Solution
Predictive analysis
A forecast is worth something only if somebody changes a decision after reading it. The useful question is therefore not the model's accuracy, but the decision it informs and how long you have to act.
The gains from forecasting
Stock that follows demand
Forecasting next week at product and site level cuts both stockouts and unsold goods, which are two ways of losing the same margin.
Maintenance triggered by condition
Acting on a signal rather than a calendar avoids both mistakes: the pointless service and the unplanned stop.
A gap you can explain
A model that says why it predicts a figure can be argued with in a meeting. An opaque one is ignored at the first disagreement.
In practice
- Time-series forecasting at the granularity you actually use, with seasonality, holidays and campaign effects
- Anomaly detection on continuous measures: consumption, flow, temperature, volume
- Factor contributions shown alongside the forecast, so the number can be argued with
- An honest comparison against the current method, including when that method is a spreadsheet that works
- Scheduled retraining and drift monitoring: a model that ages quietly is a risk
- Output delivered into the existing decision tool rather than into one more dashboard
Systems involved
- An existing warehouse or central database
- ERP, point of sale and stock management
- Meters, sensors and supervisory systems
- BI tools: Power BI, Tableau, Metabase or whichever is in place
- Useful public data: weather, calendars, public holidays
Service lineData →
What is worth forecasting
The same work, against each sector’s own constraints. Every card opens the full sector.
Banking and insurance
Monitoring of transaction flows
Deviation models over transaction and declaration flows, factor contributions shown, thresholds governed and every alert traceable to its data.
Energy and utilities
Consumption and production forecasting
Forecasting at the granularity and horizon that genuinely inform a decision, with factor contributions shown next to the figure.
Logistics and supply chain
Volume forecasting
Forecasting by site and by week, with seasonality and campaign effects, so staffing is planned rather than endured.
Manufacturing
Condition-triggered maintenance
Alerts based on the real measurements rather than the calendar, with what triggered them shown alongside.
Retail and e-commerce
Demand forecasting
Forecasting by product and by site, with seasonality, holidays and campaigns, at the horizon that replenishment actually uses.
How it runs
The decision
Which decision changes, how often, and with how much lead time. Without that answer, the forecast is an exercise in style.
Baseline
The current method is measured. A model that doesn’t beat it doesn’t deserve production, and that happens.
Model
The simplest model that does the job. Complexity is paid for in operations, every month, for a long time.
Operation
The forecast publishes on its own, the error stays tracked, retraining runs on schedule, and an alert fires when quality slips.
Dependencies and next steps
Test the fit: Predictive analysis
Describe the context, constraints and decision you need to make. The first conversation qualifies scope, boundaries and the next useful step.
Describe the situation