Checklist

Finite Scheduling — Constraint Identification & Buffer Policy Checklist

An actionable, team-run checklist that helps planners and operations identify true constraints, capture key measurements, and set a simple, testable buffer policy to protect throughput in short-term finite schedules.

Interactive Tool

Finite Scheduling — Constraint Identification & Buffer Policy Checklist

This checklist helps planning and operations teams identify true capacity constraints, capture the measurements that matter, and set a simple, testable buffer policy to protect delivery promises without overstocking or needless firefighting. Run this collaboratively with planners, supervisors, a maintenance representative, and at least one operator familiar with the work. Save the completed checklist so you can track changes and test buffer rules over time.

How to use: Fill the form with observed values from the last representative period (shift/day/week). For candidate constraints, record utilization, processing-time variability, setup/ changeover impact, and critical SKUs. Use the buffer section to capture lead time and throughput data and record a recommended buffer using the suggested heuristic formula. Treat the recommended buffer as a starting point to validate with short experiments and continuous tuning.

Who completed this checklist? Include role/department.
YYYY-MM-DD or shift date
Where is this checklist being applied?
Which shift is being evaluated?
List up to three candidate bottlenecks you want to validate (comma-separated). Example: 'Press #2, Paint cell, Final assembly'
For each candidate below, capture utilization, average processing time and variability, setup/changeover impact, and critical SKUs. Use measured data when possible—do not rely on verbal estimates.
Name or location of the candidate constraint (example: 'CNC #3' or 'Pack Line A').
Percent of available time the station is busy (exclude planned downtime). Use measured utilization over the same period as other fields.
Average cycle time for the operation. Use seconds/minutes consistent across candidates.
Record a standard deviation or a min–max range. High variability increases buffer needs.
How many setups or changeovers occur in a typical shift? Frequent setups reduce effective capacity.
Average time lost per changeover event.
Which SKUs rely on this workstation and are high priority for on-time delivery?
Based on the data above, do you confirm this is a true constraint (Yes/No)?
Attach or summarize logs, OEE snapshots, or test runs that support your conclusion.
(Repeat fields for a second candidate)
(Optional third candidate)
Use the values below to calculate a starting buffer. The formula below is a heuristic; validate with trials and adjust. Example heuristic: Buffer units ≈ lead_time_days × avg_throughput_per_day × (1 + throughput_cv × safety_factor). Record the buffer you choose and why.
The planning horizon or time between buffer placement and replenishment.
Measured average throughput for the candidate constraint.
Stddev/mean of throughput. Example: 0.3 for 30% variability.
Choose a safety factor to scale the variability term. Common starting values: 1.0–2.0.
Enter the buffer units you calculate using the heuristic (or your chosen rule). Save this value for later comparison to outcomes.
How will the buffer be replenished? Select the trigger type.
If using units threshold, reorder when buffer falls to this unit count.
If using time-based trigger, how many days before the protected lead time should you trigger replenishment?
If 'Yes', conveyors/queues may be loaded beyond buffer to keep the constraint busy; if 'No', enforce strict buffer limits.
If enforcing max WIP, enter the maximum allowed units queued at the constraint.
How often will buffer levels and performance be reviewed?
Who is responsible for keeping the buffer policy working? Include role and contact.
Record assigned actions, owners, deadlines, and suggested experiments to validate the buffer.
Examples: run a 2-week pilot of buffer values, integrate measurement into shift daily huddle, schedule root-cause work for top variability drivers.
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