Drum-Buffer-Rope-based production planning and control (PPC) approaches provide production managers with effective tools to manage production disruptions and improve operational performance. The corner stone of these approaches is the proper selection of time-buffers which are considered as exogenously defined constant. However, the majority of real-world manufacturing systems are characterized by the dynamic change of demand and by stochastic production times. This fact calls for a dynamic approach in supporting the decision making on time-buffer policies. To this end, we study a capacitated, single-product, three-operation, flow-shop manufacturing system. We propose a dynamic time-buffer control mechanism for short/medium-term PPC with adaptive response to demand changes and robustness to sudden disturbances in both internal and external shop environment. By integrating the control mechanism into the flow-shop system, we develop a system dynamics model to support the decision-making on time-buffer policies. Using the model, we study the effect of policies on shop performance by means of analysis of variance. Extensive numerical investigation reveals the insensitivity of time-buffer policies to key factors related to demand, demand due date and operational characteristics such as protective capacity and production times.
Insufficient production planning in manufacturing systems often turns a non-bottleneck resource to capacity constraint resource (CCR), which operates as a bottleneck with on average excess capacity (Goldratt, 1988). Drum-Buffer-Rope (DBR)-based production planning and control (PPC) approaches focus on the synchronization of resources and material utilization in CCRs of manufacturing systems (Goldratt and Fox, 1986 and Sivasubramanian et al., 2000). This synchronization calls for time-buffers that protect the production plans of CCR from the effects of disruptions at the preceding production resources. By means of time-buffers (i.e. constraint, assembly, shipping time-buffers), buffer management monitors the inventory in front of protected resources to effectively manage and improve system’s performance (Schragenheim and Ronen, 1990 and Schragenheim and Ronen, 1991).
The research agenda on the efficiency of DBR approach in PPC of manufacturing systems has received increased attention during the last decade. The basic assumption in all relative studies is the exogenous determination of time-buffers as a constant throughout the planning horizon. However, the majority of real-world manufacturing systems are characterized by the dynamic change of demand and by stochastic production times. Therefore, the decision making on time-buffer policies calls for a dynamic mechanism. This is exactly the purpose of this paper. More specifically, we consider a dynamic, capacitated, single-product, three-operation, flow-shop production system. We define as production time-buffer (PTB), the total of constraint and shipping time-buffers. We propose a dynamic, goal-seeking, feedback mechanism to define PTB for short/medium-term PPC. By integrating the proposed mechanism into the flow-shop system, we develop a system dynamics (SD) model to support the decision making on PTB policies. We study the shop response (dynamics of product flows, inventories, performance measures) to PTB policies under stochastic demand and production times. Since the dynamic behavior may be used to evaluate the efficiency of a specific PTB policy, the SD model can be viewed as a decision support system (DSS) for PTB-related decisions. In particular, by continuous monitoring, the actual level of PTB is adjusted to demand-driven desired values. The innovative element of the control mechanism is the endogenous definition of desired PTB values. In addition, the mechanism provides robustness to sudden disturbance occurrences in demand and shop operations. This is a positive property to cope with uncertainty issues in both external and internal shop environment. Using the SD model, we determine PTB increase/decrease policies throughout a given planning horizon and we study their effect on shop performance by means of analysis of variance (ANOVA). The examination of results obtained by extensive numerical investigation reveals the insensitivity of PTB policies to key factors related to demand, demand due date and operational characteristics such as protective capacity and production times. This is an additional appealing feature of the proposed PTB control mechanism which provides production managers with flexibility on PTB-related decisions.
The rest of the paper is organized as follows. Section 2 presents the literature review on DBR studies and applications in manufacturing systems and justifies the suitability of SD methodology in developing dynamic DBR-based PPC systems. Section 3 contains the flow-shop system under study and its performance measures, the description of the SD model, the mathematical formulation and the model’s validation. Section 4 presents the control parameters under study along with their sets of values, while Section 5 presents the adaptability and robustness properties of the dynamic PTB control mechanism. The effect of PTB policies on the shop’s performance obtained by numerical investigation is given in Section 6. Finally, in Section 7 we wrap-up with a summary, the limitations of our work and directions for model extensions.
