Disparate data integration case for connected factories using timestamps

Manufacturing data integration of machine, process, and sensor data from the shop floor remains an important issue to achieve the anticipated business value of fully connected factories. Integrated manufacturing data has been a hallmark of Industry 4.0 initiatives, because integrated data precipitates better decision-making for cost, schedule, and system optimizations.  In this paper, we extend work on optimizing manufacturing costs, describing an algorithm using timestamps to integrate previously unassociated quality and test information, enabling us to better identify and eliminate redundant tests.  Results are provided and discussed, and we suggest the approach described may be valuable for some types of heterogeneous manufacturing data integration where timestamps and event chronologies are available

Maturity level of predictive maintenance application in small and medium-sized industries: Case of Morocco

In order to remain competitive in the long term and to push the company's efficiency to its limits, entrepreneurs are more and more open to the idea of integrating into Industry 4.0 aiming mainly at filling the important downtimes and the associated productivity losses by implementing predictive maintenance. This concept, common in developed countries, is much less widespread in Morocco and even less in small and medium-sized Moroccan companies. The objective of this article is to study the maturity level of predictive maintenance in Moroccan small and medium-sized enterprises, through a questionnaire validated by experts and made available to several companies. Valid data from 115 companies throughout the kingdom operating in different sectors were collected and processed by descriptive and factorial analysis under SPSS software. The results obtained show that only 33% of our sample were able to implement predictive maintenance, and that the expected benefits of this approach are the minimization of downtime at 96.5% and the increase in productivity at 94.8%, The main challenges observed are the lack of team motivation and a corporate culture unsuited to digitalization, which represents 42.277% of the total variance, lack of financial resources at 12.916% of the total variance and lack of data protection at 11.644% of the total variance. This analysis indicates that the level of maturity regarding the application of predictive maintenance in Moroccan small and medium-sized companies is low, these rates can be used to improve the root causes

Predictive Maintenance in Industry 4.0

In the context of Industry 4.0, the manufacturing-related processes have shifted from conventional processes within one organization to collaborative processes cross different organizations, for example, product design processes, manufacturing processes, and maintenance processes across different factories and enterprises. The development and application of the Internet of things, i.e. smart devices and sensors increases the availability and collection of diverse data. With new technologies, such as advanced data analytics and cloud computing provide new opportunities for flexible collaborations as well as effective optimizing manufacturing-related processes, e.g. predictive maintenance. Predictive maintenance provides a detailed examination of the detection, location and diagnosis of faults in related machinery using various analyses. RAMI4.0 is a framework for thinking about the various efforts that constitute Industry 4.0. It spans the entire product life cycle & value stream axis, hierarchical structure axis and functional classification axis. The Industrial Data Space (now International Data Space) is a virtual data space using standards and common governance models to facilitate the secure exchange and easy linkage of data in business ecosystems. It thereby provides a basis for creating and using smart services and innovative business processes, while at the same time ensuring digital sovereignty of data owners. This paper looks at how to support predictive maintenance in the context of Industry 4.0. Especially, applying RAMI4.0 architecture supports the predictive maintenance using the FIWARE framework, which leads to deal with data exchanging among different organizations with different security requirements as well as modularizing of related functions

Predictive Maintenance in Industry 4.0

In the context of Industry 4.0, the manufacturing-related processes have shifted from conventional processes within one organization to collaborative processes cross different organizations, for example, product design processes, manufacturing processes, and maintenance processes across different factories and enterprises. The development and application of the Internet of things, i.e. smart devices and sensors increases the availability and collection of diverse data. With new technologies, such as advanced data analytics and cloud computing provide new opportunities for flexible collaborations as well as effective optimizing manufacturing-related processes, e.g. predictive maintenance. Predictive maintenance provides a detailed examination of the detection, location and diagnosis of faults in related machinery using various analyses. RAMI4.0 is a framework for thinking about the various efforts that constitute Industry 4.0. It spans the entire product life cycle & value stream axis, hierarchical structure axis and functional classification axis. The Industrial Data Space (now International Data Space) is a virtual data space using standards and common governance models to facilitate the secure exchange and easy linkage of data in business ecosystems. It thereby provides a basis for creating and using smart services and innovative business processes, while at the same time ensuring digital sovereignty of data owners. This paper looks at how to support predictive maintenance in the context of Industry 4.0. Especially, applying RAMI4.0 architecture supports the predictive maintenance using the FIWARE framework, which leads to deal with data exchanging among different organizations with different security requirements as well as modularizing of related functions

Design and Evaluation of Domain-Specific Platforms and the Special Case of Digital Healthcare

The implementation of digital innovations in the healthcare sector is faced with different barriers and challenges. The complex system of regulations, the lack of interoperability, and highly dynamic interorganisational networks lead to missing widespread adoption of eHealth solutions. Digital platforms can help to overcome these barriers by providing a holistic infrastructure. They create a modularised foundation that innovators can use to create own innovations and provide them to demanders of digital solutions. As intermediaries, they can be accessed both by healthcare professionals and eHealth solution providers. Providers can offer their eHealth services via the platform. Healthcare professionals can use these services to create own interorganisational information systems. In the field of information systems research, effects and strategies for two-sided platforms are well researched and the potentials of eHealth platforms are also discussed. However, the organisational and technological design and methods for the construction of platforms are fewer questioned. Nonetheless, platform owners can benefit from implementation strategies and architectural guidance to create sustainable platforms and surrounding ecosystems. This doctoral thesis questions how domain-specific platforms can be designed systematically. Conducting a design-science research process, it develops both a modelling system and the Dresden Ecosystem Management Method (DREEM) to support the development of platforms in different domains. Furthermore, it describes the design characteristics of two-sided platforms in the healthcare sector and provides an evaluation approach to analyse the platforms’ ability to create a viable innovation ecosystem in the healthcare sector. The doctoral thesis contributes by providing methodical guidance for platform owners and researchers to design and evaluate digital platforms in different domains and improves the understanding of platform theory in the healthcare sector.:A. Synopsis of the Doctoral Thesis 1. Introduction 2. Foundational Considerations 3. Requirements for Design Artefacts and Knowledge 4. Structure of the Doctoral Thesis 5. Conclusion B. Paper 1 - Governance Guidelines for Digital Healthcare Ecosystems C. Paper 2 - Revise your eHealth Platform! D. Paper 3 - Business Model Open ”E-Health-Platform” E. Paper 4 - Modelling Ecosystems in Information Systems F. Paper 5 - Designing Industrial Symbiosis Platforms G. Paper 6 - Management of Digital Ecosystems with DREEM H. Paper 7 - Guiding the Development of Digital Ecosystems I. Paper 8 - Towards Maintenance Analytics Ecosystems J. Paper 9- Sustainability of E-Health-Projects K. Paper 10 - ISO 11354-2 for the Evaluation of eHealth-Platform