Source-to-PayConsumption forecast
Expected quantities per item and site for the coming months, including seasonality.
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- AI predictions
Computed trends to decide one step ahead
Your order, price and delivery history holds patterns the eye does not always catch. AI Predictions uses them to estimate what is likely to happen next.
- Earlier decisionsPressure points are spotted before they hit.
- Better-timed negotiationsThe right moment to go to market is identified.
- Fewer errorsAnomalies are flagged before approval.
- Explainable AIEvery result can be understood and discussed.
Module benefits
- Future demand — Estimated quantities by item or category over the coming months.
- Price trends — Likely purchase price movements to time your negotiations.
- Likely delays — Orders at risk of delay based on supplier history.
- Anomaly detection — Unusual amounts, quantities or frequencies flagged automatically.
- Explanations — Main factors and confidence level shown for each result.
- Recommendations — Suggested actions: bundle, anticipate, follow up, check.
Fully integrated into the Source-to-Contract cycle
The AI predictions module fits into your procurement approach, from spend analysis to signed contract, and feeds operational purchasing directly.
Capabilities
Forecasts calculated on your own purchasing data
The module analyses order, price, delivery and invoice history to anticipate what will happen and flag what is out of the ordinary.
Price trends
Likely price trend per category, based on purchase history and contract revisions.
Delay risk
Probability that an open order will be late, by supplier, item and period.
Anomaly detection
Unusual price, abnormal quantity, duplicate invoice: atypical transactions are flagged.
Explanations
Each forecast shows the factors that weigh most, so it can be discussed.
Recommendations
Order earlier, consolidate, renegotiate: concrete suggestions linked to the relevant modules.
Why it matters
Useful, understandable artificial intelligence
AI only matters to procurement if it answers concrete questions: how much to order next month, when to renegotiate a price, which order is likely to be late, which invoice deserves a check. And it is only used if results can be understood.
AI Predictions applies statistical and machine learning models to your own history. Each result comes with explaining factors and a confidence level, and remains a recommendation: decisions always belong to your teams.
Anticipation
Forecast needs instead of discovering them
Past consumption contains patterns: seasonality, gradual growth, peaks linked to shutdowns or sales campaigns. The module detects them and projects needs for the coming months, item by item. Buyers and stock managers get a quantified starting point for orders and negotiations.
- Forecast by item, category and site
- Confidence interval displayed
- Known events taken into account
- Forecast vs. actual comparison
Vigilance
Spot the unusual among thousands of transactions
Nobody can reread every order and invoice. The module does so continuously and flags what departs from usual behaviour: a unit price three times the average, a quantity entered with an extra zero, an invoice very similar to one already paid. Each flag is explained and can be confirmed or dismissed.
- Checks on prices, quantities and amounts
- Likely duplicate invoices detected
- Anomaly score and explanation
- Learning from dismissed flags
Decision
Recommendations linked to action
A forecast is only valuable if it changes a decision. Each recommendation is attached to where it applies: an order proposal in inventory, a renegotiation in contracts, an alert in order tracking. Users stay in control: they accept, adjust or ignore.
- Recommendations ranked by estimated impact
- Direct link to the relevant module
- User decision recorded
- Effect of followed recommendations measured
How it works
From model to decision
Each prediction comes with a confidence level and the factors behind it, so the buyer stays in control of the decision.
1Collect
Order, price, delivery and invoice history.
2Prepare
Data cleaning and identification of special events.
3Calculate
Forecasts and anomaly scores updated regularly.
4Explain
The main factors behind each result are shown.
5Recommend
Suggestions linked to operational modules.
6Measure
Forecast vs. actual and model improvement.
Handling a recommendation
- Proposed
- Viewed
- Accepted
- Adjusted
- Ignored
- Measured
Who is it for?
What each team gains
Buyers
Receive alerts and recommendations to anticipate price increases and shortages.
Supply planners
Adjust order quantities to forecast consumption.
Accounts payable
Check invoices flagged as unusual first.
Procurement leadership
Tracks trends and prediction quality over time.
Results
What predictions deliver
01
Fewer stock-outs
Needs are anticipated before shortages.
02
Less overstock
Ordered quantities follow forecast consumption.
03
Better-prepared negotiations
Price trends are known.
04
Errors caught
Anomalies are spotted before payment.
05
Explained decisions
Every result shows its factors.
06
A model that improves
User feedback refines it.
Use cases
How organisations use it
Manufacturing
A plant anticipates its consumption of wear parts and consumables before scheduled maintenance shutdowns.
Retail and services
A distributor forecasts packaging and supplies needs ahead of peak trading periods.
Public and semi-public bodies
An organisation detects atypical invoices before they are paid.
Integration
Its place in the Source-to-Pay cycle
Modules share the same data: no re-entry between steps.
Frequently asked questions
How much history is needed?
Twelve to twenty-four months of data gives good results; quality matters more than volume.
Is our data used to train other customers’ models?
No, models are trained only on your data, in your environment.
Does the AI decide on its own?
No, it suggests; decisions remain approved by your teams.
Do models improve over time?
Yes, they are retrained regularly and learn from accepted or rejected recommendations.
Can some predictions be turned off?
Yes, each prediction type is enabled separately, by entity or category.
Are predictions available in French?
Yes, explanations are shown in the user’s language.
Source-to-Contract
Other modules in the suite
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