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Linking Process Innovation Maturity to Sustainability: Insights from a Systematic Literature Review
No full textPart 2: Digital Transformation and Organizational InnovationInternational audienceSustainability has become an evolving paradigm within academia. This emergence has led organizations to innovate their business processes to remain competitive, meet customer expectations regarding sustainability, and comply with governmental regulations to achieve sustainability goals. This necessitates organizations to innovate their business processes beyond being profit-oriented. However, knowledge about the pathways of succeeding a sustainable process innovation is currently limited. Considering capability areas and business process maturity models (MMs) as key roles in a process innovation’s pathway, this paper emphasizes the scarcity of existing sustainability measurement items for assessing and innovating business processes, highlighting the need for clearer guidance. We provide a systematic literature review (SLR) to explore the current state of literature and evaluate the progression of a related maturity quantification. Finally, our research unveils the interplay between the triple bottom line (TBL) sustainability dimensions (i.e., ecology, economy, and social), emphasizing the need for extending beyond profit-oriented business process innovation. Our insights call for integrating the sustainability dimensions to facilitate maturity when prioritizing BPM investments based on configuration theory
AI Based Search Engine to Deploy a TRIZ Pointer to Chemical Effects
No full textPart 1: AI-Driven TRIZ and InnovationInternational audiencePointers to effects are a group of TRIZ tools which helps the inventor to master a greater knowledge of scientific phenomena and laws, so to suggest him or her different directions to reach possibilities of solution. Pointers to geometric, chemical, and technological effects have been theorized, but only those to physical effects have ever had concrete developments at the research level and as commercial applications.The aim of this work is twofold: on the one hand, to bring the pointer back to chemical effects (CE), recovering little-known texts that are difficult to find but also difficult to interpret, as they have never been translated from Russian. The other aim is to contextualize these tools in the light of the recent achievements of artificial intelligence technologies in the field of information retrieval. A combination of AI tools, as NER (Named Entity Recognition), RAG (Retrieval Augmented Generation) and LLM (Large Language Model) have been combined in order to identify chemical features from several chemical sources, to index documents in order to answer user’s questions, to interact with this Knowledge-Base by a chatbot and finally to generate a complete and standardized output.A comparison is presented between recent commercial applications of AI and traditional pointers to CE from TRIZ literature. In this paper it is explained how the system works, which are the potentialities according to the AI technologies evolution and a comparative study between a SW infrastructure developed by the authors in collaboration with university spin-off software house and others current AI commercial players like GPT or Gemini based applications
Predictive Data Analysis Platform, for Optimizing and Automating the Distribution of Car Insurance Products, Based on Telematic Data
No full textPart 2: Digital Transformation, Industry 4.0, and Predictive AnalyticsInternational audienceThe purpose of this article is to present an AI technology based innovative approach, involved in a platform for digitizing processes in the Car Insurance Business field, which allows the end user (the broker of insurance or the insurer) to base his decisions on robust information to help him make a robust business forecast. Predictive analytics for insurance entails the use of special technology to sift through and analyze historical telematics data and consumer trends in effort to project future behavior. Obviously combining the AI based IT technologies with mathematical and statistical models, the integrated digitalized platform presented in this article involve also both Data Modeling and Deep Learning. Practically, the software platform presented in this article represents the backbone of any insurance brokerage business, because without such an application it is impossible to manage business processes that have hundreds or even thousands of sales agents. From structural point of view, this platform has a layered structure, the first layer being the basic brokerage application, this being extended with innovative predictive computational modules as upper layers. The technical implications mainly refers to the innovative way of involving in the digitalized system a massive amount of telematics data. The expected business implications consists in offering, by an innovative digitalized solution, the possibility for the final client (insurance broker or insurer) to receive information that will help him make a forecast of business
Immersive Human-Robot Collaboration in Restricted or Confined Spaces
No full textPart 2: Human in Command – Operator 4.0/5.0 in the Age of AI and Robotic SystemsInternational audienceThe emergence of collaborative robots in confined spaces marks a new era in manufacturing and automation. Robots designed to assist human operators in complex and physically demanding tasks are gaining popularity due to their ability to work alongside humans, minimising risks and maximising productivity. However, most applications are in open areas where operators have enough space to move out of the robot’s trajectory in the case of an emergency. This paper presents a unique case study of Human-Robot Collaboration (HRC) inside a car body, a scenario that has yet to be extensively explored. This study utilises the NASA TLX method to evaluate the workload of human operators in an HRC application. The paper presents findings of various factors such as mental, physical, and temporal demands, as well as performance, effort, and frustration levels experienced by operators while performing HRC tasks within a car body. The experiences of the participants were explored in detail through semi-structured interviews. The study indicates that operators are willing to collaborate with robots within car bodies. However, ergonomic obstacles and difficulties still exist when robots are deployed in confined spaces like car bodies. A new concept called “Immersive Human-Robot Collaboration” is proposed in this paper, aiming to overcome the challenges and enable effective HRC in confined spaces
How Lean Tools Contribute to a Production System, Investigation in the Automotive Industry
