Infor VISUAL Manufacturing Scheduling Checklist
When a VISUAL schedule does not match what the floor does, the cause is almost always data and habits rather than the scheduling engine. This checklist walks through the five places to look, in the order that usually pays off.
0 of 30 items complete
7 critical items still open - these are the highest-risk gaps.
Calendars and resources
Routing and operation data
Lead times and planning parameters
Work order release discipline
Floor feedback and reporting
Count the items you can answer yes to. Anything marked critical that is a no is the first thing to fix, because bad data here produces schedules nobody trusts.
Scheduling exceptions without the spreadsheet
Optional: a short conversation about surfacing late orders and routing variances from your VISUAL data automatically.
Optional. The tool and every export work without this. No spam.
How to use this checklist
Work through the groups with a planner and a shop floor supervisor in the same room. Tick each item only if you can point to where it is true, for example a calendar record or a routing. Mark critical items that fail, then fix those before touching anything else. Export the result to PDF to attach to an improvement ticket, or to Excel to track follow up. Repeat quarterly, because routings and calendars drift as products and shifts change.
Why schedules fail: calendars and routings
Finite scheduling can only be as realistic as its inputs. If a resource has no calendar, or runs three shifts on paper and one in practice, every date downstream is wrong. The same applies to routing times. Run times that were set when a part was first quoted often differ from actual by a wide margin. Comparing labor ticket history with standard times for your top parts is one of the cheapest accuracy gains available.
- Calendars: match real shifts and holidays.
- Resources: current capacity, no ghosts.
- Times: reviewed against actual labor.
- Units: per piece versus per hundred is a classic error.
Lead times, parameters and release habits
Planning parameters decide what gets planned and when. Lead times that are too short create orders that are born late, while lead times padded for safety stack across levels and push dates out of reach. Release discipline matters just as much. Releasing everything early floods the floor and hides the real priorities. A weekly review of past due orders, with a clear owner, keeps the schedule honest.
Common mistakes
Teams often adjust dates manually to make the schedule look right, which hides the data problem and has to be repeated every cycle. Others change calendars to force overtime instead of recording overtime as a separate decision. Finally, scheduling data has no owner, so nobody corrects routing times after a process change. Name an owner, review a short list of parts each month, and the schedule improves steadily.
Frequently Asked Questions
Does this checklist assume finite scheduling?
It works for both finite and infinite approaches. Calendars, routings and release habits matter in either case. Items about resource capacity are most valuable when you schedule finite capacity, but they also help when you only use capacity for load reports.
How often should I review routing times?
At least once a year for all active parts, and sooner for your highest volume or highest value parts. Compare labor ticket actuals against standard setup and run times and update where the gap is persistent and not explained by one off problems.
What is the fastest fix for late orders?
Check whether the orders were late at creation because lead times are too short, or became late because of material or capacity. The fix differs. Lead time data, shortage reports and a calendar check will usually tell you which one it is.
Who should own scheduling data?
A named planner or production engineer with time set aside for it. Without an owner, routing times, calendars and parameters decay. Even two hours a week reviewing a short exception list keeps the data usable.
If you want scheduling exceptions, past due orders and routing time variances surfaced automatically from your VISUAL data, we can build a read-only report or assistant for it.
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