Abstract
This study provides a system dynamics (SD) model of make-to-order (MTO) production and discusses the key factors of production improvement. The proposed system can be divided into three subsystems: income/cost, order/production, and human resources (HR). The time delay between customer demand, production demand, order quantity, material demand, and inventory is considered in a practical application. In addition, this paper considers how the cycle time is affected by the total input of HR; how unit transportation cost is influenced by the delivery quantity; and how unit penalty (shortage) cost is affected by the amount of shortage. The production capacity, yield, and holding cost needed to satisfy practical demands are all considered. A simulation approach to MTO production for meeting contract requests is presented in this study. Simulation results reveal that the amount of shortage will be the most important factor affecting the policy for the replenishment of material. Although the rise in production capacity leads to a reduced amount of shortage, it does not play a significant role. A sensitivity analysis of the replenishment of material policy is conducted to find out the best suggested policy. The SD model is also shown to quickly simulate changes in system behaviour, which allows an organisation enough time to respond to and conquer any unpredictable situation that might occur.
Highlights
Schiuma et al [31] describe systems thinking as a powerful approach to understanding the ‘real system’, which emphasises the relationships between the system’s parts rather than the parts themselves [31]
The original situation is inserted into the proposed system dynamics (SD) model in order to simulate the MTO production
This work applies an SD model to simulate the amount of shortage in an MTO production
Summary
Schiuma et al [31] describe systems thinking as a powerful approach to understanding the ‘real system’, which emphasises the relationships between the system’s parts rather than the parts themselves [31]. Schiuma et al [31] apply a systems thinking framework to assess the dynamics of knowledge assets in improving business performance Their framework provides a better understanding of why and how initiatives to manage knowledge assets better can be turned into value creation mechanisms with positive impacts on business performance; this is seen as fundamental to avoiding the misallocation of resources [31]. By inputting different parameters and various policy scenarios, the simulation can produce different results of system behaviour With this type of tool, an organisation can apply the forecasting behaviour to adjust its future input data or policy scenarios of an SD model, in order to meet the company’s goals or customers’ satisfaction. The simulation results of the proposed SD model can provide the references for adjusting the future production environment to meet such a complex and fast-changing industry environment
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