Labor flexibility integration in workload control in Industry 4.0 era

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.

Impact of Industry 4.0 adoption on workload demands in contact centers

“…Handling this amount of data requires different skills and competencies that most employees may not be yet prepared for (Costa & Portioli-Staudacher, 2021;Tommasi et al, 2021), justifying the increase in mental demand derived from the use of big data. The mixed effects of IoT and big data on the mental demand of contact centers' employees allow us to partially validate H 1 .…”

“…Challenges to its adoption in the workplace include the visualization, sharing, querying, and updating of the information generated by data sets with sizes exceeding the capacity of usual software to process within an acceptable time and value (Harford, 2014; Madden, 2012). Handling this amount of data requires different skills and competencies that most employees may not be yet prepared for (Costa & Portioli‐Staudacher, 2021; Tommasi et al, 2021), justifying the increase in mental demand derived from the use of big data. The mixed effects of IoT and big data on the mental demand of contact centers' employees allow us to partially validate H 1 .…”

“…The impacts of I4.0 adoption on employees workload and ergonomics are still underexplored in the literature (Costa & Portioli‐Staudacher, 2021; Kadir et al, 2019), and the few existing studies (e.g., Broday, 2020; Laudante, 2017; Reiman et al, 2021; Virmani & Salve, 2021) raise contradictory arguments on the relationship between I4.0 and employees workload. On the one hand, I4.0 technologies allegedly allow the automation of repetitive and monotonous activities, increasing the diversity of movements and responsibilities and positively affecting the workplace.…”

Implementing workload control through lean-based project in MTO-MTS environment

“…(2017) “How lean transformation affects scheduling” highlighting the importance of shifting from a “utilization-centric” mindset to “Lean Thinking”. While moving toward lean thinking, WLC is proven to work well in the case of high customization, particularly in MTO companies (Costa and Portioli-Staudacher, 2021). Following the lines of Bertolini et al.…”

“…Advantages of the roadmap are showed also in the study carried out by Tokola et al (2017) "How lean transformation affects scheduling" highlighting the importance of shifting from a "utilization-centric" mindset to "Lean Thinking". While moving toward lean thinking, WLC is proven to work well in the case of high customization, particularly in MTO companies (Costa and Portioli-Staudacher, 2021). Following the lines of Bertolini et al (2017), WLC is taken into consideration here along with other lean techniques.…”