Kristof Briele

Bio: Kristof Briele is an academic researcher from RWTH Aachen University. The author has contributed to research in topics: New product development & Product (category theory). The author has an hindex of 2, co-authored 9 publications receiving 11 citations.

Autonomous Data-Driven Quality Control in Self-learning Production Systems

Abstract: Shorter product lifecycles, increased individualization and disruptive technological change is said to closely correspond with the worldwide increase in production of electric vehicles and their components. Nascent production technologies, such as additive manufacturing, enable the industrial production of customized products but are often accompanied by fluctuations in product quality, as well as low process stability. This paper describes how self-learning production systems may be enabled to efficiently adapt to these disturbances through autonomous data-driven quality control. Moreover, this paper presents how the overall latency between the occurrence of an event, which directly or indirectly influences quality, and the completed implementation of process adaptions may be reduced. The core element of the presented approach is the creation of a predictive quality model from which an inverted process model and thus process adjustments can be derived. To demonstrate the proposed concept, the presented approach is applied to a Fused Deposition Modeling production system in form of model-based parameter optimization.

Nachhaltige Produktion durch Predictive Quality und Sustainability Analytics entlang der Lieferkette

Abstract: Aufgrund stetig steigender Anforderungen sind produzierende Unternehmen gezwungen ihre Produkte und Prozesse kontinuierlich zu verbessern. Dabei greifen sie immer haufiger auf datengetriebene Analysemethoden zuruck, die als Entscheidungsgrundlage fur Handlungsmasnahmen dienen. Das Potenzial, das sich durch eine horizontale Datenintegration entlang der Lieferkette ergibt, wird von Unternehmen jedoch nur selten berucksichtigt und in Mehrwert umgewandelt. Dieser Beitrag erlautert die Monetarisierung technischer Daten durch den Einsatz von Predictive Quality und Sustainability Analytics entlang der Lieferkette und beschreibt den Einfluss der Datenqualitat und der Relevanz der Daten auf den potenziellen Datenwert. Abschliesend werden die Monetarisierungseffekte anhand von zwei industriellen Anwendungsbeispielen veranschaulicht.

Extracting Customer-Related Information for Need Identification

Abstract: The increasing amounts of customer-generated content regarding a product or service published in Social Media are an important source of information for companies. Especially for product development projects or the design of service offers, the unbiased feedback expressed in so-called product reviews is most valuable. However, for the effective use of product review content, the development of automated text processing tools is essential; manual text processing approaches are very time-consuming and thus compromise the benefits provided from the extracted information. To date, automated text mining tools focus the analysis of customers preferences and emotions articulated within a product review. An automated extraction and analysis of customer-related content has not yet been investigated in detail. Customer-related content refers to information within a review, which does not primarily concern the product, but provide information about the customer himself, his usage behavior, personal environment and habits. This information is most generally expressed in an objective manner by the author (i.e. customer) and provides an authentic starting point for the identification of customer needs. Particularly for innovative product development, the consideration of customer habits and personal environment is highly relevant for the derivation of underlying needs, which can be more important than the knowledge of specific preferences regarding a product. The objective of this research is the development and validation of a text mining process for the extraction of objective content from product reviews. To this end, German reviews from Amazon.de regarding two product categories are collected and firstly annotated manually for validation reference. Thereafter, a text mining process is developed comprising text preparation, transformation, classification and performance evaluation. Three different classifiers are applied for performance comparison.

Defining Lean Production : Some conceptual and practical issues

Abstract: Purpose - This paper aims to investigate the definition of Lean Production and the methods and goals associated with the concept as well as how it differs from other popular management concepts. Methodology/Approach - The paper is based on a review of the contemporary literature on Lean Production, both journal articles and books. Findings - It is shown in the paper that there is no consensus on a definition of Lean Production between the examined authors. The authors also seem to have different opinions on which characteristics that should be associated with the concept. Overall it can be concluded that Lean Production is not clearly defined in the reviewed literature. This divergence can cause some confusion on a theoretical level, but is probably more problematic on a practical level when organizations aim to implement the concept. This paper argues that it is important for an organization to acknowledge the different variations, and to raise the awareness of the input in the implementation process. It is further argued that the organization should not accept any random variant of Lean, but make active choices and adapt the concept to suit the organization-s needs. Through this process of adaptation, the organization will be able to increase the odds of performing a predictable and successful implementation. Originality/Value - This paper provides a critical perspective on the discourse surrounding Lean Production, and gives an input to the discussion of the implementation of management models. Keywords - Lean Production, Definition, Construct Validity, Total Quality Management Paper type - Conceptual paper

