From analysis to a testable simulation
In manufacturing it is not the machine that decides but the hour: a bottleneck machine at a standstill takes the pace away from the whole line. Three stages turn the hour into a calculation.
The fourth stage happens inside the plant: after introduction, the simulated trajectory is compared with downtime, quality and machine data of the running production. What the measurement refutes gets replaced.
What a gained bottleneck hour is worth
Overall equipment effectiveness (OEE) is a product of availability, performance and quality: the weakest factor drags down the whole machine. You set both scenarios yourself; only the consequences are calculated.
Every slider is an assumption, not a fact. All initial values are fictional examples: no customer data, no industry figures, not the values of the sample plant from the six modules.
Calculation: OEE = availability × performance rate × quality rate. Gained hours = 4 bottleneck machines × 5,000 planned hours per year × (OEE target − OEE today). Additional contribution margin = gained hours × margin per hour. The margin per gained hour assumes that the additional capacity is sold. That is your assumption, not a commitment. The availability gain refers to organisational causes such as setup, ramp-up and micro-stops.
Which of the three levers you touch first and which target is realistic is decided by production management and team. The simulation only shows how the factors multiply.
Unplanned downtime as a distribution instead of a single number
Annual downtime costs vary: 500 simulated years per scenario, today versus early warning. You set both sets of assumptions yourself. The random generator starts from a fixed seed, so every visit shows the same result.
Calculation: failures per year Poisson-distributed around the set mean, failure duration lognormally distributed around the set median with a fixed spread parameter of 0.8. Annual costs = sum of durations × hourly rate. 500 years per scenario, fixed random seed, independent random streams per scenario. The metric compares self-contained runs. Very long failures only occur as far as the distribution allows: a single catastrophic loss lies beyond this curve. Both scenarios are your assumptions, not a claim of effect.
How many failures an early warning actually prevents or shortens at your plant is only shown by the pilot on your machine data.
The economic maintenance interval
The simulation plots preventive replacement against failure risk as a cost curve over the interval. With purely random failures no economic interval exists. The curve shows that too, instead of hiding it.
Calculation: lifetime Weibull-distributed. The wear characteristic is the shape parameter, and the mean lifetime determines the scale parameter via the gamma function. Cost of an unplanned failure = planned replacement plus downtime duration × hourly rate from simulation 02. Cost rate = expected cost per cycle divided by expected cycle length, integrated numerically on a grid. The optimum is the minimum of this curve.
Whether lifetime and spread match your assemblies is what your maintenance team sees in its own failure data. The curve only computes the assumptions.
What one point of scrap rate costs
This is the simplest calculation on this page: every result can be checked on a pocket calculator.
Calculation: defective parts = parts × rate. Defect costs = defective parts without rework × material cost plus reworked parts × rework cost. Savings = defect costs × reduction. New rate = rate × (1 − reduction). Machine time and energy of scrapped parts are deliberately not included.
Which defect patterns are addressable at your plant and which reduction is achievable is clarified by the analysis of your quality data. The slider does not replace it.
What this page assumes: fully disclosed
Every number on this page follows arithmetically from the slider values. In addition:
- Example values: all initial values are fictional: no customer data, no industry benchmarks and deliberately not the values of the sample plant from the six modules.
- Bottleneck hours: OEE as a product of availability, performance rate and quality rate; fixed values of 4 bottleneck machines and 5,000 planned hours per machine and year. The contribution margin per gained hour assumes that the additional capacity is sold.
- Downtime: failures per year Poisson-distributed, failure duration lognormally distributed with a fixed spread parameter of 0.8; 500 simulated years, fixed random seed, independent random streams per scenario. Very long failures only occur as far as the distribution allows: a single catastrophic loss lies beyond this curve.
- Maintenance interval: lifetime Weibull-distributed. The wear characteristic slider is the shape parameter, and the scale parameter follows from the mean lifetime via the gamma function. Numerical integration on a grid. The finding "no economic interval with purely random failures" is a property of the model, not an empirical result.
- Scrap: only material and rework costs; machine time and energy of scrapped parts are deliberately not included.
- Not additive: the four results must not be summed, because the organisational OEE gap (simulation 01) and the failure-driven hours (simulations 02 and 03) overlap at the margin. Downtime cost per hour and contribution margin per gained bottleneck hour are two different quantities and are never netted against each other.
This page is a basis for conversation, not an offer. All values are model calculations based on freely adjustable assumptions; no number is a commitment, a forecast for a specific plant or an industry figure.
In a real project your data replaces the sliders: your machine and downtime data provide failure rates and durations, your quality data the scrap rate, your MES data the OEE factors. The calculation paths stay the same.