Multi-stage assembly systems where the demand for components depends on the market-driven demand for end products, are commonly encountered in practice. Production Planning and Control (PPC) systems for this production context include Kanban, Materials Requirement Planning (MRP), Optimised Production Technology (OPT), and Demand Driven MRP (DDMRP). All four of these PPC systems are widely applied in practice and literature abounds on each of these systems. Yet, studies comparing these systems are scarce and remain largely inconclusive. In response, this study uses simulation to assess the performance of all four PPC systems under different levels of bottleneck severity and due date tightness. Results show that MRP performs the worst, which can be explained by the enforcement of production start dates. Meanwhile, Kanban and DDMRP perform the best if there is no bottleneck. If there is a bottleneck then DDMRP and OPT perform the best, with DDMRP realising lower inventory levels. If there is a severe bottleneck, then the performance results for DDMRP and OPT converge. This identification of contingency factors not only resolves some of the inconsistencies in the literature but also has important implications for the applicability of these four PPC systems in practice.

International audienceIn this paper, we report a simulation study on the role of sequence-dependent set-up times in decision making at the order release level of a workload controlled make-to-order flow-shop. The study evaluates the potential for set-ups savings, dependent on the level of workload in the shop, for two alternative strategies, namely considering set-up times centrally, within the release decision or locally, within the dispatching decision. These strategies are compared and assessed on the basis of two main performance measures namely time in system and standard deviation of the job lateness. Results indicate that the local strategy, which has been traditionally adopted in practice and in most of the studies dealing with sequence-dependent set-up times, does not always give the best results. The release frequency and the shop workload appear critical to the selection of the strategy to adopt, strongly influencing system performance

Production environments are becoming more complex and dynamics. This is influenced by external factors related with products’ characteristics and costumers’ requirements and internal factors related with processing times variability, machine failures, setup times, between others. To face this increasing complexity and dynamics, it is crucial to have effective production control methods, considering Interoperability Enablers for Cyber-Physical Systems. However, production control methods most in used today, are focused on centralised decision-making and planning, and considered inadequate to deal with the increasing dynamics of these systems. Autonomous Production Control (APC) may be an adequate alternative to face this complexity, allowing flexible and rapid reaction to possible disturbances that may occur in the production system. However, as APC is the relatively new concept, there are no existing surveys. Therefore, we review and discuss the literature on APC methods to bring more attention to this promising topic of research, highlighting future research directions.

Constant Work-in-Process (ConWIP) is a card-based control system that was developed for simple flow shops – a lack of load-balancing capabilities hinders its application to more complex shops. In contrast, load balancing is an integral part of Workload Control, a production planning and control concept developed for high-variety environments. One means of load balancing evident in the Workload Control literature is through the use of a capacity slack-based backlog-sequencing rule. This study therefore investigates the potential of the backlog-sequencing decision to improve load balancing in the context of ConWIP, thereby making it suitable for more complex, high-variety environments. Using simulation, we demonstrate that: (i) the choice of backlog-sequencing rule significantly impacts throughput times and tardiness-related performance measures; and (ii) capacity slack-based sequencing rules achieve significant performance improvements over ‘classical’ ConWIP backlog-sequencing rules. These results significantly extend the applicability of ConWIP. Results from the Workload Control literature however do not directly translate across to ConWIP. The simplified release procedure of ConWIP makes backlog-sequencing based on planned release dates dysfunctional. This negatively impacts the performance of modified capacity slack-based sequencing rules that were recently shown to be the best choice for Workload Control.