No full textPart 1: Lean Thinking Models for Operational Excellence and Sustainability in the Industry 4.0 EraInternational audienceLean Manufacturing adoption is a gradual process, and the application status of Lean tools influences enterprise performance. Only well-prepared Lean initiatives bring real benefits. This study aims to identify the effective Lean tools required for eliminating waste and also verify the main factors that motivate companies to Lean applications. The study adopted a quantitative research method. Research data were collected through a questionnaire to the target population of medium and large companies in the automotive industry in Poland. The study confirmed that Lean tools are regularly implemented in automotives and the most popular tools are 5S and Visual Management. Statistically significant relationships were identified between specific Lean tools and waste reduction. The widest relationship with waste reduction is observed in VSM application, which is not frequently used by companies. This suggests that great benefits can be obtained when an organization employs the approach focusing on the whole stream of value in the company. The research reveals a challenge with physical stream and in cooperation in value chains in front of vast part of studied companies. The study provides also a basis for a practical guidance for business how to introduce Lean tools to eliminate particular types of waste
Advances in Production Management Systems. Production Management Systems for Volatile, Uncertain, Complex, and Ambiguous Environments: 43rd IFIP WG 5.7 International Conference, APMS 2024, Chemnitz, Germany, September 8-12, 2024, Proceedings, Part v
No full textInternational audienceBook Front Matter of AICT 73
Open Data Framework for Energy Management and ISO Certification in Smart Manufacturing Systems
No full textPart 5: Open Knowledge Networks for Smart ManufacturingInternational audienceSince published in 2011, ISO50001 has become the fastest-growing ISO management standard. However, its implementation and integration within the organisation have been fuzzy and overlooked ever since. This research literature aims to understand the current implementation of ISO 50001, its benefits and improvements, gaps in its integration, drawbacks, and challenges. This paper also then presents a methodology that allows the implementation of an energy management system following the ISO 50001 standard by suggesting an open data framework that layouts a detailed plan for shortlisting the most relevant and goal-oriented energy performance indicators (EnPIs) using multi-criteria decision-making technique (MCDM) and an information model to establish relationships between EnPIs and their data entities. Next, an architecture that provides a road map for the effective implementation of ISO 50001 is presented. This architecture identifies elements of the open data framework for processes like data collection, analysis, normalisation, and insightful visualisation. This architecture presented will be helpful in managing performance metrics and enabling organisations to manage energy performance using an open data approach
Adoption of AI-Based Systems in Industrial Maintenance: Empirical Evidences from an Action Research in the Maintenance Service Business
No full textPart 2: Engineering and Managing AI for Advances in Asset Lifecycle and Maintenance ManagementInternational audienceThe adoption of AI-based systems in industrial contexts is a hot topic in the scientific and technical arena. The present paper aims to bring a contribution to this regard. It sheds light on the adoption of AI-based systems starting from the evidences gathered during an action research in the maintenance service business. The action research deals with the adoption of AI in Predictive Maintenance within the service offering of an Original Equipment Manufacturer that takes advantage of Digital Servitization. The maintenance data-driven decision-making is studied based on the triad of three related entities: technologies, humans and organizations. Moreover, the inclusion of advances potentially available from AI is discussed by means of two selected use cases where advanced data analytics embedding Machine Learning (ML) and Transfer Learning (TL) techniques are considered. As AI is infused along the Predictive Maintenance process implemented with the digital transformation, the use cases are presented as meaningful examples in order to deduce strategic considerations in terms of strengths, weaknesses, opportunities and threats as implied by their deployment in Predictive Maintenance processes at full scale
Construction Logistics Conceptualization: A Comprehensive Framework and Existing Challenges
No full textPart 6: Advances in Production Management SystemsInternational audiencePlaying a key role in the nation’s infrastructure development, construction industry has been studied extensively for several decades. However, there is a need of systemizing knowledge on construction logistics – a crucial element in improving performance of the industry. Combining multiple case study and literature review methods, this research offers a comprehensive framework for construction logistics with construction logistics activities positioned throughout construction project phases. Additionally, it explores practical challenges in on-site and prefabricated construction logistics operation, through which offering valuable insights into areas to be further developed and suggesting improvement ideas for practitioners and policymakers in construction and similar Engineer-to-Order industries
An Accessibility Assessment Algorithm for Support Structure Removal in Parts Produced by Powder Bed Fusion of Metal Using a Laser Beam
No full textPart 5: Additive ManufacturingInternational audienceIn the additive manufacturing process of Powder Bed Fusion of Metals using a Laser Beam (PBF-LB/M), the removal of support structures represents a costly step, predominantly carried out manually in current industrial practices. Particularly challenging are areas of the parts where tools for support removal struggle to access. Therefore, in the data preparation of parts manufactured via PBF-LB/M, attempts are often made to orient the parts within the build chamber in such a way that no supports are needed in the difficult-to-access areas. However, identifying these areas is error-prone and typically relies on manual evaluation by engineers. To reduce susceptibility to errors, this paper introduces an algorithm called the Accessibility Assessment Algorithm (AAA), which automatically evaluates the accessibility of parts. The algorithm is implemented and tested on ten parts. Five experts experienced in removing support structures from PBF-LB/M-printed parts are interviewed and asked to evaluate the same parts for inaccessible areas, and then to assess the calculation results of the AAA. Subsequently, the results regarding inaccessible areas are compared between the algorithm's assessment and the experts’ evaluations. The results show a 70.5% agreement, indicating its effectiveness in assessing the accessibility of parts