TL;DR & Quick Summary
A focused predictive analytics project, such as demand forecasting for one product line, typically costs about $25,000–$50,000 and takes 8–12 weeks to reach real use. Multi-site systems integrated with ERP typically cost $50,000–$120,000 and take 4–6 months. The model is the smaller part of the budget. Data preparation and integration are where the time and money go.
| Scope | Typical cost | Typical timeline |
|---|---|---|
| Focused: one product line, site or decision; output to a spreadsheet or planning tool | $25,000–$50,000 | 8–12 weeks |
| Integrated: several sites or a large catalogue, reading from and writing to ERP | $50,000–$120,000 | 4–6 months |
| Enterprise: several models, planning interface, governance and approvals | $120,000+ | 6+ months |
Data decides the budget: missing stockout records and unrecorded promotions add weeks
Start focused: one decision, a parallel run, then expand on evidence
Estimate the return first: inventory reduction, fewer stockouts, less expediting
Key Takeaway: Ask any provider how much of their plan is data work and integration. If the answer is "not much", the timeline is optimistic.
Get Started: Want a scoped estimate for your data? Schedule a strategy call with Cogniq AI or see our predictive analytics services.
Where the Budget Goes
Like any engineering work, cost follows people × weeks × rate. A typical team is one to two data and ML engineers plus part-time integration and product help. At a blended $60–$90 per hour, every week of a two-person team costs about $4,800–$7,200. What sets the number of weeks is how much of each phase your data and systems need.
A typical split of effort for a focused project:
| Phase | Share of effort | What happens |
|---|---|---|
| Discovery and data audit | 10–15% | Define the decision, the items and the horizon; check what data exists and how good it is |
| Data preparation | 30–40% | Clean history, reconstruct stockout periods, align promotions and prices, fix units |
| Modelling and backtesting | 20–25% | Build candidate models, test them against simple benchmarks on held-out data |
| Integration | 15–25% | Pull data automatically; deliver forecasts where planners work |
| Parallel run and handover | 10–15% | Run alongside the current process, measure, train the team |
The share for data preparation surprises most buyers. It's the main reason estimates that start with "which model?" go wrong.
The Phases in Practice
Discovery and Data Audit (2–3 weeks)
Decide exactly what the forecast is for: which decision (purchasing, production, staffing), which items and locations, how far ahead, and how often it refreshes. Then audit the data against those needs. Our data-readiness checklist covers what to check, including seasonal history, stockout flags, promotions and units.
The output should be a short written scope and a list of data fixes with owners. If a provider skips this phase, expect surprises later.
Data Preparation (3–5 weeks)
Usually the longest phase. The most important fix is stockouts: when an item is out of stock, recorded sales understate real demand, and a model trained on that history under-forecasts. In our tests, simply averaging sales underestimated true demand by about 8% on items that sometimes ran out. Correcting for stockouts removed that error. See how much stockouts cost you for the method.
Modelling and Backtesting (3–4 weeks)
Build several candidate approaches and test each on held-out history against a simple benchmark, scoring error, bias and value added. Our forecast accuracy metrics guide explains what to measure. The result should be a decision by segment: which method for fast movers, which for slow or intermittent items, and where the simple benchmark is good enough.
Integration (2–6 weeks)
Forecasts that live in a notebook don't change decisions. This phase connects the data sources and delivers forecasts into the ERP, planning tool or a simple shared view, on a schedule. It's the most variable phase. A clean ERP with an API takes two weeks; several legacy systems can take six or more. Our guide to connecting inventory systems to predictive reordering covers the options.
Parallel Run and Handover (2–4 weeks)
Run the new forecast alongside the current process without acting on it, compare results, and train planners. Only switch when the evidence supports it.
A Simple Way to Estimate the Return
Before committing, put rough numbers on the value. For illustration, a distributor with $2 million of average inventory:
- Inventory reduction: if better forecasts let you hold 10% less stock for the same service, that's $200,000 less inventory. At an assumed carrying cost of 25% a year, which covers capital, storage, handling and obsolescence (use your own figure), that saves $50,000 a year.
