Make a Slow ERP Fast, Starting With a Measurement, Not a Guess
We time the operations your users actually complain about, find out where those seconds go, and fix that. No tuning before a baseline exists, and every change reported back as a before number and an after number.
50+ ERP and software projects. Zero failed projects since 2021.
You cannot tune what you have not measured
Everything on this page follows from one rule: establish the baseline first, then optimise against it and show the difference.
A baseline in seconds, on the operations people complain about
We sit with the people who are waiting. They name the screens and the jobs: the order save that stalls while a customer is on the phone, the valuation run that has to start at night, the report that nobody dares open before lunch. We time each one repeatedly, at quiet hours and at peak, and write the numbers down. That baseline is the whole contract. If your real problem is governance, licensing or data quality rather than speed, an ERP audit is the honest starting point instead.
- Named operations, timed in seconds, at peak and off-peak.
- Findings ranked by how much business time each one wastes.
- The same operations re-timed after every change.
More CPU is the easiest thing to buy and rarely the constraint
When an ERP slows down, the first proposal is almost always a bigger server. It is easy to approve and it feels like action. It also buys a few months at a permanent monthly cost, because a query that reads an entire table still reads an entire table, just faster. We size infrastructure last, after the query plans, the indexes, the data volume and the custom code have been looked at. Sometimes hardware really is the answer, and we say so with evidence rather than as an opening bid.
- Infrastructure sizing assessed, not assumed.
- Query plans and index usage examined before any purchase.
- A straight answer when the server genuinely is undersized.
The six places ERP performance is won or lost
Every assessment covers all six. In most systems, three or four of them are contributing and one is doing most of the damage.
Five stages from first complaint to published numbers
What an optimisation engagement covers
Baseline report
Every named operation timed in seconds, with the peak-hour figure alongside the quiet-hour one.
Database diagnosis
Slow query analysis, execution plans, index review, table growth and lock contention, with evidence.
Custom code review
The customisations sitting in your slow paths, read line by line for queries inside loops.
Archiving plan
Retention rules agreed with finance, a tested retrieval path, and batches run against a copy first.
Remediation and scheduling
Index and query fixes, batch jobs moved off-peak, and heavy reporting separated from live transactions.
Before and after numbers
The same operations re-timed, published next to the baseline, plus monitoring to catch future drift.
What faster looks like in business terms
Already know which ERP you want checked?
The method is the same on every platform, but the tooling and the usual culprits are not. Go straight to your system.
Why teams bring us in when the ERP is slow
ERP performance questions
Why did our ERP get slower over time when nothing changed?
Will more CPU and RAM fix a slow ERP?
How do you find out what is actually causing the slowness?
How long does ERP performance optimisation take?
Does archiving old data mean we lose it?
How much faster will our system actually get?
Find Out Where Your Seconds Go
Book a free consultation. Tell us which three operations your team complains about most, and we will tell you how we would baseline them and what we would expect to find.
Book a Free Consultation →No commitment. A practical first look at what is slowing your ERP down.
What Is Inside an ERP Performance Engagement
A measured baseline, a diagnosis with evidence behind it, remediation you approve item by item, and the same operations timed again at the end so the improvement is a number rather than an impression.
From a stopwatch to a system people stop complaining about
The slow operations, timed, then traced to a cause
We name the operations with your users, time each one repeatedly at peak and off-peak, and record the seconds. Only then do we profile: slow query logs, execution plans, index usage, table sizes and row counts, background job schedules and lock waits. Every finding comes back with the evidence that produced it, so you are approving a diagnosis rather than an opinion.
- Named operations timed before anything is touched.
- Execution plans and index usage captured per slow query.
- Findings ranked by business time recovered against risk.
The two causes nobody wants to look at
Most systems slow down because nothing is ever removed, so we plan archiving for closed periods and completed transactions with retention rules agreed by finance and a retrieval path we test first. Then we read the customisations in your slow paths. Code that was fine against ten thousand records behaves very differently against five million, and a database call inside a transaction loop is the pattern we find most often.
- Archiving scoped, agreed in writing and run against a copy first.
- Custom code in slow paths reviewed for queries inside loops.
- Rewrites tested for the same results before they are released.
Heavy analytics off the system people are trying to work in
Analytical queries running against the live transactional database during business hours slow everyone down, and reports written to pull every row and filter in memory are the worst of them. We rewrite what can be rewritten, move batch jobs out of the working day, and where the volume justifies it we move analytics onto a separate reporting database. Remote office latency and screens returning tens of thousands of rows get looked at in the same pass.
- Reports rewritten to filter in the database, not the browser.
- Batch jobs rescheduled away from the working day and month-end peak.
- A separate reporting database or warehouse where volume justifies it.
Everything the engagement hands over
Baseline timing sheet
Every named operation in seconds, at peak and off-peak, recorded before a single change is made.
Diagnosis with evidence
Slow queries, execution plans, index gaps, table growth and lock waits, each tied to an operation.
Code review notes
The customisations in your slow paths, with the specific lines that cost you time called out.
Archiving and retention plan
What moves, what stays, who signed it off, and a tested route to retrieve anything archived.
Before and after report
The same operations re-timed and published next to the baseline, with the ranges we quoted.
Monitoring and runbook
Alerts on the operations that matter, plus notes so your team can spot drift without calling us.