AI for ERP
Forecasting and early warnings added to your existing ERP.
What is AI ERP?
AI for ERP is a layer alongside your existing enterprise system that forecasts demand from your own transaction history, warns when suppliers or costs drift outside normal patterns, and flags entries that look wrong before they reach the ledger.
An ERP records what happened accurately. What it rarely does is tell you what is about to happen, which is the part planners actually need.
That gap gets filled with spreadsheets. Somebody exports last year figures every month, applies judgement, and produces a forecast that lives outside the system entirely.
We add forecasting on top of the data already inside the ERP, so predictions come from the same records the accounts do, and warnings appear before a problem reaches month end rather than after.
Why we write nothing back by default
Duara builds ERP intelligence layers from Nairobi for businesses running SAP, Oracle, Odoo, Zoho or similar, where the system holds excellent data that nobody is using to look forward.
We work read only by default and write nothing back unless you specifically ask for it. ERP changes are slow, expensive and difficult to reverse, and a forecasting layer has no business modifying the system of record to do its job.
Who this is for
- You run an ERP but still plan in spreadsheets
- Stock outs and overstocking both happen
- Problems are found after period close
- Reporting needs a specialist every time
Industries we serve
- Manufacturing
- Distribution and wholesale
- Retail chains
- Agribusiness
- Construction materials
- Pharmaceutical supply
Problems this solves
These four conditions describe an ERP being used as a ledger rather than as a planning system.
- Planning happening in spreadsheets. Data is exported monthly and forecast outside the system, so the plan and the record of reality live in different places.
- Stock outs alongside overstocking. Both happen simultaneously across different lines, because reorder levels were set once and never revisited.
- Problems surfacing at period close. A cost drift or supplier slippage becomes visible weeks after it started, when correcting it is expensive.
- Reporting gated by a specialist. Any question outside the standard reports requires somebody who knows the system, so most questions never get asked.
What we deliver
A forecasting and monitoring layer reading from your ERP, producing demand predictions, supplier and cost warnings, and anomaly flags, without modifying the system of record.
Demand and stock forecasting from your own history
Early warnings on supplier performance and cost drift
Unusual entries flagged as they occur
Reporting that does not need a spreadsheet export
Confidence ranges rather than single point forecasts
A read only approach that leaves the ERP untouched
Features
- Forecasts with confidence ranges. A range rather than a single number, because a point forecast implies a certainty that does not exist and gets planned against.
- Supplier drift detection. Lead times measured from actual deliveries, so a supplier slipping is visible before it causes a stock out.
- Anomaly flagging at entry. A price keyed ten times too high is caught as it happens rather than at reconciliation.
- Read only by default. Nothing is written back to the ERP unless you explicitly request it, which keeps your system of record clean.
- Seasonal pattern learning. Recurring annual patterns are picked up from your own history rather than assumed from industry norms.
Benefits
- Planning moves back into the system. Forecasts built from ERP data reconcile with the accounts, which spreadsheet forecasts never quite do.
- Fewer stock outs on fast movers. Reorder points that adjust to real demand catch the lines that are quietly accelerating.
- Problems caught while cheap. A cost drift flagged in week two costs far less to address than the same drift found at close.
- Questions get answered directly. Reporting stops depending on specialist availability, so more questions actually get asked.
Technologies we use
- ERP data extraction. Reading through supported interfaces so nothing about the core system is modified.
- Time series forecasting. Demand prediction from your own transaction history, including seasonal and trend components.
- Anomaly detection. Statistical flagging of entries that fall outside normal ranges for that item, supplier or account.
- Supplier performance measurement. Promised against actual delivery dates, calculated continuously rather than reviewed annually.
- Reporting layer. Query access that does not require exporting data or involving a specialist.
Read only access shapes the whole design. Everything is built so the ERP remains the untouched system of record, which also keeps support agreements intact.
How the process works, step by step
An ERP layer usually takes six to eight weeks, since historical data extraction and validation take longer than the modelling.
-
Step 1
Free data review
We establish what your ERP holds, how far back, and whether the history is clean enough to forecast from.
-
Step 2
Read only connection
We connect through supported interfaces and validate that extracted totals match your existing reports.
