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QAD, IFS and Plex Data Migration Effort Calculator

Data migration is the most underestimated part of an ERP project. This calculator builds a transparent estimate from the number of objects, the hours per object, the number of mock cycles and the rework rate, so you can challenge a partner's quote or set your own budget.

Your numbers

objects

Parts, customers, suppliers, structures, open orders, balances and so on.

hours
hours
3 cycles
40 % of first cycle

Share of first-cycle effort repeated on each later cycle.

hours
hours
per hour

Your results

Total migration hours
1,660
Sum of build, cleansing, reconciliation and cutover effort.
Estimated cost
$141,100
Build effort
600
One-time effort to build the extract, mapping and load for every object.
Later-cycle multiplier
1.8
First cycle counts as 1, later cycles at the rework rate.
Cleansing effort
450
Reconciliation effort
450
Equivalent full-time weeks
41.5

Planning estimate only. Real effort depends on data quality, source complexity and partner tooling. Replace the defaults with figures from your own profiling and project history.

Save results:

Total migration hours vs mock cycles

Sweeps Mock load cycles from 1 to 6, holding your other inputs fixed.

Get a profiled migration estimate

Optional: we can discuss profiling your source data to replace these assumptions with measured figures.

Optional. The tool and every export work without this. No spam.

How to use the calculator

Count the data objects you will actually migrate, then enter hours for building the extract, mapping and load, cleansing per cycle and reconciliation per cycle. Set the number of mock loads and how much of the first-cycle effort repeats on later cycles. Add cutover weekend effort and a blended rate. The primary outputs are total hours and estimated cost, with a chart showing how total hours change with the number of mocks.

The method behind the numbers

Build effort is a one-time cost per object. Cleansing repeats each cycle but shrinks as data improves, which the rework percentage models: total cleansing equals objects times hours times one plus the later cycles at the rework rate. Reconciliation is repeated in full every cycle because each load must be proven. Cutover is added once. This is a simple additive model, intentionally easy to audit.

  • Build = objects x build hours.
  • Cleansing = objects x hours x (1 + (cycles - 1) x rework).
  • Reconciliation = objects x hours x cycles.

Reading the results

Use the chart to see the cost of an extra mock: each added cycle adds cleansing and reconciliation effort but usually reduces cutover risk. Compare your total against a quote. A partner price far below the figure from your own inputs often means cleansing and reconciliation are excluded or assumed to be done by you. Convert hours to full-time weeks to check the plan against staffing.

Common mistakes

People count only the big objects and ignore the long tail of smaller ones such as units of measure, tax codes and attachments. They also use best-case hours per object from a vendor demo instead of their own profiling. Another frequent mistake is assuming business staff time is free: cleansing and reconciliation hours land on your people, so budget their time.

Frequently Asked Questions

Does this work for systems other than QAD, IFS and Plex?

Yes. The model is ERP neutral. The title lists the systems commonly migrated to or from, but the inputs are generic hours and counts that apply to any data migration.

What is a realistic number of mock cycles?

Three is common, with a dress rehearsal on top. Complex data or poor quality may justify more. The chart shows the cost impact so you can weigh it against cutover risk.

Why does reconciliation repeat every cycle?

Each mock changes the extract, mapping or source data, so the load must be proven again. Skipping reconciliation on later cycles is how errors reach production.

Can I use this to challenge a partner's quote?

Yes. Enter their assumed objects and cycles and compare the hours. Ask which activities their price includes, especially cleansing support, reconciliation and cutover.

If you want an evidence-based estimate, Netray can profile your source tables and produce counts and quality metrics that make these inputs realistic.

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