ICIEOM2025_ABST_0112_38241

ADVANCING AI-DRIVEN PROCESS INDUSTRY OPTIMIZATION: INSIGHTS FROM THE S-X-AIPI PROJECT ASPHALT PILOT CASE

ÁREA: Special Session / SPS4 - "Convergence of AI, Data, and Robotics (ADR) Technologies in Manufacturing Business Processes ? Best Practices from HORIZON EU Projects: AIDEAS
AUTORES: ANIBAL REÑONES;DANIEL GOMEZ;LAURA SANZ;MARIO L. RUIZ;CRISTINA VEGA

10.14488/ijcieom2025_abst_0112_38241

ai in process industry, asphalt production, digital transformation, predictive maintenance, industrial ai adoption, sustainability.

The s-X-AIPI project, funded under Horizon Europe, explores AI-driven optimization strategies to enhance efficiency, sustainability, and resilience in the European process industry. This paper focuses on the asphalt production pilot, demonstrating the role of AI in improving quality control, energy efficiency, and predictive maintenance. A core innovation of the project is the Autonomic Manager (AM), a self-adaptive AI framework that monitors model performance, detects degradation, and integrates human-in-the-loop (HITL) mechanisms to ensure explainability and trust. The AI solutions implemented in the asphalt pilot optimize mix design, burner efficiency, paving conditions, and quality traceability while enabling modular scalability to other industrial sectors. With validation at Technology Readiness Level (TRL) 7, this study highlights integration challenges, human-AI collaboration, and regulatory compliance as key factors for industrial AI adoption. The findings underline the importance of real-world validation, continuous adaptation, and sustainability-driven AI deployment, reinforcing the potential of AI to drive digital transformation in the process industry.

ICIEOM2025_ABST_0120_38277

ANALYTIC HIERARCHY PROCESS APPLIED TO THE DEVELOPMENT AND USE OF BIODEGRADABLE PACKAGING IN THE FOOD INDUSTRY

ÁREA: Special Session / SPS12 - Analytic Hierarchy Process applied to circular economy, ESG, and sustainability
AUTORES: ANA CAROLINA RATTI NOGUEIRA;ANTONELLA PETRILLO;VALERIO ANTONIO PAMPLONA SALOMON;CLAUDEMIR LEIF TRAMARICO

10.14488/ijcieom2025_abst_0120_38277

analytic hierarchy process, biodegradable packaging, food industry.

The packaging originally used for food products generates a large volume of waste, which brings with it environmental problems. Thus, new types of packaging have been created with the aim of reducing environmental impacts through the use of biodegradable materials. Some types of packaging can be evaluated taking into account criteria such as costs, environmental impact, product conservation and appearance. The initial questions that this research aims to answer are: ?What are the biodegradable raw materials used and their advantages?? and ?What is the most viable type of biodegradable packaging for perishable foods (meat, fruits and vegetables)??. The general objective of this research is to reduce the use of conventional packaging and promote the commercialization of biodegradable packaging. The aim is to achieve this objective through three research methods: literature review, case study and mathematical modeling.

ICIEOM2025_ABST_0123_38123

ASSESSING COMPLEXITY IN SUPPLY CHAINS WITH THE SHANNON ENTROPY: THE INFLUENCE OF STRATEGY AND COMPETITIVE PRIORITIES

ÁREA: Special Session / SPS15 - Remanufacturing, Circularity, and Lifecycle Management Concerns
AUTORES: MIGUEL AFONSO SELLITTO;MARTA RINALDO

10.14488/ijcieom2025_abst_0123_38123

supply chain strategy, shannon’s entropy, supply chain complexity.

Entropy is a multidisciplinary concept that helps understand disorder, randomness, and uncertainty within systems [1]. One type of system in which entropy can play a relevant role in assessing uncertainty is the supply chains (SC), mainly those that integrate reverse functions, such as disassembly and dismantling, with logistics functions, such as supply, manufacture, and distribution. One classical way of measuring entropy is the Shannon entropy from the information theory [2]. The various management approaches within the SC may generate unexpected effects due to the mutual dependence among functions and players, generating unexpected or unusual behaviors. One example is the bullwhip effect. Small variations in the demand may rebound as strong variations in wholesale, production, and suppliers. In short, the interconnections among players and the various feedback loops in an SC system may generate uncertainties in key variables that are difficult to manage or forecast. The usual approach to tackling such a level of uncertainty is to provide more information about the key variables involved in the SC system [3]. Complexity is a key concept in systems theory. In systems such as SCs, complexity is associated with uncertainty, which, in turn, is associated with the level of information the system requires to operate. The more uncertainty a system holds, the more complex it is and the more information it requires [4]. Complexity has nothing to do with size or complication. A sequence with the first thousand prime numbers has zero complexity since the members are deterministic. A sequence of ten random numbers has infinite complexity since, even if small, it is impossible to enumerate then a priori. An aircraft engine with a thousand components may have a wide variety and be complicated, but it is not complex since it is linear and each component defined. A piece of art, a luxury or craft item as a tie is simple, but it is complex since it is hard to predict demands from the fashion or luxury industry. In short, simplicity is the opposite of complication, not complexity, which is the opposite of linearity. Returning to SCs, as complexity relates to the amount of required information, Shannon's entropy can measure the amount of information, and then measuring entropy can assess the SC complexity [5]. This study seeks to apply information theory's Shannon entropy [6] to evaluate and manage complexity in industrial SC. The purpose of the study is to propose a quantitative modeling method employing Shannon's entropy model to assess the complexity within SCs. The underlying assumption is that information entropy serves as a proxy for network complexity within the SC. The research method is quantitative modeling, applied to four focal companies from the agrifood and metalworking industries in Southern Brazil. Complexity is connected with the SC strategy. Four primitive strategies stand in supply chain studies, according to the level of uncertainty in both the demand and the supply chain. For the sake of simplicity, uncertainty can be considered as a binary variable, high or low, without intermediate gradations. The two earliest SC strategies are focused on efficiency (low uncertainty in both demand and supply sides) and agility or innovation (high uncertainty in both demand and supply sides). Intermediate strategies are focused on resilience (low uncertainty in demand, high uncertainty in supply) and responsivity (low uncertainty in supply, high uncertainty in demand). Early results show that efficiency SCs claim for low complexity, while agile ones claim for high complexity. Complexity is also connected with four primitive competitive priorities: cost, quality, flexibility, and dependability. Early results also show that SCs prioritizing cost and quality require lower complexity compared to those prioritizing flexibility and dependability [6]. Notably, internal connections between players related to specially engineered products and deliveries show significant differences in average entropies, indicating varying organizational complexities based on competitive priorities. Implications suggest that a focus on efficiency materialized in cost and quality control in SC management may lead to lower complexity. On the other hand, focus on innovation, materialized in flexibility and dependability control, may lead to higher complexity in SC management. Such requirements may strongly influence strategic decision-making in industrial settings, mainly when not still acknowledged activities, such as reverse logistics, disassembly, dismantling, and reuse of parts, are involved [7]. This research introduces a novel application of information entropy to assess and control complexity within industrial supply chains. Future studies can explore and validate these insights, which will contribute to the evolving field of supply chain management, mainly involving reverse chains.

ICIEOM2025_FULL_0116_38311

CIRCULAR PRACTICES IN VITICULTURE: A QUALITATIVE EXPLORATION OF BYPRODUCTS VALORISATION IN THE AGRIFOOD INDUSTRY

ÁREA: Special Session / SPS8 - Unfolding the complexity of supply chain transformation toward circularity and resilience
AUTORES: PIERA CENTOBELLI;STEFANO ABBATE;MARIA DI GREGORIO;GIUDITTA PAGNOTTO MOGROVEJO

10.14488/ijcieom2025_full_0116_38311

by-products; industrial symbiosis; wine industry; wine valorisation

The wine industry ranks among the most lucrative sectors in Italy. The total turnover was 13.8 billion euros, representing 10% of the entire agri-food turnover. The sustainability issues of the wine industry rely not only on water management, chemical use, energy consumption, and biodiversity conservation but also on its ability to manage waste and by-products efficiently. There are many opportunities for reusing waste and by-products within the sector, as well as across other industries. These utilization pathways help to reduce waste, minimize the environmental impact, and create opportunities for value-added products and resource recovery within the industry. This paper presents a multiple case study conducted in Italy, aimed at evaluating the current state of waste management practices, examining the strategies employed by wineries to handle waste and exploring existing industrial symbiosis exchanges within the wine sector. Our findings reveal the complexities of waste management practices within the Italian wine industry and underscore the potential benefits of industrial symbiosis exchanges. This research contributes to a deeper understanding of sustainable practices in the wine industry and offers practical insights for policymakers, industry stakeholders, and winery managers seeking to enhance sustainability efforts and promote circular economy principles

ICIEOM2025_ABST_0114_38183

CITIZEN SCIENCE IN SCHOOL-STREET MONITORING: A CASE STUDY FROM ITALY

ÁREA: Special Session / SPS6 - "Innovative Smart and Sustainable Mobility: findings from the research projects of the Italian National Centre for Sustainable Mobility - MOST"
AUTORES: SIMONA DE BARTOLOMEO;LEONARDO CAGGIANI;NADIA GIUFFRIDA

10.14488/ijcieom2025_abst_0114_38183

citizen science, crowdsourcing, public participation, sustainable mobility

Private motorized vehicles, particularly cars, are among the most common modes of transport used by parents to drive their children to school (Fyhri et al., 2011). McDonald and Aalborg (2009) found that 75% of parents adopt this behavior, citing factors such as comfort, time efficiency, and perceived safety. Regarding safety, nearly half of parents do not allow their children to walk to school without adult supervision (McDonald and Aalborg, 2009). This mobility pattern contributes to significant congestion in front of schools during peak drop-off and pick-up times, with the surrounding areas often crowded with vehicles. Many of these cars remain idling with their engines on while waiting, creating localized air pollution hotspots due to exhaust emissions. Additionally, the frequent driving manoeuvres required for drop-off and pick-up introduce safety risks. These issues are particularly concerning, as the negative externalities primarily affect the children attending the school, whose young age makes them more vulnerable to the consequences. To address these challenges, it is essential to promote sustainable and safe alternatives to car use (Teixeira et al., 2019) and ensure efficient transportation options for children. Furthermore, assessing the impact of these travel habits and exploring alternative transport solutions should be a priority. Traditional traffic monitoring methods can support mobility improvements by providing data to inform urban planning, traffic management, and pollution mitigation strategies. However, these conventional systems often struggle to collect detailed traffic data on specific streets, such as those around schools. Innovative approaches, such as crowdsourcing through citizen science, offer a promising solution to bridging these data gaps. Based on this premise, this study investigates the use of citizen science methodologies to collect traffic and environmental pollution data in urban school zones. By leveraging a network of crowdsourced monitoring devices near educational facilities, this research aims to assess how crowdsourcing can help bridge critical gaps in urban mobility data while empowering local communities—particularly young people—to engage in sustainability initiatives. The data collection process focuses on real-time monitoring of traffic volumes, speeds, and modal distribution using low-cost, easy-to-install traffic sensors mounted on windows. This data enables an hourly, daily, and weekly analysis of traffic patterns, including differentiation by direction. The sensors used are the Telraam S2, using AI and a proprietary tracking algorithm to detect, classify, and count road users with high precision by grabbing images from a school’s window. Case studies from pilot experiments conducted in northern and southern Italy, as part of a national research project, illustrate regional disparities and school-specific challenges. Preliminary results suggest the viability of such cost-effective solutions for localized data acquisition, offering insights into urban mobility’s contributions to pollution and traffic safety issues.

