Applied ML for Operations Decisions
Move from spreadsheet heuristics to models your planners actually trust, built on your demand, inventory, and supplier history.
Every operations group has decisions being made by a spreadsheet nobody fully understands and one person who is about to retire. This engagement replaces the most expensive of those with something defensible, and trains your team to build, evaluate, and maintain it. The measure of success is not model accuracy. It is whether your planners change what they do.
We select the target decision with you during discovery, based on where error is currently most expensive. Everything is built against that decision and your systems. Forecasting for a seasonal consumer goods network and supplier risk scoring for a regulated manufacturer are different engagements.
Forecast accuracy, inventory turns, or the decision metric selected at kickoff
For teams with historical operational data in accessible systems
10–14 weeks, tailored
What Makes This Different
Built on your history
Demand, inventory, lead times, supplier performance. Your data, your seasonality, your exceptions.
Statistics you will actually use
Enough rigor to avoid the expensive mistakes, without a semester of theory nobody applies.
The full path to a decision
Data through model through deployment through the moment a planner acts differently. Most training stops three steps early.
Adoption is part of the scope
A model nobody trusts is a sunk cost. We build the trust-building into the engagement.
What Your Team Walks Away With
Your Outcome: A deployed model against a real operational decision, and a team that can evaluate the next one, including vendor proposals.
Sound statistical intuition
What these methods can and cannot tell you, and where they fail quietly.
A built and evaluated model
On your data, against a decision that matters.
Vendor evaluation capability
The ability to tell a real ML proposal from a dressed-up regression.
Results your team can communicate
To planners, to leadership, and to finance.
Built for Organizations That…
The Engagement, and What You Keep
How the work runs
What your organization owns after
Frequently Asked Questions
What if our data is messier than we would like?
It will be. Discovery includes a data assessment, and we scope the target decision against what your systems can actually support. If the data will not carry the decision you have in mind, we say so early and tell you what it would take.
Who needs to be in the room?
The analysts who will maintain the model and the planners whose decisions it changes. Adoption workshops are part of the engagement precisely because a model the planners do not trust never moves the metric.
Do participants need a statistics background?
Comfort with spreadsheets or basic scripting is enough. We build the statistical reasoning that drives good modeling decisions and skip the derivations nobody applies.
We already bought an ML tool. Does this still apply?
Often more so. The same work that builds a model builds the ability to evaluate one, so you leave able to tell whether what you bought is doing anything and what to ask the vendor next.
“The hardest part of machine learning is rarely the algorithm. It's asking the right question and trusting the evaluation enough to act on it.”Ben Manning
Give your planners a model they will actually act on.
Your data, your decision, and the evaluation discipline to defend it.
Start With a Capability Assessment