252 research outputs found
Sort by
A Fuzzy Multi-Criteria Approach for Selecting Open-Source ERP Systems in SMEs Using Fuzzy AHP and TOPSIS
Full text linkIn a rapidly growing and competitive business era, selecting an open-source Enterprise Resource Planning (ERP) system is a critical step to support the efficiency and effectiveness of company operations. This research aims to propose an innovative methodology by integrating the fuzzy Analytical Hierarchy Process (fuzzy AHP) and fuzzy Technique for Order Preference by Similarity to the Ideal Solution (fuzzy TOPSIS) to improve the open-source ERP selection process. The method involves eight criteria and 26 sub-criteria to comprehensively evaluate 11 open-source ERP alternatives, specifically for SMEs in the transportation services sector in Indonesia. System quality has been identified as a critical factor in the selection of an open-source ERP system, with particular emphasis on aspects such as security and reliability. These sub-criteria are considered the most influential in determining the suitability of a system. The analysis further indicates that the 10th ERP alternative as the best choice, consistently outperforming others in meeting the defined criteria. Additionally, sensitivity analysis confirmed the robustness of this choice, demonstrating its stability and effectiveness despite changes in criteria weights. Beyond its practical implications for SMEs, this research contributes a versatile evaluation framework that can be adapted to other industries seeking effective ERP solutions. The findings emphasize the importance of structured decision-making in technology adoption, offering comprehensive and reliable guidance for organizations aiming to optimize their operations through open-source ERP systems. This study not only bridges a critical gap in ERP selection for SMEs but also establishes a methodological foundation for future research and applications across diverse industry sectors
Achieving Quasi-Complete Balance in U-shaped Assembly Lines Using Idle Time Elimination Methodologies
Full text linkThe problem of balancing the U-shaped production line is a well-known NP-problem in mass production, where the main objective is to allocate tasks efficiently to the workstations while minimising idle time and balancing the workload throughout the production line. The study introduces two new methods to address these issues and increase the efficiency of line balancing: Operators Arrangement for U-shaped line Balancing (OAUB) and Layout Design for U-shaped line Balancing (LDUB). OAUB optimizes the allocation of workers by strategically adjusting the assignment of tasks, ensuring that idle time is minimised and that the use of workers is maximised. By contrast, LDUB alleviates idle time by filling in the gaps between tasks with independent, low-processing tasks from non-critical paths, thus ensuring a more efficient use of available time during the transitions. Implementation of these methods has shown promising results and significantly reduced the idle time of the production processes. Specifically, OAUB achieved a reduction of only 0.9 units of idle time, while LDUB decreased idle time by 3 units. These results are particularly important from a sustainability point of view, as they contribute to reducing waste through better use of resources and increased production efficiency. Unlike traditional approaches, which are mainly focused on minimizing the number of workstations or the reduction of cycle times, our methodology provides a more integrated approach to balancing the workload between workstations. The practical implications of these methods are significant as they are applicable to the real-world production environment and offer effective and efficient solutions to achieve near complete alignment on U-shaped assembly lines, thus improving overall productivity and sustainability in an environment of mass production
Modeling Consumer Willingness to Consider Electric Motorcycles in Indonesia: A System Dynamics Approach
Full text linkSustainable transport plays a key role in the fight against climate change, particularly in developing countries where reliance on conventional vehicles is high. Motorcycles account for the majority of the fleet of motor vehicles in Indonesia and contribute significantly to emissions. In order to achieve its Paris target of a 29 percent reduction of carbon emissions, the government is encouraging electric cars with various incentives. This study develops a willingness to consider (WTC) model for electric motorcycles in Indonesia based on the powertrain technology transition market agent model (PTTMAM) and utilizes Vensim software to simulate outcomes. The WTC model is built on the assumption that consumers' willingness to consider electric motorcycles is influenced by factors such as costs, marketing, and exposures. The system dynamics model consists of four modules: the conventional motorcycle, the electric motorcycle, the marketing module, and the willingness to consider module. The simulation results show an increasing trend in consumers’ willingness to consider electric motorcycles from 2017-2035, with the WTC value reaching 0.3209 in 2035. While this indicates a positive shift toward greater consumer interest in electric motorcycles, the growth remains modest and slow, reflecting the challenges of widespread adoption. Additionally, this study evaluates three government incentive and subsidy policy scenarios. The scenario results indicate that government subsidies and incentives can increase the consumers’ willingness to consider electric motorcycles in Indonesia, thereby increasing their market share. Among the scenarios, the purchase price subsidy is the most effective, as it directly reduces the financial barrier, encouraging more consumers to make the switch to electric motorcycles
