Illustrative scenario—not a client case study.
The manufacturer ran four production lines with varying product mix and demand patterns. Capacity planning was done monthly: assess utilisation, factor in maintenance windows, overlay demand forecasts, and produce a plan for scheduling, labour, and possible overtime or temporary staffing.
The process took five to seven working days. Planning gathered utilisation data from MES and SCADA. Maintenance provided downtime and planned work from CMMS. Demand came from sales and the planning system. Each source had different formats, different definitions of “available capacity” and “utilisation,” and different cut-off dates. The planning manager spent the first three days collecting and reconciling data before any scenario could be built.
By the time the plan was ready, some of the assumptions were already stale. Urgent requests for scenario changes triggered another round of data gathering. Leadership asked why planning could not respond faster. The diagnostic was commissioned to identify the bottleneck.
Interviews revealed:
MES calculated utilisation from work order completion and planned cycle times. SCADA calculated from machine run-hours and theoretical capacity. A third calculation existed in a planning spreadsheet that combined both with manual overrides.
When planning requested “current utilisation,” the answer depended on the source. No single definition had been agreed. Operations used one for shift reporting. Maintenance used another for OEE. Planning had to reconcile before building scenarios—and reconciliation was manual.
Planned maintenance was in CMMS. It was exported periodically and shared with planning. Unplanned downtime was logged in MES or in operator notes. It often did not reach planning until the weekly production meeting—days after it occurred.
Maintenance windows for the coming month were not always finalised when planning needed them. Revisions were common. Each revision meant the capacity plan had to be rebuilt. No one owned the handoff between maintenance schedule and planning input. No standard format existed for “capacity lost to maintenance.”
Sales had a pipeline. Planning had a forecast. A key customer had provided a rolling forecast. The three were maintained separately. Capacity planning used a blend—determined by the planning manager—without a documented rule for how to combine them.
When demand assumptions changed mid-month, there was no single place to update. Sales would email. Planning would adjust the spreadsheet. The link between the change and the capacity impact was not always traced.
The bottleneck was not tooling. A new system would still require manual data gathering until these were addressed.
Before any system changes, the manufacturer:
Defined utilisation and availability for planning — A single definition was agreed: how to calculate available hours, how to treat planned vs unplanned downtime, and which system (MES) would be the source for planning. SCADA data would feed MES; planning would use MES only. The reconciliation step was eliminated.
Established a maintenance-planning handoff — Maintenance committed to providing a capacity impact view by a defined date each month. A standard format was agreed: line, date range, hours lost, reason. Revisions would be communicated in the same format. Planning no longer had to interpret raw CMMS exports.
Consolidated demand for capacity planning — A single demand view was designated for capacity planning. Sales, planning, and customer forecasts would be reconciled into this view by a defined process and owner. Capacity planning would use this view only. Changes would flow through one channel.
Assigned input ownership — Operations owned utilisation data. Maintenance owned the capacity impact of maintenance. Planning owned demand consolidation. Each had a deadline for monthly input. The planning manager’s role shifted from data gathering to scenario building and decision support.
Within two months, the capacity planning cycle was reduced from five to seven days to two days. The first day was data collection—now standardised and largely automated from agreed sources. The second day was scenario building and review.
The time savings came from:
If capacity planning is bottlenecked by data gathering rather than analysis, the constraint is governance of the inputs. See Asset and Maintenance Data Oversight for how maintenance data is governed for planning, or Data-Driven Decision Frameworks for how ownership of planning inputs is formalised.