Statistical process control consists of tools for quality control common not only in industrial areas. Statistical process monitoring and control detect process shifts caused by an assignable cause and eventually proposes a corrective action before many nonconforming parts are manufactured. The detection is typically made on process output sampled according to a certain sampling plan. Because of limited sampling and measuring capacities, the number of such samples can be much smaller than the number of produced outputs, which can lead to large delays between the occurrence of the assignable cause and its detection and consequently to additional costs connected with scrapped parts or non-detected faults. Fortunately, besides the measurements made on the sampled process output, in-process data are becoming increasingly available, which can enable to detect of a problem immediately after it appears. With rapid ICT development, much larger process data can be collected and stored with many variables and large sample sizes.
The majority of deployed solutions are however based on univariate methods that require different assumptions to be met. In the new situation, the univariate setting becomes inconvenient and the probability of assumption violation rapidly increases. AI and machine learning principles help to overcome those issues via multivariate approaches enabled by the increasing data availability. The experiment will define a process to be controlled, define and analyze the post-process and eventually also the in-process data, propose AI monitoring methods tailored to the particular situation, implement the methods, and perform their preliminary assessment on available historical data.
This will be presented to interested representatives of selected SMEs, potential overlaps will be discussed and a proposal of potential use-case project will be defined. Not only the benefits of AI in-process monitoring and control, but also their issues, potential pitfalls, and barriers will be demonstrated and discussed.
The experiment can guide the Czech companies towards novel AI-based approaches to monitoring and control of their processes. This will enforce the competitiveness of the Czech industry and other domains. Although SPC was originally intended to improve industrial manufacturing, it has also many applications in other sectors like the healthcare, environmental sector, or general services. In cases where AI cannot be deployed because of insufficient data infrastructure, the experiment can help to identify and describe the barriers and motivate the SMEs to overcome them e.g. by creating a sophisticated data platform and setting related processes.
Number of SMEs that will consider and discuss the potential use of AI, machine learning, and data science in process control and monitoring and data-driven quality management systems.
Motivate at least two SMEs to initiate preparation of data infrastructure appropriate for future AI-based quality control and monitoring.
Support at least one SME to develop a proof of concept for AI-based process control.