Optimising Manufacturing Processes For Better Output
Always Faster, Better, Cheaper and More Customised
The market is forcing factories to constantly look for ways to become smarter and leaner to keep up with customers’ demands.
The Production Line Is No Longer The Bottle-neck
Machinery used on the production line enables factories to collect an increasing amount of real-time data about the manufacturing process. The focus is turning towards how this data can be harnessed to improve the quality of output.
There Is a Lot of Data, But Little Actionable Information
For data to be actionable it needs to be connected and digitalised. This will help to weed out the noise in the data so it could be analysed and the most important KPIs could be visualised.
Letting Intelligent Machines Support Where They Can
Machine learning can help Engineers take a leap forward in decision making and optimisation. Production processes can become faster, leaner and more efficient as several variables can be controlled at once in real- time.
Production in Industry 4.0
Automate Your Production Line With The Following Features
Production Parameter Optimisation
Input and output parameters for production can be significantly improved with the use of data analytics and machine learning applications.
The optimisation process enables cutting production costs, increasing time-to-market and decreasing lead times.
For maintaining stable production capacity it’s important to have control systems in place that help to monitor the day-to-day work and eliminate issues using predictive maintenance before they become critical.
Big Data analytics and machine learning can be used to detect defects in the production process before they become apparent to the human eye, therefore providing more time to proactively fix an issue and reducing machinery downtime.
Real-Time Production Line Improvements
Machine learning applications enable to influence robot movements and parameters in real-time to compensate for differences in robots and other production line fluctuations.The aim of real-time adjustments is to keep the production output quality constant.
Scrap Rate Reduction
For keeping the production quality constant, machine learning software can further help reduce waste. It can learn from the reasons why a product was scrapped and make necessary changes in the production process or suggest possible reuses for scrapped products.
The Digital Transformation
Bring Production Into The New Era
Digitalisation is Key for Leveraging Data in Decision-Making
Automation of specific manufacturing processes has brought along a leap in production efficiency worldwide under the term Industry 3.0.
The focus of Industry 4.0 is to automate decision making by using precise data from the entire production process.
Harness All Data In Your Enterprise
Every step of the production process and every decision taken by the management can and should be measured.
Quite often this data is in analogue form, on paper or in the heads of management, or is only kept within the bounds of a specific manufacturing process. By digitalising the analogue and measuring information from every process step of manufacturing, an overall picture of the enterprise appears.
The actual situation awareness is the key that enables fully digitalised enterprises to achieve superior efficiency to their peers.
An iterative 3-step process
Proof of Concept - Fastest to Goal
To prove the project can be executed a smaller scope quick win will be agreed on. This will enable users to get fast results before committing fully to a large scale project.
Fully functional software
Main functionality of the software will be developed and made available to the client for testing.
Increased scope and ML independence
Development of additional functionality, process automatisation and modifications to the machine learning (ML) algorithms will be added. At the end of this phase the software will be fully functional.
Our Experts In Managing Production Projects
We are developers, technologists, operators and engineers who have worked on over 100 projects.
Deep experience in Big Data, Automation and production.
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