Showing 688 results for Type of Study: Research
Sina Nayeri, Mahla Zhain Vamarzani, Zeinab Asadi, Zeinab Sazvar, Nikbakhsh Javadian,
Volume 35, Issue 3 (9-2024)
Abstract
This study focuses on evaluating potential raw material providers (RMPs) as one of the critical tasks of the logistics managers. In this regard, the literature showed that the simultaneous consideration of resilience, digitalization, and circular economy in the RMP selection problem (RMPSP) has been ignored by previous studies. Therefore, to cover the mentioned gap, this research attempts to study the RMPSP by considering other crucial concepts namely resilience and Circular Economy (CE). For this purpose, by considering a real-world case study in the steel industry, the current work first specifies the indicators of the research problem. Then, the indicators’ weights are measured using the stochastic Best-Worst Method (BWM). In the next step, the RMPs are prioritized by developing a novel approach called the stochastic Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). In general, the main objective of this study is to evaluate the performance of the RMPs in the steel industry based on the CE, resilience, and digitalization aspects. According to the achieved results, “Reliability”, “Price”, “Quality”, “Reverse logistics and Waste management”, “Information systems usage”, and “Restorative Capacity”, are identified as the most desirable indicators. Moreover, the results confirm the effectiveness and validation of the developed method.
Mary Jiny D, G Navamani, Raman Kumar, Željko Stević, Darjan Karabašević, Rajender Kumar,
Volume 35, Issue 3 (9-2024)
Abstract
The increasing demand for food delivery services driven by technological innovations has led to a surge in online shopping and food ordering. Efficient logistics play a crucial role in connecting customers with restaurants seamlessly. In this context, the practical application of graphical networks is explored in this article to streamline food delivery operations. We introduce a novel parameter eternal m-certified domination number denoted by γmcer∞(G) , which represents the minimum number of guards needed to handle any sequence of single orders using multiple-guard movements, ensures that the guard arrangement consistently constitutes a certified dominating set. A case study is presented, illustrating how this concept can be employed to de-crease human resources in a food delivery start-up. This research contributes to optimizing food delivery logistics and reducing operational costs, thereby enhancing the efficiency of the food delivery industry.
Arifa Khan, Saravanan P,
Volume 35, Issue 3 (9-2024)
Abstract
Optimizing production in the plastic extrusion industry is a pivotal task for small scale industries. To enhance the efficiency in today’s competitive market being a small-scale manufacturer over their peers is challenging. With the limited resources, having constraints on manpower, capital, space, often facing fluctuations in demand and production, simultaneously maintaining high quality became very important for the success. Among the plethora of KPIS used in manufacturing, Overall Equipment Effectiveness (OEE) stands out as corner stone. In this study, we collected real-world data from a plastic extrusion company. i.e., an HDPE Pipe manufacturing company. It serves as the backdrop for our study, this is based on the plastic extrusion sector and set out a goal of enhancing OEE through a comparative investigation of various ML models. To forecast and estimate OEE values, we used various Machine Learning models and examine each algorithm’s performance using metrics like Mean Squared Error (MSE) and model comparisons using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), creating a comprehensive picture of each algorithm’s strength which enables the small businesses to make informed decisions and empowers them to stay agile and adapt to the changes in the manufacturing environment.
Makhfud Efendy, Nizar Amir, Kritsana Namhaed, Muhammad Yusuf Arya Ramadhan, Mochamad Yusuf Efendi, Mohammed Kheireddin Aroua, Misri Gozan,
Volume 35, Issue 3 (9-2024)
Abstract
The production of food-grade salt from crude solar salt has been examined through a techno-economic evaluation. This study aimed to investigate a salt factory to analyze its technical and economic aspects to determine the precise parameters for improving the quality of food-grade salt. The primary process of this factory involves grinding, washing, draining, drying, and fortification, supported by equipment like brine management, conveyors, sieves, and packaging. The proposed salt plant, designed for a 3-ton daily output over 15 years, requires 30 months for construction and a 4-month startup. The total capital outlay is USD 1,921,000, with USD 310,000 for technology and equipment. Economic indicators, including a Net Present Value (NPV) of USD 7,862,000, an Internal Rate of Return (IRR) of 46.48%, payback in 1.56 years, and a Return on Investment (ROI) of 64.28%, demonstrate feasibility. Establishing a salt plant in Indonesia supports food-grade salt production, stabilizes solar salt prices and enhances the welfare of traditional salt farmers. Ultimately, the results of this study can provide valuable insights for evaluating the feasibility of establishing a food-grade salt production plant in Indonesia.
