37 research outputs found
Impact of overbooking reservation mechanism on container terminal's operational performance and greenhouse gas emissions
No full textTruck appointment systems facilitate coordination between container terminals and drayage trucks in container pick-up operation reservation. However, in many cases, trucks with a reservation do not arrive at the scheduled appointment. As the number of no-shows increases, the container terminal's productivity will plummet, and drayage trucks that failed to get reservations will lose their opportunity to get service. This research proposes an overbooking reservation mechanism (ORM) to alleviate the negative impact of these no-shows. This research scrutinizes the detailed process mapping of the existing reservation mechanism, proposes an ORM, and conducts agent-based simulations to evaluate the ORM's performance against the regular and go-show reservation mechanisms at different levels of no-shows and working occupancies. The application of an ORM can improve productivity and service levels while minimizing such negative externalities as queue length, overtime, and greenhouse gas emissions. High overtime intensities only appear when the container terminal's workload is exceptionally high, at 200% of maximum capacity, with a low level of no-shows. Even in exceptionally high demand conditions, the drayage trucks wait only up to 16 min before receiving service
Improving Café Reputation: Machine Learning Analytics for Predicting Customer Engagement on Google Maps
Get PDFBackground: Online reviews is a powerful tool in shaping customer decisions, as they significantly influence a business’s reputation and the ability to attract new customer. Given the growing reliance on digital platforms, understanding engagement levels is crucial for business that want to enhance online presence. By analyzing these customer activities, business owners can leverage Machine Learning (ML) analytics to predict engagement on Google Maps reviews.
Objective: This study aimed to develop the most suitable ML model in order to predict customer engagement levels in café business on Google Maps, and determine the online review features that have the greatest impact on engagement. Additionally, the analysis aimed to provide actionable recommendations to help business owners improve online reputation and engagement strategies.
Method: A total of 5,626 online reviews data were collected using web scraping methods during the analysis. The data was then preprocessed by extracting major review features, calculating engagement levels, and addressing class imbalance with SMOTE method. In the study, K-Means clustering was used to segment engagement levels, while sentiment analysis through VADER Lexicon was applied to measure sentiment content. Various ML models were trained and validated using a 10-fold cross-validation method. Finally, Analysis was conducted using Spearman's correlation to identify relationships among features derived from the best-performing ML models.
Results: The result of the analysis showed that Random Forest model achieved the highest accuracy and PR AUC in predicting engagement levels. The four most influential factors were review length (16.23%), photos (15.57%), total rating (12.35%), and author review count (10.19%). Spearman's correlation analysis showed a positive relationship among review length, photos, and author review count, signifying the combined impact on engagement levels.
Conclusion: This study described the effectiveness of Random Forest model in predicting engagement levels in Google Maps reviews. Specifically, the model identified review length, photos, total rating, and author review count as the key factors influencing engagement. These results would provide valuable guidance for business owners that desire to improve customer engagement and online reputation. Building on this, future studies should explore larger datasets, integrate additional features, and examine how the engagement contribute to long-term customer retention.
Keywords: Online Reputation Management, Customer Engagement, Behavior, Machine Learning, Google Maps Review, Predictive Analytic
Warehouse Specification Proposition for Urbanis (Urban Farming Company) Using Discrete Event Simulation Method
Get PDFTwo percent of the world's surface use for cities, yet it consumes 75 percent of its resources. Urban farming is an emerging alternative food network that could supply some of the food needs in cities with less emission, healthier food, and the environment. Urbanis is a company that likes to contribute to the acceleration of urban farming, especially in Indonesia, by utilizing vacant land and labor. In 2021, Urbanis plans to scale up the production capacity to 10 tonnes per month or 400 kg per day. It requires us to have a warehouse to store the food product that has not been absorbed by the market. The purpose of this study is to find warehouse specifications for Urbanis and the amount of labor and rack inside the warehouse alongside capital and operational expenditure. This research uses a layout with an area of 5x14 meters for experimental design. The model then translated into a discrete-event simulation model named Anylogic. The results show, for each amount of arrival, the number of labor that utilizes effectively are two labors with a maximum number of rack 50. Given these results, the author conducted operational and capital expenditure, which consist of variable analysis and additional variables such as a table, fan, and chair. The result is Urbanis need Rp 50.738.000 for capital expenditure while Rp 10.871.337 for operational expenditure
Agent-based inter-organizational systems in advanced logistics operations
