“…Thus, also the amount or extent of reuse. A product portfolio which is defined in advance, i.e., the number of machine variants are pre-defined, offers a great opportunity to consider differences and commonalities (and thus potentials for reuse) already during the development of the machines [56][57][58]. In contrast, special machinery engineering is a project-based built-to-order business, and therefore no (or only limited) awareness exists about the machines to be built next.…”
Section: Characterization Of Special Machinery Engineeringmentioning
Special machinery engineering is of great importance to the manufacturing industry and makes a comparatively large contribution to many economies. Digital transformation is already well advanced in many producing industries, and modern ICT technologies such as agents, service orientation, digital twins and artificial intelligence are being used with increasing success. In addition to improving specific product characteristics such as reliability or flexibility, the adoption of modern ICT technologies to ensure sustainability is being intensively discussed. So far, however, there has been little uptake of these technologies in the special machinery industry; sustainability receives little attention. This article examines in detail the reasons for and impediments to the adoption of modern ICT technologies based on a study among special machinery manufacturers. Observations of existing challenges were gathered during daily work, described in detail, and used to derive conclusions about causal barriers. From this, detailed requirements are derived to promote the adoption of modern ICT technologies in the special machinery engineering sector and, ultimately, to bring sustainability more into focus.
“…Thus, also the amount or extent of reuse. A product portfolio which is defined in advance, i.e., the number of machine variants are pre-defined, offers a great opportunity to consider differences and commonalities (and thus potentials for reuse) already during the development of the machines [56][57][58]. In contrast, special machinery engineering is a project-based built-to-order business, and therefore no (or only limited) awareness exists about the machines to be built next.…”
Section: Characterization Of Special Machinery Engineeringmentioning
Special machinery engineering is of great importance to the manufacturing industry and makes a comparatively large contribution to many economies. Digital transformation is already well advanced in many producing industries, and modern ICT technologies such as agents, service orientation, digital twins and artificial intelligence are being used with increasing success. In addition to improving specific product characteristics such as reliability or flexibility, the adoption of modern ICT technologies to ensure sustainability is being intensively discussed. So far, however, there has been little uptake of these technologies in the special machinery industry; sustainability receives little attention. This article examines in detail the reasons for and impediments to the adoption of modern ICT technologies based on a study among special machinery manufacturers. Observations of existing challenges were gathered during daily work, described in detail, and used to derive conclusions about causal barriers. From this, detailed requirements are derived to promote the adoption of modern ICT technologies in the special machinery engineering sector and, ultimately, to bring sustainability more into focus.
“…This renders them insufficient for a systematic, model-driven approach. Interdisciplinary product lines [23] aim to model the variability of CPPS resources, maintaining a domain-specific view on the modeled variability, but do not consider products and resources. Caesar et al [7] aim at a context-aware re-configuration of CPPSs using VM and semantic approaches.…”
The Industry 4.0 initiative envisions the flexible and optimized production of customized products on Cyber-Physical Production Systems (CPPSs) that consist of subsystems coordinated to conduct complex production processes. Hence, accurate CPPS modeling requires integrating the modeling of variability for Product-Process-Resource (PPR) aspects. Yet, current variability modeling approaches treat structural and behavioral variability separately, leading to inaccurate CPPS production models that impede CPPS engineering and optimization. This paper proposes a PhD project for integrated variability modeling of PPR aspects to improve the accuracy of production models with variability for CPPS engineers and production optimizers. The research project follows the Design Science approach aiming for the iterative design and evaluation of (a) a framework to categorize currently incomplete and scattered models and methods for PPR variability modeling as a foundation for an integrated model; and (b) a modeling approach for more accurate integrated PPR variability modeling. The planned research will provide the Software Product Line (SPL) and CPPS engineering research communities with (a) novel models, methods, and insights on integrated PPR variability modeling, (b) open data from CPPS engineering use cases for common modeling, and (c) empirical data from field studies for shared analysis and evaluation. CCS CONCEPTS • Software and its engineering → Software product lines.
“…Testing and evolution [28,33,37,38,41,43,44,45,48,52,64,67,69,72,73,83,91,92,97,104,105,107,111,113,114,115,117,121,122,124,128,129,130,132,134,135,139,141,143,144,150,152,159,160,161,162,164,165,166,168,170,176,…”
Feature models have been used since the 90's to describe software product lines as a way of reusing common parts in a family of software systems. In 2010, a systematic literature review was published summarizing the advances and settling the basis of the area of Automated Analysis of Feature Models (AAFM). From then on, different studies have applied the AAFM in different domains. In this paper, we provide an overview of the evolution of this field since 2010 by performing a systematic mapping study considering 423 primary sources. We found six different variability facets where the AAFM is being applied that define the tendencies: product configuration and derivation; testing and evolution; reverse engineering; multi-model variability-analysis; variability modelling and variability-intensive systems. We also confirmed that there is a lack of industrial evidence in most of the cases. Finally, we present where and when the papers have been published and who are the authors and institutions that are contributing to the field. We observed that the maturity is proven by the increment in the number of journals published along the years as well as the diversity of conferences and workshops where papers are published. We also suggest some synergies with other areas such as cloud or mobile computing among others that can motivate further research in the future.
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