Yuri Delano Regent Montororing,
Volume 35, Issue 3 (9-2024)
Abstract
Technological advancements have fueled heightened competition in manufacturing, compelling companies to adopt strategies prioritizing swift, timely, and high-quality customer service. This necessitates seamless integration of supportive systems such as resources, equipment, facilities, and workforce, underscoring the criticality of scheduling in aligning activities and resources for on-time task completion. Scheduling, inseparable from sequencing, is pivotal in optimizing manufacturing and service industries' operations. However, challenges arise when tasks converge with limited facility capacities, necessitating effective resource allocation. By leveraging mathematical techniques and heuristic methods, scheduling optimizes resource utilization, minimizes production costs, and enhances service quality. Despite its significance, existing models often overlook critical aspects like identical job consideration and sequence-dependent setup times, limiting real-world applicability. This research addresses these gaps by proposing robust mathematical models for intricate scheduling requirements. The proposed approach seeks to optimize manufacturing operations by effectively handling complex scheduling needs, thereby minimizing production costs and enhancing operational efficiency. This research endeavours to advance scheduling optimization strategies through real-world implementation and evaluation and contribute to the manufacturing industry's sustainable growth.
Iffan Maflahah, Wila Wirvikananda, Hamzah Fansuri, Dian Farida Asfan, Raden Faridz,
Volume 35, Issue 3 (9-2024)
Abstract
Seablite salt (Suaeda maritima) was a unique product currently under development. Seablite is a low-sodium salt essential for modern society, particularly for those who prioritize their health. This investigation aims to employ a dynamic system approach to evaluate the revenue and profit generated by the salt production system. The dynamic systems approach steps: the construction of the causal loop diagram, the development of the stock-and-flow model, the parameterization of the model, the simulation to analyze the system's behavior under various conditions, the verification and validation, the development of policy recommendations, and the conclusion with a summary of the core findings. The model was developed using four submodels: (1) demand, (2) supply, (3) production cost, and (4) revenue. The moderate scenario demonstrates that the salt flow requirements can be satisfied by utilizing the dynamic system to protect the revenue and production costs. It was consistent with the escalating production expenses. According to the optimistic scenario, the salt demand can be satisfied until 2026. The company's revenue is insufficient to cover production costs due to the rise in raw material prices. Farmers begin to reap the rewards in this scenario. It's because the overall revenue exceeds the production costs.
Pardis Roozkhosh, Amir Mohammad Fakoor Saghih,
Volume 35, Issue 3 (9-2024)
Abstract
The reliability of each component in a system plays a crucial role, as any malfunction can significantly reduce the system's overall lifespan. Optimizing the arrangement and sequence of heterogeneous components with varying lifespans is essential for enhancing system stability. This paper addresses the redundancy allocation problem (RAP) by determining the optimal number of components in each subsystem, considering their sequence, and optimizing multiple criteria such as reliability, cost uncertainty, and weight. A novel approach is introduced, incorporating a switching mechanism that accommodates both correct and defective switches. To assess reliability benefits, Markov chains are employed, while cost uncertainty is evaluated using the Monte-Carlo method with risk criteria such as percentile and mean-variance. The problem is solved using a modified genetic algorithm, and the proposed method is benchmarked against alternative approaches in similar scenarios. The results demonstrate a significant improvement in the Model Performance Index (MPI), with the best RAPMC solution under a mixed strategy achieving an MPI of 0.98625, indicating superior model efficiency compared to previous studies. Sensitivity analysis reveals that lower percentiles in the cost evaluations correlate with reduced objective function values and mean-variance, confirming the model's robustness in managing redundancy allocation to optimize reliability and control cost uncertainties effectively.