ICIEOM2025_ABST_0103_38310

DEAR LOCAL ORGANIZING TEAM, WE HOPE THIS MESSAGE FINDS YOU WELL. WE WOULD LIKE TO INFORM YOU ABOUT A FEW RECENT UPDATES AND REQUESTS REGARDING THE PRESENTATION SCHEDULE: ITALO CESIDIO FANTOZZI, AUTHOR OF PAPER 38213, HAS REQUESTED TO PRESENT ON JUNE 23RD, AS HE WILL NOT BE AVAILABLE ON THE FOLLOWING DAYS DUE TO MEDICAL REASONS. PAPERS 38263 AND 38154 WILL BE PRESENTED BY MARLENE AMORIM. PAPER 38171 WILL BE PRESENTED BY IZABELA RAMPASSO. VALERIO SOLOMON, AUTHOR OF PAPERS 38277, 38253, AND 38131, WILL NOT BE PRESENT ON JUNE 25TH. HE HAS REQUESTED TO MOVE ALL THREE PRESENTATIONS TO JUNE 23RD OR 24TH. MARTA MENEGOLI (PAPER 38233) HAS ASKED TO BE SCHEDULED AS THE FIRST PRESENTER IN HER SESSION, AS SHE WILL NEED TO LEAVE BY 7:00 PM. REGARDING PAPER 38214 (AUTHORED BY MICHELE): THE AUTHOR FACED PAYMENT ISSUES AND COULD NOT REGISTER IN ADVANCE. SHE WILL COMPLETE HER REGISTRATION ON-SITE. COULD WE PLEASE INCLUDE HER IN THE PROGRAM? THANK YOU FOR YOUR SUPPORT AND COLLABORATION IN MAKING

ÁREA: Artificial intelligence in Human-centric Production Systems / AI for education, training and skills development in manufacturing
AUTORES: SERENA PROIETTI

10.14488/ijcieom2025_abst_0103_38310

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AI is reshaping the landscape of higher education by enabling new forms of content interaction, personalization, and student support. However, many existing platforms fall short in addressing structural issues such as high dropout rates, limited accessibility for students with learning disabilities, and excessive workload for faculty. In this work, we present BloomyLabs, a Learning Experience Platform AI by design developed to tackle these challenges through a human-centered approach. BloomyLabs allows educators to upload a wide range of content types (PDFs, slides, videos, spreadsheets), which are processed and indexed by an AI engine capable of understanding and interacting with the material. Students, in turn, engage with the content through a simple interface where they can ask questions, request summaries, generate concept maps, and receive personalized cognitive support tailored to their individual learning profiles, including support for DSA (specific learning disorders).

ICIEOM2025_ABST_0081_38135

DEVELOPMENT OF STRATEGIC ROADMAP FOR INDUSTRY 4.0 BASED ON MATURITY AND READINESS

ÁREA: Industry 4.0/5.0 / Digital Transformation and Data Science
AUTORES: SANDRO BREVAL SANTIAGO;JOSE REINALDO SILVA;ELINILSON VITAL

10.14488/ijcieom2025_abst_0081_38135

industry 4.0; maturity model; roadmap.

Distributed manufacturing is a critical driver of economic competitiveness and a characteristic of Industry 4.0. The shift towards Industry 4.0 (I4.0) requires manufacturing firms to implement structured roadmaps for digital transformation. However, companies face significant challenges depending on their initial maturity level and readiness to transition . This paper presents a framework that integrates maturity and readiness analysis to develop a strategic production arrangement with horizontal integration, focusing on a cloud-based distributed manufacturing approach. The proposed model supports decision-makers by identifying key variables influencing digital transformation and providing structured guidance for implementation.

ICIEOM2025_ABST_0104_38305

DISPOSAL OF SPACE DEBRIS UTILIZING A SPACE TUG

ÁREA: Product Lifecycle Management / Innovation, Product and Service Development
AUTORES: ANDRII VOLODIMIROVICH POHUDIN

10.14488/ijcieom2025_abst_0104_38305

space debris, space tug, sun-synchronous orbits

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ICIEOM2025_ABST_0114_38201

EFFICIENT TRAFFIC MANAGEMENT IN CAV ENVIRONMENT USING LANE DROP STRATEGIES: THE CASE OF TANGENZIALE DI NAPOLI

ÁREA: Special Session / SPS6 - "Innovative Smart and Sustainable Mobility: findings from the research projects of the Italian National Centre for Sustainable Mobility - MOST"
AUTORES: SALVATORE VISCIO;MARIO MARINELLI;NADIA GIUFFRIDA;FRANCO FILIPPI;ROBERTA DI PACE

10.14488/ijcieom2025_abst_0114_38201

lane drop, cav, traffic management

The on-ramp area is a high-risk conflict zone where traffic accidents frequently occur. Connected and automated vehicles (CAVs) offer the potential to enhance merging safety through effective cooperative control strategies. One possible solution for optimising the merging process involves managing the on-ramp merging lane by shielding it from the main traffic flow. To achieve this, in addition to conventional control strategies such as variable speed limits and ramp metering, geometric lane drops (e.g., work-zone or design-based) or virtual lane drops on the main road could be considered. These measures can help reduce conflicts in the on-ramp merging zone when a hard shoulder is unavailable and Hard Shoulder Running is not a viable option. However, implementing such solutions introduces a bottleneck on the main road, necessitating effective merging strategies to enhance safety upstream [1]. The literature proposes various lane merging strategies, including early/late merge techniques, lane-changing advisory systems, and optimization strategies for mixed and fully automated CAV environments [2-5]. Based on this premise, this study explores various network management approaches to identify key factors that influence efficient traffic control in a CAV environment. We conduct a real-scale case simulation using a microscopic traffic model to evaluate the impact of geometric and virtual lane drops upstream of an on-ramp zone. Additionally, both static and dynamic merging systems are analyzed, considering the presence of CAVs. The case study focuses on the Tangenziale di Napoli (TaNa), a major arterial road in Naples, Italy. The methodology involves implementing a lane drop of the outermost lane on the main road (TaNa) to mitigate conflicts in the merging zone near the entrance ramp (Figure 1). A What-If analysis is conducted using the PTV Vissim microscopic traffic simulation model, considering the following scenario parameters: ? fixed downstream closure distance (lv); ? variable upstream closure distances (lm); ? variable warning distances (la); ? variable lane drop durations (tc). Preliminary results indicate negligible variations in overall network performance, with the most noticeable effects on the ramp. At the same time, a greater impact is observed on safety indicators, particularly in reducing conflicts. Future research will focus on formulating a multi-objective optimization problem to determine the optimal strategy, validating compliance with regulatory frameworks and real-world lane drop operations, and integrating control strategies such as variable speed limits while expanding the spatial scope of the analysis.

ICIEOM2025_ABST_0123_38124

ENHANCING EFFICIENCY IN THE NONWOVEN INDUSTRY THROUGH DISCRETE EVENT SIMULATION: RETROFITTING AND REFURBISHING AN INACTIVE CUTTING MACHINE

ÁREA: Special Session / SPS15 - Remanufacturing, Circularity, and Lifecycle Management Concerns
AUTORES: LUÍSA MÜLLER PEREIRA;LEANDRO GAUSS;MARCELLO FERA;MARIO CATERINO;MIGUEL AFONSO SELLITTO

10.14488/ijcieom2025_abst_0123_38124

discrete event simulation, nonwoven industry, retrofitting.

The research addresses a critical issue faced by a nonwoven industry company striving to enhance its efficiency and reduce operational costs. The relocation, retrofitting and refurbishing of a cutting machine is the main focus aiming to meet the growing demand for goods with diameters under 600 mm, a market segment that is becoming more and more important in the sector. The motivation of the study is the need to enhance production capacity without affecting final delivery schedules. A second motivation is providing a quicker response to market demands and reducing logistical and operational cost. To this end, discrete event simulation (DES), a tool capable of exploring various alternatives and production scenarios without interrupting actual operations [1], was chosen. The purpose of the study is to evaluate the impact of reactivating a cutting machine, previously employed in line A, to line B. Materials must be redirected from warehouse 1 to warehouse 2. The goal of the modification is to improve production efficiency, save operating costs, and eliminate the necessity for outsourcing cutting services. In particular, the study aims to increase the processing capacity of materials with diameters smaller than 600 mm, optimize buffer space, and reduce labor. The results were measured by the percentage increase in the line’s throughput and the number of trucks required for transfers to warehouse 2. AnyLogic software supports DES, due to its capacity to accurately depict manufacturing process uncertainties and variabilities [2]. The research had four steps: (i) conceptual modeling and validation; (ii) collection and modeling of input data; (iii) construction, verification, and validation of the computational model; and (iv) analysis of simulated scenarios. The conceptual modeling was validated by company experts, ensuring that all process stages were accurately represented. Input data included information on demand, processing times, and setup times, collected over the past six months to ensure the currency of information. The model’s construction allowed for the simulation of different scenarios and the analysis of the impact of various layout and resource configurations [3]. The results indicated a 19.9% increase in the cutting capacity for materials with diameters less than 600 mm, along with the optimization of the warehouse layout for 24 loading positions, without a reduction in the production volume. The analysis of simulated scenarios revealed that Scenario 4 is the most viable, as it balances cost reduction with the optimization of infrastructure. The scenario keeps the cutting machine's efficiency at 95% while reducing the buffer area by 50% and labor by 75%. Regular preventative maintenance and increased process reliability are now possible as the new capacity of 300 tons per month exceeds the demand of 227 tons per month. The discussion highlights the efficiency of DES in assessing production scenarios, highlighting its capacity to combine stochastic and dynamic elements [4]. The study also emphasizes how crucial it is to take into account outside factors, including supplier delivery, in order to maximize production sequencing and save inventory expenses [5]. Aiming for a more thorough understanding and looking for an optimal production alignment, the research also highlights the necessity of investigating integration with the subsequent manufacturing process, extrusion. By identifying bottlenecks and suggesting layout and resource-use improvements, the simulation offered a solid scientific foundation for strategic decision-making. The findings convey implications that may be generalized to other companies in the nonwoven industry. Relocating the cutting machine within a facility can reduce the requirement for outsourcing, which will boost production efficiency and optimize operating costs. Regular preventative maintenance, which improves process dependability, lowers downtime, and avoids product quality issues, becomes possible when production capacity surpasses demand [6]. The study contributes to the advancement of DES in the nonwoven industry by offering a new approach to the analysis and simplification of industry-specific processes. The study also demonstrates the benefits of simulation by enabling companies to test out different scenarios and possibilities without changes on the shop floor. Simulation provides greater security and minimizes errors in future scenario forecasts, aligning installed capacity with market demand and driving advances in production management [7].

