Apoio à Definição de Arquiteturas IIoT Inteligentes

As plataformas IIoT (Industrial Internet-of-Things) são um facilitador na transformação digital, no âmbito da indústria 4.0, promovendo a flexibilidade, para uma adaptação mais rápida às necessidades do mercado, e permitindo às organizações ter uma visão clara sobre o seu estado atual. No entanto, as PMEs (Pequenas e Médias Empresas) estão a encontrar dificuldades na mudança para este novo paradigma, devido à falta de: i) recursos qualificados que são necessários para desenvolver e implementar as suas próprias soluções de digitalização; ii) um entendimento claro sobre a reengenharia necessária que envolve a digitalização, na adoção de soluções IIoT, e iii) modelos adequados para a especificação de soluções IIoT orientadas às PMEs. Com intuito de ultrapassar estes desafios, discute-se uma solução numa dupla perspetiva, em que: i) por um lado, procura-se a automatização da especificação das arquiteturas de plataformas IIoT, de acordo com as necessidades específicas do negócio, reduzindo o investimento necessário para desenvolver este tipo de facilitadores I4.0, e; ii) por outro lado, fomentar o entendimento partilhado do IIoT, entre os especialistas do domínio e as organizações, promovendo o envolvimento de ambas as partes neste processo de especificação. Neste contexto, a semântica desempenha um papel importante, permitindo a acomodação do conhecimento multidisciplinar das arquiteturas IIoT num modelo semântico alavancado por capacidades de raciocínio. Esta solução foi avaliada num caso de estudo, em que a arquitetura produzida pela solução foi comparada, em termos de utilidade, com a arquitetura implementada. O resultado foi que a arquitetura produzida correspondia aos requisitos impostos, pelo que esta foi aprovada pelos especialistas do domínio do caso de estudo, validando a solução.IIoT (Industrial Internet-of-Things) platforms are an enabler for the digital transformation in the scope of industry 4.0, promoting flexibility for a faster adjustment to market and allowing organisations to have a clear vision over its current status. However, SMEs (Small and Medium Enterprises) are struggling to shift into this new paradigm, due to the lack of: i) qualified resources needed to develop and implement their digitalization solutions; ii) a clear understanding about the digitalisation reengineering related IIoT adoption, and; iii) suitable models for SME-oriented IIoT solutions specification. In order to overcome these challenges, a solution is discussed within a twofold perspective: i) on one hand, it looks for the automation of the specification of IIoT platform’s architectures, according to the specific business needs, reducing the investment needed to develop these kind of I4.0 digital enablers, and; ii) on the other hand, it fosters a shared understanding of the IIoT between the domain expert’s and the organisations, promoting the involvement of both parties in this specification process. In this context, semantics plays an impacting role, allowing the accommodation of the multidisciplinary knowledge of IIoT architectures in a semantic model leveraged by reasoning capabilities. This solution was evaluated in a case study, where the architecture produced by the solution was compared, utility wise, with the implemented architecture. The result was that the produced architecture matched the imposed requirements, and so, it was approved by the case study’s domain experts, validating the solution

Ontology based semantic engineering framework and tool for reconfigurable automation systems integration

Digital factory modelling based on virtual design and simulation is now emerging as a part of mainstream engineering activities, and it is typically geared towards reducing the product design cycle time. Reconfigurable manufacturing systems can benefit from reusing the existing knowledge in order to decrease the required skills and design time to launch new product generations. The various industrial simulation systems are currently integrating product design, matching processes and resource requirements to decrease the required skills and design time to launch new products. However, the main focus of current reconfigurable manufacturing systems has been modular production lines to support different manufacturing tasks. Additionally, the design data is not transferrable from various domain-specific software to a collaborative and intelligent platform, which is required to capture and reuse design knowledge. Product design is still dependent on the knowledge of designers and does not link to the existing knowledge on processes and resources, which are in separate domains. To address these issues, this research developed an integration method based on semantic technologies and product, process, resource and requirements (PPRR) ontologies called semantic-ontology engineering framework (SOEF). SOEF transferred original databases to an ontology-based automation data structure with a semantic analysis engine. A pre-defined semantic model is developed to recognise custom requirement and map existing knowledge with processing data in the automation assembly aspect. The main research contribution is using semantic technology to process automation documentation and map semantic data to the PPRR ontology structure. Furthermore, this research also contributes to the automatic modification of system simulation based on custom requirements. The SOEF uses a JAVA-based command-line user interface to present semantic analysis results and import ontology outputs to the vueOne system simulation tool for system evaluation

An Ontology-Aided Computer-Based Approach for Business Model Innovation Ideation

Business model innovation (BMI) is a key leverage for the successful long-term performance of a business. Still, there are almost no computer-based tools to support the BMI process. Most available tools focus on the initiation phase of the BMI process: to map out the current business model. Unfortunately, the later steps, such as the ideation phase, are currently not supported by computer-based tools. However, computer-based tools can assist managers in the decision- making process of elaborating new BMI ideas. This research addresses the research question on how a computer-based tool can suggest BMI cases based on a business’s business model, enterprise architecture, business innovation case characteristics, or criteria for which they are looking for in a potential BMI. A particular focus is on the combination of BMI with information technology. The research has shown that although a computer-based and ontology-aided approach for facilitating BMI would be beneficial for managers, additional research is required. A more detailed conceptualization of a business model than the current status quo can contribute to achieving such a computer-based tool for BMI recommendation. This thesis follows a design science research strategy. As part of the awareness phase, a literature review was conducted as well as three BMI cases were collected for further analysis of their characteristics. Since this research aimed to develop an ontology-aided approach for supporting the BMI ideation, a BMI ontology and a matching method had to be developed. The BMI ontology is built modularly, and therefore, it contains the individual ontologies for a business model, business capability, business innovation case, and enterprise architecture and their interconnections. Additionally, a construction industry-specific ontology was developed and added to the BMI ontology to showcase the possibility for extensions for industry-specific criteria since the developed BMI ontology is an unspecific industry ontology and thus, applicable for every business. Further, the ontology was implemented into a computer-based tool with case- based-reasoning ability to evaluate the possibility of matching BMI cases with a business model, enterprise architecture, or business innovation case criteria. Additionally, to have the functionality of filtering BMI cases, a matching method between BMI cases and filtering criteria was developed and evaluated by leveraging the collected BMI cases during the awareness phase. Through a computer-based tool, browsing, matching, and comparing BMI possibilities become an efficient and straightforward task