Udisubakti Ciptomulyono

Manufacturing remains a major contributor to greenhouse gas emissions and resource depletion, accelerating the transition toward circular business models. Although circular manufacturing seeks to generate economic, social, and environmental value, significant uncaptured value persists and is insufficiently prioritized. This study systematically identifies and prioritizes uncaptured values within circular manufacturing business models. A literature review identified 35 uncaptured values related to sustainable manufacturing business models targeting six captured values aligned with the SDGs. A Fuzzy Delphi survey involving nine experts in heavy equipment and machinery recovery operations was conducted to ensure industrial relevance. Nine uncaptured values met the consensus (75%) and threshold (0.2) criteria and were subsequently prioritized using fuzzy evaluation scores. The retained values primarily reflect core operational constraints, particularly weak quality control in recovery activities, limited availability of viable end-of-life products, and unfavorable recovery end-of-life characteristics. These findings indicate that bottlenecks in reverse supply and recovery execution predominantly drive uncaptured value in circular manufacturing rather than downstream market factors. By providing an expertconsensus prioritization framework, this study advances research on circular business models and offers a practical foundation for targeted mitigation strategies to strengthen value capture and enhance the long-term resilience of circular manufacturing systems.

Circular business models (CBMs) are increasingly being adopted in heavy equipment manufacturing to extend the value of end-of-life (EoL) components through recovery practices. However, existing sustainability risk assessments largely rely on generic literature-based risk lists without verifying their contextual relevance to specific industries. This study addresses this gap by systematically exploring and validating sustainability risks that are specifically relevant to circular manufacturing in the heavy equipment sector. An initial set of 32 sustainability risks was identified through literature review and cross-industry exploration. These sustainability risks were then evaluated using the Fuzzy Delphi Method (FDM) to manage uncertainty and establish expert consensus on their relevance to circular business models in the heavy equipment manufacturing sector. Based on the consensus criteria (d < 0.2; agreement ≥ 75%), 14 risks were validated as contextually relevant. The findings reveal that the most critical risks are concentrated in key circular activities, particularly those related to occupational health and safety hazards in EoL component recovery, and inaccurate or insufficient evaluation of the quality of components or products to be recovered. The main contribution of this study lies in moving beyond generic sustainability risk identification toward context-specific validation of sustainability risks in circular manufacturing. By filtering and confirming sustainability risks that truly reflect industrial realities, the results provide a robust foundation for targeted sustainability risk assessment and mitigation. Practically, the validated sustainability risk set provides decision-makers and engineers with a more precise basis for prioritizing sustainability risks and enhancing the resilience of circular manufacturing systems.

The national energy transition encourages the application of biomass co-firing technology in Steam Power Plants (PLTU) as an effort to reduce greenhouse gas emissions and increase the renewable energy mix. The success of the implementation of co-firing is greatly influenced by the selection of the right biomass raw materials, considering the trade-off between benefits, costs, and operational risks. This study aims to determine the most optimal co-firing biomass raw materials in coal-fired power plants using the Benefit-Cost–Risk Analysis approach combined with the Analytical Hierarchy Process (AHP) method. The study was conducted on one of the coal-fired power plants in East Java by comparing three alternatives, namely sawdust, rice husks, and coal without co-firing. The weighting of criteria was carried out through expert assessment using AHP paired comparisons. The selected alternatives were then analyzed for their environmental impact using the Life Cycle Assessment (LCA) approach with the scope of cradle to gate. The results show that sawdust has the highest Benefit value, a Benefit–Cost (BCR) ratio above one, and a level of risk that can still be managed compared to other alternatives. The LCA analysis identified boiler units as the main hotspots of environmental impact, so operational improvements were recommended through water-steam system control. This research provides a basis for strategic decision-making in the selection of co-firing biomass that supports the energy transition and environmental sustainability.

Service quality in aviation fuel supply operations is critical due to its direct impact on flight punctuality, safety, and overall airport performance. This study aims to systematically identify service quality gaps, prioritize customer requirements, and determine actionable technical responses to improve refueling services at DPPU Juanda. An integrated framework combining SERVQUAL, Importance Performance Analysis (IPA), Focus Group Discussion (FGD), Best Worst Method (BWM), and Quality Function Deployment (QFD) is employed to translate customer expectations into prioritized technical initiatives. The results indicate that schedule integration with stakeholders, real-time monitoring of refueling operations, and strengthening apron audit systems emerge as the most critical technical priorities for service quality improvement. The proposed framework provides a structured decision-support approach that supports managerial decision-making and offers practical guidance for enhancing service reliability in aviation fuel supply operations.

