Eva Hoos

The Data-driven Factory - Leveraging Big Industrial Data for Agile, Learning and Human-centric Manufacturing

Global competition in the manufacturing industry is characterized by ever shorter product life cycles, increasing complexity and a turbulent environment. High product quality, continuously improved processes as well as changeable organizational structures constitute central success factors for manufacturing companies. With the rise of the internet of things and Industrie 4.0, the increasing use of cyber-physical systems as well as the digitalization of manufacturing operations lead to massive amounts of heterogeneous industrial data across the product life cycle. In order to leverage these big industrial data for competitive advantages, we present the concept of the data-driven factory. The data-driven factory enables agile, learning and human-centric manufacturing and makes use of a novel IT architecture, the Stuttgart IT Architecture for Manufacturing (SITAM), overcoming the insufficiencies of the traditional information pyramid of manufacturing. We introduce the SITAM architecture and discuss its conceptual components with respect to service-oriented integration, advanced analytics and mobile information provisioning in manufacturing. Moreover, for evaluation purposes, we present a prototypical implementation of the SITAM architecture as well as a real-world application scenario from the automotive industry to demonstrate the benefits of the data-driven factory.

ValueApping: An Analysis Method to Identify Value-Adding Mobile Enterprise Apps in Business Processes

Mobile enterprise apps provide novel possibilities for the optimization and redesign of business processes, e.g., by the elimination of paper-based data acquisitioning or ubiquitous access to up-to-date information. To leverage these business potentials, a critical success factor is the identification and evaluation of value-adding MEAs based on an analysis of the business process. For this purpose, we present ValueApping, a systematic analysis method to identify usage scenarios for value-adding mobile enterprise apps in business processes and to analyze their business benefits. We describe the different analysis steps and corresponding analysis artifacts of ValueApping and discuss the results of a case-oriented evaluation in the automotive industry.

Analysis Method for Conceptual Context Modeling Applied in Production Environments

Context-awareness is a well-accepted approach to adapt applications to the needs of a user. Yet, it is hardly used in enterprise information systems, especially in production environments. Production environments are complex due to multi-faced actors, products, manufacturing equipment and processes. Hence, the modeling of context is sophisticated, particularly to determine relevant context. The goal of this paper is to facilitate and support context modeling in production environments. Our contribution is an analysis method for conceptual context modeling suited for Industry 4.0 and an extensible engineering context model as starting point for modeling of different Industry 4.0 use cases. The analysis model consists of a graphical notation for a simplified and abstract context model and a template-based concept to detail the model. Furthermore, we evaluate the approach by modeling a real use case from the car manufacturing industry.

The Stuttgart IT Architecture for Manufacturing

The global conditions for manufacturing are rapidly changing towards shorter product life cycles, more complexity and more turbulence. The manufacturing industry must meet the demands of this shifting environment and the increased global competition by ensuring high product quality, continuous improvement of processes and increasingly flexible organization. Technological developments towards smart manufacturing create big industrial data which needs to be leveraged for competitive advantages. We present a novel IT architecture for data-driven manufacturing, the Stuttgart IT Architecture for Manufacturing (SITAM). It addresses the weaknesses of traditional manufacturing IT by providing IT systems integration, holistic data analytics and mobile information provisioning. The SITAM surpasses competing reference architectures for smart manufacturing because it has a strong focus on analytics and mobile integration of human workers into the smart production environment and because it includes concrete recommendations for technologies to implement it, thus filling a granularity gap between conceptual and case-based architectures. To illustrate the benefits of the SITAM’s prototypical implementation, we present an application scenario for value-added services in the automotive industry.

Improving Business Processes Through Mobile Apps

Mobile apps offer new possibilities to improve business processes. However, the introduction of mobile apps is typically carried out from a technology point of view. Hence, process improvement from a business point of view is not guaranteed. There is a methodological lack for a holistic analysis of business processes regarding mobile technology. For this purpose, we present an analysis framework, which comprises a systematic methodology to identify value-added usage scenarios of mobile technology in business processes with a special focus on mobile apps. The framework is based on multi-criteria analysis and portfolio analysis techniques and it is evaluated in a case-oriented investigation in the automotive industry.

Automated Creation and Provisioning of Decision Information Packages for the Smart Factory

In recent years, Industry 4.0 emerges as a new trend, enabling the integration of data-intensive cyber physical systems, Internet of Things, and mobile applications, into production environments. Even though Industry 4.0 concentrates on automated engineering and manufacturing processing, the human actor is still important for decision making in the product lifecycle process. To support correct and efficient decision making, human actors have to be provided with relevant data depending on the current context. This data needs to be retrieved from distributed sources like bill of material systems, product data management and manufacturing execution systems, holding product model and factory model. In this article, we address this issue by introducing the concept of decision information packages, which enable to compose relevant engineering data for a specific context from distributed data sources. To determine relevant data, we specify a context-aware engineering data model and corresponding operators. To realize our approach, we provide an architecture and a prototypical implementation based on requirements of a real case scenario. This article is a revised and selected version of the previous work.

Context-Aware Decision Information Packages: An Approach to Human-Centric Smart Factories.

Industry 4.0 enables the integration of new trends, such as data-intensive cyber physical systems, Internet of Things, or mobile applications, into production environments. Although it concentrates on highly data-intensive automated engineering and manufacturing processing, the human actor is still important for decision making in the product lifecycle process. To support correct and efficient decision making, human actors have to be provided with relevant data depending on the current context. This data needs to be retrieved from distributed sources like bill of material systems, product data management and manufacturing execution systems, holding product model and factory model. In this paper, we address this issue by introducing the concept of decision information packages, which enable to compose relevant engineering data for a specific context from distributed data sources. To determine relevant data, we specify a context-aware engineering data model and corresponding operators. To realize our approach, we provide an architecture and a prototypical implementation based on requirements of a real case scenario.

Design method for developing a Mobile Engineering-Application Middleware (MEAM)

Mobile Apps running on smartphones and tablet pes offer a new possibility to enhance the work of engineers because they provide an easy-to-use, touchscreen-based handling and can be used anytime and anywhere. Introducing mobile apps in the engineering domain is difficult because the IT environment is heterogeneous and engineering-specific challenges in the app development arise e. g., large amount of data and high security requirements. There is a need for an engineering-specific middleware to facilitate and standardize the app development. However, such a middleware does not yet exist as well as a holistic set of requirements for the development. Therefore, we propose a design method which offers a systematic procedure to develop Mobile Engineering-Application Middleware.