Analytics and decision support for manufacturers and industry
Most operations teams already have the data they need to make better inventory and production decisions. It is just scattered, inconsistent, and nobody has time to pull it together.
The problem is rarely a lack of data
You have an ERP that holds sales history. You have spreadsheets a planner maintains, because the ERP does not do what they need. You have purchase orders in email and quality records on paper. Every number exists somewhere. Getting a defensible answer out of them takes two days, so decisions get made from experience instead.
That works until it does not. A long-lead-time item goes out of stock. Working capital climbs because safety stock was set once and never revisited. A customer asks for a delivery commitment and the honest answer is a guess. None of this is a failure of judgement. It is the predictable result of asking people to plan without a reliable picture.
Analytics and decision support closes that gap. We take the data you already produce, make it consistent enough to trust, and build the specific calculation or model that answers the question your team keeps having to guess at.
The five services
Each is scoped and priced on its own. Most clients begin with one and add others once the first has paid for itself.
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Put the right inventory in the right place. Set smarter stock targets, identify excess and shortages, and rebalance stock to protect service while reducing working capital.
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Anticipate demand and plan with greater confidence. Turn demand patterns into forecasts planners can act on, and focus attention on the products and locations that need it.
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Turn complexity into clear actions. Move past reporting what happened to recommending what to make, order or stock next, with the reasoning shown.
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Surface risks early and prioritise replenishment and production decisions, in a planning tool built around how your team already works.
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Build a consistent, reliable foundation for planning. Standardise data across systems, automate the updates, and add quality checks so everyone works from the same definitions.
How a decision gets made
Every engagement follows the same path from scattered records to something a planner acts on.
What we deliver
Concrete outputs, not a slide deck. Depending on the service, an engagement produces some combination of the following.
- Reorder point and safety stock tables per item and location, with the service level each one is set to achieve.
- Forecast models with documented accuracy against a baseline, and a monthly refresh you can run yourselves.
- Planning tools and dashboards built into the software your team already opens, not a new login.
- An automated data pipeline that refreshes the inputs on a schedule and tells someone when a source breaks.
- Written documentation of the method, the assumptions and the limits, so the work survives staff changes.
What you need to get started
Less than most people expect. We have never turned down a project because the data was too messy; we have occasionally said the first phase needs to be data engineering.
- Two to three years of sales or consumption history, at the item level. A CSV export is fine. Less history is workable, with wider uncertainty.
- A current item list with whatever attributes you hold — category, supplier, lead time, cost, unit of measure.
- Open or recent purchase orders, if you have them, so we can see real lead times rather than the ones in the system.
- One person who knows why the data looks the way it does. This matters more than any file.
Spreadsheets and ERP exports are a normal starting point. So are gaps, duplicate item codes and a column someone has been using for two different things since 2019. We expect all of it.
Typical outcomes
Described in kind, not in numbers. We do not publish percentages we cannot attribute to a named client, and we will not quote you someone else's results as a forecast of yours.
- Working capital released from stock that was not protecting service, and redirected to items that were genuinely short.
- Fewer stockouts on the items that matter most, because safety stock reflects actual demand variability and real supplier lead times.
- Planners spending their time on the exceptions the model flags instead of rebuilding the same spreadsheet every Monday.
- Delivery commitments a salesperson can make without checking with three people first.
- A number the leadership team agrees on, because everyone is reading the same definition of it.
What any individual client sees depends on where they started. The assessment is where we form a view about that, and we would rather say so up front than promise a figure.
Where this applies
Common questions
What is decision support, in plain terms?
Reporting tells you what happened. Decision support tells you what to do about it. A report might show that three products went out of stock last month. Decision support says which quantities to order this week, for which products, and what it assumed to get there. The difference is whether the output ends in a conversation or an action.
Do we need a data warehouse first?
No. Most of our clients start with spreadsheets, an ERP export and some manual records. That is a normal starting point, not a problem to fix before we can begin. We build the data foundation only as far as the decision you want to make actually needs it.
Is this machine learning, or is it statistics?
Both, depending on what the data supports. Classical statistical methods are often more accurate than machine learning on short histories and intermittent demand, and they are easier to explain. We choose the method that performs best on your history and we show you the comparison, rather than leading with a technique.
How do you measure whether it worked?
We agree the measure before we build. It is usually a service level, an inventory value, a forecast error measure such as MAPE or MASE, or the time a planner spends preparing a number. We take a baseline first, so the comparison afterwards is real rather than asserted.
What happens to the models when the engagement ends?
They are yours. We hand over the code, the documentation and the assumptions, and we walk your team through them. We do not hold models in a system you cannot access.
Can you work with our existing BI tool?
Usually, yes. If your team already uses Power BI, Tableau or something similar, building into it is normally better than adding another place to look. We will tell you when a dedicated tool is genuinely the better option, and why.
Find out what your data can already tell you
A free 30-minute data readiness assessment. We look at what you have, tell you what it can support today, and name the first thing worth building. No preparation needed.