Integrating Learning and Business Process Management

Recent research activities in the field of TEL have created a new awareness for intelligent learning infrastructures. To foster the usage of innovative TEL in the workplace, it must be integrated into organizational business operations and aligned with their learning requirements. Being the semantic interface of organizational ICT infrastructure, business processes represent the potential linkage between learning and business IS. Today, most organizations and their supporting ICT systems have incorporated processes as central objects of control. They manage their businesses along their processes, starting with process design over process execution up to process control and monitoring that feed back into improved business process design. As this process lifecycle has become the central instrument of BPM, it lends to be the vehicle for a businessintegrated learning management. This paper aims to position the thesis of a reciprocal relationship between business and learning processes being the prerequisite for prospective integrated workplace learning

Powertrain Assembly Lines Automatic Configuration Using a Knowledge Based Engineering Approach

Technical knowledge and experience are intangible assets crucial for competitiveness. Knowledge is particularly important when it comes to complex design activities such as the configuration of manufacturing systems. The preliminary design of manufacturing systems relies significantly on experience of designers and engineers, lessons learned and complex sets of rules and is subject to a huge variability of inputs and outputs and involves decisions which must satisfy many competing requirements. This complicated design process is associated with high costs, long lead times and high probability of risks and reworks. It is estimated that around 20% of the designer’s time is dedicated to searching and analyzing past available knowledge, while 40% of the information required for design is identified through personally stored information. At a company level, the design of a new production line does not start from scratch. Based on the basic requirements of the customers, engineers use their own knowledge and try to recall past layout ideas searching for production line designs stored locally in their CAD systems [1]. A lot of knowledge is already stored, and has been used for a long time and evolved over time. There is a need to retrieve this knowledge and integrate it into a common and reachable framework. Knowledge Based Engineering (KBE) and knowledge representation techniques are considered to be a successful way to tackle this design problem at an industrial level. KBE is, in fact, a research field that studies methodologies and technologies for capturing and re-using product and process engineering knowledge to achieve automation of repetitive design tasks [2]. This study presents a methodology to support the configuration of powertrain assembly lines, reducing design times by introducing a best practice for production systems provider companies. The methodology is developed in a real industrial environment, within Comau S.p.A., introducing the role of a knowledge engineer. The approach includes extraction of existing technical knowledge and implementation in a knowledge-based software framework. The macro system design requirements (e.g. cycle time, production mix, etc.) are taken as input. A user driven procedure guides the designer in the definition of the macro layout-related decisions and in the selection of the equipment to be allocated within the project. The framework is then integrated with other software tools allowing the first phase design of the line including a technical description and a 2D and 3D CAD line layout. The KBE application is developed and tested on a specific powertrain assembly case study. Finally, a first validation among design engineers is presented, comparing traditional and new approach and estimating a cost-benefit analysis useful for future possible KBE implementations

An Adaptive Contextual Recommender System: a Slow Intelligence Perspective

This paper introduces an Adaptive Context Aware Recommender system based on the Slow Intelligence approach. The system is made available to the user as an adaptive mobile application, which allows a high degree of customization in recommending services and resources according to his/her current position and global profile. A case study applied to the town of Pittsburgh has been analyzed considering various users (with different profiles as visitors, students, professors) and an experimental campaign has been conducted obtaining interesting result

Mass Customization of Housing: A Framework for Harmonizing Individual Needs with Factory Produced Housing

Integrated processes for design and fabrication have guided mass customization of architectural systems and components. Providing affordable and accessible housing, a vital segment of the building industry, is a multifaceted process that witnessed various manifestations towards individualization over the past few decades. Design flexibility in housing systems is becoming a crucial aspect, informed by consumers’ lifestyles, demographic patterns, and lifecycles change at a rapid pace. As the housing market demands more personalized, efficient, and agile strategies, prefabricated building systems have always presented a viable alternative for flexibility and cus-tomization, following a rise of interest in the last decade focused on new modes of digitized design and production. This paper presents an overview and appraisal of various practices to implement customization in the housing industry, with specific focus on empowering a systemic approach. We then propose an open framework that could accommodate emergent design technologies and production protocols, with the aim of taking advantage of advanced research efforts, and coupled with current industry application

