Towards smart assessment: A metamodel proposal

International audienceAssessment initiatives in organisations are focused on the evaluation of organisational aspects aiming to obtain a critic view of their status. The assessment results are used to lead improvement programs or to serve as base for comparative purposes. Assessment approaches may comprise complex tasks demanding a large amount of time and resources. Moreover, assessment results are highly dependent on the assessment input, which may have a dynamic nature due to the constant evolution of organisations. The assessment results should be adaptable to these changes without much effort whilst being able to provide efficient and reliable results. Therefore, providing smart capabilities to the assessment process or to systems in charge of performing assessments represents a step forward in the search for more efficient appraisal processes. This work proposes a metamodel defining the elements of a Smart Assessment, which is guided by elements related to the smartness concept such as knowledge, learning, reasoning and inferring capabilities. The metamodel is further specialized considering a Business Process In-teroperability Smart Assessment scenario

Industry 4.0 Maturity Assessment: A multi-dimensional indicator approach

Purpose - Industry 4.0 has offered significant potential for manufacturing firms to alter and rethink their business models, production processes, strategies, and objectives. Manufacturing organizations have recently undergone substantial transformation due to Industry 4.0 technologies. Hence, to successfully deploy and embed Industry 4.0 technologies in their organizational operations and practices, businesses must assess their adoption readiness. For this purpose, a multidimensional analytical indicator methodology has been developed to measure Industry 4.0 maturity and preparedness. Design/methodology/approach- A weighted average method was adopted to assess the Industry 4.0 readiness using a case study from a steel manufacturing organization. Findings- The result revealed that the firm ranks between Industry 2.0 and Industry 3.0, with an overall score of 2.32. This means that the organization is yet to achieve Industry 4.0 mature and ready organization. Practical Implications- The multi-dimensional indicator framework proposed can be used by managers, policymakers, practitioners, and researchers to assess the current status of organizations in terms of Industry 4.0 maturity and readiness as well as undertake a practical diagnosis and prognosis of systems and processes for its future adoption. Originality/ value- Although research on Industry 4.0 maturity models has grown exponentially in recent years, this study is the first to develop a multi-dimensional analytical indicator to measure Industry 4.0 maturity and readiness

Methods and Models for Industrial Internet of Things-based Business Process Improvement

Over the last three decades, the Internet of Things (IoT) has gained significant importance and has been implemented in many private, public, and business contexts. Leveraging and combining the IoT's capabilities enables far-reaching transformations and disruptive innovations that are increasingly recognized, especially by industrial organizations. In this regard, the Industrial IoT (IIoT) paradigm has emerged, describing the use of IIoT technology in the industrial domain. One key use of the IIoT is the incremental or radical improvement of business processes. This goal-oriented change of business processes with IIoT technology to accomplish organizational goals more effectively is called IIoT-based Business Process Improvement (BPI). Many use cases demonstrate the benefits of IIoT-based BPI for all types of industrial organizations. However, the interconnection between IIoT and BPI lacks theoretical knowledge and applicable artifacts that support practitioners. Moreover, a significant number of related projects fail or do not achieve the anticipated benefits. This issue has drawn attention in recent scholarly literature, which calls for further research. The dissertation at hand approaches this research gap by extending and advancing existing knowledge and providing valuable contributions to managerial practice. Three critical challenges for conducting IIoT-based BPI projects are addressed in particular: First, the essential characteristics of IIoT-based BPI applications are explored. This enables their classification and a foundational comprehension of the research field. Second, the required capabilities to leverage IIoT for BPI are identified. On this basis, industrial organizations can assess their maturity and readiness for implementing corresponding applications. Third, the identification, specification, and selection of appropriate applications are addressed. These activities enable the successful practical execution of IIoT projects with BPI potential

Methods and Models for Industrial Internet of Things-based Business Process Improvement

