A System Approach for Creating Employee-Oriented Quality Control Loops in
Production for Smart Failure Management System in SMEs
Authors: Refik Caglar Turgut , Jochem Roland 
Published: February 2023

The basis of effective processes for failure elimination and prevention
is formed by quality control loops, which aim to detect and analyse
deviations/failures from quality specifications and to take appropriate
measures to correct them in time. Quality methods can either be used as
controllers (e.g. statistical process control, failure mode and effects
analysis) in quality control loops or as a special form of these (e.g.
Plan-Do-Check-Act cycle, 8D Report, Six Sigma methodology). For the
successful introduction of quality control loops, relevant data should be
systematically collected, evaluated and interpreted in order to derive
targeted measures as a consequence. Small and Medium Enterprises (SME)
rarely have a systematic quality management system for the comprehensive
collection and analysis of quality data and their embedding in quality
control loops. On the other hand, the increasing complexity of production
systems requires the digitalisation and expansion of quality control loops
already in use, although they have delivered good results so far. At this
point, Artificial Intelligence (AI) is a future key technology that holds
significant potential for future value creation. AI-driven data science
methods (e.g. machine learning) enable the explanation of complex,
correlationally directed relationships in large amounts of data and
accordingly contribute to process improvement as well as failure management.
In this context, the expansion of quality control loops through digitalised
elements and AI methods can help to achieve a smart failure management
system.In terms of content, failure management also includes the term
"failure prevention" and is not a one-time process, but a continuous process
that requires the motivation and understanding of all employees.
Furthermore, the concept of "Total Productive Management" also aims at
defect-free products and effective production processes and involves all
employees in improvement activities to maximise plant efficiency and
minimise losses. At this point, SMEs need intelligent, digital and
employee-oriented error management systems. The core objective of the paper
is to present the conceptual development of a smart failure management
system that is in a continuous learning process through interaction with the
employee and in this way learns human cognitive problem-solving skills. This
approach is intended to detect failures on the shop floor at an early stage
in order to identify possible causes of problems and derive measures. If the
defect type, cause or measure are not known, the system suggests suitable
methods/tools of quality and data science to support employees in problem
solving process. In order for the assistance system to have human cognitive
problem-solving capabilities, the system must be trained in advance by
qualified employees who have extensive technical and methodological
knowledge and can apply it confidently. With this in mind, the failure
management system is expanded to include two additional subsystems. Firstly,
the less competent employees must be taught missing methodological knowledge
on the basis of digital learning methods. Then, the learning phase is
followed by the employee training, in which the employee is supported
digitally and dialogue-based in the selection and implementation of methods
as well as the interpretation of results.