Die Erfahrungen aus der Vergangenheit zeigen deutlich, dass weder ausgeprägte technik- noch humanzentrierte Gestaltungsparadigmen zu einer nachhaltigen und deutlichen Verbesserung der Wettbewerbsfähigkeit beitragen, sondern u. U. sogar negative Auswirkungen haben können. Im Gegensatz dazu konnten mit organisationszentrierten Ansätzen zur Gestaltung von Produktionssystemen deutliche Fortschritte in der Verbesserung der Wettbewerbsfähigkeit erzielt werden. Die Hypothese lautet, dass der Erfolg der ausgerufenen vierten industriellen Revolution wesentlich davon abhängt, ob es gelingt diese nachhaltig in der Organisation zu verankern und zielgerichtet umzusetzen. Humane und technische Aspekte sind demzufolge an die Strukturen und Prozesse der Organisation anzupassen und auszurichten. Dabei besteht in einem zunehmend dynamischen und komplexen Wettbewerbsumfeld für Unternehmen die Möglichkeit, die Arbeits- und Organisationsgestaltung in verschiedene Richtungen zu entwickeln. Im Rahmen eines organisationszentrierten Ansatzes ist im Sinne der soziotechnischen Arbeits- und Produktionssystemgestaltung die systemische Prozesssicht in den Vordergrund zu rücken. Konkret erfordert die Gestaltung von CPPS u. a. neue dezentrale Führungs- und Steuerungsformen, neue kollaborative Formen der Arbeitsorganisation mit einem hohen Maß an selbstverantwortlicher Autonomie, einen verstärkten Aufbau entsprechender Systemkompetenz sowie damit einhergehend eine wachsende technische Unterstützung auf Basis der Anpassung der Arbeit an den Menschen.

The application of Industrial Data Science in context of connected Smart Products requires modeling and structuring data for its design, development and use. Especially for Smart Products, a comprehensive handling of data quality is mandatory, because of their interdisciplinary character and broad range of heterogeneous stakeholders covering the entire product lifecycle. The overall goal of data preparation is to provide high-quality data for application and evaluation by users. Established process models for industrial data analysis often treat the specification and assurance of data quality as a single-point activity with a defined conclusion. Providing end-to-end data quality has received little attention in the field of industrial data analytics. In this paper, we will (1) structure four distinct phases for ensuring end-to-end data quality along data analytics activities, (2) define a set of criteria and measures for meeting and quantifying data quality requirements based on established criteria, and (3) provide a step-by-step model for establishing and maintaining high Data Quality for Industrial Data Science applications. The quality criteria aim to identify pointwise and continuous actions during the data analysis process. Such criteria target a shared responsibility for maintaining data quality during analyses between analyst and user. The developed model provides an actionable approach for assessing and ensuring the requirements of Data Analysis Quality.

Delivery times represent a key factor influencing the competitive advantage, as manufacturing companies strive for timely and reliable deliveries. As companies face multiple challenges involved with meeting established delivery dates, research on the accurate estimation of delivery dates has been source of interest for decades. In recent years, the use of machine learning techniques in the field of production planning and control has unlocked new opportunities, in both academia and industry practice. In fact, with the increased availability of data across various levels of manufacturing companies, machine learning techniques offer the opportunity to gain valuable and accurate insights about production processes. However, machine learning-based approaches for the prediction of delivery dates have not received sufficient attention. Thus, this study aims to investigate the ability of machine learning to predict delivery dates early in the ordering process, and what type of information is required to obtain accurate predictions. Based on the data provided by two separate manufacturing companies, this paper presents a machine learning-based approach for predicting delivery times as soon as a request for an offer is received considering the desired customer delivery date as a feature.

Manufacturing companies are progressively applying digital manufacturing tools to respond to increased product complexity in shortened product lifecycles. The application results in a comprehensive documentation of the product emergence process. Furthermore at Daimler, a controlled natural language has recently been established, which enables automated analysis of natural language work task description texts. This work proposes a methodology, which enhances planning efficiency by automatically presenting a set of potentially suitable work plans for novel products. The presented work plans are reused from past planning activities. Assessment of work plan suitability is based on a statistical analysis that employs Methods-Time Measurement (MTM) data as well as work task descriptions in a controlled natural language (cnl). The proposed methodology is compared to a previously presented approach, in which text mining is used instead of a controlled natural language. The test comprises 104 work tasks of a Daimler assembly line. While result quality is only slightly improved for the cnl based approach, mapping results from product clusters to assembly sequences are simplified and analysis effort can be reduced if a cnl is already established. Future investigations should focus on investigations of applicability to different production and assembly domains