Emad Hajjat, Majed Alzoubi, Leqaa Al-Othman, Lu'ay Wedyan, Osama Hayajneh,
Volume 35, Issue 3 (9-2024)
Abstract
This study examines the role of forensic accounting in enhancing financial transparency and reducing fraud in Jordanian institutions. Using a mixed-method approach, data were collected from 150 respondents including chartered accountants, auditors, financial managers. through a structured questionnaire. The findings reveal that forensic accounting significantly contributes to fraud prevention by supporting government investigations, providing courtroom testimony), and developing financial management systems. Additionally, forensic accountants play a crucial role in preparing key reports for government activities. The correlation analysis shows strong interdependencies between forensic accounting’s roles in arbitration and fraud detection. While most hypotheses were confirmed, challenges were noted in applying forensic accounting within the public sector. The study concludes by recommending that policymakers strengthen the integration of forensic accounting into Jordan's financial regulatory framework to enhance its effectiveness, particularly in the public sector. This research highlights the vital role of forensic accounting in maintaining financial integrity and provides a foundation for future studies.
Nur Islahudin, Dony Satriyo Nugroho, Zaenal Arifin, Helmy Rahadian, Herwin Suprijono,
Volume 35, Issue 4 (12-2024)
Abstract
The Internet of Things (IoT) emerged as a pivotal catalyst in shaping the landscape of Industrial Revolution 4.0. Its integration within the manufacturing sector holds transformative potential for enhancing productivity on the production shop floor. Real-time monitoring of production processes becomes feasible through the implementation of IoT. Allows companies to promptly assess whether production outcomes align with predetermined plans, facilitating agile adjustments for swift improvements. In the face of volatile consumer demand, the company can efficiently strategize planned production approaches in response to significant shifts in consumer needs. This study endeavours to design a robust real-time production monitoring system employing the Internet of Things paradigm. The system's architecture emphasizes embedding sensors within the production floor processes to discern product types. Subsequently, a web platform enables seamless dissemination of production data to all relevant components. By leveraging real-time monitoring capabilities through IoT, the company gains the agility to swiftly decide and adapt production strategies, especially amid dynamic shifts in consumer demand.
Tuan Ngo, Bao Ngoc Tran, Minh Duc Tran, the Long Tran, Trang Dang,
Volume 35, Issue 4 (12-2024)
Abstract
Improving hard machining efficiency is a growing concern in industrial production, but environmentally friendly characteristics are guaranteed. Nanofluid minimum quantity lubrication (NF MQL) has emerged as a promising solution to improve cooling and lubrication performance in the cutting zone. This paper utilizes Box-Behnken experimental design to identify the influences of Al2O3/MoS2 hybrid nanofluid MQL hard turning using CBN inserts on surface roughness and cutting forces. Mathematical models were employed to predict thrust cutting force, tangential cutting force, and surface roughness in hard turning under MQL conditions using Al2O3/MoS2 hybrid nanofluid. The study results reveal that the minimum thrust force (Fy) occurs at a nanoparticle concentration of 0.5%, air pressure of 5 bar, and flow rate of 236 l/min. In comparison, the tangential force (Fz) reaches its minimum at a nanoparticle concentration of 0.8%, air pressure of 5 bar, and airflow rate of 227 l/min. The minimum surface roughness was achieved with a nanoparticle concentration of 1%, air pressure of 4.7 bars, and airflow rate of 186 l/min. Additionally, based on the multi-objective optimization, an optimal parameter set of NC=1%, p=5 bar, and Q = 210 l/min was identified to bring out the minimal values of surface roughness (Ra) of 0.218 µm, thrust force (Fy) of 115.9 N, and tangential force (Fz) of 93.3 N.
