Demand and capacity planning
Estimate future workload at a useful planning level and horizon. Connect forecasts to staffing or inventory scenarios while showing the uncertainty that matters for the decision.
Data & decision intelligence
Predictive analytics
Use your available data to explore likely outcomes and inform forward planning.
Built around a specific business need.
Planning is difficult when reports describe what has already happened but offer little help with what may happen next. Demand, workload, cost and completion forecasts can support better preparation, provided the prediction target is clear and the uncertainty is visible. A precise-looking number is not useful if it answers the wrong question.
We develop predictive analytics around a defined operational decision and an honest evaluation of available data. Simple benchmarks are tested before more complex models. Forecasts are presented with their assumptions, limitations and intended use, so decision makers can judge when the output is informative and when additional evidence is needed.
Who this is for
- Operations teams planning demand, workload or resource capacity.
- Project and commercial teams comparing forward-looking scenarios.
- Businesses with historical data seeking a defensible forecasting approach.
Where this can make a practical difference.
Completion or cost forecasting
Use dated project or operational records to explore likely outcomes. Preserve what was known when each forecast was produced, avoiding retrospective information that would make evaluation unrealistically favourable.
Early attention signals
Identify records or situations that may benefit from review, using a clearly defined outcome. Design the response process before generating alerts, so predictions create manageable action rather than another unattended queue.
How we approach the work.
Define the decision and target
Agree what is being predicted, when it must be available and how it will influence action. Clarify the relative consequences of different errors.
Examine historical information
Assess coverage, missingness and changes in operating conditions. Check that input fields would genuinely have been available at the point when a prediction is needed.
Establish meaningful benchmarks
Compare simple rules or historical averages with candidate models. Use evaluation splits that reflect the deployment setting, including time-based separation for forward forecasting.
Evaluate usefulness and uncertainty
Review errors across relevant periods and operating segments. Examine prediction intervals or other uncertainty estimates where appropriate, and test whether the output supports the intended decision.
Deploy with monitoring
Record model versions, inputs and subsequent outcomes. Agree review triggers for changing data or deteriorating performance, alongside a practical fallback when predictions cannot be trusted.
The sequence is tailored to the agreed scope, dependencies and risk. It describes our delivery approach, not a fixed timeline or performance guarantee.
What the engagement can deliver.
- Prediction target, decision context and evaluation plan.
- Prepared datasets with documented assumptions.
- Benchmark comparisons and selected model implementation.
- Forecast or alert interface with uncertainty information.
- Monitoring, review and retraining responsibilities.
The proposal identifies the deliverables, acceptance evidence, responsibilities and support arrangements included in your engagement.
Measure what matters to this workflow.
Forecast error against benchmark
Compare relevant error measures with a simple alternative at the actual decision horizon.
Interval coverage
Where intervals are used, assess how often outcomes fall within them and whether their width is useful.
Operational response quality
Examine whether predictions lead to useful preparation or review, including the burden created by false alerts.
These are measures to discuss and baseline. Targets are agreed for the specific system and data; no universal improvement percentage is assumed.
What to prepare for discovery.
- A specific planning question and named decision owner.
- Historical records with dates, outcomes and source definitions.
- Agreement on how predictions will be reviewed and acted upon.
Questions worth asking.
How much data is enough?
There is no universal threshold. Required coverage depends on variation, seasonality, target rarity and the decision horizon. We assess the available history before recommending a modelling approach.
Can a predictive model explain what causes an outcome?
Historical relationships alone do not establish causation. We distinguish prediction from causal analysis and avoid presenting a useful forecasting feature as proof that changing it will change the outcome.
What happens when business conditions change?
We monitor relevant inputs and realised outcomes, then review the model against agreed triggers. A simpler fallback or temporary human-led process may be preferable while new conditions are understood.