Integrating Financial Metrics with Production Simulation Models

A production system is traditionally considered as a combination of the materials supply, production planning, scheduling, control and material transformation functions. Many production control strategies have been proposed and used to manage production system operations. Such control strategies aim to regulate key metrics such as Work In Progress (WIP), cycle time or throughput, as well as their inter-relationships. Moreover simulation technology is available for developing and optimizing production control. Methods such as Discrete Event Simulation (DES) enable complex process chains to be examined to understand bottlenecks, excess inventory, overproduction, etc.. By representing a production system inside a simulation model it is possible to examine in virtual scenarios how a real system might work under predefined conditions. The influence of key characteristics, such as WIP or throughput can be readily examined during such virtual scenarios. A key weakness of the current state-of-the-art in this area is the lack of non-engineering metrics typically modelled. For decision makers the critical metrics are often not solely engineering but also financial, for example, cash-flow, profitability or Return on Investment. However automatically generating financial metrics from simulation output production metrics is a non-trivial task. Moreover the nature of financial and production metrics are typically dissimilar in fidelity and interval and careful attention is required to standardise data for robust decision making. The documented research demonstrates an integrated simulation methodology which represents the calculation of both the critical engineering and financial metrics appropriate for volume production systems. The P&Q production problem and the DES software QUEST are used. In particular the paper investigates how a Design of Experiments approach can be used to systematically create simulation combinations to understand and quantify the critical interactions between engineering and financial metrics.