Every supply chain team collects data. Not every team can say with confidence what it means. That gap is usually where the risk sits.
We build the models, test the assumptions, and leave you with a way of working that holds up under scrutiny, whether that’s a board meeting, a tough trading period, or a decision with real money behind it.
Common statistical analysis challenges:
Decisions get made on instinct or legacy process.
No one source of truth or trust when it comes to data.
KPI’s are volatile or are being measured incorrectly.
Forecasts and plans get built, but they are not followed.
Recurring operational issues and firefighting.
What does statistical analysis actually involve?
Statistical analysis starts with description: taking the data you already collect and turning it into patterns anyone in the business can read, in a dashboard or a report, rather than a spreadsheet only one person understands.
From there, it moves into harder questions. This is where inferential statistics comes in, testing whether the relationships you think you’re seeing are real, how much confidence to place in a forecast, and which factors are actually driving a result rather than simply correlated with it.
Depending on the problem, a project might include:
- Performance dashboards that separate genuine signal from ordinary noise
- Testing whether a change in the business, a new supplier, a process change, a pricing move, had a measurable effect
- Modelling demand variability and lead time uncertainty, not just the average
- Running simulations to see how different scenarios play out before you commit to one
- Identifying which variables actually explain the outcomes you care about, and which are a distraction
The Trym Approach
Every project is different, but most follow the same shape.
Understand the data
Before any modelling starts, we work out what’s actually available, how good it is, and where the gaps sit. This is usually where we learn the most about how the business really operates.
Frame the right questions
Data can answer almost anything if you ask it the right thing. We work with you to define the actual decision at stake.
Build the model
We build a ‘digital copy’ of the challenge you are facing, using all the data and variables we have collected.
Pressure-test it
We run the model against different assumptions and scenarios to see where the answer holds and where it’s sensitive to change, so you know how much weight to put on it.
Make it actionable
The output is built to be used: a dashboard, a report, or a tool your team can actually use to solve problems.
Continued support
We’ll leave you with everything you need to take action, but we’ll be there if you need us.
From the field: modelling a network under pressure
A third-party logistics provider came to us mid-way through winning a major new customer. The customer’s growth plans depended on a change in pricing policy, and our client needed to know what network of sites would actually support that growth over the coming years, with no firm volume forecast to work from.
We started by building an honest picture of the existing network: what each site could realistically handle, where the spare capacity sat, and which sites were candidates to exit as leases came up. With no detailed forecast to rely on, we built a data model that let the team explore different growth scenarios instead of waiting for a forecast that was never coming. What happens if most of the growth lands with one type of customer? What if the average product size shifts by a fifth?
From there, we built a network optimisation model to test the cost of serving the customer base under different site combinations and volume assumptions. Sixteen weeks in, the business had a framework it could use to make decisions with its eyes open: what needed to happen regardless of how the future played out, and where it made sense to wait and see.
For organisations concerned with the use of data and decision-making in the face of uncertainty, statistics is vital to identifying the factors that contribute most to successful outcomes.
Statistical analysis done well doesn’t produce a report that sits in an inbox. It changes how decisions get made.
If your team is making big decisions on partial information, we can help build the evidence behind them.
Getting your demand forecast right:
Decisions backed by evidence
A clear sense of how much confidence to place in a forecast
Know the difference between normal variation and real shift
Dashboards and models your team actually use
Faster, more confident calls when the stakes are high
Latest in Statistical Analysis
Frequently asked Questions.
Demand planning is focused specifically on forecasting future demand. Statistical analysis is broader.
It’s the toolkit we draw on across all our work, from demand planning to cost-to-serve to network design, whenever a decision needs to be backed by evidence rather than assumption.
No. Most projects start with data that’s incomplete, spread across systems, or hasn’t been properly checked in years. Understanding what you’ve actually got, and what’s missing, is usually the first step, not a precondition.
It depends on the question. A focused piece of analysis to answer a specific question might take a few weeks.
Longer projects are more sophisticated and have more moving parts. We always give detailed time estimates on project before we start, so you understand the real timeframes.
We work with whatever you already have, whether that’s Excel, a WMS, an ERP, or a BI tool. We are system agnostic, and only recommend new tools where there’s a genuine gap they’d fill.