ICIEOM2025_ABST_0114_38198

EVALUATION OF FACTORS INFLUENCING DEMAND FOR ELECTRIC VEHICLE CHARGING STATIONS

ÁREA: Special Session / SPS6 - "Innovative Smart and Sustainable Mobility: findings from the research projects of the Italian National Centre for Sustainable Mobility - MOST"
AUTORES: MARIO MARINELLI;ALEKSANDRA COLOVIC;MICHELE OTTOMANELLI

10.14488/ijcieom2025_abst_0114_38198

electric vehicles; regression model; charging infrastructure

The estimation of EV charging demand, both at macro and micro levels, is crucial for infrastructure and policy development, placement of charging facilities, and management of real-time operations for electric vehicles (EVs). From a macroscopic point of view, the EV charging demand is mostly focused on long-term predictions at the city level. At the same time, the question of power supply and energy pricing is essential for energy management on a daily basis. Usually, the estimation of several parameters such as vehicle characteristics, driver behaviour (driving style, heating usage, battery state) and environmental impacts is crucial. In addition, the placement of EV charging infrastructure is the subject of several characteristics related to the specific area of implementation and several aspects such as socio-demographic, spatial, and economic attributes, travel time/energy accessibility of considered zones, etc. Since high-density residential zones with a notable frequency of people and a larger amount of attraction points (e.g., shopping malls and restaurants) could generate more EV users, it becomes worthwhile to investigate their influence on charging station usage. Following this trend, many charging stations have been installed on private land for public use, such as supermarkets or shopping centres. In recent years, several papers have dealt with machine learning approaches for short-term and long-term demand management of charging facilities. Those included variables that depict information on EVs related to their charging season data, state of charge, real-time GPS data, daily energy usage, demographic factors related to their adoption (Wang et al., 2023a-b). For example, Orzechowski et al. (2023) considered a three-year dataset from 11 stations in Scotland (UK) for forecasting EVs charging demand. In another study, a comparison among different deep learning models showed the advances of the gated regression unit (GRU) model when predicting charging demand for EV users in the Morocco case study (Boulakhbar et al., 2022). Koohfar et al. (2023) provided a long-term prediction of EV charging demand using historical real-world EV charging records from 25 public stations related to a case study of Boulder (Colorado, USA). However, there is a lack of charging demand prediction models that explore the variables that might be significant for proving better EV charging infrastructure accessibility, such as the presence of POIs, measured through their driving distance from charging stations. This work proposes a Stepwise Linear regression model for predicting the utilization of the EV charging stations considering a real dataset of 393 stations installed in the Lombardy region (Italy) and an open dataset of POIs of the same region. The Stepwise Linear regression model has been performed based on driving distance between charging stations and the nearest POI of each category, within a predefined service radius, to fit existing charging station usage in the case study area. For instance, zones with a smaller number of POIs can have an influence on lower demand and, thus, on the predicted charging demand. As a result of the stepwise regression, a total of 60 variables were selected based on different combinations of POI categories. Thus, those predictors have been considered for the analysis, each one of them representing the nearest attraction point of each category from a charging station within the service radius. In this way, the investigated area included more than 90,000 POIs belonging to various categories which can be classified as churches, art centres, banks, bars and cafes, buses, cinemas, clinics and hospitals, dentists, community centres, shops, post offices, sports centres, railway and train stations, taxi, restaurants and supermarkets, touristic attractions, etc. Also, the attraction of charging points and the willingness-to-use of EV drivers have been considered through a service range of 1500 m, which take

ICIEOM2025_ABST_0104_38146

EXPERTISE DISTANCE AND PROBLEM-SOLVING EFFECTIVENESS IN HACKATHONS

ÁREA: Product Lifecycle Management / Innovation, Product and Service Development
AUTORES: ANJA TEKIC

10.14488/ijcieom2025_abst_0104_38146

expertise distance, hackathons, problem solving, innovation

To generate valuable solutions to complex challenges, companies increasingly engage external contributors through co-creation initiatives such as crowdsourcing contests, ideation workshops, and hackathons (Füller, 2010; Tekic & Willoughby, 2019). Aiming to understand how companies may increase the problem-solving effectiveness in co-creation, management research has highlighted the importance of determining characteristics of contributors that organizations should target to obtain high-quality input (Acar, 2019; Pollok et al., 2019). Prior research considers the expertise distance of participants—that is, the extent to which their knowledge aligns with or diverges from the problem domain—a key factor influencing the quality of contributions (Boons & Stam, 2019; Piezunka & Dahlander, 2015). The main debate evolves around the question whether contributors with distant or proximate expertise generate more valuable solutions. One stream of research emphasizes the importance of expertise distance for increasing the problem solving effectiveness in co-creation, as it provides contributors with a very different and unrelated perspectives on the problem, allowing them to develop novel solutions that are considered to be the most valuable for companies’ innovation projects (e.g., Jeppesen & Lakhani, 2010; Poetz & Schreier, 2012). In contrast, there is another stream of research, which emphasizes that valuable solutions are only the ones that are useful and relevant for the company and that such solutions may only come from contributors whose expertise is proximate and related to the problem domain (e.g., Dahlander & Gann, 2010; Piezunka & Dahlander, 2015). Recent research (Boons & Stam, 2019) shows that distant expertise is required for the development of valuable solutions and, when accompanied by proximate expertise provides contributors with the great potential for developing solutions that are highly valuable, under the assumption that such solutions are both novel and useful for companies’ innovation projects. Building on this debate, in this study we aim to respond to the following research question: How does contributors’ expertise distance relate to novelty and usefulness of their solutions, and their overall value for companies? Taking into account that individuals with combined expertise are rare, in contrast to previous research that has focused on crowdsourcing contests, in this study we focus on hackathons. In hackathons, a contributor is not a single individual, but a team, providing us with an appropriate research setting to understand whether novel and/or useful solutions come from contributors with expertise domains that are distant and/or proximate to the problem domain. Using multilevel structural equation modelling (ML-SEM), we examined how expertise distance influences solution outcomes and whether novelty and usefulness mediate the relationship with overall value of solutions in hackathons. Data was collected from 38 teams (186 students) participating in seven hackathons, submitting 150 solutions. Each team consisted of 5-6 members, with participation across 4–5 hackathons. For each submitted solution we used a Likert scale (1-7) questionnaire to ask: (1) their contributors (all team members) to indicate the degree to which the problem of the hackathon was close the area of their expertise (788 individual responses in total, aggregated to create team-level constructs), and (2) jury members (product managers from the sponsoring company) to rate the degree of the solutions’ novelty and usefulness, and their overall value for their companies’ innovation. Our study provides empirical evidence about the interplay of expertise distance, solutions’ novelty and usefulness, and the overall value they have for companies. In this way, it contributes to open innovation research focused on the issues of distant search and problem solving (e.g., Poetz and Schreier 2012; Boons and Stam 2019). While focusing specifically on hackathons as an increasingly popular concept in both research and practice (Granados & Pareja-Eastaway, 2019; Lifshitz-Assaf et al., 2021), this study also offers practical insights for innovation managers about how to increase problem-solving effectiveness in such endeavours.

ICIEOM2025_ABST_0107_38206

EXPLORING PUBLIC POLICIES IN THE CONTEXT OF THE SDGS, CLIMATE CHANGE, AND HUMANITARIAN OPERATIONS

ÁREA: Operations for social sustainability / Humanitarian Operations Management
AUTORES: RAISSA ZURLI BITTENCOURT BRAVO;BEATRIZ SARGES DUTRA;CRISTIANO DIAS BARROS;BRENDA DE FARIAS OLIVEIRA CARDOSO;LUIZA RIBEIRO ALVES CUNHA

10.14488/ijcieom2025_abst_0107_38206

public policies, sdgs, system thinking, climate change, humanitarian operations

The interconnected challenges of climate change (CC), sustainable development, and humanitarian crises underscore the urgent need for integrated public policies that address systemic inequalities between global production and consumption patterns that intensify environmental challenges and social inequalities, exacerbating the vulnerabilities of different regions. Addressing these imbalances requires a policy framework that aligns economic incentives with environmental responsibility and social equity. This study explores how public policies can mitigate these asymmetries by advancing the Sustainable Development Goals (SDGs), fostering climate resilience, and enhancing humanitarian operations (HOs) through a system-thinking approach. Therefore, this research employed a Systematic Literature Review (SLR), encompassing searchers in Scopus and Web of Science databases with a string that combined keywords related to SDGs, CC, HOs, and system thinking to ensure comprehensive coverage. Several documents reviewed highlighted the implementation of public policies spanning economic, social, environmental, and governance perspectives. In the economic field, Chapariha (2022) highlighted income redistribution and the implementation of progressive taxes as measures to reduce social inequalities in Iran, while initiatives such as introducing carbon taxes and eliminating fossil fuel subsidies, observed in Japan (Kanai and Yamamoto, 2022), aim to encourage more sustainable practices. Mechanisms for payments for ecosystem services were evidenced in the Colombian Páramos (Benavides et al., 2019). Regarding infrastructure, urban management policies have gained prominence, such as flood control plans and investments in green infrastructure described in Japan (Kanai and Yamamoto, 2022). Additionally, efforts to strengthen climate-resilient sanitation systems were identified as critical in Pakistan (Shin et al., 2022). In the environmental field, nature-based solutions (NBS), as exemplified in the Netherlands by Keesstra et al. (2018), have proven to be a cost-effective approach to mitigating climate risks and promoting biodiversity. Conservation and reforestation initiatives, such as those implemented in Nepal and India, were also highlighted by Verma et al. (2021), demonstrating how long-term policies can restore degraded ecosystems and promote climate balance. From a health and well-being perspective, efforts aimed at expanding vaccination campaigns and strengthening public health systems, as described in Yemen (Harpring et al., 2021), complemented food security programs and support for small farmers in countries like Nigeria (Echendu, 2022). Social and humanitarian policies also gained prominence, with initiatives including direct financial assistance and the integration of vulnerable communities into climate adaptation strategies, as described in Yemen (Harpring et al., 2021) and Nigeria (Echendu, 2022). Policies for environmental education and incentives for remaining in fragile ecosystems have been essential to reducing migration and preserving key ecosystem services (Benavides et al., 2019). Finally, governance and international collaboration were identified as pillars for the success of various policies. Kanai and Yamamoto (2022) analyzed the case of Japan and highlighted the importance of articulation among governments, the private sector, and academia to mitigate disasters and promote resilience. Meanwhile, Side (2022) reinforced the need for debt relief linked to social protection programs, emphasizing how global issues require greater international coordination. It is important to highlight that the public policies analyzed impact multiple SDGs, reflecting the interconnection between social, economic, and environmental dimensions. For example, The "Room for the River" policy and the blue-green infrastructure align with sustainable cities and communities policy (SDG 11), integrating climate risk management, such as flood control, with the creation of green spaces to enhance urban resilience (Li et al., 2023). The carbon taxation and energy transition policies are linked to climate action (SDG 13), promoting the reduction of fossil fuel use, funding renewable energy, and phasing out harmful subsidies (Chapariha, 2022). Meanwhile, community forest management and environmental conservation policies connect to life on land (SDG 15), encouraging sustainable forest management, biodiversity protection, and support for international conservation programs (Verma et al., 2021). The preliminary findings of this SLR highlight that public policies must be integrated and adapted to local contexts, considering economic, social, and environmental factors. Synthesizing public policy research assists in bridging the gap between theory and practice, ensuring policies are both evidence-based and context-sensitive.