Unveiling the Landscape of Sustainable Logistics Service Quality: A Bibliometric Analysis
Full text linkIn today's environmentally conscious world, where environmental sustainability and consumer demand for responsible business practices are Sustainable Logistics Service Quality (SLSQ) has emerged as a critical focus in supply chain management, driven by increasing environmental concerns and consumer demand for responsible business practices. This study conducts a bibliometric analysis of 546 Scopus-indexed documents published between 1994 and 2024, systematically uncovering key research trends, thematic clusters, and gaps in SLSQ. Findings reveal a marked increase in SLSQ research since 2013, spurred by regulatory pressures, advancements in digital technologies, and growing consumer expectations for sustainable logistics. Dominant themes include the integration of cutting-edge technologies such as artificial intelligence (AI), big data analytics, blockchain, and sustainable transportation methods, which collectively enhance logistics service quality while reducing environmental impacts. Additionally, a notable trend is the alignment of logistics services with sustainability goals, reflecting both academic interest and industry imperatives to lower carbon footprints and improve resource efficiency, particularly in sectors like e-commerce. Despite these advancements, the study identifies significant gaps, particularly the lack of multidimensional metrics capable of comprehensively evaluating SLSQ across social, environmental, and economic dimensions. This highlights an urgent need for standardized and holistic frameworks to guide logistics providers in achieving operational efficiency and sustainability objectives. By bridging service quality and sustainability, this research addresses an underexplored area and provides a foundation for future scholarly work in SLSQ. Practical implications include guiding logistics providers and policymakers in formulating sustainable practices that align with regulatory requirements and enhance customer satisfaction. For academia, it offers a pathway to develop robust SLSQ metrics and frameworks, advancing sustainable logistics strategies and fostering a more efficient, eco-friendly, and customer-centric logistics ecosystem.
Enhancing Above-Knee Prosthetic Design for Inclusive Workplaces: Ergonomic Considerations in Manual Material Handling
Full text linkEmployment is crucial for economic sustainability and social inclusion, yet individuals with disabilities face significant barriers. Globally, only 44% of disabled individuals are employed compared to 75% of those without disabilities. Manual material handling (MMH) relies heavily on stability and control in demanding industries such as manufacturing and logistics. Such demands create challenges for individuals with above-knee prostheses, as most current designs focus on walking and do not adequately support the postural and load-bearing requirements of MMH tasks. This study aims to evaluate the performance of transfemoral prosthesis designs during MMH, analyzing the effects of container type, load mass, and their interaction on gait efficiency, discomfort, and stability. Eight male unilateral above-knee amputees (24–39 y) carried handled and handle-less boxes loaded from 4 to 10 kg in a randomised within-subject trial. Gait deviation, perceived discomfort, and steadiness were captured with self-report measures. Two-way analysis of variance analyses showed a significant container × load interaction: handle-less 10 kg loads produced the greatest lateral trunk lean toward the prosthetic side, whereas lighter handled loads minimised deviation. Increasing load also elevated discomfort in the back, waist, stump and contralateral arm and reduced perceived stability. Observed lateral lean and impact-related knee extension suggest three priority modifications: (1) add socket adduction within an ischial-containment design to improve femoral stabilisation, (2) increase knee-swing friction to soften terminal impact, and (3) fit dual-keel feet to cushion heel strike. Implementing these changes may reduce gait errors and fatigue, raising safe lifting capacity for transfemoral prosthesis users in MMH task. Nonetheless, the male-only sample may not capture gender-specificgait strategies; future trials should include female participants and a larger cohort to verify generalisability. These preliminary findings still offer insights into improving prosthetic designs to enhance safety, functionality, and inclusion in industrial MMH tasks
