Manufacturing is one of the areas in business where analytics should be used to get a better-quality product. Unfortunately, due to a lack of knowledge or cost for infrastructure, many manufacturing businesses forego any sort of deep analytics. Recent developments in the area of analytics have made it a viable prospect to explore analytics anew. Digital data collection through IoT makes it more cost-effective for manufacturing companies. However, data is only as valuable as the application you use it for. The value in collecting digital data is how a company leverages that data to improve its product quality and efficiency. That’s where analytics comes into the picture.
Data Collection for Testing and Design
Before a company can improve its product quality and efficiency, it needs a base of information to work with. Collecting data from manufactured products through IoT devices allows that data to be stored within a database. The database provides a centralized location for storing the data and comparing later iterations of products with earlier ones. The production parameters can be tweaked and adjusted so that the final real-world model meets a predetermined virtual model. The term “Digital Twin” is usually used in this case since it tries to pattern the physical product with a digital, virtualized model (the “twin”).
Analysis of Process Parameters
Manufacturing processes have several parameters that are tracked on the shop floor. By monitoring these parameters and adjusting them along a sliding scale, the IoT devices can collect the resulting generated data. The company can then use this data to determine the optimal production parameters, as well as any that produce noticeable anomalies within the production line. Automation can use these analytics to automatically adjust parameters if the output (the result data) surpasses a particular threshold value. Human monitoring is still necessary, but for the most part, a business can leverage automation to create a feedback loop that ensures that quality is maintained.
Quality Control and Testing
Testing usually happens with a random sample in most manufacturing facilities. Depending on the rate of failure, the batch can be deemed sound or not. If a particular batch shows faults in its creation, the output data of the IoT stream can be compared to previous batches that lacked the same defect to find out what went wrong. If the testing shows a more robust product, the current standards by which the analytical engine tests the results may be updated to improve the overall product quality. In this way, a manufacturing company may evolve its product by using data and analytics to determine improvements.
Implementing analytics is no small feat, and it usually requires some form of analytics consulting to grasp what should be done. Regardless of how the company implements its analytics, it should seek constant improvement in the quality of its product and the efficiency of its processes. Analytical engines can work in tandem with data stores like SAP to maintain a single source of truth that the company can pull from. As data about the processes are updated, they are reflected in real-time via a dashboard, allowing engineers to keep abreast of how the manufacturing system develops.