Operations teams live in the present tense. Which truck is late, which machine is down, which branch is out of stock. Every one of those surprises was predictable hours or days earlier, and the data that would have predicted it was already sitting in your systems. Predictive analytics for operations is mostly the work of closing that gap.
This is not a data science moonshot. The four problems below account for most of the value we have delivered to ops teams, and none of them needed more than two people and a quarter to reach production.
Demand forecasting: order against tomorrow, not last month
Most operations plan against a trailing average. Last month we sold 400 units, so order 400. A forecast model adds what the average misses: seasonality, payday cycles, weather, promotions, public holidays. In South African retail the month-end salary spike alone can move daily demand 60 percent, and a trailing average smears that signal into nothing.
Start with your top 50 products or routes by value, not all of them. Forecast daily demand two weeks out, compare against what the buyer would have ordered, and count the rand difference in waste and stockouts. That number is your business case.
Arrival time prediction: stop making customers guess
For EvolveVision Logistics we built delivery ETA prediction from GPS traces, historical route times, and time of day. The model itself was ordinary. The impact was not: proactive delay notifications cut "where is my delivery" calls by 43 percent, which freed two full-time dispatchers. The lesson is that the prediction only pays when it changes an action, in this case sending the notification before the customer phones.
43%
fewer inbound delivery-status calls
11 weeks
from first data pull to production
2 FTEs
of dispatcher time freed for exceptions
Predictive maintenance: fix it before it fails
If you run equipment, breakdowns are your most expensive surprise. The good news is you rarely need exotic sensor work to start. Maintenance logs, fault histories, and usage hours already predict a lot. A model that ranks assets by failure risk over the next 30 days turns a reactive maintenance team into a scheduled one.
Stock-out and shortage warnings
Combine the demand forecast with current stock and supplier lead times and you get the simplest, most loved output we ship: a daily list of items likely to run out before the next delivery, ranked by margin impact. No dashboard archaeology, just a short list in the ops channel every morning at 07:00.
How to run your first project
- 1Pick one recurring surprise that costs real money. One, not four.
- 2Find where the outcome is already recorded: late deliveries, breakdowns, stockouts. No recorded history means you start by logging, not modelling.
- 3Build the dumbest useful baseline first: a moving average, a rule of thumb. Measure it.
- 4Train a model and only keep it if it beats the baseline by a margin that matters operationally.
- 5Wire the prediction into an existing routine: the morning stand-up list, the dispatch screen, the maintenance planner. A prediction nobody sees at decision time is worth nothing.
- 6Review accuracy monthly with the ops team, and retrain when the world shifts.
What it costs and what to expect
A first production predictor of this shape typically runs 10 to 14 weeks with a two-person team, assuming your operational data is reachable. Expect the first model to be 15 to 30 percent better than the human baseline, not magic. That is usually plenty: a 20 percent reduction in emergency call-outs or expired stock pays the project back within the year.
The compounding effect is cultural. Once one prediction is trusted in the morning routine, ops teams start volunteering the next problem, and the second project ships faster because the pipelines exist. You can read how this played out end to end in the EvolveVision Logistics story, or talk to our data and analytics team about which of your surprises to attack first.