This review paper aims to explore state-of-the-art research and scientific literature about Enterprise Architecture (EA) resilience. Based on a systematic literature review, 850 articles have been subjected to evaluation for relevance. Based on the findings in 58 selected papers, we conclude that the field of EA resilience is still in its infancy. We identified several definitions and classified six types of resilience measures, based on information type (qualitative/quantitative), the source of the disruption (internal/external), and the duration of the resilience (short-term/long-term). Based on the review, we found 19 metrics that are candidates for EA practitioners to consider for the design of measurement and assessment methods for EA resilience. In addition, we identified relevant research from Information Systems sub-domains and other sciences that can be incorporated to create a holistic view on EA resilience. Based on published definitions of resilience in the selected papers, we propose a definition of the concept of EA resilience. This definition is validated using expert opinion and creates a starting point for reasoning about EA resilience and future research.
Disruptions and exceptions are an important source of risks in logistics, as far as the planning of transportation services is concerned. Failing to rapidly react on and handle such events may lead to serious depreciation of the transported cargo and reputation damage. The Internet of Things seems to be the technology capable of providing the tools required to detect exceptions nearly real-time. However, currently, there is little research on how to enhance the detected exceptions with related information from internal or external sources. Furthermore, most exception detection capabilities rely on experience and not much research exist on how to improve the accuracy of using third-party knowledge. In this paper, we propose a reference architecture for situation-aware logistics. The architecture specification follows the key principles derived from an extensive requirements analysis, the state of the art literature, and the ideas promoted by the Industrial Data Space initiative. The proposed architecture has been instantiated and tested by means of a prototype designed for the case of temperature-controlled transportation services.
The aim of this doctoral consortium paper is to introduce my doctoral research proposal in the field of enterprise computing. The scientific problem that I address in my research is the limited usage of real-time data, originating from Industry 4.0 (I4.0) technologies (e.g. smart IoT devices and sensors), by Small-and Medium sized Enterprises (SMEs) in the logistics industry. I argue that the development of an industry platform for real-time data streaming and analytics would allow SMEs to benefit from such data and help them streamline their operational processes and overall performance. The main contribution of my research is a reference architecture for such a platform, geared for the needs of SMEs, and incorporating: 1) a logistics canonical data model to collect and harmonize I4.0 data, 2) an automatic schema matcher to map SME data to the logistics canonical data model, 3) autonomous data mining agents, 4) an adoption strategy based on the concept of intelligence amplification and 5) key performance indicators to measure adoption effects on operational and decisional performance.
In logistics, questions as "Where is my container?" and "When does my container arrive?" can often not be answered with sufficient precision, which restricts the ability of logistics service providers to be efficient. Since logistics is complex and often involves multiple transportation modes and carriers, improving efficiency and saving costs in the supply chain requires communication between the different parties and the usage of real-time data is critical. Currently, logistics service providers (LSPs) use real-time data to a very limited extent, mainly for tracking the progress of a specific part of a given shipment. This data is retrieved manually from a number of websites and sharing with other actors is not even considered. This leads to lack of end-to end visibility and delays in planning. This research proposes an architecture and a common data model for an integration platform that allows the automated collection of real time container tracking data enabling LSPs to plan more efficient. Currently, there is no common data model available that contains all the information required and enables LSPs to track their shipments real-time. The common data model is designed via a bottom-up approach using results of interviews, observations at different logistics service providers, analyses of open data on websites, and serves the information needs of the business processes involving such data. The model is also validated against industry standards. Based on the proposed architecture a prototype was built that is tested in real operating conditions with a fourth party logistics company.
Designing, developing, and implementing applications based on the concepts of Intelligence Amplification (IA) is a complex process. Although some design theories are present in literature, to our best knowledge, no comprehensive IA design approach exists for practitioners. Based on action design research, an IA design canvas is developed and guiding design principles are derived in two iterations. The main contribution of this research is a comprehensive IA design approach, consisting of an IA design canvas and four guiding design principles. Evaluation of the IA design canvas in three concurrent design workshops with 25 participants representing, 14 organizations, provides empirical support that the proposed IA design approach can ease the design processes, especially during the emphasize, ideate, and conceptualize stages of design thinking. Generalization is however not possible. Future research can explore the broader use of the IA canvas for explanation, analysis, prediction, and quantification, and formalize the IA design approach in a design theory.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.