ICIEOM2025_ABST_0089_38120

FINANCIAL SUSTAINABILITY IN RESILIENT RURAL COLLABORATIVE NETWORKS: A MATURITY ASSESSMENT INSTRUMENT

ÁREA: Logistics and Transportation / Supply Chain Risk Models and Resilience
AUTORES: GIOVANA DEGASPARI PINTO;VITOR MELÃO CASSÂNEGO;ÍCARO GUILHERME FÉLIX DA CUNHA;FELIPE FERRARI GAVIOLI;DAISY APARECIDA DO NASCIMENTO REBELATTO

10.14488/ijcieom2025_abst_0089_38120

financial maturity, collaborative networks, small rural producers

Although significant in the agricultural sector, small producers face challenges within long and complex agri-food chains, including high operational expenses, bureaucratic requirements, and dependence on intermediaries, which reduce their competitiveness [1]. In this context, collaborative networks emerge as a strategic alternative, enabling the exchange of resources and knowledge among producers, which reduces costs and expands market access [2]. However, a structured financial management system is essential to ensure sustainability, something often overlooked [3]. Without a transparent model, these networks face administrative challenges that impact their long-term viability [4]. Financial maturity assessment emerges as a tool to identify weaknesses and improve management [5]. Despite the extensive literature on financial management, there is still a lack of specific tools to assess the financial management maturity in collaborative networks, making it challenging to identify areas for improvement. In light of this gap, this study aims to develop an instrument to measure the degree of financial management maturity in organizations, considering different stages of development, from early-stage initiatives to consolidated networks. The methodological development was based on Zamanzadeh et al. [6], who propose the design of three stages: determination of content domain, generation of the item and construction of the instrument, and measurement of the item. The first stage was crucial for defining the instrument’s components. This study identified a gap in the literature on financial management maturity scales and adapted the Capability Maturity Model Integration (CMMI) framework for this context. Structured in five progressive stages, the model assesses organizational maturity levels, from chaotic processes (level 1) to continuous improvement through technological innovations (level 5). According to Ross et al. [4], the pillars of financial management include controlling inflows and outflows, critically analyzing figures, and strategic planning, which underpin process areas (PA) such as financial reporting, financial analysis, and planning, using indicators like liquidity, solvency, and profitability to inform diagnoses and promote sustainable growth. In the second stage, the specific objective was developed based on the characteristics of each maturity level, adapted from the CMMI model. The maturity level classification uses a scale based on the relationship between variables [7], where the final practice of each objective determines progression to the next level. The third stage, evaluation, uses binary values segmented by the three processes. The system is progressive, starting at level 2, as level 1 represents “informal chaos”. While some organizations may adopt advanced practices, sequential progression ensures comprehensive analysis, making level 5 unlikely without this foundation. Unlike the CMMI model, which allows isolated improvements, this instrument emphasizes that levels 2 and 3 are crucial for institutionalizing processes, ensuring stability, and reducing risks stressed. The following research steps include validation, a critical stage involving expert confirmation of content validity. It is suggested that this validation occurs in three stages: defining the specialist panel, content evaluation, and validity quantification. According to Zamanzadeh et al. [6], the panel should consist of at least five experts, possibly extending to ten. A pre-test stage with two specialists is recommended to assess clarity, pertinence, and relevance before formal validation. The evaluation can be qualitative-quantitative, considering grammar and connotation (qualitative) and applying the Content Validity Index (CVI) using a 4-point scale (quantitative). Future studies may also integrate the Kappa coefficient, ranging from - 1 to +1 [8], to assess agreement beyond chance, with values above 0.60 considered substantial [9]. This approach ensures methodological rigor and reliability in the validation process. This research contributes to the financial management of collaborative networks formed by small rural producers, considering the specificities of short agri-food chains and their challenges. Providing a simple, structured method helps these networks understand their financial situation and develop plans to enhance their maturity, fostering sustainability and resilience. The study has significant theoretical and practical implications. Practically, the proposed scale aids financial management transformation in the agri-food sector, supporting decision-making and enhancing competitiveness in local markets. Theoretically, the research adapts the CMMI model to a new context, expanding the application of established methodologies to address financial and operational challenges in dynamic sectors. The instrument’s development strengthens financial resilience, improving management and contributing to the economic viability of these networks.

ICIEOM2025_ABST_0093_38118

MAPPING GLOBAL TRENDS IN SUPPLY CHAIN EDUCATION: A DATA-DRIVEN COMPARATIVE STUDY

ÁREA: Higher Education in Industrial Engineering and Management / Teaching and Learning in Industrial Engineering and Operations Management
AUTORES: HEET SHAH;ALI VAEZI

10.14488/ijcieom2025_abst_0093_38118

supply chain management education, global curriculum analysis, scm program comparison.

The increasing complexity and technological advancement of global supply chains have intensified the demand for highly skilled supply chain professionals. In response, educational institutions worldwide are developing specialized Supply Chain Management (SCM) programs that integrate theoretical foundations with practical applications. Despite this trend, limited research exists on the variations in these programs across institutions, particularly in terms of curriculum design, technology integration, and focus areas such as sustainability, digital transformation, and international trade. This study addresses this research gap by conducting a data-driven, comparative analysis of SCM degree offerings from leading global universities. By leveraging machine learning (ML) and advanced analytical techniques, this research provides an in-depth assessment of how academic institutions tailor their supply chain curricula to meet the evolving demands of the industry. This study identified prominent universities from the top 20 economies ranked by GDP, using established global rankings such as Times Higher Education, QS World University Rankings, and US News. This selection ensured diverse representation across academic and economic landscapes. A systematic review of university rankings and official program descriptions facilitated the identification of specialized SCM degree programs. The final dataset encompassed approximately 400 programs across nearly 20 countries, focusing on key aspects such as curriculum structure, course content, departmental affiliations, and thematic specializations. To ensure data reliability, information was manually verified against official university sources. Descriptive analytics and text analysis techniques were employed to reveal patterns and emerging trends within SCM curricula. Initial findings suggest that universities structure their SCM programs based on institutional priorities and regional economic factors. While some institutions emphasize digital transformation by integrating courses on artificial intelligence, big data analytics, and blockchain technology, others focus on sustainability, circular supply chains, and global logistics. Topic modeling, a machine learning technique used for textual data analysis, uncovered significant thematic trends in SCM education. The analysis confirmed that logistics and management remain foundational components across most programs, with increasing emphasis on risk management, resilience planning, and ethical sourcing in response to global disruptions such as the COVID-19 pandemic. Additionally, the research found that universities renowned for SCM excellence typically offer more specialized courses and extensive industry collaborations compared to general top-tier institutions, which often provide broader, management-oriented curricula. This comparative analysis offers critical insights into the distinctive characteristics of SCM education across global institutions. The study underscores the growing importance of data-driven decision-making and advanced analytics in SCM training, reinforcing the role of emerging technologies in shaping modern educational practices in industrial engineering and operations management. The findings of this study contribute to the ongoing discourse on SCM education and lay the groundwork for future research aimed at enhancing curriculum design and integrating advanced analytical techniques into training methodologies. These insights can assist educators, policymakers, and industry leaders in making informed decisions about curriculum development, ensuring that students acquire the necessary skills to thrive in the contemporary supply chain environment. In conclusion, this research highlights the transformative impact of artificial intelligence, automation, and sustainability on SCM education. By systematically analyzing global SCM programs, this study deepens the understanding of current academic trends while prov

ICIEOM2025_FULL_0123_38268

MATURITY MODEL FOR REMANUFACTURING THROUGH THE LENS OF THE PARADOX THEORY

ÁREA: Special Session / SPS15 - Remanufacturing, Circularity, and Lifecycle Management Concerns
AUTORES: PIERA CENTOBELLI;DANIEL DE MATTOS NASCIMENTO;GIUDITTA PAGNOTTO MOGROVEJO

10.14488/ijcieom2025_full_0123_38268

circular economy, maturity model, paradox theory, remanufacturing

Paradox theory offers critical insight to guide companies today, which, driven by increasing globalization and increasingly competitive and innovative markets, face a dynamic and complex scenario. Tensions that seem irresolvable, such as sustainability and efficiency or even innovation and stability, if appropriately managed, turn into opportunities to promote sustainable growth (Smith & Lewis, 2011). In fact, the industrial world aims to reduce waste and maximize resource utilization, and concepts, such as reuse, repair, refurbishing, remanufacturing, and recycling are the basis of a sustainable system as described by the butterfly diagram. According to the current literature, there are several tensions associated with the circular economy strategy. To support organizations in a more sustainable approach, maturity models have been introduced, which allow the maturity level of a specific domain to be assessed based on defined criteria. Using them, it is possible to start the development process by measuring the maturity of a company against a predefined target situation and improving it (Król & Zdonek, 2020; Kayikci et al., 2022). In this paper, we specifically focused on remanufacturing strategy, which helps to reduce environmental impacts and production costs in several industries. While remanufactured products result in reduced emissions and environmental costs, they often have lower margins than new ones, due, primarily, to consumer perceptions of their durability. In addition, this strategy creates internal conflicts as companies are concerned that remanufactured products may take market share away from products that generate higher profit margins, while also making production more challenging when dealing with non-standardized products (Shi et al., 2020). However, for many companies, remanufacturing represents a sustainable practice that enhances both efficiency and cost reduction. Nevertheless, there are still few studies on maturity models for remanufacturing. Based on these premises, this paper aims to bridge this gap by defining a maturity model for manufacturing and identifying the key paradoxes associated with remanufacturing, integrating insights from paradox theory. Relying on a careful scoping review, focus group interviews and multiple case studies, this paper proposes a new maturity model for remanufacturing that assess company maturity according to five levels, focusing on three main perspectives People & Organizational Culture, Sustainable Production, Supply Chain Optimisation. Each perspective was further divided into additional categories. This maturity model serves as a practical tool for companies to evaluate their ability to adopt remanufacturing, identify areas for improvement, and address paradoxes associated with remanufactured products.

