# Performance-focused machine capacity simulation - Part 3: Example scenarios and benefits

> Effective scenario testing reveals how shifts and real vs planned data shape performance for smarter decision making.

## Scenario Testing Methodology
Effective scenario testing is essential for exploring how various operational choices influence system performance in a simulation model. To provide a clear basis for interpreting the model’s behaviour, four experimental configurations were created, comparing two data sources (Planned vs. Actual production data) combined with two shift structures (regular shift and regular shift with one hour of overtime).

### Experimental Configurations
*   **Runs 1 & 2 (Planned Data):** Represents expected production with predefined job sequences and stable input values.
*   **Runs 3 & 4 (Actual Data):** Incorporates real production data including unplanned delays.
*   **Shift Structures:** Regular shift (06:00–14:00) vs. regular shift with 1 hour of overtime.

Parameters such as random failure probability (0.5), maintenance duration (15 minutes), and defect rate (0.01) remained constant across all runs to ensure performance variations resulted solely from shift duration and data source differences.

## Analysis of Simulation Results
The results demonstrate how different data sources and working hours influence the production line’s performance using the FIFO scheduling rule:

*   **Throughput and OEE:** Planned data configurations achieved higher throughput and OEE compared to Actual data. Planned runs reached throughput values of 11.5 and 10.62 units, while Actual runs achieved only 7.07 and 7.75 units—a 30–40% reduction in output under real conditions.
*   **Efficiency:** Total OEE dropped from 25.97% (planned) to 18.0% (actual), indicating that machine utilization and process flow were less efficient in real production.
*   **Time-based Metrics:** Total waiting time accounted for the majority of the simulation duration, suggesting that products spent significant time in queues.
*   **Impact of Overtime:** Extending working time by one hour had only a marginal impact on throughput or OEE. This indicates that system constraints are the primary barrier to workflow coordination, rather than available working time. Improving job sequencing and reducing idle times yields more performance improvements than simply adding shift hours.

## Strategic Benefits for SMEs
The developed simulation model serves as a decision-support tool to enhance operational and strategic planning:

*   **Anticipatory Planning:** Planners can visualize how adjustments to orders, resources, or shift extensions affect production, throughput, and OEE before implementation.
*   **Scalability:** The model is accessible to SMEs, even those without extensive databases or advanced IT infrastructure, by focusing on bottleneck workstations to gain insights into production performance.
*   **Prioritization Testing:** Firms can test different scheduling rules (e.g., FIFO, shortest processing time, or key customer) to align operational decisions with strategic goals.
*   **Collaborative Transparency:** The simulation acts as a shared visualization tool, helping stakeholders align expectations, reduce conflicts regarding delivery commitments, and develop more feasible production plans.

## Project Information
This article is part of the *Simulation models in industrial processes* project.

*   **Project Website:** [https://sitepro.savonia.fi/](https://sitepro.savonia.fi/)
*   **Previous Parts:**
    *   [Performance-focused machine capacity simulation – part 1: Introduction](https://sitepro.savonia.fi/)
    *   [Performance-focused machine capacity simulation – part 2: Simulation description](https://sitepro.savonia.fi/)
*   **Author:** Sorayya Amirahmadi, Project Specialist, Savonia University of Applied Sciences

## References
*   Goldratt, E.M. & Cox, J., 1984. *The Goal: A Process of Ongoing Improvement*. Croton-on-Hudson, NY: North River Press.