Industry 4.0 is facilitating human capability, and at the center of the research are the workers—Operator 4.0—and their knowledge. For example, new advances in augmented reality and human–machine interfaces have facilitated the transfer of knowledge, creating an increasing need for labor flexibility. Such flexibility represents a managerial tool for achieving volume and mix flexibility and a strategic means of facing the uncertainty of markets and growing global competition. To cope with these phenomena, which are even more challenging in high-variety, low-volume contexts, production planning and control help companies set reliable due dates and shorten lead times. However, integrating labor flexibility into the most consolidated production planning and control mechanism for a high-variety, low-volume context—workload control—has been quite overlooked, even though the benefits have been largely demonstrated. This paper presents a mathematical model of workload control that integrates labor flexibility into the order review and release phase and simulates the impact on performance. The main results show that worker transfers occur when they are most needed and are minimized compared to when labor flexibility is at a lower level of control—shop-floor level—thus reducing lead time.
Highlights
Growing global market competition and the increased diversity of customer demands have led to the rapid development of manufacturing (Tao et al 2017)
Having introduced the synergism between production planning and control (PPC) and labor flexibility enabled by Industry 4.0 technologies, as both target the delivery of goods on time, making companies more profitable in the market, this study aims to present and test through computer-based simulation a new PPC model for high-variety, low-volume companies that integrates labor flexibility
To assess WLWorker+ against WLWorker, we show for both models the workload at each station—measured as corrected shop load (CSL), as in Oosterman et al (2000)— and the number of workers at each station
Summary
Growing global market competition and the increased diversity of customer demands have led to the rapid development of manufacturing (Tao et al 2017) These phenomena make the demand more difficult to predict along, affecting the setting of reliable due dates. Companies that fail to establish long-lasting relationships with customers because of unreliable due dates are destined to disappear from the market (Kingsman and Hendry 2002). This is the reason why companies must shorten lead times and improve due-date estimation, which is especially challenging for companies that are producing on a to-order basis.
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