Get PDF“Agent-based Inter-organizational Systems (ABIOS) in Advanced Logistics Operations” explores the concepts, the design, and the role and impact of agent-based systems to improve coordination and performance of logistics operations. The dissertation consists of one conceptual study and three empirical studies. The empirical studies apply various research methods such as a multiple-case study research, coordination mechanism design, and predictive analytics using big data. The conceptual study presents a theoretical exploration and synthesis explaining the demand for inter-organizational systems (IOS) and the corresponding IOS functionalities. The first empirical study presents a multiple-case study exploring real-life ABIOS implementations in the warehousing and transportation business. The second empirical study provides an auction based coordination mechanism design for the container’s pick-up/delivery appointment reservation problem that involves the seaports and drayage operators. The third empirical study presents a seaport service rate prediction system that could help drayage operators to improve their predictions of the duration of the pick-up/delivery operations at a seaport by using the subordinate trucks’ trajectory data. Based on these studies, the dissertation offers new insights on the role of inter-organizational systems in mitigating coordination equivocality and uncertainties; the interplay among the ABIOS applications, required structural adjustments, and the potential of business performance improvement opportunities; and the development of two ABIOS prototypes: an auction based coordination mechanism and a predictive analytics application based on big data
Agent-based Truck Appointment System for Containers Pick-up Time Negotiation
Get PDFCongestion in the seaports area is a common issue in many parts of the world. Fluctuating truck arrival has been identified as one of the significant determinants of congestion. In response, a truck appointment system (TAS) is introduced to manage truck arrival, particularly at peak times. In the existing TAS mechanism, the scheduling decision is centralized and disregards the concerns of trucking companies. Moreover, TAS may complicate the business operation of trucking companies that already have a constrained truck schedule. This study proposes a decentralized negotiation mechanism in TAS that allows trucking companies to adjust arrival times by utilizing the waiting time estimation provided by the terminal operator. We develop an agent-based model of a TAS in the container terminal pick-up procedure. The simulation results indicate that compared to the existing TAS mechanism, the negotiation TAS mechanism generates a shorter average truck turnaround time regardless of truck arrival rates. In terms of average net time cost, the negotiation TAS mechanism provides better value under high truck arrival rate conditions. The incentive for trucking companies to participate in the negotiations is even higher at peak times
Unraveling the Most Influential Determinants of Residential Segregation in Jakarta: A Spatial Agent-Based Modeling and Simulation Approach
No full textThis study involves the analysis of the residential segregation patterns in Jakarta, Indonesia, one of the largest global metropolitan cities. Our objective is to determine whether similarities in religion or socioeconomic status are more dominant in shaping residential segregation patterns in Jakarta. To do so, we extended Schelling’s segregation agent-based model incorporating the random discrete utility choice approach to simulate the relocation decisions of the inhabitants. Utilizing actual census data from the 2010–2013 time period and the Jakarta GIS map, we simulated the relocation movements of the inhabitants at the subdistrict level. We set the inhabitants’ socioeconomic and religious similarities as the independent variables and the housing constraints as the moderating variable. The segregation parameters of the inhabitants (i.e., dissimilarity and Simpson indexes) and the spatial patterns of residential segregation (i.e., Moran index and segregation maps) were set as the dependent variables. Additionally, we further validated the simulation outcomes for various scenarios and contrasted them with their actual empirical values. This study concludes that religious similarity is more dominant than socioeconomic status similarity in shaping residential segregation patterns in Jakarta
Industry 5.0 Research in the Sustainable Information Systems Sector: A Scoping Review Analysis
Get PDFIndustry 4.0, centered on cyber-physical production systems, has been criticized for prioritizing profit over social and environmental concerns. In contrast, Industry 5.0 emphasizes AI efficiency while promoting human-centric, resilient, and sustainable approaches, integrating economic, social, and environmental systems. Previous research has often focused solely on conceptual frameworks and technologies, overlooking Industry 5.0\u27s sector-specific impacts. This study addresses that gap by conducting a scoping review to map research findings, identify trends, and highlight knowledge gaps and future research opportunities. By systematically analyzing literature from the Scopus database (2016-present), the study refined a large dataset to focus on Industry 5.0\u27s relevance. The analysis revealed significant attention to sectors like Industry and Producer Services, while Agriculture and Retail, particularly natural resource-based sectors like agriculture and fisheries, are often neglected. Key findings indicate that Industry 5.0 is likely to be driven by the industrial sector, followed by product services and financial industries. The study also highlights the strong connection between IoT and AI in optimizing operations with real-time data and automation and identifies blockchain as a promising technology for enhancing transparency and security, despite existing implementation challenges. This research not only serves as a foundational record of Industry 5.0\u27s implications across various sectors but also provides valuable insights into its role in Information Systems (IS). It lays the groundwork for future exploration of Industry 5.0 in diverse sectors and industries
Integrated Multi-Income Stream Performance Dashboard: a Japanese Corporate Banking Case
