Product Lifecycle Management Challenges of CPPS

Gerhard, Detlef

doi:10.1007/978-3-319-56345-9_4

Cited by 14 publications

(7 citation statements)

References 10 publications

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“…Tooling complexity viewpoint: This viewpoint relates to the sophistication of the IT landscape because of the extensive use of highly advanced and speci c IT systems required in design (Gerhard, 2017). The high sophistication of the IT landscape is caused by the diversity and dynamics of the relationships between project partners, manufacturers, vendors, and suppliers (Gerhard, 2017).…”

Section: Complexity Viewpointsmentioning

confidence: 99%

“…Complexity rises due to this sophistication as multiple (specialized) tools and databases are needed, dozens of data formats with different semantics are used, and interoperability is not supported (Gerhard, 2017).…”

Section: Complexity Viewpointsmentioning

confidence: 99%

“…This variation can include quantity, variety and dynamic of elements and their states, their interaction within the system, and their interaction with the environment (Ehrlenspiel & Meerkamm, 2017;Lindemann et al, 2009a;Mehr & Lüder, 2019). Furthermore, to meet customization demands, there can be multiple instances and variants of a base system (Gerhard, 2017).…”

Section: Complexity Viewpointsmentioning

confidence: 99%

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Engineering Complexity beyond the surface: Discerning the viewpoints, the drivers, and the challenges

Morales

Nizamis

Bonnema

2022

Preprint

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Complexity is often regarded as a “problem” to solve. Instead of yet again attempting to solve complexity, we follow systems engineering practices and switch back to the problem domain. A major obstacle in the problem domain is the impossibility to universally define complexity. As a workaround, we explored complexity characterization and identified shortcomings of the existing characterizations. The shortcomings include lack of standardization, inconsistent semantics, system-centricity, insufficiently transparent reasoning, and lack of validation. To address these shortcomings, we proposed a framework to characterize complexity by adapting three questions (who, why, what) from the Five Ws information-gathering method. The answer to the WHO-question proposed four complexity viewpoints; the answer to the WHY-question proposed a two-dimensional structure to identify complexity drivers; and the answer to the WHAT-question derived generalized complexity challenges. We used a systematic mapping study (SMS) to validate the framework. In general, our findings suggest that papers with complexity solutions do not frame their research within the complexity problem domain, hindering the contribution evaluation. Through the viewpoints, we identified general research gaps of six solution directions. From the drivers, we noted three observations in the discourse of complexity origins: 1) a system-driven tendency, 2) a preference for concreteness vs. abstraction, and 3) an unclear distinction between origins and effects. Through the challenges’ findings we supported two hypotheses: 1) a system-centric preference; and 2) a solution-oriented vision. This application of our framework exemplifies its potential to facilitate and structure future research, both in the problem and solution domains.

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Section: Complexity Viewpointsmentioning

confidence: 99%

Section: Complexity Viewpointsmentioning

confidence: 99%

Section: Complexity Viewpointsmentioning

confidence: 99%

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Engineering Complexity beyond the surface: Discerning the viewpoints, the drivers, and the challenges

Morales

Nizamis

Bonnema

2022

Preprint

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“…A core aspect of a DT are digital and seamless information flows, which are an essential aspect [16] to efficiently and effectively run through the ALC-phases. Furthermore, the vertical data integration across the ALC-phases, as well as horizontal integration are necessary [17,18].…”

Section: Figure 1 the 6 Dimensions Of Dtmentioning

confidence: 99%

A Knowledge-Based Digital Lifecycle-Oriented Asset Optimisation

Passath

Huber

Biedermann

et al. 2021

Teh. glas. (Online)

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The digitalisation of the value chain promotes sophisticated virtual product models known as digital twins (DT) in all asset-life-cycle (ALC) phases. These models. however, fail on representing the entire phases of asset-life-cycle (ALC), and do not allow continuous life-cycle-costing (LCC). Hence, energy efficiency and resource optimisation across the entire circular value chain is neglected. This paper demonstrates how ALC optimisation can be achieved by incorporating all product life-cycle phases through the use of a RAMS²-toolbox and the generation of a knowledge-based DT. The benefits of the developed model are demonstrated in a simulation, considering RAMS2 (Reliability, Availability, Maintainability, Safety and Sustainability) and the linking of heterogeneous data, with the help of a dynamic Bayesian network (DBN).

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“…Cyber-Physical Production Systems and Digital Threads open the vision on the importance of data modelling and management in the smart factory over multiple perspectives, ranging over the product lifecycle [6] (e.g., product design, manufacturing, product quality control), the production process phases (e.g., process monitoring), work centers and shop floor. This vision is also coherent with the RAMI 4.0 Reference Architectural Model for Industry 4.0 [7], where the dimension of the lifecycle value stream (IEC 62890), spanning over the development, production and maintenance phases, is interleaved with the IEC 62264/IEC 61512 levels (from product to the connected world level) and with the smart factory layers (starting from industrial assets up to the business layer).…”

Section: Introductionmentioning

confidence: 99%

Multi-perspective Data Modelling in Cyber Physical Production Networks: Data, Services and Actors

2022

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In recent years, Cyber Physical Production Systems and Digital Threads opened the vision on the importance of data modelling and management to lead the smart factory towards a full-fledged vertical and horizontal integration. Vertical integration refers to the full connection of smart factory levels from the work centers on the shop floor up to the business layer. Horizontal integration is realised when a single smart factory participates in multiple interleaved supply chains with different roles (e.g., main producer, supplier), sharing data and services and forming a Cyber Physical Production Network. In such an interconnected world, data and services become fundamental elements in the cyberspace to implement advanced data-driven applications such as production scheduling, energy consumption optimisation, anomaly detection, predictive maintenance, change management in Product Lifecycle Management, process monitoring and so forth. In this paper, we propose a methodology that guides the design of a portfolio of data-oriented services in a Cyber Physical Production Network. The methodology starts from the goals of the actors in the network, as well as their requirements on data and functions. Therefore, a data model is designed to represent the information shared across actors according to three interleaved perspectives, namely, product, process and industrial assets. Finally, multi-perspective data-oriented services for collecting, monitoring, dispatching and displaying data are built on top of the data model, according to the three perspectives. The methodology also includes a set of access policies for the actors in order to enable controlled access to data and services. The methodology is tested on a real case study for the production of valves in deep and ultra-deep water applications. Experimental validation in the real case study demonstrates the benefits of providing a methodological support for the design of multi-perspective data-oriented services in Cyber Physical Production Networks, both in terms of usability of the data navigation through the services and in terms of service performances in presence of Big Data.

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Product Lifecycle Management Challenges of CPPS

Cited by 14 publications

References 10 publications

Engineering Complexity beyond the surface: Discerning the viewpoints, the drivers, and the challenges

Engineering Complexity beyond the surface: Discerning the viewpoints, the drivers, and the challenges

A Knowledge-Based Digital Lifecycle-Oriented Asset Optimisation

Multi-perspective Data Modelling in Cyber Physical Production Networks: Data, Services and Actors

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