Inaccurate Forecasting
Production schedules planned using assumed cycle-time (45 sec). Actual cycle-time varied 45-54 seconds. Schedules missed targets frequently. Planners used conservative assumptions but still underestimated variance.
What Became Visible
Real cycle-time data showed actual distribution: 10% of cycles 45-46 sec, 50% of cycles 47-48 sec, 35% of cycles 49-54 sec, 5% of cycles 55+ sec. Forecasting could now account for actual distribution instead of average or assumption.
Data-Driven Capacity Planning
Production forecasts now based on actual cycle-time distribution from real data, not assumptions. Schedule buffer adjusted for actual variance rather than estimated variance.
How it worked: Real cycle-time data enabled probabilistic forecasting. Schedules now account for actual performance distribution, not assumed performance.
Results
more reliable delivery
more predictable schedules
less over-conservative planning
Forecasting accuracy depends on data accuracy. Real cycle-time distribution enables realistic, reliable throughput forecasts.
Operational Reality
Forecasting was based on assumptions, not data. Real data enabled realistic planning and improved schedule adherence.