Demand and workload
Plan volumes, inventory and resources for expected changes.
Predictive Analytics
Know earlier.
Plan more accurately.
We develop predictive analytics from historical data, helping identify patterns and apply forecasts to management decisions.
History → Forecast → Decision
Forecasts with clear limitsHorizon · Factors · Quality
Data helps you prepareLook beyond the past.
Look beyond the past.
Plan the next step.
Forecasts complement your team's experience. They must connect to a specific decision and the cost of possible errors.
Equipment
Identify signs of changing equipment conditions and support maintenance planning.
Risks and scenarios
Compare actions using history and external factors.
The horizon mattersOne forecast.
One forecast.
Several horizons.
The model estimates likely developments. Select a horizon to see why both the predicted value and its uncertainty matter.
Short-term horizonProcessing diagram
Historical observationsForecast and possible range
For operational planning and short decision cycles. Uncertainty usually grows with the forecast horizon.
Medium-term horizonProcessing diagram
Historical observationsForecast and possible range
For resource preparation, purchasing and expected workload assessment. Uncertainty usually grows with the forecast horizon.
Long-term horizonProcessing diagram
Historical observationsForecast and possible range
For scenario planning with greater attention to uncertainty. Uncertainty usually grows with the forecast horizon.
Practical applicationsForecasts help
Forecasts help
where decisions are made.
Consider industry processes, data availability and the consequences of errors.
Retail
Demand, stock, seasonality and pricing factors to support purchasing and inventory decisions.
Manufacturing
Equipment parameters and failure history for risk forecasting and maintenance planning.
Finance
Risk analysis, fraud indicators and scoring tasks. Requirements and the role of outputs are agreed separately.
Logistics
Times, routes and workload accounting for weather, traffic, seasonality and vehicle condition.
Five quality factorsA good forecast
A good forecast
starts before the model.
Study the forecasting foundation and agree output limitations.
History
Completeness, quality and comparability of historical observations, and how well they represent the current process.
Parameters
Feature usefulness and reliability. More parameters do not always mean better forecasts.
Horizon
How far ahead to forecast and how often decisions are made.
External factors
Seasonality, weather, market changes and other events that can affect the process.
Model
Select the approach for the data and task. Validate on historical periods held apart from training.
Validation before deploymentA model must pass
A model must pass
the test of time.
Evaluate how the approach would have performed on later data. Relate errors to their business impact.
Later time period
Separate training from validation. Test forecasts on data unavailable to the model during training.
Baseline comparison
Compare with a simple or existing method. Improvement should be visible on an agreed metric.
Error costs
Underestimation and overestimation may have different business costs. Plan monitoring after launch.
How we workA clear process.
A clear process.
A verifiable result.
First define the business question and available history, then validation and application of results.
-
01
Problem definition
Define what to forecast, the horizon and the decision that depends on it.
-
02
History collection
Analyse company data and suitable external sources.
-
03
Research
Check relationships, historical quality and feature usefulness.
-
04
Model and deployment
Develop, test on past periods and agree how forecasts will be used.
Related tasksBuild the foundation.
Build the foundation.
Get more from your data.
Forecasting can form part of a broader solution.
Questions answeredDiscuss the essentials.
Discuss the essentials.
Before launch.
How much history is needed?+
It depends on event frequency, seasonality and the horizon. We assess available periods, process changes and observation quality before evaluating a pilot.
Can future results be guaranteed?+
A forecast estimates likely developments. Quality is validated on history and monitored after deployment. External changes can affect results.
How often should the model be updated?+
Frequency depends on process changes, new data availability and operational needs. Quality monitoring and review conditions are agreed for the project.
What if our data is fragmented?+
Start with sources and a common structure. Data preparation can be a separate stage before forecast development.
Start with your challengeWhich decision
Which decision
needs a forecast?
Tell us about the metric, available history and horizon. We will propose a research and validation plan.
- Metric and business decision
- Sources and historical depth
- Horizon and error costs
Solution scope, timelines and terms are agreed for your project.
Loading the contact form…