Demand forecasting
Project demand by product, location or customer group. Give planners an earlier view of likely stock pressure and excess cover.
Useful for replenishment and capacity planning.
Supply chain analytics
Predictive analytics from Batley, West Yorkshire
Predictive Analytics That Reach Decisions Before the Cost Does
Operational losses often leave clues in the weeks before they reach the accounts. Griffith's Digital Insights Ltd. builds AI forecasting solutions on your own historical data, tests them against past outcomes, then puts useful signals into the tools your team already uses.
Plan demand, spot likely equipment failure, flag churn, forecast cash flow and investigate unusual activity sooner. Each model is checked before it informs a live decision.
Demand forecast
Illustrative view · next 12 weeks
Forecast range
Weekly units · central estimate with confidence band
Hover view · projected demand
HistoryCurrent weekForecast horizon
Supplier risk review
Priority flagged for review
Unusual activity
Alert linked to source record
Illustrative interface only. Example labels and values are not client results.
Use your own history
Model choice follows the data you have and the outcome you record.
Test before release
Back-testing shows where forecasts hold up and where they miss.
Put signals to work
Send outputs into dashboards, alerts or planning reports.
Keep checking accuracy
Monitor drift and review thresholds as conditions change.
Practical model use cases
Where Forecasting and Risk Models Pay Back Fastest
Start with a costly decision that arrives too late. Build the model around the action someone can take when an early signal appears.
Predictive maintenance
Score failure likelihood using asset history and operating signals, so maintenance teams can assess risk before an unplanned stoppage.
Put critical assets at the front of the review queue.
Manufacturing analytics
Churn and retention
Identify accounts showing early signs of disengagement. Give customer teams time to review the relationship and choose a response.
Signals support a conversation, not an automatic verdict.
Customer analytics
Cash-flow forecasting
Project receipts and payments using ledger history and sales pipeline data. Compare trading scenarios to see where timing could tighten.
Make the assumptions visible to finance teams.
Financial analytics
Risk scoring and anomaly detection
Flag unusual transactions, claims patterns or supplier behaviour for review. Link each signal to its source so investigation can start with evidence.
Calibrate alerts to the cost of a miss and a false alarm.
Risk and commercial analytics
A measured modelling process
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02
03
04
From Data Assessment to a Validated Model
The first task is checking whether the available records can answer the business question. Then we test model options against historical periods and review the misses with your team.
Evidence comes first.
01Assess the data
Review history depth, missing values, record frequency and whether the target outcome is captured consistently. If the evidence is too thin, we say so before modelling begins.
02Find useful signals
Check which operational factors appear before the event being predicted. Keep features tied to a real process and make their meaning clear to the people who will use the result.
03Compare model approaches
Test suitable methods and weigh accuracy against explainability and the effort needed to maintain the model. Choose the approach that fits the decision, not the one with the most impressive label.
04Back-test and review
Run predictions against past periods the model has not seen. Review false positives and missed events with the business before any output is allowed to influence a live decision.
A forecast is useful only when its limits are understood. Validation gives teams a clear view of where a model is dependable and where human review remains essential.
Useful after launch
Models Deployed Into Daily Workflows, Then Watched
A prediction has to reach the person who can act on it. Griffith's Digital Insights Ltd. puts model outputs where planning and review already happen.
Forecast tiles in existing dashboards
Show the estimate, confidence range and key drivers alongside current performance measures.
Alerts tied to agreed actions
Route a threshold breach to the right role, with context that helps them decide what to check next.
Planning reports and forecast columns
Feed scores and projections into scheduled reports or planning systems your team already knows.
Reasons people can inspect
Show the main factors behind a prediction so staff can challenge an output when operational context changes.
Model monitoring view
Example measures to agree for each model. Values are illustrative.
Forecast error
Within agreed band
Input drift
Review threshold
Outcome hit rate
Tracked by period
Review cadence set with the team
Monitoring tracks error, drift and outcomes against thresholds agreed for the use case. Scheduled refreshes, seasonal recalibration or retraining can follow when the evidence calls for it.
Anomaly detection and response
Anomaly Detection That Produces an Action, Not a Chart
A useful alert separates routine variation from a change worth investigating. Thresholds are tuned against your data and the cost of interrupting someone unnecessarily.
Urgent · immediate review
Route a high-severity exception to the responsible role with a direct link to the related transaction or asset record.
Review · investigate this shift
Add the exception to a dashboard queue or scheduled digest for a closer check during the working day.
Low · include in trend review
Record lower-priority changes for a routine review without sending an immediate interruption.
Every signal needs an owner
Define who receives an exception, where it appears and what evidence accompanies it. Review alert effectiveness over time so thresholds reflect the work, not just the model.
Calibrate the thresholdCompare normal variation with meaningful exceptions, then tune sensitivity with the people handling alerts.
Route by severity and roleSend a dashboard exception, scheduled digest or threshold notification to the right team.
Link back to the sourceInclude the underlying transaction, claim or machine record so investigation starts with context.
Keep threshold changes accountableAgree who can change alert rules and how the team reviews missed signals and false alarms.
Clear performance measures
What Good Forecast Performance Looks Like in Practice
Performance is reported in terms the business can use: what the model predicted, what happened and how early a team could respond. Measures depend on the data and decision.
Unplanned stoppages
Track failure signals, confirmed faults and stoppages avoided after planned intervention.
Forecast error by line
Compare projected and actual demand by product, region or planning period.
Earlier arrears signals
Measure lead time between a risk flag, a review and a recorded payment outcome.
Exceptions worth review
Report unusual claims or transactions flagged, investigated and confirmed by the team.
Good fit, or a reporting problem first?
Predictive analytics needs consistent history and a recorded outcome to learn from. If definitions change between systems or teams, a dashboard and cleaner reporting may be the right first step. We’ll scope the data and decision before recommending a model.
Start with the decision
Find Out What Your Own Data Can Predict
Tell us which decision is still based on instinct or arrives after the reporting cut-off. We’ll discuss the available history, the outcome you need to predict and what a realistic first model could show.
The first conversation defines the scope before any modelling programme is agreed. Start with one decision.
Discuss a forecasting project