Over the last three decades, the Internet of Things (IoT) has gained significant importance and has been implemented in many private, public, and business contexts. Leveraging and combining the IoT's capabilities enables far-reaching transformations and disruptive innovations that are increasingly recognized, especially by industrial organizations. In this regard, the Industrial IoT (IIoT) paradigm has emerged, describing the use of IIoT technology in the industrial domain. One key use of the IIoT is the incremental or radical improvement of business processes. This goal-oriented change of business processes with IIoT technology to accomplish organizational goals more effectively is called IIoT-based Business Process Improvement (BPI). Many use cases demonstrate the benefits of IIoT-based BPI for all types of industrial organizations. However, the interconnection between IIoT and BPI lacks theoretical knowledge and applicable artifacts that support practitioners. Moreover, a significant number of related projects fail or do not achieve the anticipated benefits. This issue has drawn attention in recent scholarly literature, which calls for further research. The dissertation at hand approaches this research gap by extending and advancing existing knowledge and providing valuable contributions to managerial practice. Three critical challenges for conducting IIoT-based BPI projects are addressed in particular: First, the essential characteristics of IIoT-based BPI applications are explored. This enables their classification and a foundational comprehension of the research field. Second, the required capabilities to leverage IIoT for BPI are identified. On this basis, industrial organizations can assess their maturity and readiness for implementing corresponding applications. Third, the identification, specification, and selection of appropriate applications are addressed. These activities enable the successful practical execution of IIoT projects with BPI potential

Framework For The Successful Set-up Of A Common Data Model In The Context Of An Industry 4.0-ready Plant Design Process

The production plant design process consists of a multitude of individual engineering disciplines, which rely on a variety of digital models. The individual tasks build up on each other, while each discipline consumes information from the previous processes. However, sharing relevant data across multiple companies is challenging and susceptible to miscommunication and delays. Furthermore, integrating diverse software systems, tools, and technologies create compatibility issues and hinder seamless integration. As a result, a heterogeneous, non-automated data and information landscape is created, characterized by a high level of manual data transfer. This represents a major problem on the way towards Industry 4.0. The goal of this paper is to provide a framework for the successful set-up of a common data model in the context of an Industry 4.0-ready plant design process across and along the value chain. For this purpose, a literature review of current problems in the cross-company and cross-departmental collaboration in the plant design process is provided and requirements for the framework are derived. Existing solutions and research projects are compiled and evaluated against the requirements, from which the framework's structure is concluded. The framework itself is intended to be holistic and must therefore not only include technical aspects (e.g. data interfaces, semantics), but also enable the entire organization and value chain to implement the common data model as part of the digital transformation process (e.g. employee skills, business strategy, legal conditions). Based on this, the framework is further elaborated by deducing calls for action for a successful set-up of a common data model within the research project DIAMOND (Digital plant modeling with neutral data formats). The focus should be on employees and their competencies, as these are prerequisites for shaping digital transformation. Future research must prioritize these actions to enhance technology readiness and organizational Industry 4.0 preparation

Assessment of the readiness and maturity for Industry 4.0 adoption in Indian automobile industries

Purpose: This paper addresses the urgent need to comprehensively assess the preparedness of the Indian automobile industry for adopting Industry 4.0 technologies, a critical imperative for sustaining global competitiveness in one of the world's largest and fastest-growing automotive sectors. The study introduces the Maturity Assessment and Readiness for Industry 4.0 in the Indian Automobile Industry (MARI-IA) Scale, offering a novel contribution to the scientific discourse on this vital issue.Literature Review: The existing literature review underscores the scarcity of tailored tools specifically designed to evaluate Industry 4.0 readiness in the distinctive context of the Indian automotive industry. Methodology: To bridge this gap, the paper employs a survey methodology involving 55 participants from 14 diverse organisations, spanning original equipment manufacturers (OEMs), supplier industries, and service centers. The chosen research object is these organisations, strategically selected to represent the spectrum of the industry. Utilising the MARI-IA Scale, the study systematically assesses maturity and readiness across five pivotal dimensions: Vision, Machines, Practices, Products, and People. Results: The findings reveal discernible variations in readiness levels, with OEMs exhibiting the highest preparedness, followed by supplier and service industries. Large-scale industries consistently outperform their medium, small, and micro-scale counterparts, indicating a pronounced scale-dependent disparity. Notably, the 'People' dimension garnered the highest rating, suggesting an existing readiness for skill enhancement initiatives and heightened customer awareness initiatives. In contrast, the 'Vision' dimension is rated the lowest, signalling a pressing need for increased strategic commitment and top management involvement in implementing Industry 4.0 initiatives. Value: The empirical analysis conducted substantiates the relevance and applicability of the MARI-IA Scale in effectively evaluating ndustry 4.0 readiness in the unique context of the Indian automobile industry. Beyond a mere assessment tool, the results of this study carry significant practical implications for stakeholders, offering a roadmap for enhancing Industry 4.0 preparedness and maintaining a competitive edge in the global automotive landscape. This research is a foundational resource for scholars, industry practitioners, and policymakers navigating the dynamic landscape of Industry 4.0 adoption in the Indian automobile sector