Product
Quality
Product quality analysis is important for every manufacturing industry. Data availability has exploded due to the simplicity and availability of inexpensive sensors and data storage. The constant emergence of new technologies continues to improve connectivity and the speed at which data becomes available. These advances offer unique opportunities to apply product quality analysis to maximize throughput.
In manufacturing, defective products reduce customer satisfaction and risk losing sales. The results can be devastating. Suboptimal production processes lead to lost productivity. Products that do not meet quality standards must be scrapped, but that is not the only loss. The raw materials, machine time and labor that produce these inferior products are often wasted as well. A company's long-term success therefore depends on the quality of its goods and services. Combined with the latest machine learning techniques, there is an opportunity to further improve production by collecting large amounts of data and new production data at higher resolutions. Product quality analysis helps increase production in the following ways:
Faster quality checks:
Combining machine learning/computer vision with new data sources such as image data can accurately and efficiently identify manufacturing defects, reducing the need for tedious and time-consuming human inspection. Quickly identify defects and machine problems for faster troubleshooting. This allows the plant to return to optimal production levels as quickly as possible.
Predictive Quality Analysis:
Predictive models provide early warning of potential downstream quality and equipment issues. If corrective action is not possible, the intermediate product can be scrapped prematurely, saving even more lost manpower and machine time.
Prescriptive quality analysis:
Artificial intelligence and optimization techniques help operators take preventative action when possible. Examples of this include intelligently updating process parameters or intelligently changing the execution schedule of a particular batch.
Maximize Throughput:
Industry often makes conservative production decisions to ensure quality standards are met. Product quality analysis can reveal how to maximize production efficiency, machine efficiency and raw material utilization while maintaining required quality standards. Product Quality Analytics analyzes production data and provides real-time forecasts and recommendations. This maximizes throughput and greatly increases the value of production quality analysis over traditional methods that rely on analyzing historical data.
The InfiTekPro project team applied causal inference techniques to provide general and detailed insight into the factors that affect product quality. The engineered solution combines machine learning with causal inference and automation to simultaneously optimize these settings and ensure high quality production. Our team uses dynamic non-linear modeling techniques to determine the exact settings of these machine parameters throughout the factory, thereby optimizing product quality.