Forecasting and Predictive Analytics
Most business plans are built by taking last year and adding a number that feels right.
Why It Matters
Seasonality, customer buying cycles, the gap between enquiry and order, and the effect of a price change are all sitting in your transaction data. A model reads them together, which is difficult to do by hand across thousands of records.
Every projection comes with a confidence range and the assumptions behind it, so you can plan capacity for the middle case and know what the downside looks like. Accuracy is measured against actuals each period, so the forecast earns its credibility over time.
Projected sales by month, product and region, with the confirmed order book separated out.
Expected collections week by week, based on how each customer actually pays rather than terms.
Item-level demand for planning purchase and stock, with seasonality handled properly.
The plant hours, machines or people the projected order book will need, and where it gets tight.
Which accounts are likely to grow, slow down or stop ordering, based on their own pattern.
Where margin is heading if current input cost and product mix continue as they are.
We start from the decisions you need to make, then pick the measures that support them.
We pull two to three years of records and clean the gaps, duplicates and one-off events.
Models are tested on past periods you already know, so accuracy is measured before release.
Each projection is published with its confidence range, drivers and assumptions.
Each period we compare forecast against actual, report the gap and retune the model.
Behind the annual plan
View of a cash squeeze
Stock held against a guess
Capacity and hiring decisions
Confidence on every number
Accuracy, period after period
We build ERP, so we know where the gaps and one-off entries in your history are.
Every model is checked against periods you already know, so accuracy is a measured fact.
We fit the method to your data pattern and horizon rather than forcing one technique.
We publish confidence ranges and say plainly where the forecast is weak.
The forecast is an input to your planning meeting. Your team makes the commitments.
It is projecting what is likely to happen next in your business, using your own history rather than a flat percentage on last year. Predictive analytics reads patterns in sales, orders, collections, production and customer behaviour, projects revenue, cash, demand and capacity forward, and states how confident it is in each projection.
Accuracy depends on how stable your business is and how much clean history exists. Rather than claim a single number, every projection carries a confidence range and we report accuracy against actuals each period, so you can see how the forecast is performing and treat it accordingly.
Two to three years of transaction history gives the most reliable result, because it lets the model separate seasonality from trend. Twelve to eighteen months is usually workable for shorter-horizon forecasts, with a wider confidence range to reflect the thinner history.
The usual set is revenue and order intake, cash inflow and collection timing, product-level demand, plant or team capacity needs, and customer behaviour such as which accounts are likely to reduce or stop ordering. What we build depends on the decisions you need it for.
Yes. Each projection shows the drivers behind it, such as the order book already confirmed, seasonal pattern, recent trend and known events like a price change. The assumptions are written out so your team can challenge them and adjust.
No. It gives your planning meeting a starting position built on evidence instead of a guess. Your team adjusts for what the data cannot see, such as a new customer being signed or a line being shut for maintenance, and the plan stays a human decision.
Related
The other services in this section, and related pages from TCB Infotech.
No commitment. A practical look at what your data supports.
