Curates research findings on data management, highlighting trends and challenges.

Discusses the requirements for data valuation methods in AI and data-driven systems.

Daten entwickeln sich für Produktionsunternehmen zu einem wesentlichen Produktionsfaktor. Dies hat zu Folge, dass sich Produktionsunternehmen von einer Physik-dominierten Produktionslogik hin zu einer Software-dominierten Produktionslogik entwickeln, kurz gesagt: „Software wins Manufacturing!“. Da jedoch Unternehmen nicht über alle notwendigen Daten verfügen, entsteht die Notwendigkeit für Datenmärkte bzw. Datenökonomien.

Proposes a data quality index for evaluating data valuation in the data economy.

Proposes a foundational trust framework for AI and identifies emerging research opportunities.

Explores unsupervised anomaly localization techniques using explainable AI in multi-sensor environments.

Investigates the conceptualization of data ecosystems for enhancing industrial food production.

Explores how conceptual models can bridge the gap between mental models and machine learning models.

Describes a framework for transitioning from qualitative to quantitative data valuation in manufacturing.

Discusses approaches to the monetary valuation of data in the context of accounting.

Presents a methodology for integrating conceptual modeling with machine learning techniques.

Explores smart resilience services designed for industrial production environments.

Proposes AI-based methods to enhance resilience in manufacturing processes. Resilience describes a system’s recovery from disruptions or the capability to deal with future shocks. Not only the current COVID-19 pandemic has highlighted the importance of resilience: floods, earthquakes, bankruptcies, politics and similar events regularly disrupt the manufacturing industry. Yet, only few companies work on managing their resilience, partially since it (or the lack thereof) only becomes evident after catastrophic events. A better understanding of a firm’s resilience provides the basis for an active resilience management, which is necessary in order to survive as a manufacturing company in the future. Both qualitative and quantitative approaches for determining manufacturing resilience exist, especially in the context ofsupply chain management. Quantitative approaches can be further classified into deterministic and probabilistic approaches. Whilst deterministic approaches to manufacturing resilience lack complexity to describe manufacturing systems with several participants, quantitative approaches to manufacturing resilience fail to leverage advances in AI technologies up to this date. However, advances in AI technologies coupled with an ever-increasing amount of available data from manufacturing networks create an opportunity to approach the measurement of manufacturing resilience in a fundamentally new way. Such a new data-driven approach will become more and more valuable as manufacturing companies operate under increasingly volatile, uncertain, complex, and ambiguous conditions. The SPAICER project addresses this problem by focusing on a quantitative and data-driven approach to manufacturing resilience. Previous measurement of resilience come with several shortfalls: (1) They are usually based on qualitative inputs that need to be manually gathered via e.g. surveys, interviews or questionnaires with huge amounts of questions which potentially leads to (2) a subjective assessment of the resilience level; (3) Traditional approaches are neither data-driven nor automated, which hinders scalability and constant measurement; (4) They usually focus on certain parts of the company (e.g. production resilience, logistics resilience, etc.) and only incorporate input from few individuals within area of focus, not providing a holistic view and neglecting e internal dependencies; (5) Most research focuses on event studies after certain disruptions occured, which implies that resilience can only be measured ex-post focusing on reactive resilience and mostly ignoring anticipative resilience; (6) In terms of data, only single data sources are used. Moreover, most approaches only consider internal data sources such as ERP systems and neglect external data sources (weather, news, traffic, etc.) to better understand the whole environment impacting the processes. With our approach, we intend to overcome these shortfalls by utilizing an AIbased approach for manufacturing resilience on a micro- (local) meso- (company-wide) and macrolevel (global, network-oriented). The goal is to address manufacturing resilience through an objective and data-driven lens by using AI technologies and taking a holistic and cross-company view and thus enabling a reactive as well as anticipative resilience analysis.

Due to the booming e-commerce, innovative smart services are needed to attract customers to the stores and enhance shopping experience to remain competitive. A smart service that has gained in importance in e-commerce and has found its way into stationary retail is the recommendation system. However, the shopping experience in stationary retail stores vary from the one in online shops. Previous approaches neglect the stress of customers during shopping process in stores in order to optimize the placement of product recommendations on mobile devices as well as enhance shopping experience in general. Focusing on two shopping scenarios in a laboratory retail store, we introduce the application of a situation-specific smart retail service prototype - a service system that reacts according to the perceived stress level of the customer during shopping.

Presents a smart retail service that adapts based on customer vital signs.