Demand plans, inventory levels, maintenance windows and capacity decisions all rest on a prediction. Most of those predictions are built on history that was never cleaned, in a model nobody can explain, and the planners quietly override them. We build forecasting that earns its overrides: the data underneath it is reconciled, the model is explainable, and the accuracy is measured against what actually happened.
Demand forecasting, inventory optimization and predictive maintenance built on reconciled data, with accuracy measured against outcomes rather than asserted.
Forecasting and optimization is the practice of predicting future demand, failure or capacity from historical data, then using that prediction to set a decision such as inventory level, maintenance schedule or staffing. It works when the underlying history is reconciled and the model is explainable enough that planners trust it instead of overriding it.
Forecasting and optimization predicts demand, failure or capacity and turns that prediction into a decision: how much stock to hold, when to service a machine, how much capacity to buy. Thinklytics builds these on reconciled data, makes the model explainable to the planners who use it, and measures accuracy against what actually happened.
Demand and revenue forecasting that planners act on rather than override, because they can see what drives each number.
Inventory and safety stock optimization built on real service levels, not a static reorder point set years ago.
Predictive maintenance that reads machine signals and flags failures before they stop a line.
Accuracy measured in production, against what happened, and reported back, so the model earns or loses trust on its record.
A model dropped on data nobody reconciled. If history is wrong, the forecast is confidently wrong.
A black box. A prediction a planner cannot explain to their director is a prediction that gets ignored.
A one-time model build. Forecasts drift, and a model with no monitoring quietly stops working.
A replacement for planning judgment. The forecast informs the decision, the planner still owns it.
A data readiness pass on the history the forecast will learn from: gaps, duplicates, and definition drift found and fixed before any modelling.
The forecasting or optimization model itself, built in your stack, with the drivers exposed rather than hidden.
A backtest against held-out history, so accuracy is a measured number and not a claim.
Monitoring and alerting on drift, so the model tells you when it has stopped being right.
An enablement transfer: your planners understand the model well enough to challenge it.
Inventory carrying cost removed by AI-driven demand forecasting. Stockouts fell from 312 to 81 a quarter.
Avoidable downtime surfaced across six plants once OEE was calculated in real time. Unplanned stoppages fell 31 hours a month.
Annual saving from predictive maintenance on 340 compressor stations, shifting from fixed-interval servicing to condition-based.
They cannot see what drives it, so they trust their own judgment more. An unexplainable forecast is an ignored forecast, regardless of its accuracy.
Drift. Demand patterns moved, the model did not, and nothing was monitoring the gap. Without drift alerting, a model degrades silently for months.
Two teams forecast the same thing and get different answers.
They are learning from different histories, or the same history with different definitions of the measure. This is a semantic layer problem wearing a forecasting costume.
Buffer stock is what an organization buys instead of forecast confidence. The carrying cost is the price of the mistrust.
What is forecasting and optimization in a data consulting context?
It is predicting future demand, failure or capacity from historical data, then converting that prediction into an operational decision such as an inventory level, a maintenance window or a staffing plan. The consulting work is mostly in the data underneath and the trust around it, not in the choice of algorithm.
We do not quote an accuracy figure before seeing the data, because accuracy is a property of your history rather than our model. We backtest against held-out history and report the measured number. If the achievable accuracy is not good enough to act on, that is a finding worth having early.
You need it before the forecast is trustworthy, which is why the first phase is a readiness pass rather than a model build. A model trained on unreconciled history produces predictions that are confidently wrong, which is worse than no forecast at all.
What is the difference between forecasting and predictive maintenance?
Both predict a future state from signals. Forecasting usually predicts a quantity, such as demand or revenue. Predictive maintenance predicts an event, such as a failure, from machine telemetry like vibration, temperature and cycle time. The data work and the trust problem are the same shape.
Yes. Most planning suites have a capable engine that is underused because the data feeding it was never reconciled. We fix the input before recommending you replace the tool.
Drift monitoring, delivered as part of the engagement. The model reports on its own accuracy against actuals, so degradation surfaces as an alert rather than as a bad quarter.
Forecasting is worth doing when a decision actually changes based on the number. If nothing downstream moves, the forecast is decoration.
A recurring decision, such as how much to stock or when to service, is being made on judgment because the numbers are not trusted.
You carry buffer inventory or buffer capacity that exists mainly to absorb forecast error.
Machine or sensor data is being collected and nothing is reading it until something breaks.
A model was built, went live, and quietly stopped being accurate.
The history the forecast would learn from is unreliable: start with Data Cleaning and Preparation.
Two teams disagree on what the measure even means: that is Semantic Layer Engineering.
You need the models you already have monitored and kept alive in production: see MLOps Consulting.
You are not sure whether your data can support any model yet: start with the Analytics Truth Audit.
We do not publish a rate, because the modelling is rarely the expensive part. What moves the effort is the state of the history and the number of decisions the forecast has to serve.
How many years of usable history exist, how many gaps and duplicates sit in it, and whether the measure was defined the same way throughout. This is usually the largest single factor.
Forecasting one aggregate demand line is a different job from forecasting at SKU and location level across thousands of combinations.
Machine telemetry, point of sale, weather, promotions and pricing each need a pipeline before the model can read them.
A number in a dashboard is cheaper than a forecast that writes back into a planning system and triggers a replenishment order.
A model you will run for years needs drift detection and a retraining path. A one-off analysis does not.
Every engagement starts with a readiness pass on the data, so scope is measured against what is actually in your history before any number is discussed.
Profile the history the forecast will learn from. Gaps, duplicates, definition drift and missing signals surfaced before any modelling starts.
Establish what a naive forecast achieves, then measure any model against it on held-out history. A model that cannot beat the baseline is not worth deploying.
The model, in your stack, with drivers exposed so a planner can see why the number moved and challenge it.
Wire the output into the decision it serves, then instrument drift so the model reports its own accuracy against actuals.
The common failure is not a weak algorithm. It is a model that planners cannot interrogate, built on history nobody validated.
The history the model will learn from, profiled and reconciled first.
The readiness work a forecast depends on, as a service in its own right.
Keeping models alive, monitored and retrained after they ship.
Putting the prediction in front of the person making the call.