Manufacturing analytics for manufacturers

Each one is built on the same foundation: integrated data from production systems and ERP, governed KPI definitions and dashboards reviewed in the daily, weekly and monthly meetings where decisions are made.

A common approach is to start with one or two use cases, prove the value in a single plant and then extend the same data model to other sites and product families. Because KPI definitions and integrations are reused, each additional use case is usually faster to deliver than the first.

The use cases below are listed in a typical order, but the sequence should follow your own priorities. A plant with capacity constraints may start with OEE, while a business under margin pressure may start with product profitability. A plant facing customer complaints may start with quality and scrap.

Every use case is delivered with documented KPI definitions, a named owner and a review cadence, so the dashboards become part of how the plant is managed. Each one names the decision it supports, such as which line to improve first or which product to reprice.

Start with the use case closest to your most urgent decision. If capacity is the constraint, that is usually OEE. If margin is the concern, it is cost and product profitability. If customers are complaining, it is quality.

Whichever comes first, the data model is designed for the rest, so the second use case reuses the integrations, keys and definitions built for the first. Each use case answers one question a leader asks every week, and each one is measured against a baseline taken before the work starts.

These benefits build on each other. Once production and finance data share a model, questions that used to require a special project, such as the true cost of a product line or the margin effect of a new shift pattern, can be answered from existing dashboards. That is where manufacturing business intelligence starts to change how decisions are made, not only how they are reported.

The financial case is direct. Analytics helps reduce costs by showing which losses are worth fixing first: the changeover that costs the most capacity, the defect that drives the most scrap, the product that earns the least margin. Improvement budgets then go where the data says the return is highest.

Each role gets its own view, built on the same numbers, so meetings between operations and finance start from agreement rather than debate.

We start with advice on one plant and one use case, reviewed with your team before any build is proposed. Training follows where your team needs it, and the build starts only once definitions are signed off and the plant data and team are ready. We then prove the value and reuse the same model across other sites. Operations, finance and IT are involved from the first week, so system access, production data and cost data line up from the start rather than being reconciled at the end.

Delivery is mostly remote, with plant visits where they add value, and sessions are scheduled around shift patterns and time zones. Each step ends with a sign-off from the people who will use the result.

The first dashboard is shown on real plant data during the build step, and adjusted with the supervisors and managers who will use it every day. Timelines are confirmed in writing after the plant and data review.

After go-live, usage is reviewed with the plant team. Views nobody opens are fixed or retired, and new questions are added to the backlog for the next phase, so the reporting stays focused on the decisions that matter.