ICIEOM2025_ABST_0119_38299

MIND BEFORE CALLING IT “CIRCULAR”: UNDERSTANDING CIRCULAR PRODUCT OPTIONS

ÁREA: Special Session / SPS11 - Circular Ecosystems and systemic innovation ? actors, structures, goals and flows for sustainability
AUTORES: SILVIA COLABIANCHI;CHIARA GROSSO;LUCA FRACCASCIA;FABIO NONINO

10.14488/ijcieom2025_abst_0119_38299

sustainable products, circular supply chain, circular ecosystem

In the looming paradigm shift from linear to circular models of production and consumption the road map for companies is fraught with challenges for transitioning their capabilities to circular targets. Owing to the several challenges, Industry and academia are committed to research on drivers and barriers for transitioning certain high resource-consuming economic sectors to full circularity. The increasing commitment of academics and practitioners is confirmed by a literature on circular economy (CE) dense with research on multiple issues of CE. To cite a few recent studies on CE per industry sector, across decades, CE in intersection with industry (re) evolution, business model and metrics. Studies on achieving product-level CE multiple loops of circular design and design approaches. Frameworks to guide key stakeholders (policy makers, managers, designers) involved into the paradigm shift. Noteworthy, literature on CE agreed that the functionality of circular models of production and consumption can be effective only if the products and services are designed for circularity. According to Chiaroni, a functional CE can follow essentially two ways to transform the product life cycle from linear to circular: product upcycling and product redesign. Nevertheless, the literature is still fragmented on what are the options for taking a product to full circularity. The present study addresses the gap with the aim of identifying those products that are the likeliest candidates for moving from linear to circular and characterise the circular product options in terms of upcycling and redesign.

ICIEOM2025_ABST_0101_38197

MULTI-OBJECTIVE OPTIMIZATION OF A WIND-SOLAR HYBRID SYSTEM WITH BATTERY ENERGY STORAGE USING ROBUST PARAMETER DESIGN

ÁREA: Circular Economy and Sustainable Development / Renewable Energy Systems and OM
AUTORES: AGLAUCIBELLY MACIEL BARBOSA;PAULO ROTELLA JUNIOR;ARTHUR LEANDRO GUERRA PIRES;ANRAFEL DE SOUZA BARBOSA;KAREL JANDA

10.14488/ijcieom2025_abst_0101_38197

hybrid energy systems, multi-objective optimization, robust parameter design.

The search for sustainable energy solutions has driven the development of Hybrid Renewable Energy Systems (HRES), which integrate multiple renewable sources to meet the growing energy demand. In particular, the combination of wind and solar energy, coupled with battery energy storage, offers a promising alternative by mitigating the individual limitations of each source, such as generation intermittency. The main challenge of these systems is optimizing their performance and balancing economic and environmental variables under uncertain conditions, such as climate change and demand fluctuations. This work proposes a multi-objective optimization model for a hybrid wind-solar battery storage system. The methodology is based on Robust Parameter Design (RPD), which incorporates uncertainties in critical system parameters, such as temperature and energy demand, to ensure optimal performance is maintained under adverse operating conditions. The multi-objective optimization methodology was implemented considering two main objectives: maximizing the Net Present Value (NPV) and minimizing the Levelized CO₂ Emissions (LE). Robust Parameter Design was employed to address uncertainty in environmental variables, such as temperature and energy demand levels. Response Surface Methods and statistical analysis techniques were used to model these variables' relationships. The study considered scenarios with different proportions of wind and solar power generation and various demand profiles. The optimization included using a Desirability Function, which weighs the results according to the weights assigned to each objective (economic and environmental), allowing the search for balanced solutions. The model also incorporated Mean Squared Error (MSE) analyses to evaluate the impact of control and noise variables on the system's performance. As a result, the study revealed that the proportion of wind energy and demand highly influenced NPV. Scenarios with a higher proportion of wind generation resulted in lower NPV due to the higher initial costs of installing and operating wind turbines. However, this negative impact was mitigated in high energy demand scenarios, showing that the system is economically viable when operated under high utilization conditions. Regarding CO₂ emissions, increasing the proportion of wind energy led to a significant reduction in LE, confirming the potential of this renewable source to mitigate environmental impact. However, energy demand showed a positive correlation with emissions, indicating that increased demand tends to raise the amount of CO₂ emitted, especially in scenarios where renewable energy is insufficient to meet the total load, requiring conventional sources. The results indicated that the multi-objective optimization applied to the wind-solar hybrid system with battery storage effectively balances economic and environmental goals. Robust Parameter Design ensured that the system was resilient to climate and demand variations, with temperature being highlighted as a critical variable affecting both wind generation and battery efficiency. Optimization with the Desirability Function allowed the exploration of different weights for the NPV and LE objectives. In scenarios where environmental sustainability was prioritized, higher penetration of wind energy and lower CO₂ emissions were observed, while optimization focused on financial performance favored scenarios with higher demand and moderate integration of wind energy. Thus, it is concluded that this study demonstrated that it is possible to optimize renewable energy hybrid systems robustly and efficiently, meeting both financial and environmental objectives. The Robust Parameter Design methodology effectively reduced operational uncertainties' impact and ensured the system's long-term viability. The results suggest that the ideal configuration to maximize NPV and minimize LE involves a balanced proportion of wind and solar energy, adjusted according to energy demand and climatic conditions. Future research may explore integrating other renewable sources, such as biomass, and analyzing more complex trade-offs between costs and environmental benefits in scenarios of public policies promoting renewable energy incentives.

ICIEOM2025_ABST_0104_38150

NAVIGATING AI-TRANSFORMATION: A TYPOLOGY OF COMPANIES IN THE ERA OF AI

ÁREA: Product Lifecycle Management / Innovation, Product and Service Development
AUTORES: ZELJKO TEKIC;JOHANN FUELLER

10.14488/ijcieom2025_abst_0104_38150

ai-transformation, typology, companies

As business interest in AI grows, so does the proliferation of concepts, variables, and factors related to AI transformation, increasing the complexity of the phenomenon. This paper introduces a comprehensive typology of companies in the AI era, providing a structured framework for understanding and navigating AI transformation. The typology categorizes companies based on three primary dimensions: AI readiness of their business model, control over high-quality data, and proficiency in AI algorithms (technology). From this foundation, five distinct company archetypes emerge: AI-born Companies, Digital Champions, Problem Owners, AI Tools Providers, and Digital Zombies.

ICIEOM2025_ABST_0109_38306

OPTIMIZING DEBRIS REMOVAL DURING CARGO RETURN MISSIONS

ÁREA: Special Session / SPS1 - Advancements and Opportunities for In-Space Manufacturing
AUTORES: PAOLO SCARABAGGIO;NICOLA MIGNONI;RAFFAELE CARLI;MARIAGRAZIA DOTOLI

10.14488/ijcieom2025_abst_0109_38306

debris removal, trajectory optimization, debris mitigation.

Abstract. Low earth orbit has become increasingly crowded with orbital debris, posing serious sustainability challenges. Despite guidelines mandating post-mission disposal, the accumulation of defunct satellites and fragmentation debris continues, raising the risk of a cascading collision phenomenon known as Kessler syndrome. Space agencies and industry are responding with ambitious debris mitigation initiatives [1]. While most research emphasizes either debris prevention or dedicated active debris removal missions [2-3], this abstract considers integrating debris capture operations into routine cargo return flights. Indeed, vehicles such as SpaceX?s Dragon capsule reenter the atmosphere after ISS resupply missions with excess capacity, providing a potential opportunity for opportunistic debris removal at marginal additional cost.

ICIEOM2025_ABST_0108_38283

PROCESS MANAGEMENT IN A BRAZILIAN FEDERAL HIGHER EDUCATION INSTITUTION

ÁREA: Operations for social sustainability / Operations in the Public Sector
AUTORES: ELIZABETE RIBEIRO SANCHES DA SILVA;CARLOS HENRIQUE PEREIRA MELLO;GABRIELA MOLINA FRANÇA

10.14488/ijcieom2025_abst_0108_38283

process mapping, management, higher education institution.

Process management is very important in that it enables the managers of any organization to obtain more efficient control over the use of resources, as it presupposes the use of productivity indicators throughout the entire process. In the case of public organizations, the impacts are even more relevant because they use public resources and seek transparent management of the use of these resources. For years, management methodologies have been applied more in the context of industrial companies and very little in public administration. In order for public resources to be managed transparently, innovations in management models and techniques are needed in Higher Education Institutions. In this context, this article aims to analyze the state of the art in process management in Higher Education Institutions and to critically analyze the models adopted for mapping the processes of a Brazilian Federal Higher Education Institution (FHEI). In order to achieve the aim of this article, a theoretical review of publications on the subject and a case study of process mapping at a federal university will be carried out. Improvements are expected in the management of FHEI processes, and in the transparency of resource management are also expected. The next steps include a survey directly with the sectors involved in process mapping at the university, to identify problems and suggestions for future improvements.

ICIEOM2025_ABST_0123_38125

REAL-TIME PREDICTION OF MELT FLOW INDEX IN HIGH-DENSITY POLYETHYLENE PRODUCTION USING MACHINE LEARNING

ÁREA: Special Session / SPS15 - Remanufacturing, Circularity, and Lifecycle Management Concerns
AUTORES: NILSON DANOWSKI PEREIRA;MARTA RINALDO;MARCELLO FERA;MARIO CATERINO;LEANDRO GAUSS

10.14488/ijcieom2025_abst_0123_38125

high-density polyethylene; machine learning; melt flow index prediction

The research addresses a critical challenge in the petrochemical industry, particularly in the production of high-density polyethylene (HDPE). As the industry faces increasing competitiveness and pressure to cut operational costs, there is a growing need for creative solutions to improve efficiency and decrease reliance on conventional laboratory tests [1]. The objective of the research is to create a computational model capable of forecasting the Melt Flow Index (MFI), an important specification variable, in real time. The goals of the project include achieving lower processing costs and waste generation while improving general operations conditions. The purpose of this study is to develop and validate a machine-learning model that can predict the MFI in an HDPE-producing facility. The model aims to eliminate the frequent requirement for laboratory analysis and manual sampling, enabling quick modifications to the production process. The study intends to apply the CRISP-DM (Cross-Industry Standard Process for Data Mining) framework as an organized method for creating this predictive model [2]. Improvements in the plant's operational effectiveness and product quality are among the expected outcomes. The methodology's foundation is the CRISP-DM framework, which comprises six stages: modeling, assessment, outcomes discussion, data preparation, business understanding, and data understanding. This methodical approach makes a comprehensive examination and development of the prediction model easier. In order to construct the model, the study uses a quantitative methodology and machine learning techniques, including neural networks, linear regression, and other sophisticated algorithms [3]. Historical operational data from the facility were collected and processed to ensure that the model accurately and reliably reflected the operations. The collection and treatment enables the model to generalize effectively with new data inputs, facilitating its training and validation [4]. The results of the study are promising. The model that achieved the best results on the MFI was a Neural Network. The model explains 83.2% of the variance in MFI, with a Mean Absolute Error (MAE) of 0.0544 MFI units, evidencing high accuracy and reliability. The model also shows a high level of resilience, validated by a cross-validation procedure, which showed that it worked well under various operational circumstances. This validation highlights the model's ability to retain its prediction accuracy in dynamic production situations, which is essential. In the discussion section, the applicability of machine learning methods to address the complexities existing in petrochemical processes is reviewed. According to the study, reducing overfitting is a continuous process that will require constant monitoring to ensure the long-term effectiveness of innovative models. Adding more variables, like the H2/C2 Ratio, increased the expected accuracy of the model. This finding underlines the importance of continually updating the model to meet changing production needs and conditions, which is currently a characteristic of the industry. The study has implications. It evidences that, when implementing a predictive model for MFI, companies in the petrochemical industry can significantly reduce their need for laboratory analyses, leading to substantial cost savings and increased operational efficiency [5]. The ability to predict MFI in real-time enables immediate process adjustments, reducing the discard of production by unacceptable quality [6]. Moreover, the research adds to the scope of machine-learning applications in the petrochemical sector, thus improving companies' technological capabilities and preparing them for the future needs of the fast-evolving industry [7]. In short, this study presents strong evidence of the use of machine-learning models in the petrochemical sector. The study details a possible path toward more efficient and less expensive processes that serve as a basis for future advances in the industry. The case demonstrates the advantages of this approach, highlighting its potential to facilitate a deeper integration of data science within the industry. By using the CRISP-DM methodology, this pathway serves as a flexible and scalable framework, enabling the development of sophisticated predictive models tailored to meet specific industry needs. This structured process not only enhances the effectiveness of data-driven decision-making but also fosters innovation and efficiency in the industry.

