Twinworks Factory Simulation
Simulate a production line: throughput, lead time, WIP, OEE and the bottleneck, with CIs.
Try an example: 3-station line template, 5 replications · Own model: two machines and a buffer, deterministic · Line with failures over 2 days, 3 replications
How it works
Runs the Twinworks discrete-event simulation on a factory model (sources, buffers, machines with failures and setups, assembly, conveyors, quality checks, operators and shift calendars). Returns throughput per hour, lead time, WIP with a Little’s-law check, per-station states and OEE, the bottleneck and a cost per good part, with Student-t confidence intervals across seeded replications.
- Discrete-event simulation with a binary-heap event calendar; ties are processed in (time, priority, insertion order) so every run is reproducible.
- Parts are pushed downstream with blocking after service: a machine that cannot hand its part on is “blocked”; a machine without parts is “starved/idle”.
- Each random quantity (arrivals, cycle times, failures, routing choices) has its own seeded stream, so scenario changes do not shift other random numbers (common random numbers).
- Statistics are reset after the warm-up. Replications use derived seeds; intervals are Student-t intervals of the replication means.
- Little’s law is checked as L (time-average WIP) versus λ·W (departure rate × mean time in system); differences above 5 % signal a non-stationary system or too short a run.
- OEE = availability × performance × quality with planned time = time on shift minus planned maintenance; availability losses = breakdowns + setups; performance = ideal cycle time × parts ÷ run time (starving and blocking reduce it); quality = good ÷ processed.
- Bottleneck: active-period method (Roser et al.) — at each moment the station with the longest uninterrupted active period (working, setup, repair, maintenance, waiting for an operator) is the bottleneck; overlaps between consecutive bottlenecks count as shifting.
A simulation is only as good as its input data. Validate cycle times, failure data and routings against your shop floor before making investment decisions.
Sources
- Law, A. M.: Simulation Modeling and Analysis, 5th ed. (McGraw-Hill, 2015) — event scheduling, replications, confidence intervals (McGraw-Hill)
- Roser, Nakano & Tanaka: A practical bottleneck detection method (Winter Simulation Conference 2001) (WSC)
- Hopp & Spearman: Factory Physics, 3rd ed. — Little’s law, VUT/Kingman equation, line throughput (Waveland Press)
- Nakajima, S.: Introduction to TPM — Overall Equipment Effectiveness = availability × performance × quality (Productivity Press)
- Little, J. D. C.: A proof for the queuing formula L = λW (Operations Research 9(3), 1961)
For scripts and AI agents
- JSON API:
POST https://twinworkssimulate.usefulweb.site/api/v1/calculate(same calculation as this page) - Interface description: OpenAPI 3.1 · AI guide: llms.txt
- MCP server (Streamable HTTP):
https://twinworkssimulate.usefulweb.site/mcp· tool nametwinworks_simulate
Example request
curl -s -X POST https://twinworkssimulate.usefulweb.site/api/v1/calculate \
-H 'content-type: application/json' \
-d '{"template":"three-station-line","replications":5}'Related tools
- Twinworks Bottleneck Analysis – Find the constraint of a production line and test fixes: ranked bottlenecks with what-if gains.
- Twinworks Buffer & Parameter Sweep – Sweep a buffer size (or any parameter) and get the throughput curve with confidence intervals.
- Twinworks Line Balancing – Assign assembly tasks to stations for a takt time: efficiency, balance delay and smoothness index.
- Twinworks Queueing Check – M/M/1, M/M/c, M/D/1, M/G/1 and Kingman waiting times — optionally verified by simulation.