Maryam Arshi, Abdollah Hadi-Vencheh, Adel Aazami, Ali Jamshidi,
Volume 35, Issue 4 (12-2024)
Abstract
Linguistic variables (LVs) provide a reliable expression of cognitive information. By inheriting the advantages of LVs, we can express uncertain and incomplete cognitive information in multiple attribute decision-making (MADM), and they do so better than existing methods. In the decision-making process, we can consider decision experts’ (DEs’) bounded rationality, such as cognition toward loss caused by the DEs’ cognitive limitations during the decision process. Therefore, it is necessary to propose a novel cognitive decision approach to handle MADM problems in which the cognitive information is expressed by LVs. In this paper, we employ LVs to represent uncertain and hesitant cognitive information. Then, we propose a mathematical programming approach to solve the MADM problems where attributes or cognitive preferences are not independent. Moreover, the validity and superiority of the presented approach are verified by dealing with a practical problem.
Dwi Kurniawan, Aghnia Nazhiifah Ulhaq, Aditya Fadhilah Althofian, Rubby Nur Rachman,
Volume 35, Issue 4 (12-2024)
Abstract
In industrial and commercial settings, inventory systems often involve managing multiple products with diverse demand patterns, making the direct application of the single-item newsvendor model inefficient. To address this complexity, this study proposes an adaptation of the newsvendor model through demand aggregation, where related items are grouped into a product family. By aggregating demand and financial parameters, the traditional newsvendor approach can be extended to multi-item systems, simplifying the inventory management process. This method was tested in two different case studies—a coffee roaster company and a meatball producer—demonstrating its validity and applicability. The aggregated newsvendor model was found to enhance inventory accuracy and efficiency, reducing random error and improving operational performance. This approach offers a valuable extension of the newsvendor model, with potential for broader application across various industries.
Agus Ristono,
Volume 35, Issue 4 (12-2024)
Abstract
This paper proposes a decision-support model for supplier selection based on integrating the step weight assessment ratio analysis (SWARA), the method based on the removal effects of a criterion (MEREC), and Additive Ratio Assessment (ARAS) using a case study of the leather industry in Indonesia. The model starts by identifying the main criteria using the opinions of leather industry experts using Delphi. The second stage is to weigh them based on the main criteria, using compromising of objective and subjective weighting methods, namely MEREC and SWARA. The suppliers are selected and ranked based on the main criteria. Lastly, a sensitivity analysis will be performed to check the robustness. Delphi methodology adopted in this study gives managers in Indonesia's leather industries insights into the factors that must be considered when selecting suppliers for their organizations. The selected approach also aids them in prioritizing the criterion. Managers can utilize the supplier selection methodology suggested in this study to rank the suppliers based on various factors/criteria. This study makes three novel contributions to the supplier selection area. First, Delphi is applied to the Indonesian leather industry and integrates MEREC, SWARA, and ARAS into supplier selection. Second, sensitivity analysis allows the determination of the impact of modifications in the primary criteria on the ranking of suppliers and assists decision-makers in assessing the resilience of the process. Last, we find it essential to develop a simple methodology for managers of the Indonesian leather industry to select the best suppliers. Moreover, this method will help managers divide complex decision-making problems into more straightforward methodologies.
Parinaz Esmaeili, Morteza Rasti-Barzoki,
Volume 35, Issue 4 (12-2024)
Abstract
This paper examines the simultaneous decisions regarding advertising, pricing, and service to supply chain coordination involving one manufacturer and one retailer. Demand is impacted by these decisions, with service playing a crucial role in enhancing customer loyalty and boosting sales. The study employs three well-known game theory approaches—Nash, Stackelberg-Retailer, and Cooperative games—to analyze their effects on the supply chain. Optimal strategies for both the manufacturer and the retailer are identified within each approach, and the strategies' results are compared. Results show that the retailer manufacturer, and the entire system achieves higher profits through the Stackelberg-Retailer game compared to the Nash game, while the Cooperative game results in the highest overall profits. Finally, the Nash bargaining model is outlined and analyzed to assess opportunities for sharing profits.