- Fewer lost sales: estimate current lost sales from stockout history, then the share a better forecast would recover. The method is in our lost sales guide.
- Less expediting and waste: rush freight, emergency orders and written-off stock.
Compare the total with the build cost plus a year of running costs. If the case only works with optimistic assumptions, start with a smaller project to test them.
Are You Ready to Start?
A project goes faster and costs less when a few things are in place before kickoff. You're ready if:
- You can name the decision the forecast should improve, and the person who makes it
- You have at least a year of sales history at the level you stock, and two years if demand is seasonal
- You can export stock levels or stockout dates, so lost demand can be corrected
- Someone can provide system access to ERP, sales and purchasing data within the first two weeks
- There's a current method to compare against, even if it's a spreadsheet
You're not ready yet if nobody owns the decision, the history is only a few months long, or the data sits in systems nobody can export from. In that case, a short paid data audit is a better first step than a full project. It costs a fraction of the build and tells you exactly what to fix.
An Example Timeline
For illustration, a distributor with 2,000 active items across three warehouses wants better weekly purchase quantities:
| Weeks | Milestone |
|---|---|
| 1–2 | Scope agreed; data exported from ERP; audit finds stockout gaps and unrecorded promotions |
| 3–6 | History cleaned; stockout periods corrected; promotions flagged with sales |
| 7–9 | Models backtested by segment against the current spreadsheet method; winners chosen |
| 10–11 | Forecasts delivered weekly into a shared view and a purchasing export |
| 12–15 | Parallel run with planners; results compared; switch-over for the segments that improved |
That's about 15 weeks. With a team of 1.5–2 people at $60–$90 an hour, that's roughly 900–1,200 hours, or about $54,000–$108,000, in the integrated band. Most of the variation comes from weeks 3–6, which is why the data audit matters so much.
Running Costs After Launch
- Computing and storage: usually modest for forecasting workloads
- Monitoring: accuracy and bias by segment, with alerts when they drift
- Retraining and recalibration: as products, customers and seasons change
- Pipeline maintenance: when source systems or exports change
- Ownership: someone in planning who owns the forecasts and their use
Build, Buy or Both?
If your ERP or planning software already forecasts, the first question is whether it does well enough. A fair bake-off against a custom model, using the same items, holdout and metrics, answers that. See demand forecasting software vs a custom model. Many businesses end up with a hybrid: the planning system stays the workflow, and a custom model feeds the segments where it wins.
What to Ask Before You Sign
- How much of the plan is data preparation, and what happens if our data is worse than expected?
- Which benchmark will the model be compared against, and on which held-out period?
- How will forecasts reach our planners and systems?
- Will there be a parallel run before we act on the forecasts?
- What will we own: code, models, pipelines, documentation?
- What does it cost to run and maintain after launch?
- What acceptance criteria define success?
Clear answers to these predict a successful project better than any model choice.
Common Reasons Projects Overrun
- Starting without a decision. "Forecast everything" never finishes. "Improve purchasing for our top 500 items at three warehouses" does.
- Discovering data problems late. Stockouts, promotions and unit changes found in week eight push everything back. Audit first.
- No benchmark. Without a simple comparison, nobody can say when the model is good enough, so tuning never ends.
- Integration left to the end. Access to ERP data and write-back should start in week one.
- No owner in the business. Forecasts nobody is responsible for using don't change decisions.
Conclusion
A focused predictive analytics project typically costs $25,000–$50,000 and takes 8–12 weeks. Larger integrated systems cost more and take months. In both cases the data work and integration decide the budget, not the model. Start with one decision, audit the data first, test against a simple benchmark, run in parallel, and expand on evidence.
Schedule a strategy call with Cogniq AI for a scoped estimate based on your data, or explore our predictive analytics services.