Investment in chemical/oil tankers is crucial for enhancing operational performance and maintaining competitiveness in the global shipping industry. The background of this research is the need to provide solutions to challenges in ship investments that often fail to meet targets, whether in technical performance, compliance with international regulations, or cost efficiency. These conditions affect the competitiveness of companies, as most chemical/oil tanker fleets are over 15 years old, require high maintenance costs, and pose risks of detention by Port State Control (PSC) and rejection by major oil companies. The methodological approach is the integration of Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Analytic Network Process (ANP) to analyze the selection of chemical/oil tanker ship investments. DEMATEL is used to identify cause-effect relationships so that dominant factors can be prioritized. Once the dominant factors are obtained, ANP processes the relationships and dependencies among criteria in the network model and determines more precise weighting. Subsequently, priority calculations are carried out by constructing a supermatrix that encapsulates the relationships among elements. This supermatrix is analyzed to produce final priorities for determining the best alternative. The research data were collected through questionnaires and interviews with stakeholders, including top management and experts. DEMATEL revealed key influencing factors such as Initial Investment, Operating Costs, Regulatory Compliance, Market and Geopolitical Risks, and Technological Risks. These factors are analyzed in the ANP method to obtain a model, network, and pairwise comparisons.

The main challenge in electricity distribution in Riau and Riau Islands is the high incidence of power line faults. One effort to reduce power line faults is through the installation of FIOHL (Fault Indicator Overhead Line). FIOHL is a device used to detect faults on power lines, which can help quickly identify the location of the fault. In this study, PLN UP2D Riau plans to install 35 FIOHL units on 198 power lines. The objective of this study is to determine the priority order of power lines as the location for installing FIOHLs accurately. By using the Entropy method as a criterion weighting method and PROMETHEE (Preference Ranking Organisation Method for Enrichment Evaluations), the priority order of alternative solutions for determining the power lines can be obtained. The installation of FIOHL must meet eight criteria: number of outages, outage duration, Energy Not Served (ENS), number of outages without cause, feeder load, network length, number of customers, and keypoint ratio for each feeder. The number of customers has the highest weight at 0.175, followed by network length at 0.152 and ENS at 0.151. The priority locations for FIOHL installation using the PROMETHEE method show that the Maroko feeder ranks first with Phi = 0.6329, followed by the Laos feeder with Phi = 0.6247. The allocation of FIOHL installations across each Implementation Unit (UP3) includes 14 units for UP3 Bangkinang, 14 units for UP3 Dumai, 4 units for Pekanbaru, and 3 units for UP3 Rengat..

Inconsistencies in product weight during fertilizer bagging can lead to material losses and reduced operational efficiency. This study investigates the use of machine learning to predict weight deviations in the Urea Bagging Unit at PT Petrokimia Gresik. Four algorithms were used: an Artificial Neural Network (ANN), Random Forest Regression (RFR), Linear Regression (LR), and Support Vector Regression (SVR). The dataset used consisted of nine numeric sensor variables. Among the models, RFR achieved the highest predictive accuracy (R² = 0.9638, RMSE = 0.0496, MAE = 0.0338). Feature importance analysis identified the clamping time and air pressure as the most influential variables. A Smart Bagging System was developed using the RFR model, integrating real-time monitoring and automated parameter adjustment. The simulation results show that the system can reduce overweight losses by up to 95%, with potential annual savings of approximately IDR 29 billion. While promising, these results are based on controlled conditions and a limited dataset; further field validation is recommended. The proposed system demonstrates the potential of machine learning to support cost-efficient, real-time process control in industrial bagging operations. This work aligns with SDG 9 and SDG 12 by promoting industrial innovation and reducing resource waste.

Manufacturing is the third largest contributor to greenhouse gas emissions in the world. Exploration of resources to produce raw materials is one process that contributes to greenhouse gas emissions. Using recycled materials in manufacturing with the circular economy principle is one way to reduce the carbon footprint. However, the application of the principle of a circular economy has various challenges and risks. Therefore, sustainability risks in circular manufacturing need to be developed and studied in more depth. This research was conducted using bibliometric analysis using R Software and literature review analysis. The results of the bibliometric analysis produced three research clusters that showed new potential with topics that could be collaborated on. Research on circular business models and risks is still rarely developed but has a high level of relevance, so it still has the potential to be developed. The literature review results strengthen the bibliometric analysis findings by creating a study on sustainability risks in circular manufacturing, the recycling industry, and other recovery industries. Research that examines the impact of sustainability risks on circular manufacturing that processes solid waste can be recommended in further research.

The environmental background for reducing greenhouse gas emissions drives a shifting program to gradually replace the internal combustion engine vehicles (ICEVs) with electric vehicles (EVs). Electric vehicles are part of low-carbon emission vehicles promoted in sustainability transportation. In Indonesia, EV sales volume has risen significantly during the last three years. However, it is still far from the targeted number stated in the roadmap of EV development. Economic factors are the main barriers to EV adoption and production, as are other sustainable product problems. Therefore, the research evaluates the structure of the nine economic barriers related to users, EV manufacturers, EV charging station providers, and the government using the DEMATEL method. The result concludes that the most significant barrier is the domination of global original equipment manufacturers (OEMs), followed by EVs as the disruptive technology and products; most automotive customers are economy users, and the EV purchase prices are higher than ICEV prices. The result benefits as the feed for further decision-making analysis determined by the government for more effective affirmative policy to sustainable products.