What will demand be next month?
Decision: adjust inventory, purchasing, staffing or capacity before the expected change arrives.
Predictive Analytics
Graphite Note builds predictive models for business planning, forecasting and prioritisation. The aim is not a model score in isolation. It is a prediction at the level where a team can actually make a decision.
Use predictive analytics when your question is
Start with the decision that follows the prediction, then choose the model, horizon and granularity around it.
Decision: adjust inventory, purchasing, staffing or capacity before the expected change arrives.
Decision: prioritise retention effort where risk and expected customer value justify intervention.
Decision: rank limited operational capacity around the cases with the strongest predicted risk or opportunity.
Common patterns
Estimate future demand or revenue with uncertainty at the store, product, region or business-unit level.
PropensityRank opportunities by likelihood or expected value so teams spend limited capacity where it matters.
ValueEstimate future value and retention risk for more disciplined acquisition and retention decisions.
Decision-ready forecasting
Graphite Note focuses on the operational unit, horizon and uncertainty that the business needs. A single top-line forecast may be accurate and still be useless if planners allocate people, inventory or budget at a much finer level.
Demand sensing is useful when short-horizon plans need to react to newer signals such as recent POS movement, stock position, orders, weather or other variables available at prediction time. The goal is not to rename forecasting. It is to refresh the forecast quickly enough that the business can still change the plan.
When the next question is which feasible action to take under capacity, budget, stock or policy constraints, the workflow moves from prediction into optimisation. See forecasting vs optimisation.
Build the forecast around when the team can still act.
Store, brand, customer, region or another unit that maps to real execution.
Expose uncertainty where it changes planning or risk tolerance.
Direct answers
Predictive analytics estimates what is likely to happen. Causal analytics asks what would change if an intervention changed. Both can be useful in the same decision workflow.
Yes, when they are relevant and available. External signals can improve forecasts if they carry information about the future outcome and are available at prediction time.
Demand sensing is short-horizon forecasting that refreshes expected demand using the newest useful signals available at prediction time, such as recent POS, inventory, orders, weather or market changes. It is most valuable when that update can still change replenishment, allocation, staffing or another near-term plan.
That depends on the business decision. The prediction can feed planning, prioritisation, a causal analysis, optimisation or a machine-readable recommendation through the Decision API.
Start with the planning question, not the algorithm.