An Integrated Framework for Ergonomic Performance Assessment in Food Manufacturing: A Case Study Using Ergo-VSM, AHP, and TLS
Full text linkIn food processing industries, particularly nut-based production that relies heavily on manual labor, ergonomic challenges related to repetitive motion, prolonged static postures, and thermal stress are increasingly prominent due to rising production demands. These issues are often concentrated at specific workstations and tend to be overlooked in conventional performance evaluations. To address this gap, this study proposes an integrated Ergonomic Performance Assessment (EPA) framework designed to evaluate ergonomic performance comprehensively across the entire production line. The framework integrates Ergonomic Value Stream Mapping (Ergo-VSM) for process visualization, the Analytical Hierarchy Process (AHP) for assigning weights to ergonomic criteria, and the Traffic Light System (TLS) for intuitive performance classification. A case study was conducted in a peanut processing facility, involving 8 workstations. Data were gathered through direct observations, detailed task analyses, and expert input from three experts via Focus Group Discussions (FGDs). Ergonomic indicators were derived from literature and expert consensus, weighted using AHP based on pairwise comparisons, and assessed using structured observational metrics. The results were visualized within the Ergo-VSM framework using TLS. Ergonomic performance was quantified through the Manufacturing Ergonomic Score (MES), which reached 69.15%. Based on a three-tier classification system low (<60%), moderate (60–90%), and high (>90%) this score falls within the moderate category, indicating several areas require improvement. Musculoskeletal disorder risks and high working temperatures were identified as the most critical concerns, particularly at thermally intensive and physically demanding workstations. The EPA framework enabled the visualization of ergonomic variation between workstations, allowing for systematic identification of priority areas for improvement. This research contributes to ergonomic evaluation literature by offering a structured, data-driven approach and provides practical insights for enhancing worker well-being and operational productivity
Optimizing Demand Forecasting Method with Support Vector Regression for Improved Inventory Planning
Full text linkProblems arising from suboptimal production planning can cause inventory management to be less effective and efficient in the company. The lack of integrated presentation of information also causes less efficiency in making decisions. This study aims to obtain the best kernel function forecasting model by predicting ground rod sales using the Support Vector Regression (SVR) method in order to determine the level of forecasting accuracy and the results of ground rod forecasting in the future which are presented in an optimal data visualization. This problem-solving is done with the Support Vector Regression method, which consists of linear kernel functions, polynomial kernel functions, and radial basis function (RBF) kernel functions with the Grid Search Algorithm. Based on the results of the best parameter search that has been done using the grid search algorithm, it can be concluded that the best kernel function forecasting model is a linear kernel function with a value of C = 100 and ε = 10-3. The accuracy of this forecasting model has a MAPE value of training data and testing data of 2.048% and 1.569%, where this value is the smallest MAPE value compared to the MAPE value of the other two functions. After getting the best model, forecasting was carried out within five months, obtaining an average of 6,647 monthly pieces. The results of forecasting and historical sales are reviewed in a visualization of Business Intelligence data so that it is well exposed, where the forecasting shows an increase from every month
Business Incubators and Technology-Based Startups in Emerging Economies: A Bibliometric Analysis
Full text linkIn the context of rapid technological advancement and the global rise of entrepreneurship, business incubators have become essential mechanisms for supporting technology-based startups, particularly in emerging economies. These incubators play a strategic role in bridging resource gaps, fostering innovation, and enhancing the survival and growth of early-stage ventures. Despite their increasing importance, there remains a limited understanding of how incubator performance directly influences startup outcomes. This study addresses that gap through a comprehensive bibliometric analysis of 920 scholarly articles published between 2010 and 2022, sourced from Scopus and Google Scholar. Using VOSviewer, the analysis identifies key research trends, influential publications, and thematic clusters related to incubator performance. The findings reveal a