Get PDFIn response to the complex operational challenges faced by Japanese Corporate Banking (JCB), arising from the coexistence of disparate core banking systems post-merger, this study aims to address inherent issues affecting marketing performance monitoring. The existing condition at JCB is characterized by data inconsistency, limited system interoperability, and fragmented income tracking through multiple Excel reports and management systems. Recognizing the gaps in the current setup, the research question revolves around how to enhance marketing performance monitoring effectively. The research objectives, therefore, encompass the development and implementation of a tailored integrated report utilizing the CRISP-DM methodology. This innovative performance dashboard harmoniously consolidates data from diverse sources, presenting a cohesive representation crucial for comprehensive marketing performance assessment. Leveraging advanced methodologies like data normalization and cross-platform integration, the research approach ensures streamlined income tracking, mitigating existing limitations. The data, drawn from various product applications, undergoes meticulous processing to facilitate a unified view on the integrated dashboard. The anticipated result is a significant improvement in monitoring efficiency, heightened data accuracy, and an empowered decision-making process within JCB\u27s operations. The business implication of this initiative is the tangible enhancement of the bank\u27s ability to comprehensively assess income performance, thereby elevating the quality of strategic decision-making and reinforcing JCB\u27s competitive positioning in the banking sector
STRATEGIC DECISION MAKING FOR BUSINESS DEVELOPMENT AND PROFIT OPTIMIZATION USING THE ANALYTICAL HIERARCHY PROCESS: THE CASE OF KLINIK PRATAMA SINDANG SARI
Get PDFHealth is important for the well-being of the community, and basic clinics are important places for people in Indonesia to get health treatment. Klinik Pratama Sindang Sari has been committed to providing great service since it opened in 1997. In 2024, it received "Paripurna" (excellent) accreditation. Even though the clinic knows this, it still has trouble being financially stable, mostly because it relies heavily on BPJS capitation, which makes up 80% of its income. Rising costs of doing business and not enough variety make it even harder for it to earn profit. This study looks into ways for Klinik Pratama Sindang Sari to build its business in a smart way that will help its finances. The study uses Value-Focused Thinking (VFT), the Analytic Hierarchy Process (AHP), Stakeholder Analysis, Focus Group Discussions, and the Kepner-Tregoe Problem Analysis to find and rank alternative service innovations. The Analytic Hierarchy Process (AHP) is very important for judging four proposed strategies: Vaccine Service Implementation; Dental Aesthetic Care; Corporate Health Screening Cooperation; and BPJS Capitation Optimization. It does this by looking at factors like cost, regulation, market demand, profit potential, and implementation challenges
ENHANCING CREDIT SCORING PREDICTION IN ISLAMIC BANKING WITH RANDOM FOREST MACHINE LEARNING MODEL : THE ROLE OF MARITAL STATUS
Get PDFThis study explores the application of machine learning techniques, particularly the Random Forest algorithm, to predict default risk in Islamic consumer financing, with a specific focus on marital status as a key demographic factor. Conducted in the context of Islamic banking in Indonesia where ethical compliance and prudent risk assessment are critical the research examines whether incorporating marital status can improve credit risk classification. Utilizing historical financing data from an Islamic bank, the study addresses three central research questions: (1) How accurate is the Random Forest model in predicting default risk when marital status is considered? (2) How effective is the Random Forest algorithm in identifying default risk for Islamic consumer financing based on marital status? (3) What marital status related factors significantly influence the performance of the Random Forest model in this context? The methodology involves standard machine learning procedures, including data preprocessing, categorical feature encoding, and model evaluation using confusion matrices and classification metrics. Feature importance analysis is also conducted to identify influential variables. This research contributes to the emerging synergy between Islamic finance and artificial intelligence, demonstrating how demographic factors such as marital status can enhance Sharia-compliant credit risk assessments in modern Islamic banking systems.Penelitian ini mengeksplorasi penerapan teknik machine learning, khususnya algoritma Random Forest, untuk memprediksi risiko gagal bayar dalam pembiayaan konsumen Islam, dengan fokus khusus pada status pernikahan sebagai faktor demografis utama. Penelitian ini dilakukan dalam konteks perbankan syariah di Indonesia di mana kepatuhan terhadap prinsip etika dan penilaian risiko yang cermat sangat krusial. Penelitian ini mengevaluasi apakah integrasi status pernikahan dapat meningkatkan klasifikasi risiko kredit. Dengan menggunakan data pembiayaan historis dari sebuah bank syariah, studi ini menjawab tiga pertanyaan penelitian utama: (1) Seberapa akurat model Random Forest dalam memprediksi risiko gagal bayar dengan mempertimbangkan status pernikahan? (2) Seberapa efektif algoritma Random Forest dalam mengidentifikasi risiko gagal bayar pada pembiayaan konsumen syariah berdasarkan status pernikahan? (3) Faktor-faktor terkait status pernikahan apa yang secara signifikan memengaruhi kinerja model Random Forest dalam konteks ini? Metodologi yang digunakan mencakup prosedur standar machine learning, termasuk pra-pemrosesan data, pengkodean fitur kategorikal, dan evaluasi model melalui confusion matrix serta metrik klasifikasi. Analisis pentingnya fitur juga dilakukan untuk mengidentifikasi variabel yang berpengaruh. Penelitian ini memberikan kontribusi terhadap sinergi yang berkembang antara keuangan syariah dan kecerdasan buatan, dengan menunjukkan bagaimana faktor demografis seperti status pernikahan dapat meningkatkan penilaian risiko kredit yang sesuai dengan prinsip syariah dalam sistem perbankan Islam modern
