AAOM Handbook

[OFFICIAL]

Figure 7 - Variability within processes and buffer levels

2.4 Prioritization of scenarios to meet Business Expectations A combination of the VSM and VDT BSP models provides a consistent set of tools for evaluating production and service strategy scenarios. The BSP model is a dynamic model using probabilistic distributions with specified input parameters. The stochastic BSP model takes into consideration the interdependencies and variability of processes in the value stream to produce the overall probabilistic output histogram. The BSP model uses Monte Carlo simulation to determine the most likely outcome of all its processes and final outputs based on input parameters. It supports the prioritization of technical and business improvements to address constraints and opportunities such as: • Integrating throughput, cost, operating hours, recovery and product quality • Reviewing current and future constraints and opportunities • Allocating required resources to the constraining process • Identify areas with surplus capacity • The BSP model is thus used as a tool to enable initial setting of realistic performance targets and confidence levels at the start of each planning cycle. See Figure 8 below. Final targets are set after full iteration into SPS, SSS, OMS and SES, however these need initial target distributions that connect to Business Expectations.

Figure 8 - Example of Probabilistic Output

Operational Planning: Building a Business Structure Performance Model Page 36 of 39