Workload Control

Author(s):  
Nuno O. Fernandes

Global competition and changing customer requirements are putting major challenges in the production planning and control systems of manufacturing enterprises. Production planning and control requires robust methods able to cope with demand variability, processing times variability, routing sequences, resource availability, etc., between other sources of variation. Production planning and control significantly influences target performance criteria such as the delivery time, on-time delivery, work-in-process inventory, and resources utilization. Therefore, production planning and control is strategically important for the economic success of these enterprises and innovative production planning and control methods are required. This chapter describes how workload control, a leading production planning and control system for small- and medium-sized enterprises, operates and illustrates how it aids in shortening and stabilising throughput times based on a simulation study of a small made-to-order production system.

Author(s):  
Federica Costa ◽  
Alberto Portioli-Staudacher

AbstractThe 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.


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