significant increase in research activity over the past decade, with a peak in 2018, and a strong concentration of publications in journals focused on technology transfer and innovation management. Prominent themes include academic entrepreneurship, incubator performance, technology transfer offices, and the role of innovation ecosystems involving academia, industry, and government. These themes highlight the multifaceted nature of incubator success and the importance of cross-sector collaboration. The study also emphasizes the need for integrated evaluation frameworks to enhance incubator effectiveness and guide institutional and policy-level strategies. The novelty of this research lies in its synthesis of bibliometric insights to propose future research directions and methodological improvements for assessing incubator performance. By mapping the intellectual landscape of incubator research, this study contributes to a deeper understanding of how incubators can be optimized to support sustainable startup development and economic growth in emerging markets
A Framework for Sustainable Supplier Selection Integrating Grey Forecasting and F-MCDM Methods: A Case Study
Full text linkSelecting appropriate suppliers is critical for healthcare organizations to ensure high-quality, reliable, and sustainable patient care services. In an increasingly competitive environment, hospitals must optimize supplier selection not only based on economic factors but also by integrating environmental and social sustainability considerations. This study aims to create a strong system for choosing sustainable suppliers in healthcare by combining fuzzy-based multi-criteria decision-making (MCDM) methods with Grey Forecasting GM(1,1) to handle uncertainty and changes in performance over time. The proposed framework applies the Fuzzy Best-Worst Method (F-BWM) to determine the relative importance of sustainability criteria, while the Fuzzy Additive Ratio Assessment (F-ARAS) method is used to rank suppliers based on these weighted criteria. Grey Forecasting GM(1,1) is employed to predict supplier performance for future periods, with forecasting accuracy evaluated through Mean Absolute Percentage Error (MAPE). All supplier forecasts achieved MAPE values below 5%, indicating very high prediction reliability. Empirical results from a case study at a general hospital in Indonesia confirm that social aspects, such as patient safety and reputation, are prioritized over economic and environmental considerations. Practically, the proposed framework enables healthcare institutions to holistically evaluate suppliers, specifically reducing risks related to supply disruptions and quality inconsistencies. The model performs best under conditions of limited or uncertain data availability, where supplier historical performance trends can be leveraged to forecast future reliability and sustainability outcomes. The prioritization of sustainability criteria yields social criteria (weight = 0.3703) as the most important, followed by economic (0.3609) and environmental (0.2688) criteria
Enhancing Sustainable Performance in Hotel Industry: Supplier Innovativeness and Supply Chain Integration
Full text linkThe hotel industry relies on supply chain to deliver value added products and services, therefore selecting suppliers significantly affects the company's competitiveness in the market to improve sustainability performance. This research is important to determine how supplier innovativeness can improve sustainable performance. It provides a new contribution in assessing the influence of supplier innovativeness and supply chain integration on sustainable performance in the hotel industry, however their combined impact remains underexplored. The study examines the effect of supplier innovativeness on sustainable performance by focusing on the mediating role of supply chain integration in the hotel industry. The study employed a non-probability sampling method using a purposive sampling technique. The sample was selected based on the criterion that respondents held managerial or equivalent positions, as they were responsible for decision-making in hotel operations. A total of 111 respondents participated in the study and the hypotheses were analysed using SmartPLS software. Supplier innovativeness has a significant effect on supply chain integration and also contributes significantly to the improvement of sustainable performance. Indirectly, supplier innovativeness also significantly impacts sustainable performance through supply chain integration. Supply chain integration partially mediates the relationship between supplier innovativeness and sustainable performance. Emphasizing these factors can help hotels to achieve their sustainability goals, offering valuable insights for managers and policymakers. Hotel managers should actively engage in partnerships with innovative suppliers and invest in strengthening integration across their supply chains. This research contributes to the growing body of literature on sustainable supply chain management, particularly within the hospitality industry