Organizational Neuroscience of Industrial Adaptive Behavior

Organizational neuroscience is recognized in organizational behavior literature as offering an interpretive framework that can shed new light on existing organizational challenges. In this paper, findings from neuroscience studies concerned with adaptive behavior for ecological fitness are applied to explore industrial adaptive behavior. This is important because many companies are not able to manage dynamics between adaptability and stability. The reported analysis relates business-to-business signaling in competitive environments to three levels of inference. In accordance with neuroscience studies concerned with adaptive behavior, trade-offs between complexity and accuracy in business-to-business signaling and inference are explained. In addition, signaling and inference are related to risks and ambiguities in competitive industrial markets. Overall, the paper provides a comprehensive analysis of industrial adaptive behavior in terms of relevant neuroscience constructs. In doing so, the paper makes a contribution to the field of organizational neuroscience, and to research concerned with industrial adaptive behavior. The reported analysis is relevant to organizational adaptive behavior that involves combining human intelligence and artificial intelligence

Value Creation with Extended Reality Technologies - A Methodological Approach for Holistic Deployments

Mit zunehmender Rechenkapazität und Übertragungsleistung von Informationstechnologien wächst die Anzahl möglicher Anwendungs-szenarien für Extended Reality (XR)-Technologien in Unternehmen. XR-Technologien sind Hardwaresysteme, Softwaretools und Methoden zur Erstellung von Inhalten, um Virtual Reality, Augmented Reality und Mixed Reality zu erzeugen. Mit der Möglichkeit, Nutzern Inhalte auf immersive, interaktive und intelligente Weise zu vermitteln, können XR-Technologien die Produktivität in Unternehmen steigern und Wachstumschancen eröffnen. Obwohl XR-Anwendungen in der Industrie seit mehr als 25 Jahren wissenschaftlich erforscht werden, gelten nach wie vor als unausgereift. Die Hauptgründe dafür sind die zugrundeliegende Komplexität, die Fokussierung der Forschung auf die Untersuchung spezifische Anwendungsszenarien, die unzu-reichende Wirtschaftlichkeit von Einsatzszenarien und das Fehlen von geeigneten Implementierungsmodellen für XR-Technologien. Grundsätzlich wird der Mehrwert von Technologien durch deren Integration in die Wertschöpfungsarchitektur von Geschäftsmodellen freigesetzt. Daher wird in dieser Arbeit eine Methodik für den Einsatz von XR-Technologien in der Wertschöpfung vorgestellt. Das Hauptziel der Methodik ist es, die Identifikation geeigneter Einsatzszenarien zu ermöglichen und mit einem strukturierten Ablauf die Komplexität der Umsetzung zu beherrschen. Um eine ganzheitliche Anwendbarkeit zu ermöglichen, basiert die Methodik auf einem branchen- und ge-schäftsprozessunabhängigen Wertschöpfungsreferenzmodell. Dar-über hinaus bezieht sie sich auf eine ganzheitliche Morphologie von XR-Technologien und folgt einer iterativen Einführungssequenz. Das Wertschöpfungsmodell wird durch ein vorliegendes Potential, eine Wertschöpfungskette, ein Wertschöpfungsnetzwerk, physische und digitale Ressourcen sowie einen durch den Einsatz von XR-Technologien realisierten Mehrwert repräsentiert. XR-Technologien werden durch eine morphologische Struktur mit Anwendungsmerk-malen und erforderlichen technologischen Ressourcen repräsentiert. Die Umsetzung erfolgt in einer iterativen Sequenz, die für den zu-grundeliegenden Kontext anwendbare Methoden der agilen Soft-wareentwicklung beschreibt und relevante Stakeholder berücksich-tigt. Der Schwerpunkt der Methodik liegt auf einem systematischen Ansatz, der universell anwendbar ist und den Endnutzer und das Ökosystem der betrachteten Wertschöpfung berücksichtigt. Um die Methodik zu validieren, wird der Einsatz von XR-Technologien in zwei industriellen Anwendungsfällen unter realen wirtschaftlichen Bedingungen durchgeführt. Die Anwendungsfälle stammen aus unterschiedlichen Branchen, mit unterschiedlichen XR-Technologiemerkmalen sowie unterschiedlichen Formen von Wert-schöpfungsketten, um die universelle Anwendbarkeit der Methodik zu demonstrieren und relevante Herausforderungen bei der Durch-führung eines XR-Technologieeinsatzes aufzuzeigen. Mit Hilfe der vorgestellten Methodik können Unternehmen XR-Technologien zielgerichtet in ihrer Wertschöpfung einsetzen. Sie ermöglicht eine detaillierte Planung der Umsetzung, eine fundierte Auswahl von Anwendungsszenarien, die Bewertung möglicher Her-ausforderungen und Hindernisse sowie die gezielte Einbindung der relevanten Stakeholder. Im Ergebnis wird die Wertschöpfung mit wirtschaftlichem Mehrwert durch XR-Technologien optimiert