Capacity and capability to adapt have become a key factor of competitiveness in the industry today. It can be considered on both micro and macro levels, and it encompasses a range of circumstances, from the optimal operational response to disturbances at the shop floor level, to business decisions related to changing market trends.
To some extent, the capacity to adapt is related to access to relevant data, owned by the enterprise or from external providers, while capability corresponds to the enterprise ability and skills to effectively interpret, understand and process this data and make best-informed decisions on the industry’s operations and market environment trends. The former (capacity to adapt) is/will be achieved in so-called sensing enterprise, the enterprise which continuously listens to its internal and external environment, by using the technologies such as sensors, embedded electronics, and multi-agent systems. The paradigms of Cyber-Physical Systems and Internet of Things are considered as critical enablers for the sensing enterprise development strategies. The latter (capability to adapt) is related to making sense of all this data, and it is expected to be achieved in the so-called AI-enabled enterprise.
Enterprise Information Systems (EIS) aim to solve common problems, such as lack of process automation, flexibility, responsiveness, scalability, traceability and integration in as less as possible intrusive way. However, they often fail to address those issues consistently and the typical reason for that is the complexity of the process of EIS development and/or implementation in which different and diverse stakeholders fail to effectively communicate around the business objectives and key factors for their achievement. Complexity is multiplied with the factor of the long duration of the software development lifecycle in which those objectives could easily become legacy. An emerging solution to this problem is Model-Driven Engineering (MDE) which facilitates near real-time system customization and efficient response to a change. However, MDE relies on the human perceptions of the realities of the enterprise which are sometimes incomplete or inaccurate. AI-enabled enterprise would effectively address this challenge by embedding machine-learned understanding of the enterprise realities represented by raw data, into the respective models. Furthermore, the AI-enabled enterprise is capable of implementing those models and to make decisions based on those models in a real-time.
Relevant topics and fields
The objective of this Special Issue (SI) is to provide evidences on the maturity of the technologies which facilitate AI-enabled EIS, where this maturity may be considered at the levels of development, implementation and integration with existing EIS. Technologies that are commonly known as parts of AI stack are Machine Learning (including Deep Learning), Natural Language Processing (NLP) and knowledge representation and reasoning. Different industries would be taken into accounts, such as manufacturing, logistics, banking and finance, agriculture, healthcare and others. In those domains, SI seeks for the solutions to the common problems, such as process automation, process mining and discovery, security, compliance, decision making support, image recognition and processing, marketing automation, negotiation automation, etc.
The Special Issue seeks for original contributions in using so-called AI technologies, namely data science and predictive analytics, Machine Learning (including Deep Learning), Natural Language Processing (NLP), knowledge representation and reasoning and similar, in the following topics and fields processing in EIS and/or existing within an AI-enabled enterprise:
- Decision making support
- Process automation
- Process discovery and mining
- Marketing automation
- Smart Customer Relationship Management
- Smart Human Resources Management
- Facilitating AI models development lifecycle
- Cloud-based AI platforms and services
- AI-enabled software agents
- Business models for AI-enabled enterprises
- AI-enabled enterprise blockchain
- AI-enabled EIS and Internet of Things
- Legal issues of AI-enabled enterprises
- Security issues of AI-enabled enterprises
- Maturity models for AI-enabled enterprises
- Applied AI-enabled EIS solutions in different domains
- Other relevant topics and fields
Prospective authors should submit via the Enterprise Information Systems submissions site. Submitting authors should answer yes to the question: “Is this submission for a special issue?”, and then select “Model-driven data-intensive Enterprise Information Systems” from the subsequent drop-down menu.
Enterprise Information Systems considers all manuscripts on the strict condition that:
- the manuscript is your own original work, and does not duplicate any other previously published work, including your own previously published work.
- the manuscript has been submitted only to Enterprise Information Systems ; it is not under consideration or peer review or accepted for publication or in press or published elsewhere.
- the manuscript contains nothing that is abusive, defamatory, libellous, obscene, fraudulent, or illegal.
- Publishing of call: Mid-March 2019
- Manuscripts Due: 15 November 2019
- First decision Date: 15 February 2020
- Revision Due: 15 April 2020
- Final decision Date: End of September 2020
Dr Milan Zdravković
Laboratory for Intelligent Production Systems (LIPS),
Faculty of Mechanical Engineering,
University of Niš, Serbia
Prof. Dr Hervé Panetto,
Université de Lorraine,
CNRS, CRAN, France