ICIEOM2025_ABST_0099_38164

ROLE OF DIGITAL TECHNOLOGIES IN ENABLING CIRCULAR ECONOMY: A SYSTEMATIC LITERATURE REVIEW

ÁREA: Circular Economy and Sustainable Development / Data-driven Circular Manufacturing
AUTORES: HUSSEIN GHAZI ABULROB;ANABELA SOARES;LINH DUONG;VIKAS KUMAR

10.14488/ijcieom2025_abst_0099_38164

circular economy, digital technologies, industry 4.0.

This systematic review aims to uncover the role of Digital Technologies (DTs) in redefining Business Models (BMs) in manufacturing firms through facilitating Circular Economy (CE). Research suggests that implementing DTs supports firms? initiative to adopt CE and thus enhancing their performance and operational capabilities [15]. Previous reviews have struggled to present a comprehensive evaluation in this regard. They either focused on a limited number of DTs [7,8] and/or perspective [3,6], drivers and barriers of adopting DTs to drive CE [2,11], limited analysis of the integration between Industry 4.0 and CE [1,13], or product life cycle [10]. Therefore, there is a need to analyse the role of DTs in enabling Circular Business Models (CBMs) in a systematic and holistic approach [9,14]. The authors seek to fill this knowledge gap through synthesizing DTs? contribution against a comprehensive list of CE strategies, namely the (10R) [12] circular strategies, developed specifically for the manufacturing industry. These strategies were extended to include a BM dimension in addition to ?Upgrade? and ?Cascade? strategies [4]. To achieve this, this paper adopted a systematic literature review method where only peer reviewed articles were included in the analysis. The review finds out that DTs contribute toward enabling the (10R) circular strategies, namely, ?Refuse?, ?Rethink?, ?Reduce?, ?Repair?, ?Reuse?, ?Refurbish?, ?Remanufacture?, ?Repurpose?, ?Recycle?, and ?Recover?. DTs also contribute toward enabling ?Upgrade? and ?Cascade? strategies. By promoting these CE strategies, companies can meet CE objectives of resource efficiency, extending product?s lifetime, and reusing material. By achieving these CE objectives and through applying the logic of business model innovation, companies can redefine their CBMs. This study highlights the pivotal role of DTs in facilitating the transition towards CE, particularly through supporting CE strategies. It demonstrates how implementing DTs can systematically and holistically drive CE transformations. This study incorporates a broad spectrum of DTs in its analysis, offering a comprehensive understanding of their role in facilitating various CE strategies, an aspect that previous reviews have struggled to adequately address. This study provides practitioners with valuable insights to support their transitioning to CE, offering a strategic roadmap for facilitating a digitally enabled CE transformation. Furthermore, it contributes to the theoretical understanding of the role of DTs in enabling various CE strategies. Specifically, this study presents a novel exploration of how DTs drive key CE objectives, including resource efficiency, product lifetime extension, and material reuse.

ICIEOM2025_ABST_0088_38140

SMART MEASUREMENT OF HUMAN FACTORS AND PRODUCTIVITY IN INDUSTRIAL CONTEXTS

ÁREA: Operations for social sustainability / Ergonomics and Human Factors
AUTORES: JOSHUA EMANUEL LEYTON VALLEJOS;CAROLINA ROJAS CÓRDOVA;ALEXIS GÓMEZ HERNÁNDEZ;BRIAN KEITH NORAMBUENA

10.14488/ijcieom2025_abst_0088_38140

human factors, performance, artificial intelligence

The integration of Artificial Intelligence (AI) and integrated sensors have enabled the measurement and improvement of worker performance and system performance across different industries. Despite these advances, Human Factors (HF) remain underrepresented in management and engineering research literature, often relegated to occupational safety and human resources. This study conducts a systematic review of 39 empirical case studies over the past 14 years analyzing the impact of HF on industrial task performance using automatic measurement technologies. Findings indicate that research predominantly focuses on mental workload (51%), situational awareness (26%) and postural impact (21%), while the least explored constructs among the identified ones are physical fatigue (10%) and stress (8%). Although 87% of studies are laboratory-based, key challenges in industrial implementation include system interoperability, technological development and worker acceptance. The study highlights the potential of HF measurement to improve productivity and workers well-being through adaptive and augmentation systems, emphasizing the need for further real-world applications

ICIEOM2025_ABST_0101_38131

STRATEGIC ENERGY PLANNING FOR DAIRY INDUSTRY EXPANSION: A MULTI-CRITERIA APPROACH

ÁREA: Circular Economy and Sustainable Development / Renewable Energy Systems and OM
AUTORES: PETERSON DOS SANTOS MAURÍCIO;VALERIO ANTONIO PAMPLONA SALOMON;DENILSON PAULO SOUZA DOS SANTOS;JORGE KENNETY SILVA FORMIGA

10.14488/ijcieom2025_abst_0101_38131

strategic energy planning, multi-criteria decision analysis (mcda), renewable energy sources, dairy industry, mavt (multi-attribute value theory), ahp (analytic hierarchy process), energy cost-benefit analysis, operational efficiency, sustainable energy solutions

The "Cooperlat" dairy company is experiencing continuous growth in the national market and is now attracting interest from an investment fund to support its expansion and international sales. In this context, the company's operational director, supported by a decision-making system composed of production managers and industry specialists, is leading a strategic planning process. This process utilizes decision-making methods such as MAVT (Multi-Attribute Value Theory) to analyze utility functions and compare evaluation criteria, as well as AHP (Analytic Hierarchy Process) for structured comparisons between different energy sources. The objective is to identify the most cost-effective and efficient alternative energy source to be implemented at the plant. The selected energy solution must not only reduce operational costs but also enhance the cooperative?s profitability, ensuring competitiveness in both domestic and international markets.

ICIEOM2025_ABST_0098_38171

SUSTAINABLE LOGISTICS: A SYSTEMATIC LITERATURE REVIEW ON DISTRIBUTION LOGISTICS AND SUSTAINABILITY

ÁREA: Circular Economy and Sustainable Development / Sustainable Operations and Supply Chain Management for Cleaner Production
AUTORES: ALEXIS VICENCIO;HERNÁN LESPAY;IZABELA SIMON RAMPASSO

10.14488/ijcieom2025_abst_0098_38171

logistics; literature review; sustainability; transport.

The performance of logistics systems was evaluated focusing on economic aspects for many years, considering aspects such as costs reduction and operational efficiency (Beske-Janssen et al., 2015). Nevertheless, sustainability aspects has been included in the performance assessment of these systems more recently. In special, environmental aspects are being included in the analysis (Vega-Mejía et al., 2019). This focus is especially important when the impact of transport sector has on greenhouse gas emissions (IPCC, 2022). In this context, this research aimed to analyse the literature about supply chain logistics and sustainability, identifying trends and research opportunities. For this, a search in Web of Science was performed using the following string: (“VRP” OR “Distribution logistic” OR “Urban logistics” OR “Intermodal transportation” OR ”Last-mile Delivery” OR“Truck rout*” OR “Freight transportation” OR “Train logistics” “Maritime rout*” OR “Vehicle routing problem” OR “Urban freight transport” OR “Last-mile logistic’“Sea rout*” OR “Shipping rout*”) AND (“Environmental Impact” OR “Sustainab* impact” OR “Environmental performance” OR “Environmental assessment” OR “Environmental measurement” OR “Environmental analysis” OR “Sustainab* assessment” OR “Sustainab* analysis” OR “Sustainab* measurement” OR “Sustainab* performance” OR “Carbon footprint analysis” OR “sustainability indicator” OR “sustainability metric” OR “Carbon footprint measurement” OR “Carbon footprint assessment” OR “Carbon footprint impact” OR “measure environmental impact” OR “measure sustainab*”). The data collected was analysed though Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) (Moher et al., 2009). The steps followed were: identification, selection, quality analysis, and synthesis. To select the documents, the inclusion criteria established was: papers that consider sustainability assessment related to supply chain logistics. For the exclusion criteria, it was removed from the sample papers that do not focus on sustainability assessment and/or papers that do not focused on supply chain logistics. Papers from Proceedings were also eliminated. VosViewer (Eck and Waltman, 2010) was also used to analyse the relationships between words. For this, Thesaurus was used to eliminate words such as “paper” and “article”, and to establish the synonyms that the software should consider for the analysis. The Bibliometrix was also used for data analysis. The initial search in Web of Science resulted in 397 articles. After removing the papers from Proceedings, 339 articles were left. The titles and abstracts of these articles were analysed to apply the inclusion and exclusion criteria. After this analysis, 235 papers were selected. Analysing the results of this research, although there are papers in the sample since 2005, it was possible to verify an increase of publications from 2019. In 2022, 41 papers were published on the theme. Regarding the journals, the three with the highest number of publications were Sustainability, Transportation Research Part B, and Journal of Cleaner Production. Regarding the amount of papers per author, it was observed that most of authors (95.1%) published a single paper on the theme; 3.8% of the authors published two papers, 0.8% had three papers, and 0.4% presented four publications. When considered the countries of these authors, China is in the first position, with 73 papers, followed by Italy (61) and United States (48). In Europe, Germany (39), France (27) and United Kingdom (28) are highlighted; while in Latin America, Brazil (20) and Colombia (16) have more evidence. When considered the evolution of the themes throughout the years, it is worth to highlight that “efficiency”, “performance” and “e-commerce” were relevance in more recent periods, while “pollution” and “supply chain management” were highlighted only in the first period of analysis (2005-2019). Regarding the thematic diagrams for each period, for the motor themes in the first period (2005-2019), the following terms can be highlighted: “model”, “framework” and “system”; in the second period (2020-2021), “vehicle routing problem”, “CO2 emissions”, “energy”, and “circular economy” outstand. For the third period (2022-2023), “efficiency”, “city logistics”, and “emissions” can be mentioned. In the last period (2024), “last mile delivery”, “b2b e-commerce”, and “algorithm” can be evidenced. Regarding the future research opportunities, it is important to evidence the need to compare the models use to evaluate sustainability assessment in different supply chain transport models. In addition, social sustainability in this scenario needs to be more explored.

