With the ongoing digitization of the manufacturing industry and the ability to bring together data from manufacturing processes and quality measurements, there is enormous potential to use machine learning and deep learning techniques for quality assurance. In this context, predictive quality enables manufacturing companies to make data-driven estimations about the product quality based on process data. In the current state of research, numerous approaches to predictive quality exist in a wide variety of use cases and domains. Their applications range from quality predictions during production using sensor data to automated quality inspection in the field based on measurement data. However, there is currently a lack of an overall view of where predictive quality research stands as a whole, what approaches are currently being investigated, and what challenges currently exist. This paper addresses these issues by conducting a comprehensive and systematic review of scientific publications between 2012 and 2021 dealing with predictive quality in manufacturing. The publications are categorized according to the manufacturing processes they address as well as the data bases and machine learning models they use. In this process, key insights into the scope of this field are collected along with gaps and similarities in the solution approaches. Finally, open challenges for predictive quality are derived from the results and an outlook on future research directions to solve them is provided.

Quality assessment in industrial applications is often carried out through visual inspection, usually performed or supported by human domain experts. However, the manual visual inspection of processes and products is error-prone and expensive. It is therefore not surprising that the automation of visual inspection in manufacturing and maintenance is heavily researched and discussed. The use of artificial intelligence as an approach to visual inspection in industrial applications has been considered for decades. Recent successes, driven by advances in deep learning, present a possible paradigm shift and have the potential to facilitate automated visual inspection, even under complex environmental conditions. For this reason, we explore the question of to what extent deep learning is already being used in the field of automated visual inspection and which potential improvements to the state of the art could be realized utilizing concepts from academic research. By conducting an extensive review of the openly accessible literature, we provide an overview of proposed and in-use deep-learning models presented in recent years. Our survey consists of 196 open-access publications, of which 31.7% are manufacturing use cases and 68.3% are maintenance use cases. Furthermore, the survey also shows that the majority of the models currently in use are based on convolutional neural networks, the current de facto standard for image classification, object recognition, or object segmentation tasks. Nevertheless, we see the emergence of vision transformer models that seem to outperform convolutional neural networks but require more resources, which also opens up new research opportunities for the future. Another finding is that in 97% of the publications, the authors use supervised learning techniques to train their models. However, with the median dataset size consisting of 2500 samples, deep-learning models cannot be trained from scratch, so it would be beneficial to use other training paradigms, such as self-supervised learning. In addition, we identified a gap of approximately three years between approaches from deep-learning-based computer vision being published and their introduction in industrial visual inspection applications. Based on our findings, we additionally discuss potential future developments in the area of automated visual inspection.

The importance of customer-oriented marketing has increased for companies in recent decades. With the advent of one-customer strategies, especially in e-commerce, traditional mass marketing in this area is becoming increasingly obsolete as customer-specific targeting becomes realizable. Such a strategy makes it essential to develop an underlying understanding of the interests and motivations of the individual customer. One method frequently used for this purpose is segmentation, which has evolved steadily in recent years. The aim of this paper is to provide a structured overview of the different segmentation methods and their current state of the art. For this purpose, we conducted an extensive literature search in which 105 publications between the years 2000 and 2022 were identified that deal with the analysis of customer behavior using segmentation methods. Based on this paper corpus, we provide a comprehensive review of the used methods. In addition, we examine the applied methods for temporal trends and for their applicability to different data set dimensionalities. Based on this paper corpus, we identified a four-phase process consisting of information (data) collection, customer representation, customer analysis via segmentation and customer targeting. With respect to customer representation and customer analysis by segmentation, we provide a comprehensive overview of the methods used in these process steps. We also take a look at temporal trends and the applicability to different dataset dimensionalities. In summary, customer representation is mainly solved by manual feature selection or RFM analysis. The most commonly used segmentation method is k-means, regardless of the use case and the amount of data. It is interesting to note that it has been widely used in recent years.

The Digital Product Passport (DPP) is a concept for collecting and sharing product-related information along the life cycle of a product. DPPs are currently the subject of intense discussion, and various development efforts are being undertaken. These are supported by regulatory activities, especially in the case of the battery passport. The aggregation of product life-cycle data and their respective use, as well as the sharing of these data between companies, entrepreneurs, and other actors in the value chain, is crucial for the creation of a resource-efficient circular economy. Despite the urgent need for such a solution, there is currently little attention given to the digital infrastructure for the creation and handling of the DPPs (i.e., the so-called DPP system). Moreover, there is so far no common understanding of what the requirements for a DPP system are. This is the background and underlying motivation of our paper: we identify the requirements for a DPP system in a structured way, i.e., based on stakeholder involvement and current literature from science and industry. In addition, we compose, categorize, and critically analyze the results, i.e., the list of requirements for DPP systems, in order to identify gaps. Summarized, our research provides insights into the criteria to be considered in the creation of an actual DPP system.

New levels of cross-domain collaboration between manufacturing companies throughout the supply chain are anticipated to bring benefits to both suppliers and consumers of products. Enabling a fine-grained sharing and analysis of data among different stakeholders in an automated manner, such a vision of an Internet of Production (IoP) introduces demanding challenges to the communication, storage, and computation infrastructure in production environments. In this work, we present three example cases that would benefit from an IoP (a fine blanking line, a high pressure die casting process, and a connected job shop) and derive requirements that cannot be met by today's infrastructure. In particular, we identify three orthogonal research objectives: (i) real-time control of tightly integrated production processes to offer seamless low-latency analysis and execution, (ii) storing and processing heterogeneous production data to support scalable data stream processing and storage, and (iii) secure privacy-aware collaboration in production to provide a basis for secure industrial collaboration. Based on a discussion of state-of-the-art approaches for these three objectives, we create a blueprint for an infrastructure acting as an enabler for an IoP.