Use production data to improve performance and guide what comes next.
Mayker helps manufacturers move beyond reporting what already happened. We use industrial data analytics to explain performance, uncover patterns and support both operational improvements and long-term growth.
Blind spots in production hold back long-term growth.
Manufacturers already collect plenty of production data. The challenge is turning what sits in OT systems into information Operations can actually work with. A production dashboard or manufacturing KPIs can make part of the picture visible, but numbers alone do not explain what is driving performance or where to act first.
Without that visibility, Operations remains focused on reacting to today’s problems. There is little room left to spot larger opportunities, make confident investment decisions or use data to support the company’s long-term strategy.
Find the right way forward with production data.
You may still be deciding where data can make the biggest difference, or already have a clear ambition to bring to life. Mayker can help you work out what makes sense next and make it happen.
Connect your data ambitions to the long-term direction of the business. {Data roadmap}
Together, we identify where data can make a meaningful difference, prioritise the strongest opportunities and determine which capabilities are needed to realise them. The result is a step-by-step direction that connects operational needs with the company’s longer-term strategy.
Usefull when {skip}
There are plenty of ideas around data, analytics or AI, but no shared view on where to start, what to prioritise or how the different initiatives should fit together.
What you get {skip}
- A prioritised set of data and AI use cases
- A clear link between operational opportunities and business goals
- A phased roadmap for building the required capabilities
- A practical direction for what to do next
Make the right architecture decisions before you invest. {Data architecture assessment}
We map the current data landscape and compare it with what the intended use cases require. You see what can stay, where the gaps are and which technology decisions need to be made before you start building.
Usefull when {skip}
You want to move forward with manufacturing analytics, AI or an industrial data platform, but are unsure whether the existing architecture can support it.
What you get {skip}
- A map of the current data landscape
- A target architecture
- A gap analysisTechnology options and their trade-offs
- Prioritised actions with effort estimates
Bring one high-value data or AI use case to life. {Data accelerators}
An accelerator takes one defined production challenge and develops it into a working data or AI use case. We collect the required production data, build and deploy a working prototype, and use the results to determine its value and what should happen next. Predictive maintenance, production planning and energy monitoring are examples of use cases we can approach this way.
Usefull when {skip}
You have identified a concrete opportunity for data or AI and want to move it from an idea into the reality of your operation.
What you get {skip}
- A working prototype built with your production data
- The required data flows, analytics or models
- A deployment in the intended environment
- A business case based on the results
- A clear direction for further development or scaling
Make industrial data work across multiple use cases. {Industrial data platform}
An industrial data platform takes industrial data beyond one-off reports and experiments. It provides the production-grade pipelines and infrastructure needed to run analytics and AI reliably over time , from data intake to model monitoring and retraining.
Usefull when {skip}
You want to support several use cases with the same industrial data, but do not have the platform, expertise or internal capacity to build and operate that foundation.
What you get {skip}
- An industrial data platform built for your environment
- Production-grade data pipelines
- A foundation for reporting, integrations, analytics and AI
- The data and architecture expertise your internal team needs
- Support as the platform and its use cases develop
Build production data capabilities that last beyond the first use case.
A promising use case is not enough. The architecture, internal expertise and ownership around it determine whether it remains an experiment or becomes part of how the organisation works. That is why we look at both what industrial data can enable and what your organisation is ready to support.
Match the next step to your data maturity
Some manufacturers are exploring their first data use case. Others already have platforms, teams and several initiatives running. We first understand what is already in place before deciding what makes sense next.
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Let business value set the priority
The most advanced data or AI use case is not automatically the right one. We weigh business impact against technical feasibility and connect immediate opportunities to the company’s longer-term direction.
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Make sure someone can act on it.
Insights only create value when someone uses them to make decisions. We look at who needs the information, who follows up on it and how the use case becomes part of daily operations rather than remaining an experiment.
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Bring in the support your team needs.
Sometimes you need a second opinion. Sometimes you need specialists working alongside your team, or a partner who can take responsibility for the delivery. Mayker’s role reflects the expertise and capacity already available inside your organisation.
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The tools depend on what the data needs to do.
Some use cases need an industrial historian or time-series database. Others require a broader data platform, visualisation layer or machine-learning environment. We work across that landscape and select the technologies that fit the existing architecture and how the data will be used.
From the shop floor, into practice.
Energy management with Etex
Etex used data analysis to cut energy waste at its plasterboard plants, uncovering excess consumption after every production downtime.
OEE the Mendix way
Today we’re talking about OEE, or Overall Equipment Effectiveness for those in the know. OEE is a metric for measuring productivity of any given process, primarily within the manufacturing industry.
A few things you may want to know.
Learn more about how Mayker scopes, builds and supports applications for production environments.
Start with the operational or business decisions you want data to improve. Then determine which use cases could create meaningful value, whether the required production data is available and what your organisation can realistically support.A Roadmap Definition helps rank those use cases by business impact and technical feasibility, and turns them into a practical plan with priorities, costs and milestones.
Start by mapping the current data sources, flows, systems and constraints against what the intended use cases require. This shows what can stay, what is missing and which technical decisions need to be made before investing further.Mayker’s Architecture Assessment delivers a current-state view, target architecture, gap analysis and prioritised actions.
Yes. An accelerator focuses on one defined use case and develops it with real production data. This gives you practical evidence of how it works, what value it could create and whether it deserves further investment.That first use case could involve predictive maintenance, production planning or energy monitoring. The results show whether it should be developed further, changed or stopped.
Not necessarily. Existing machines, historians and production systems may already contain part of the required data. The first step is to determine whether that data is accessible, reliable and presented in a way that supports the decisions people need to make.
The right solution could be a focused production dashboard, an integration with an existing system or a broader production monitoring system. A Roadmap Definition or Architecture Assessment can clarify what is genuinely missing.
Begin with a shared definition of what each manufacturing KPI measures, which data it uses and how it should be interpreted. The same KPI can produce conflicting results when sites, systems or teams calculate it differently.A useful KPI also needs a reliable data source and clear ownership. Someone must know when performance changes, understand what sits behind the number and be able to act on it.