Iffan Maflahah, Dian Farida Asfan, Selamet Joko Utomo, Fathor As, Raden Arief Firmansyah,
Volume 35, Issue 4 (12-2024)
Abstract
Madura Island, comprising four regencies, exhibits a diverse array of agricultural resource potential, particularly in paddy, maize, cassava, and soybeans. Althought the Gross Regional Domestic Product assesses economic progress. it inadequately reflects the whole spectrum of potential within each region. A comprehensive observation of this diversity is required to facilitate a more focused development approach. This study aims to employ a hybrid hierarchical clustering method to delineate and classify the geographical regions of Madura Island according to their agricultural potential. K-means clustering, that part of hybrid hierarchical clustering approach was used to achieve aims of research. Number of farmers, land area, and commodities production were variable that used to classify regional based on its potentials. First, hierarchical method was performed to determine the appropriate number of clusters then K-means clustering was applied to classify the regions based on agricultural commodities. The results show effectively determined Madura Island's agricultural potential using the hybrid hierarchical clustering method, which categorizes locations based on characteristics of agricultural production. The research reveals six clusters, each characterized by a unique profile of primary commodity production, including paddy, corn, soybeans, and cassava. Implication of this result is offering insights into regional development of Madura based on agricultural potential.
Faikul Umam, Hanifudin Sukri, Ach Dafid, Firman Maolana, Mycel Natalis Stopper Ndruru,
Volume 35, Issue 4 (12-2024)
Abstract
Robots are one of the testbeds that can be used as objects for the application of intelligent systems in the current era of Industry 4.0. With such systems, robots can interact with humans through perception (sensors) like cameras. Through this interaction, it is expected that robots can assist humans in providing reliable and efficient service improvements. In this research, the robot collects data from the camera, which is then processed using a Convolutional Neural Network (CNN). This approach is based on the adaptive nature of CNN in recognizing visuals captured by the camera. In its application, the robot used in this research is a humanoid model named Robolater, commonly known as the Integrated Service Robot. The fundamental reason for using a humanoid robot model is to enhance human-robot interaction, aiming to achieve better efficiency, reliability, and quality. The research begins with the implementation of hardware and software so that the robot can recognize human movements through the camera sensor. The robot is trained to recognize hand gestures using the Convolutional Neural Network method, where the deep learning algorithm, as a supervised type, can recognize movements through visual inputs. At this stage, the robot is trained with various weights, backbones, and detectors. The results of this study show that the F-T Last Half technique exhibits more stable performance compared to other techniques, especially with larger input scales (640×644). The model using this technique achieved a mAP of 91.6%, with a precision of 84.6%, and a recall of 80.6%.
Hana Catur Wahyuni, Rahmania Sri Untari, Rima Azzara, Marco Tieman, Diva Kurnianingtyas,
Volume 35, Issue 4 (12-2024)
Abstract
This research discusses the application of the Failure Mode and Effect Analysis (FMEA) method in designing a blockchain system for mitigating food safety and halal risks in the beef supply chain. The complexity of the meat supply chain involving various parties increasing the risk of contamination and changes in the halal status of the meat. This research aims to identify food safety and halal risks, prioritise the risks, and design blockchain-based mitigation solutions. Blockchain was chosen for its advantages in providing high transparency and accountability, enabling real-time tracking at every stage of the supply chain. The research results show that most of the risks in the meat supply chain fall into the low category, but there are some critical medium risks, especially related to the slaughtering process. The proposed blockchain design includes product traceability features, halal certification, temperature monitoring, and smart contracts to ensure automatic validation of food safety and halal compliance. The implementation of this blockchain is expected to increase consumer trust in meat products, reduce the risk of contamination, and strengthen accountability throughout the meat supply chain.
