Smart Brewing Production Simulation, with Parameter Variation Experiment, DOE and ANOVA in Minitab скачать в хорошем качестве

Smart Brewing Production Simulation, with Parameter Variation Experiment, DOE and ANOVA in Minitab

The Brewing project, which involves creating a simulation model of a craft brewery's production process, utilizes AnyLogic Simulation Software for modeling and analysis. Here's how AnyLogic is involved, along with DOE (Design of Experiments) and ANOVA (Analysis of Variance): 1. AnyLogic Simulation Software AnyLogic is a multi-method simulation tool that supports discrete event, agent-based, and system dynamics modeling. It is widely used for simulating complex systems in manufacturing, supply chains, logistics, and more. In this context, AnyLogic is used to build a discrete event simulation (DES) model of the craft brewery. This involves simulating the brewing process in batches, tracking inventory, fulfilling orders, and measuring KPIs like throughput and order lead time. 2. Parameter Variation and DOE (Design of Experiments) Parameter variation involves adjusting key parameters (such as production strategy, operator experience, and number of fermenters) to observe how these changes affect the brewery's performance. This is crucial for understanding the system's behavior under different operational conditions. DOE (Design of Experiments) is a statistical method used to design and analyze experiments. In this case, a 2×2×3 full factorial design is applied to study the effects of three factors: – Production strategy (MTO vs. MTS) X1 ​ – Operator experience (Junior vs. Senior) X2 ​ – Number of fermenters (1, 2, or 3) X3 Each of these factors has two levels, resulting in 12 combinations. For each combination, 10 independent replicates are run to capture variability in results. 3. ANOVA (Analysis of Variance) After running the simulation with the DOE design, the results for key performance indicators (KPIs) like throughput and lead time are analyzed using ANOVA. ANOVA helps assess the impact of each factor and its interaction on the performance measures. This allows for determining which factors (e.g., production strategy, operator experience, number of fermenters) significantly affect the brewery's output. For each KPI: The mean of the replicates for each combination of factors is calculated. ANOVA is used to test for statistical significance, providing insights into which factors have the most influence on performance and where improvements can be made. Summary of Key Techniques: AnyLogic Simulation: Used to model the brewing process, simulate the production flow, and calculate the KPIs. DOE (Design of Experiments): Used to systematically vary key factors and explore their effects on performance. ANOVA (Analysis of Variance): Used to analyze the results and understand the statistical significance of each factor. This combination of simulation modeling and statistical analysis (through DOE and ANOVA) provides valuable insights into the operational performance of the brewery under different conditions, guiding decisions on production strategies, operator experience, and fermenter capacity.