ICIEOM2025_ABST_0104_38147

TEAM MOTIVATION IN HACKATHONS: A FUZZY-SET QUALITATIVE COMPARATIVE ANALYSIS

ÁREA: Product Lifecycle Management / Innovation, Product and Service Development
AUTORES: ANJA TEKIC

10.14488/ijcieom2025_abst_0104_38147

team motivation, hackathons, innovation performance

To collect valuable solutions for problems they face, companies turn to co-creation initiatives, such as hackathons, crowdsourcing contests, lead user workshops, etc. We witness a competition among the companies and open innovation intermediaries for active and prolific contributors, whose time and attention are in high demand (Franke et al., 2013; Tekic & Alfonzo Pacheco, 2024). Thus, companies need to be able to attract problem-solvers who may provide appropriate contributions to co-creation initiatives. Having the focus on how to enhance problem-solving effectiveness in co-creation, innovation management research has highlighted the importance of understanding motivation of problem-solvers (Ta et al., 2021; Zheng et al., 2011). The key debate centers on identifying the motivational drivers that encourage solvers to participate in co-creation and invest time and effort into developing innovative solutions (Acar, 2019; Hossain, 2018; Mack & Landau, 2015). While primarily focusing on crowdsourcing contests, prior studies acknowledge the significance of extrinsic (e.g., monetary rewards, career benefits), internalized extrinsic (e.g., peer recognition, reputation, skill acquisition), and intrinsic (e.g., enjoyment, intellectual curiosity) motivational factors. However, with the rising popularity of hackathons, companies face the lack of understanding of teams’ motivation to join and contribute to such initiatives. Also, by focusing on the influence of independent motivation factors on effective problem-solving in co-creation, previous research fails to capture complex and multifaceted notion of motivation and leaves it unclear whether intrinsic, extrinsic and internalized motivation act as complements or substitutes in driving problem-solvers to develop and contribute high-quality solutions. In this study, we aim to fill these gaps in research and respond to the following research question: Which specific combinations of motivation factors drive teams’ problem-solving effectiveness in hackathons? Building upon the main premises of self-determination theory (Deci et al., 1989), we adopt a neo-configurational perspective (Misangyi et al., 2016) to explore the interaction effects of extrinsic, internalized extrinsic and intrinsic motivation on the teams’ problem-solving effectiveness in hackathons, as an increasingly popular form of co-creation between companies and individual external contributors. We employ fuzzy-set Qualitative Comparative Analysis – fsQCA on the data collected from seven student hackathons, sponsored by six different companies, involving 186 students organized in 38 teams, who submitted 150 solutions in total. As it takes into account multiple conjunctural causation, causal equifinality and causal asymmetry among the multiple variables (Misangyi et al., 2016), fsQCA is ideal for the analysis of the complex relations between extrinsic, internalized extrinsic and intrinsic motivation factors of participating teams (i.e., the fsQCA conditions) and their problem-solving effectiveness in hackathons (i.e., the fsQCA outcome). Our results provide empirical evidence about the interplay of distinctive motivation factors and their mutual effect on the teams’ problem-solving effectiveness in hackathons, showing that motivation factors act not only as complements, but also as substitutes, in determining the teams’ performance. By adopting a neo-configurational perspective, this study provides a more in-depth understanding of which combinations of motivation factors drive teams of problem-solvers to co-create value for companies. In this way, this study contributes directly to innovation management research focused on the motivational underpinnings of contributors’ self-selection to participate in co-creation initiatives and their potential to develop appropriate solutions (Acar, 2019; Hossain, 2018; Mack & Landau, 2015). Additionally, as previous research has mainly looked into crowdsourcing for innovation, by focusing on hackathons as a co-creation setting, we add to sparse research on this still under-investigated, but increasingly popular topic in management research (Endrissat & Islam, 2021; Granados & Pareja-Eastaway, 2019). Finally, having in mind that hackathons have attracted significant attention in the world of practice, our study also offers useful practical guidelines for open innovation managers about how to design a proper incentive structure and attract problem-solvers who have potential to achieve high level of performance in hackathons.

ICIEOM2025_ABST_0098_38153

THE FUTURE OF AGRICULTURE? CRISPR GENE EDITING CROPS

ÁREA: Circular Economy and Sustainable Development / Sustainable Operations and Supply Chain Management for Cleaner Production
AUTORES: DAVID BAUER;THI KIEU TRANG DONG;SIMONE LUZI;BENEDETTA SABATINO;LORENZA MORANDINI

10.14488/ijcieom2025_abst_0098_38153

crispr-cas9, agriculture, gene-editing, crop production

The effects of rapid population growth and climate change are straining agriculture supply chains and global food security. Gene editing crop seeds using CRISPR-Cas9 enables precise and desirable modifications of genomes facilitating potential solutions. This paper examines: What are the impact and challenges of CRISPR-Cas9 gene editing on the agriculture supply chain?s crop production? To answer the research question a literature search and review were conducted. Relevant library sources, like Frontiers in Plant Science and Genome Editing, were searched guided by keywords and synonyms for the population (industry), intervention (technology) and outcomes (impact and challenges). Source inclusion/exclusion and quality were assessed on an individual basis. The following results were summarized in a two-sided argumentative structure. CRISPR-Cas9 represents a considerable advancement in genetic engineering, surpassing earlier technologies like ZFNs and TALENs in precision, simplicity, and adaptability [1]. CRISPR targets specific DNA sequences, enabling the growth of crops with enhanced traits, including improved crop size, nutritional value [2][3] and resistance against diseases, herbicides and environmental stressors such as drought, salinity and heat [4][5]. Additionally, CRISPR's ability to produce non-transgenic plants addresses public and regulatory concerns associated with the earlier genetically modified organisms (GMOs) [6]. Its application to the re-domestication of local plants by promoting their existent natural advantages not only enhances crop resilience to extreme weather conditions worsened by climate change but also contributes to sustainable agricultural practices by reducing chemical inputs, minimizing resource usage and leaving the biodiversity of ecosystems undisturbed [7]. In addition, allowing for higher yields, both in quality and quantity positively affects the development of urban-adapted crops advancing the practicality of urban agriculture and subsequently reducing supply chain lengths and mitigating the environmental impact of trans-equatorial food logistics [7]. Reduced timelines for developing new crop varieties, enhanced productivity, and lower production costs through lower resource usage offer significant economic benefits [1]. Existent use cases of gene-edited crops like tomatoes with improved growth traits and resistance to diseases demonstrate the potential for cost-effective, high-yield agriculture [2]. These are particularly relevant in regions like sub-Saharan Africa, particularly vulnerable to food insecurity and climate variability. By improving local crop resilience and productivity, CRISPR can reduce reliance on food imports and promote economic self-sufficiency [8]. Despite its positive impact, the integration of CRISPR into the agricultural supply chain faces significant challenges. Technical hurdles include variability in editing efficiency across plant species and possible unintended long-term genetic effects [9]. Regulatory inconsistencies globally intensify these issues, with regions like the European Union imposing strict GMO-equivalent regulations, opposing more lenient policies in the United States, Brazil, and the United Kingdom. Such disparities create market disruptions and trade barriers, complicating the global adoption of gene-edited crops [10]. Ethical considerations further complicate a fast, vast and smooth integration. Concerns about biopiracy, intellectual property rights, and corporate monopolization highlight potential sources of further economic disparities between nations. Environmental risks, including the unintentional spread of edited traits to wild relatives and effects on non-target organisms, require careful management [11]. Consumer scepticism regarding food safety and the cultural implications of genetic modifications further stresses the need for transparent communication and ethical governance. Overall, CRISPR technology has the potential to reform

ICIEOM2025_ABST_0110_38309

TOWARDS THE D-BEST DRIVEN CIRCULAR ECONOMY DIGITAL INNOVATION HUB (CE-DIH): ARCHITECTURE, PRINCIPLES, AND EARLY INSIGHTS

ÁREA: Special Session / SPS2 - Educational Development for a Digital and Circular Transition
AUTORES: HERNAN RUIZ OCAMPO;SOFIA HAMIKA;CLAUDIO SASSANELLI

10.14488/ijcieom2025_abst_0110_38309

circular economy, digital innovation hub, education, skills

The Circular Economy Digital Innovation Hub (CE-DIH), developed within the European CERES project, represents a novel, multi-stakeholder digital platform designed to catalyse and coordinate the circular economy (CE) transition across key industrial sectors. Anchored in the D-BEST reference model (Data, Business, Ecosystem, Skills, and Technology), the CE-DIH operationalises a coherent innovation ecosystem that addresses both the digital and circular transformation challenges faced (in particular by SMEs, and students and vocational learners). The preliminary architecture, strategic aims, and implementation blueprint of the CE-DIH are here presented. In line with the D-BEST framework articulated by Sassanelli and Terzi (2022), the CE-DIH is structured to activate four core dimensions: - Data: through the integration of a knowledge centre encompassing good practices, case studies, CE-related policies, and funding opportunities, - Business: by supporting SMEs in the automotive, textiles, e-waste and windmill sectors with tailored upskilling, networking, and business development guidance - Ecosystem: by fostering an interconnected community of learners, entrepreneurs, NGOs, academia, and public authorities - Skills and Technology: via scalable digital tools (e.g., MOOCs) that facilitate lifelong learning and practical CE implementation. The CE-DIH will be deployed through an off-the-shelf platform chosen for its robustness in delivering a user-centred, AI-supported educational and collaborative environment. The hub?s initial services include Massive Open Online Courses (MOOCs), circular business development support, and a community-building strategy to foster innovation across diverse value chains and alignment with EU policy frameworks. The digital infrastructure ensures open access, low entry barriers, and high scalability, cornerstones of the European Digital Innovation Hubs (EDIHs) framework. The CE-DIH?s architecture notably integrates operational principles prioritising transparency, trust-building, and sustained stakeholder engagement. The single-entry platform aims to serve as a dynamic node for exchanging experiential knowledge and co-developing practical circular economy solutions, contributing directly to the European Green Deal (EGD) objectives and climate resilience strategies. Preliminary feedback indicates strong interest from both learners and SMEs in accessing modular, CE-focused educational content, and networking opportunities. In conclusion, this abstract outlines an emerging innovation infrastructure that embodies the D-BEST model's strategic vision and demonstrates practical alignment with digital and green policy agendas. Future work will involve testing, refining, and expanding the CE-DIH to ensure long-term sustainability and maximum societal impact, inviting open collaboration and discussion on scaling and replication.

