When someone says, ‘Smart Factories’, we immediately think about the Manufacturing Execution System (MES) that controls the heartbeat of the operations. While the MES helps track vital information from many different sources to create a holistic system view, it should mirror the physical operations. Typically, an MES can enable leaner operations that drive down manufacturing costs at a plant level while increasing product quality, order accuracy and ultimately customer satisfaction.
The traditional view of quality is well summarised in the following statement;
“On the topic of quality, it does no good to design a product with great quality specs if it can’t be built accordingly. Quality must be planned and executed with the greatest of precision. And, it must do so in an environment where specifications, designs and product options are frequently quite dynamic” – Manufacturing Transformation (3 Ways an MES / MOM System Can Improve Quality)
Not unreasonable, but the traditional approach needs to evolve to maximise the opportunities offered by new or emerging manufacturing themes and technologies. Call it digitizing the factory, Industry 4.0, or Smart Factories, by complimenting the traditional with additional data points a more holistic approach to quality is achieved.
At Hitachi, we understand the value of adding predictive analytics to the Smart Factory. The ability to observe, interpret and provide recommendations is invaluable for improving operations. This is why we have developed our Smart Image Analysis System to detect signs of facilities failures and deviations in front-line worker activities that when addressed contribute to increased quality and productivity through linked operations within the MES ecosystem.
We believe that making quality a core part of the production operations within each plant is a vital aspect often overlooked by traditional MES providers. Daicel, a global producer of injectors for automotive airbags, is an excellent example of a company who recognised the challenges with the traditional approaches to quality and made a strategic decision to digitize their quality management system.
Their goal: increase the certainty of product quality, reduce the cost of internal re-work, and root cause eradication.
Our solution: Digitization of Man, Machine, Method, and Material.
Through the aggregation of 3D image analysis, data from traditional MES, and IoT devices, Hitachi enabled Daicel greater defect prediction and improved quality management processes. A short video demonstrating the solution can be view here.
You could say we are at the dawn of a new ear of manufacturing intelligence, underpinned by improved access to data to support MES, improving product quality, reducing cycle times, and streamlining plant operations. But many have already witnessed the new dawn, have you?
In recent years, mega-recalls in various industries have brought about a renewed awareness of the importance of accumulating and managing manufacturing performance data to identify the causes of product defects, and to implement countermeasures. In the advanced manufacturing workplaces of the future, it will be necessary to gather a wide range of work related performance data, including manufacturing performance and inspection data and the results of visual checks by workers, achieve new traceability by establishing mutual links among these different forms of product performance data. All of this will be achieved by introducing new manufacturing execution systems that incorporate IoT technologies