Quality forecasts in manufacturing using autoregressive models
Authors: Mayer Jan , Jochem Roland 
Published: February 2023

Companies in the manufacturing industry are facing a variety of
challenges such as increasing product complexity and variety and the
accompanying complexity of production processes. The developments for more
sustainability and optimized use of resources are additional societal
requirements. Consequently, the demands of efficient solutions in quality
management are also increasing. Innovative processes are needed to meet
industrial challenges. Therefore, enhanced availability of data in
production offers an opportunity. Hence, the combination of associated
process and manufacturing knowledge and data availability creates the
possibility to improve product- and process-related quality as well as the
use of resources. As a consequence, machine learning methods are used to
utilize and evaluate the collected data volumes. Their application in
quality control enables the operation of smart solutions like the detection
of anomalies in both product and process quality. However, there is no
standardized algorithm to implement in any desired production environment.
Conclusively, the application of specific algorithms is highly dependent on
the desired project output, human factors and the underlying infrastructure.
As main manufacturing branch, mass production combines the potential
benefits of machine learning applications and their occurring challenges for
product and process and monitoring. Existing reporting tools like the
statistical process control (SPC) enhance process owners to continuously
monitor manufactured products and processes. Nonetheless, the execution of
the SPC is naturally reactive, once the monitored products have been already
produced. Thus, process owners require a proactive, user friendly and
interactive forecast application regarding their product and process
quality.Predictive quality control is one way of improving product- and
process-related quality while taking advantage of greater data availability.
It represents an implementation of quality control in conjunction with
data-driven quality forecasting. This application enables companies to
conduct data-driven forecasts of product- and process-related quality. The
aim is to use machine predictions as a basis for decision-making for action
measures to be derived by the user. On the basis of the large amounts of
data and algorithmic evaluation, measures can be derived by process and
utilization investigations. Among other things, future events with influence
on the quality can be controlled in an improved way. In quality management,
decision-making processes are based on extensive data collection and
analysis. Predictive quality should be seen as a supplement to conventional
methods, e.g. SPC.Convenient implementation methods are key to achieve
effective quality monitoring in terms of product and process control. For
this reason, automated machine learning can be used to ease the realization
of forecasting methods. Specifically, autoregressive models are robust and
optimized statistical methods which fit to both forecasts of product and
process quality. An observed evaluation metric like the mean absolute error
for the next ten forecast items has been decreased by more than 50% from
0.141 to 0.66 with an underlying data range from 0.38 to 1.998. Since this
calculation was processed including a univariate feature vector,
improvements can be achieved by adding connected features, i.e. sensor data,
for a higher accuracy in the forecasting results.