QAD Mock Conversion Load Log Worksheet
A QAD migration lives or dies on data. This worksheet gives you one row per data object per mock cycle so the extracted, loaded and rejected counts always tie out. It works for QAD Enterprise Edition to Adaptive ERP moves and for any conversion into or out of QAD.
| Data object | Mock cycle | Owner | Records extracted | Records loaded | Records rejected | Load % | Unexplained gap | Status | |
|---|---|---|---|---|---|---|---|---|---|
98.1% | 8 | ||||||||
100% | 0 | ||||||||
98.8% | 0 | ||||||||
96.6% | 300 | ||||||||
93.8% | 20 | ||||||||
100% | 0 |
Use one row per object per cycle. Duplicate the object rows for each new mock cycle so you keep history. The unexplained gap column is the one to watch: every extracted record must be either loaded or rejected with a reason.
Automate your conversion reconciliation
Optional: we can review your extract logic and suggest queries that fill this log from source and target counts.
Optional. The tool and every export work without this. No spam.
How to use the log
List every conversion object you plan to load: item master, customers, suppliers, product structures, routings, open orders, inventory balances and GL balances. For each mock cycle enter the record count taken from the source extract, the count that loaded cleanly, and the count rejected by validation. The load percentage and unexplained gap compute automatically. Export to Excel and circulate it to the data owners after every mock so each person sees their own objects.
Why the unexplained gap matters
Most reconciliation failures are not rejected records, they are records that silently disappeared: filtered by a join, dropped by a trailing space in a key, or skipped by a status filter. If extracted minus loaded minus rejected is not zero, someone must explain the difference before the object is signed off. Treat any non-zero gap as a defect, not a rounding issue.
- Compare counts and also control totals such as inventory quantity and value.
- Keep the rejection reason list with the log, not in someone's email.
- Re-run the same extract logic each cycle so counts are comparable.
Reading the results across cycles
Load percentage should climb with every mock. A realistic pattern is a rough first mock, then most objects above the mid 90s by the second, and all objects reconciled by the dress rehearsal. If an object does not improve between mocks, the root cause is usually upstream data quality or a mapping rule, not the load program. Escalate blocked objects early because open orders and balances are loaded last and carry the cutover risk.
Common mistakes
Teams track only counts and skip financial and quantity control totals, so a load that is complete by count can still be wrong by value. Others overwrite the previous cycle instead of keeping history, which hides regressions. A third mistake is letting the migration team own all objects: business owners should confirm the loaded data in the target system, not just the project team.
Frequently Asked Questions
Is this specific to QAD Adaptive ERP?
No. The columns are generic to any ERP data conversion, so it works for QAD EE, QAD Adaptive ERP or any other target. The object names in the sample rows are typical manufacturing objects you would replace with your own list.
How many mock cycles should I plan?
Most manufacturers plan at least three mock loads plus a dress rehearsal before cutover. The right number depends on data volume, complexity and how clean the source data is. Add a cycle when an object is still not reconciled.
What is an acceptable load percentage?
The goal for cutover is every object fully reconciled, with any rejected record explained and either fixed or accepted by the business owner. Earlier mocks can be lower. There is no universal threshold, so agree it with your data owners.
Can I export it to Excel?
Yes. Use the export buttons on the tool to download the worksheet as Excel, CSV or PDF. The computed columns export with their values so recipients do not need formulas.
If your conversion extracts come from a legacy QAD database, Netray can help build repeatable reconciliation queries so this log fills itself from real counts.
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