# Performance-focused machine capacity simulation - part 2: Simulation description

> Performance-focused capacity simulation for SMEs: modeling bottlenecks, job flow, and real-time decision impacts.

## Overview
This article explains the simulation models designed for high-mix, low-volume Small and Medium-sized Enterprises (SMEs). These businesses often face challenges such as limited operations capacity, fluctuating order volumes, variable setup durations, and varied customer demands, which frequently lead to delays in order delivery.

The model focuses on the main bottleneck workstation, aligning with the Theory of Constraints (TOC). By emphasizing the management of the system’s primary bottleneck, simulation studies have shown that throughput and resource utilization can be significantly improved. The simulation evaluates scenarios like customer prioritization and job sequencing, measuring KPIs such as lead time, queue length, and throughput to provide actionable insights for capacity planning.

## Simulation Components
The simulation model is built upon two primary pillars: the database and the configuration layer.

### 1. Database
A structured database is essential for capturing production behavior and timing relationships. The dataset includes:
* Customer ID
* Actual machining and setup durations
* Planned machining and setup times
* Start date and expected finish time
* Execution order or job priority

### 2. Configuration Layer
This layer allows users to adjust parameters to evaluate scenarios under different conditions, including:
* **Time mode:** Planned vs. actual data for performance comparison.
* **Shift patterns:** Regular (06:00–14:00), overtime, or two-shift setups.
* **Resource allocation:** Machine (1–3) and operator (1–2) capacity.
* **Failure and maintenance:** Breakdown probability, duration, and daily maintenance time.
* **Quality and setup:** Defect rates and setup time reduction options.

## Simulation Process Flow
The model utilizes components from the Process Modelling Library (Source, Queue, Seize–Delay–Release, and Sink blocks):

* **Data Source (PlannedProduction / ActualProduction):** Releases agents (jobs) based on user choice. Data is linked to an external Excel interface for easy updates from ERP systems.
* **CheckStartDate and Wait Block:** Acts as a control gate, releasing jobs only when their scheduled start time matches the simulation clock.
* **Queue and Seize Blocks:** Manages resource allocation and job sequencing. Jobs are sorted by priority (e.g., Shortest Processing Time) and processed only when both machine and operator are available.
* **SetupTime, ProductionTime, and RandomFailure Blocks:** Core processing components where preparation and machining occur. The RandomFailure block simulates real-world downtime by applying user-defined probability and duration ranges.
* **Release and Sink:** The Release block frees resources for the next job. The Sink block completes the job and collects performance data for the results dashboard.

## Performance Metrics and Results
The Results tab provides a comprehensive overview of simulation outcomes, including:

* **Throughput:** Total number of completed jobs.
* **Overall Equipment Effectiveness (OEE):** Calculated via:
    * **Availability:** Proportion of scheduled time equipment runs.
    * **Performance:** Operating speed vs. maximum speed.
    * **Quality:** Percentage of good units produced.
* **Time-based Metrics:** Total Simulation Duration, Working Hours, Production Time, Setup Time, Failure Time, and Waiting Time.

## Project Information
This article is part of the *Simulation models in industrial processes* project.
* **Project Website:** [https://sitepro.savonia.fi/](https://sitepro.savonia.fi/)
* **Related Articles:**
    * [Performance-focused machine capacity simulation – part 1: Introduction](https://sitepro.savonia.fi/)
    * [Performance-focused machine capacity simulation – part 3: Example scenarios and benefits](https://sitepro.savonia.fi/)

**Writer:** Sorayya Amirahmadi, Project Specialist, Savonia University of Applied Sciences

## References
* Golmohammadi, D. (2015) ‘A study of scheduling under the theory of constraints’, International Journal of Production Economics, 165, pp. 38–50. [https://doi.org/10.1016/j.ijpe.2015.03.015](https://doi.org/10.1016/j.ijpe.2015.03.015)
* Gundogar, E., Sari, M. and Kokcam, A.H. (2016) ‘Dynamic bottleneck elimination in mattress manufacturing line using theory of constraints’, SpringerPlus, 5, p. 1276. [https://doi.org/10.1186/s40064-016-2947-1](https://doi.org/10.1186/s40064-016-2947-1)