-
Step 3
Backtesting
Forecasts are run against past periods to see how they would have performed, which is the only honest accuracy test.
-
Step 4
Warning thresholds
Alert levels are set with your planners so warnings are useful rather than constant.
-
Step 5
Live running
Forecasts and warnings go to the planning team, with accuracy tracked against actuals each period.
Quality assurance and how we work
The commitments attached to working alongside a system of record.
- Read only unless you ask otherwise. We write nothing back to the ERP by default. A forecasting layer has no business modifying the system of record.
- Forecasts are backtested before use. We show how the model would have performed on past periods rather than asking you to trust it prospectively.
- Ranges, never single numbers. Every forecast carries a confidence range, because a point estimate gets planned against as though it were certain.
- Extracted totals are reconciled. Before anybody relies on it, our figures are checked against the reports your finance team already produces.
- Accuracy is tracked publicly. Forecast against actual is reported every period, including when the model performed badly.
Frequently asked questions about AI ERP
Do we need to replace our ERP?
No. This sits alongside what you already have and reads from it. Replacing an ERP to add forecasting would be a poor trade.
Will this affect our ERP support agreement?
Reading through supported interfaces does not modify the system. We work read only by default precisely to keep that clean.
Which ERP systems do you work with?
The common ones including SAP, Oracle, Odoo and Zoho. We confirm your version and available interfaces during scoping.
How much history do you need?
A year is usually enough to capture seasonal patterns. Less can work for anomaly detection but weakens demand forecasting.
How accurate are the forecasts?
We backtest against your past periods and show you the result, rather than quoting a general accuracy figure that means nothing for your business.
Why ranges rather than a single number?
Because a point forecast implies certainty that does not exist, and people plan against it as though it were fixed.
Does it write back into the ERP?
Not by default. If you want forecasts written back we can, but the default is that your system of record stays untouched.
Can it flag data entry errors?
Yes, and catching a price keyed ten times too high as it happens is often the fastest payback in the whole project.
How does it measure supplier performance?
Promised delivery dates against actual ones, calculated continuously from your own receipts rather than reviewed once a year.
How long before the ERP work is live?
Six to eight weeks. Historical extraction and validation take longer than the modelling work itself.
What if the forecast is wrong?
Accuracy is reported every period against actuals, including when it performed badly. A model whose failures are hidden cannot be improved.
Do we need a data scientist?
No. The output is forecasts and warnings in the planners existing workflow, not a modelling environment.
Can it handle multiple warehouses or branches?
Yes, and forecasting per location usually reveals differences that a consolidated view hides entirely.
Will it replace our planners?
No. It gives them a starting forecast and warnings. Judgement about promotions, new lines and market changes stays with people.
What about products with no history?
New lines cannot be forecast from history and we say so rather than producing a number. Analogous products can inform an estimate.
Does it work with agricultural seasonality?
Yes, and strongly seasonal businesses usually get more from forecasting than steady ones, provided the history covers full cycles.
Can it detect fraud in entries?
It flags anomalies for a person to examine. It does not conclude that something is fraudulent, because that judgement carries consequences.
Is our ERP data secure?
Extraction happens under your access controls, data stays within your environment, and nothing is used to train public models.
Which of our people need to take part?
Whoever runs planning and whoever administers the ERP. Both, since one defines usefulness and the other defines access.
What are the ongoing ERP automation costs?
Data volume and forecast frequency determine it. We project the monthly cost from your own transaction counts before anything is agreed.
Glossary of terms
ERP and forecasting terms in plain language.
- System of record
- The authoritative source for a given set of data, which for transactions is your ERP.
- Backtesting
- Running a forecast against historical periods to measure how it would have performed.
- Confidence range
- The band a forecast is expected to fall within, as opposed to a single predicted figure.
- Anomaly
- An entry falling outside the normal range for that item, supplier or account.
- Lead time
- The actual elapsed time between placing an order and receiving it.
- Seasonality
- A repeating annual pattern in demand that a forecast must account for to be useful.
Related services in AI Implementation
Talk to us about AI ERP
Tell us which ERP you run and how far back its history goes. Those two facts determine feasibility faster than anything else.
Start your project