ICIEOM2025_ABST_0119_38297

TWIN TRANSITION AND DIGITAL PRODUCT PASSPORT: DRIVING COMPETITIVE ADVANTAGE IN CIRCULAR TEXTILE

ÁREA: Special Session / SPS11 - Circular Ecosystems and systemic innovation ? actors, structures, goals and flows for sustainability
AUTORES: CHIARA GROSSO;IDIANO D'ADAMO

10.14488/ijcieom2025_abst_0119_38297

sustainable business model, digital product passport, circular ecosystem

The "twin transition" involves the digitalization of production processes and the adoption of sustainable models, an approach increasingly relevant in industrial and scientific research. The European Union emphasizes the role of clean digital technologies in climate action, supporting sustainability goals and the circular economy. However, the twin transition presents significant challenges for businesses [1], such as the need to integrate digital transformation as a cross-functional strategy and the lack of a coordinated approach, particularly among small and medium-sized enterprises (SMEs). Moreover, not all digital innovations are beneficial for sustainability. To properly monitor progress toward more sustainable business practices, it is essential to consistently collect, analyze, and interpret data. In this regard, European directives, including the Green Deal and the Circular Economy Action Plan, require that all products sold in the EU must have a Digital Product Passport (DPP) starting from 2024. The DPP provides a "digital identification document" that collects detailed information about the product's composition, production, lifecycle, sustainability credentials, and ownership history. The DPP system operates through technological components that enable the collection, storage, and sharing of information throughout the product's lifecycle. Each product with a DPP can be scanned with compatible devices to access this information in an easy and direct way. Currently, DPP projects are still in the conceptual or prototype phases in many sectors, with some pilot projects under testing. DPP design setting requires to manage data avoiding information overload and supporting in identify useful data for decision-making. Therefore, it is essential to design a targeted and functional DPP. The aim of the present study is to bridge companies' perspectives in designing a DPP that effectively addresses their evolving need to adapt their business models while transitioning toward more sustainable and interconnected production systems. To this purpose, the study focused on the DPP to explore its role as a tool for supporting companies in the textile industry. Specifically, the research objective (RO) is to identify key data to be included in the Digital Product Passport (DPP) to enhance competitive advantages and enable companies to track their progress toward more sustainable practices, including facilitating a circular interconnection with other companies

ICIEOM2025_ABST_0116_38269

UNLOCKING SUSTAINABLE INNOVATION: CIRCULAR START-UPS WITHIN COMPLEX ADAPTIVE ECOSYSTEMS

ÁREA: Special Session / SPS8 - Unfolding the complexity of supply chain transformation toward circularity and resilience
AUTORES: ROBERTO CERCHIONE;RENATO PASSARO;IVANA QUINTO;VIVIANA SICARDI

10.14488/ijcieom2025_abst_0116_38269

circular economy, complex adaptive system (cas), start-ups

In the business context, industrialisation and sustainability issues are closely interconnected. The importance of environmental sustainability in the industrial sector has grown significantly in response to the urgent challenges posed by ecological degradation and climate change (Averina et al., 2022). Given their crucial influence on the future of our world, corporations must progressively support sustainable practices (Johnson and Schaltegger, 2020). In this scenario, the transition to a sustainable and circular entrepreneurial ecosystem becomes imperative. For this purpose, the implementation of a Circular Economy (CE) at the micro, meso, and macro levels allows for a reduction in the demand for natural resources and energy. In contrast to conventional supply chains, circular supply chains engage a wider range of stakeholders involved in circular operations. The stakeholders encompass enterprises and start-ups operating in various supply chain sectors, waste management companies, reverse logistics service suppliers, post-sale services, and additional external organisations such as regulatory bodies, worldwide organisations, universities, research institutions, and non-governmental organisations (Braz and Marotti De Mello, 2022; Massari et al., 2024). All aforementioned stakeholders operate synergistically by creating a sustainable entrepreneurial ecosystem (SEE). Notably, SEE aims to promote entrepreneurial initiatives that prioritise economic, environmental, and social sustainability, thereby playing a pivotal role in the transition to a sustainable economy. According to the complex adaptive system (CAS), an SEE is described as a dynamic network of interconnected actors in continuous evolution, wherein the complex nature of the 'system' increases as the number of components and their interactions grow. This paradigm allows for the analysis of intricate relationships among challenges associated with environmental sustainability, entrepreneurial aspirations, and the dynamic interplay of resources within a complex adaptive framework (Chandra et al., 2024). In this dynamic environment, circular start-ups play a pivotal role as catalysts of innovation and sustainability. Circular start-ups considerably reduce environmental impact and promote circular economy concepts by utilising digital technologies, circular business strategies, and resource-efficient strategies. Based on these premises, by employing a qualitative data analysis of 3 Italian circular start-ups, this research analyses the interactions of the circular start-ups and their environment, emphasising CAS as a catalyst for promoting circularity across many industries. Therefore, in this research, to enhance the construct validity, we employed the data triangulation technique (Roeck et al., 2020; De Massis et al., 2015). Notably, the primary data were obtained through semi-structured interviews. Moreover, several secondary sources were adopted, including company reports, websites, scientific papers, and public and internal documents. In order to analyse the data, this study employs within-case analysis for each company interviewed and then a cross-case analysis to examine similarities and differences between cases, thus increasing the external validity of the study (Eisenhardt, 1989). All the circular start-ups interviewed interact with several internal and external entities to develop innovative and circular goods and services. Moreover, several circular start-ups establish their process and operations within defined geographic boundaries in order to ensure efficiency, sustainability, and quality control. Notably, the implementation of a short supply chain system entails the promotion and alignment of local economies, the identification of traditions and the connection to the region, as well as the reduction of the overall environmental impact. Moreover, the outcomes highlight the interconnection of circular start-ups with agents that work in diverse sectors in order to develop innovative, scalable, and sustainable products or services. These networks seek to overcome operational challenges and improve process efficiency by creating synergies between autonomous actors that, while preserving their individual identities and aims, collaborate to achieve a common advantage. Therefore, for circular start-ups, cross-sectoral networking among different economic participants helps to provide sustainable solutions.

ICIEOM2025_ABST_0104_38149

UNPACKING AI PRODUCT SUCCESS: IDENTIFYING AND ANALYZING CRITICAL SUCCESS FACTORS USING ISM

ÁREA: Product Lifecycle Management / Innovation, Product and Service Development
AUTORES: IVAN SOROKIN;ZELJKO TEKIC

10.14488/ijcieom2025_abst_0104_38149

ai-products, success factors, ism.

As artificial intelligence (AI) continues to transform industries, developing AI-based products requires a nuanced understanding of the factors that drive success. This study identifies 25 critical success factors (CSFs) in AI product development and examines their interdependencies using Interpretive Structural Modeling (ISM). Through in-depth, semi-structured interviews with ten AI experts from both business and technical domains, the research highlights how AI product success emerges from a cohesive system of interconnected factors. Technical expertise and ethical considerations serve as foundational elements, influencing key aspects such as data quality, user adoption, and long-term scalability. By mapping these relationships, this study provides a structured framework for understanding AI product success. The findings contribute to the literature on AI innovation and product development, offering valuable insights for businesses and researchers navigating the complexities of AI-driven solutions.

ICIEOM2025_ABST_0098_38114

UNTANGLING THE KNOTS FOR CIRCULAR ECOSYSTEM EVOLUTION IN THE ELECTRONICS SECTOR: A PARTICIPATORY MODELING APPROACH

ÁREA: Circular Economy and Sustainable Development / Sustainable Operations and Supply Chain Management for Cleaner Production
AUTORES: MARLY MONTEIRO DE CARVALHO;ALYSON RODRIGUES;LUIS PAES;SUSANA PEREIRA;ROBERTA SOUZA PIAO

10.14488/ijcieom2025_abst_0098_38114

circular ecosystem, circular economy, eletroeletronics.

Abstract. The value proposition of circular ecosystems aims to explore different circularity strategies, which require interdependent collaboration among suppliers, customers, complementors, research centers, and diverse public policy actors [1, 2]. This interdependence generates complex networks that drive the transition through collaboration, experimentation, and platformization [3]. Unlike linear ecosystems, circular ecosystems seek to minimize emissions, waste, and natural resource consumption [4], prioritizing biomass flows and recycled by-products from other ecosystems [5] while integrating the principles of sustainable development [6]. Despite conceptual advancements, theorization on circular ecosystems still lacks cohesion and terminological clarity [7, 8, 9]. As noted by [10], circular ecosystems may inherit characteristics from various ecosystem types?such as innovation ecosystems, digital ecosystems, entrepreneurial ecosystems, and service ecosystems?depending on the circular value proposition and the strategies adopted. This complexity poses challenges for the operationalization of the concept. Moreover, there remains a significant need for empirical evidence [11]. In this context, this research aims to investigate the barriers to the formation and development of circular ecosystems in the electronics sector by exploring the perspectives of different stakeholders. The research approach applies system dynamics (SD) modeling, which enables the understanding of complex system behaviors and the examination of causal relationships [12]. However, SD modeling often lacks effective impact-based performance management tools, which are crucial for sustainability transformations [13]. To address these limitations, we adopted a participatory approach, combining backcasting techniques with system dynamics modeling [14]. While participatory SD approaches may vary in specific methodologies, they converge on the centrality of facilitation as the key organizing principle, with model accuracy and participant consensus serving as primary output measures [13]. A participatory SD modeling workshop was conducted, drawing qualitative data from key stakeholders within the Brazilian electronics sector. The workshop lasted four hours, utilizing facilitation techniques and multiple rounds of discussions at different tables to integrate diverse stakeholder perspectives. Participants (n = 19) collaboratively built causal loop diagrams (CLDs) to identify key barriers and obstacles to ecosystem evolution while also suggesting potential courses of action to mitigate these barriers. Throughout the workshop, expert modelers simultaneously captured discussions in the form of causal loop diagrams, which were then refined and validated with participants. All discussions were recorded, transcribed, and analyzed. The resulting causal loop diagram represents ecosystem members? perceptions of the determinants and causes hindering ecosystem evolution. The diagram can be broken down into distinct domains, including consumer influence, complementor bottlenecks, policy constraints, and overarching economic factors. This causal loop diagram serves as a foundation for ecosystem-led planning, engaging multiple levels of existing systems and structures to support a more effective circular economy transition in the electronics sector. Keywords: Circular Ecosystem, Circular Economy, Eletroeletronics. References 1. Gomes, L.A.V., Homrich, A.S., Facin, A.L.F., Silva, L.E.N., Castillo-Ospina, D.A., Trevisan, A.H., Ometto, A.R., Mascarenhas, J., Carvalho, M.M.: Enablers for circular ecosystem transformation: A multi-case study of Brazilian circular ecosystems. Sustainable Production and Consumption 49, 249?262 (2024). 2. Zucchella, A., Previtali, P.: Circular business models for sustainable development: A ?waste is food? restorative ecosystem. Business Strategy and the Environment 28, 274?285 (2019). 3. Konietzko, J., Bocken, N., Hultink, E.J.: A tool to analy