Deep Transfer Learning for Industrial Automation: A Review and Discussion of New Techniques for Data-Driven Machine Learning

Abstract: Deep learning has greatly increased the capabilities of "intelligent" technical systems over the last years [1]. This includes the industrial automation sector [1]-[4], where new data-driven approaches to, for example, predictive maintenance [2], computer vision [3], or anomaly detection [4], have resulted in systems more easily and robustly automated than ever before.

Process monitoring for material extrusion additive manufacturing: a state-of-the-art review

Abstract: Qualitative uncertainties are a key challenge for the further industrialization of additive manufacturing. To solve this challenge, methods for measuring the process states and properties of parts during additive manufacturing are essential. The subject of this review is in-situ process monitoring for material extrusion additive manufacturing. The objectives are, first, to quantify the research activity on this topic, second, to analyze the utilized technologies, and finally, to identify research gaps. Various databases were systematically searched for relevant publications and a total of 221 publications were analyzed in detail. The study demonstrated that the research activity in this field has been gaining importance. Numerous sensor technologies and analysis algorithms have been identified. Nonetheless, research gaps exist in topics such as optimized monitoring systems for industrial material extrusion facilities, inspection capabilities for additional quality characteristics, and standardization aspects. This literature review is the first to address process monitoring for material extrusion using a systematic and comprehensive approach.

Fashion involvement, opinion-seeking and product variety as stimulators for fashion e-commerce: an investigated model based on S-O-R model

Abstract: PurposeThis study's aim was to investigate the stimulators of fashion e-consumers within e-commerce environments. The study proposed a framework utilizing stimulus–organism–response (S-O-R) theory to suggest that fashion involvement and opinion-seeking would act as sociopsychological stimuli, while product variety as an objective stimulus. Perceived quality is proposed as an organism, moderated by perceived price. Consumer buying behavior within e-commerce environments presents the framework's response. The study looked at variables with deeper insights into Malaysian fashion consumers.Design/methodology/approachA quantitative method was used to assess the significance of relationships within the proposed model. Partial least squares structural equation modeling technique was implemented to assess the framework's relationships with a sample size of 374.FindingsResults indicate that fashion involvement is significantly associated as a sociopsychological stimulus, with product variety being an objective stimulus for Malaysian fashion e-consumers. Perceived quality is significantly represented as an organism through the framework, and buying behavior is the latent response. Price would significantly moderate the relationship between fashion involvement and quality. Opinion-seeking was found not to be a significant stimulus for Malaysian e-consumers.Originality/valueContribution of this study goes to the existing literature by providing a deeper understanding of Malaysian e-consumer behavior by applying S-O-R theory. Malaysian fashion e-consumerism was suggested to be influenced by product involvement, quality, price, opinion-seeking and product range offered; therefore, a proposed framework was demonstrated and tested.

A framework for data-driven design in a product innovation process: data analysis and visualisation for model-based decision making

Abstract: The paper presents a four-layer framework for the application of data-driven design in a product innovation process. The framework builds on the Knowledge Value Stream and on the Product Value Streams of a product innovation process and indicates how data-driven activities shall be structured and organised in relation to the different phases of a model-based decision process. Visualisation is proposed as a communication enabler at the top of the framework to overcome the comprehensibility barrier between data science and engineering design models. The framework is implemented in the case study of a construction equipment encompassing the analysis of operational machine data and the experimentation of suitable visualisation techniques. Ultimately, a list of challenges for the implementation of data-driven design is presented, and the capability of the framework to support the transition toward data-driven design is discussed in relation to the emergence of product-service systems solutions.