Hasbullah Hasbullah, Zulfa Fitri Ikatrinasari, Humiras Hardi Purba,
Volume 35, Issue 4 (12-2024)
Abstract
SMEs (Small and Medium Enterprises) play a vital role in developing countries like Indonesia, contributing 12.85% to the GDP. However, Indonesia ranks low in the Global Index of Digital Entrepreneurship Systems by the Asian Development Bank. A study in Bekasi regency found that nearly 100% of SMEs still rely on conventional systems, facing common issues like low stock accuracy and lack of transparency. While software solutions exist, they often fail to address the real issues SMEs face in the real world. This research aims to create a digital transformation framework tailored to the real issues of SMEs, confirmed by stakeholders. This study used exploratory mixed methods, identifying seven steps for digital transformation: defining customer needs, identifying gaps, setting goals, selecting technology, addressing current problems, planning and financing, and evaluation. These steps cover six dimensions: Customer needs, Processes, Planning and Strategy, Technology, Resources, and Financing. The findings highlight that digital transformation is not just about adopting technology but involves a comprehensive approach grounded in customer needs. This framework offers significant value as a main contribution to academics, practitioners, policymakers, and stakeholders by addressing SMEs’ real-world challenges and ensuring that digital transformation is effective and relevant
Erni Puspanantasari Putri, Erwin Widodo, Jaka Purnama, Bonifacius Raditya Sri Pramana Putra, Agatha Hannabel Avnanta Puteri,
Volume 36, Issue 1 (3-2025)
Abstract
Micro- and small-scale industries (MSIs) are the pillars of Indonesia’s national economy. MSIs face several issues as their businesses grow. Performance evaluation is one way to identify MSI’s effectiveness. The research objective is to evaluate the MSI’s performance in East Java Province, Indonesia. It is an effort to improve the MSI's performance. The stepwise modeling approach (SMA) and data envelopment analysis (DEA) methods were applied to identify MSIs' effectiveness, determine the classification of inefficient MSIs, and formulate an inefficient MSI development strategy. In the existing SMA concept, the remaining variables in the END step are the selected variables (model X-Y). This study proposes that variables from the initial step to step n+1 are considered in creating efficiency score models. There are five proposed models, including model 4X-3Y, model 3X-3Y, model 3X-2Y, model 2X-2Y, and model 2X-Y. The research result indicated that the proposed ES model 3X-3Y is the best. 54% inefficient and 46% efficient DMUs make up the model 3X-3Y. Six cities and fourteen regencies make up the inefficient SMI classification. Cluster_A (50%) consists of four cities and six regencies. Cluster_B (25%) consists of two cities and three regencies. Cluster_C contains two regencies (10%). Cluster_D comprises three regencies (15%).
Martin D Arango-Serna, Cristian G Gomez-Marin, Conrado Augusto Serna-Uran, Silvana Ruiz-Moreno,
Volume 36, Issue 1 (3-2025)
Abstract
In recent years changes in freight transport demand, both locally and internationally, have significantly increased cargo flows to and from logistics centers. As a result, it is essential to develop effective methods for assessing freight accessibility to road corridors designated for land cargo transportation. This paper proposes a methodology that facilitates the freight accessibility analysis to a road corridor for land cargo transportation. The accessibility analysis considers several key variables such as the mobilized tons, the overall conditions of the roads, the route lengths connected to the corridor, and origin-destination nodes associated with the productive chains mobilized by this transportation mode. We validate the methodology through a comprehensive case study conducted in Colombia. The results reveal road corridors such as Llanos de Cuivá (Yarumal) - La Apartada (Córdoba), and Soledad - Barranquilla present the lowest accessibility measure and require infrastructure investments to enhance road corridor accessibility and promote the efficient transportation of goods. Furthermore, it offers valuable insights into characterizing areas with significant cargo generation and reception, enabling targeted improvements in transportation industry responsiveness.
