Demand Forecaster
Send 24 months of requisition history. Get back a monthly hiring forecast for every job profile in the file, along with the error rate that forecast earned on months it was never shown.
Turn the requisitions you have already raised into a hiring plan you can defend.
Demand Forecaster reads your own requisition history, works out which real job each entry belongs to, and forecasts how many people every one of those jobs will need month by month. It picks the model that fits each job separately, proves that choice against months of your history it was never shown, and hands the accuracy back to you alongside the number.
9% average error, calculated for every job profile separately.
This is the average amount past forecasts missed by, not a band around next month. Every run reports the error your own file produced. How it is calculated
Three things a hiring forecast has to get right before anyone will act on it.
Know what the job really is
A job title is typed by a person in a hurry. The skills asked for in the description are what the job actually is, so that is what the forecast gets built on.
Fit the model to the job
A steady delivery role and a rare specialist behave nothing alike. One model for the whole company has to average the two, and gets both of them wrong.
Show the error, not just the number
A forecast with no error rate cannot be trusted or improved. Every number here arrives with the accuracy it earned on months it never saw.
Solve the things that make a hiring forecast wrong in the first place.
Most teams forecast hiring from a spreadsheet of last year's numbers plus a percentage. These are the five reasons that keeps going wrong, and each one is handled before a model is ever trained.
The same job written many ways
One role appears as four different titles across three business units. Counted as text, a single job looks like several small ones and none of them gets a usable forecast.
Requisitions that describe a person, not a job
Plenty of requisitions say little more than that somebody left and needs replacing. Reading the description for skills recovers the actual job behind that line.
A count that hides the work
Two requisitions with the same title can ask for completely different skills. Forecast the count alone and you plan a headcount nobody is actually able to staff.
Seasonal patterns mistaken for noise
Hiring has rhythms tied to your financial year and your delivery cycles. Two full years of history is what lets a genuine pattern be told apart from a random month.
Nobody ever checks the miss
A plan produces a number and the number is never marked against what happened. Without an error rate there is no way to tell a good forecast from a lucky one.
How a forecast gets made, start to finish.
Four steps. You only have to do the first one.
Send one file
An export of the requisitions you have already raised. Three columns are all it needs, and the file is checked the moment it arrives.
Find the real job
The same job gets written many different ways. Each description is read for the skills it asks for, so all the versions come back together as one job.
Check the plan, then it runs
We read your dates and suggest how to split them. Say yes, or change one thing, and the rest happens on its own.
Get your report
A full report for whoever defends the number, and one page for whoever approves it. Both are charted for you as well.
The three decisions that make the difference.
A model is chosen for each job profile, not one for your whole company.
Multiple models are trained on each job profile's own history. Every one of them is scored on the same withheld months, and the one that scored best is the one kept. Nothing is tuned by hand and no model is chosen in advance, so a rare specialist role is never forced to share a model with a high volume delivery role.
Trend, seasonality and your own hiring signals all feed the monthly result, and the report names which model won for each profile.
The last months of your history are hidden, then predicted blind.
This is what turns a forecast into evidence. Before any model goes anywhere near your future, the most recent months of your own history are held back. The model is trained without them, asked to predict them, and then marked against what actually happened.
Models compete on an earlier slice of your history. The winner is then scored on a final slice it has never touched, so the number you read is not the best of many attempts.
Your report shows three things: how far the chosen model missed, whether it missed high or low, and how it did against assuming this month looks like the same month last year. Hiring is seasonal, so that is the benchmark that counts.
A thin history gets a wider forecast, and the report says so.
Confidence depends on how much history a job profile has. A role with two years of steady requisitions is forecast far more tightly than one with a handful of records, and pretending otherwise is how a forecast loses the room.
So confidence is shown for each profile rather than averaged across the file. If a role is thin, you can see that it is thin, and you can treat it as a range to plan around instead of a number to commit budget to.
What one run involves.
What lands in your hands when the run finishes.
One upload produces one full report and one short summary. The platform will chart either of them for you, so you do not have to open a spreadsheet at all if you would rather not.
The monthly demand curve
How many people each job profile needs, month by month, for as far ahead as your history supports.
History against forecast
Your actual hiring and the predicted line on the same chart, per profile, so the join between them is visible.
How far each model missed
Error for every model on every job profile, split by how far ahead it was forecasting. This is the tab where our 9% either holds up on your data or does not.
Whether it missed high or low
Direction as well as size. Two profiles can share an error rate while one leaves you short of people and the other leaves you paying for a bench.
The check against last year
Each model set against assuming this month looks like the same month a year ago, which is the benchmark a seasonal business should be judged on.
The seasonal pattern in your hiring
The rhythm the model found in your own year, which is often the first time a planning team sees it written down.
The skills behind the demand
The skills appearing most often across your requisitions, so the plan can be handed to recruitment as something actionable.
Built first for the people who staff sold work, and for everyone else who has to answer for the hiring number.
Professional services is where a wrong hiring number costs twice, once on the bench and once on the work you cannot staff. Each of these starts from the same forecast and uses a different part of it.
Professional services staffing
See which roles the delivery pipeline will need before the work is sold, so the bench and the unstaffable work stop happening at the same time.
Run a forecastCapacity and capability planning
Take a defensible number into the planning cycle, with the error rate attached, instead of last year plus a percentage.
Run a forecastRecruitment demand planning
Know which pipelines to open first and how deep they need to be, months before the requisitions actually arrive.
Run a forecastWorkforce data and HR technology
Get a forecast out of the requisition data you already hold, with no cleaning project and no new system to stand up first.
Run a forecastYour own private storage
Files go into storage scoped to your organisation alone as soon as they pass validation, and are not shared outside it.
Deleted after 30 days
Your upload and its report are both removed automatically, so download anything you want to keep before then. If the number has to survive a budget review months later, talk to us about longer retention for the report.
Charged only when it works
Credits come off once, at the moment a report is generated. A rejected file or a failed run costs you nothing.
A forecast tells you what is coming. It does not tell you how to cover it.
This worker produces the forecast. The rest of the OpenKnowra platform then works on those same numbers, so the plan and the forecast never drift apart. Internal mobility looks at the roles you can fill from the people you already employ, before anybody goes to market. What if analysis lets you change the plan and see what that does to the hiring you need, before any budget is committed.
Your questions, answered.
What is workforce demand forecasting?
How accurate is it?
What exactly does the error rate measure?
How much history do we need?
Our job titles are a mess. Does that break it?
What file formats can we send?
How long does a forecast take?
What happens to our data?
What does it cost?
How is this different from headcount planning in our HR system?
Run your first forecast.
Send 24 months of requisitions and get a monthly forecast for every job profile in the file, with its error rate attached.
Or bring us your data first.
Not sure your requisition history is in good enough shape? Tell us what you have and we will tell you what a forecast on it would be worth.