Showing 711 results for Type of Study: Research
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.
Nia Budi Puspitasari, Anggit Kurnia Alfiati Devytasari, Aries Susanty ,
Volume 36, Issue 1 (3-2025)
Abstract
Sustainable Development Goals (SDGs) 12 promotes environmentally responsible consumption and production. One of its sub-objectives is to improve sustainable public procurement practices, in line with national policies and priorities. Sustainable Public Procurement (SPP) is a process of public organizations carrying out goods/services procurement activities that consider economic, social, and environmental aspects. This study identifies and evaluates the factors that drive the implementation of SPP in Yogyakarta Provinces, and seeks recommended solutions based on these driving factors. The respondents selected as the object of this study were 30 procurement actors in Yogyakarta Province. In this study, the driving factors for the application of SPP were divided into 6 factors with 22 subfactors. The analysis method used is the RII method. RII is a method for identifying the relative importance of causation of an event based on its likelihood and effect using the Likert Scale. The results showed that 6 of the 22 subfactors that encourage the implementation of SPP are the availability of sustainable products, sustainable goods/services procurement policies and procedures, the availability of sustainable human resources, the availability of sustainable product/service suppliers, organizational values, and the cost of sustainable products/services.
Mehdi Dadehbeigi, Ali Taherinezhad, Alireza Alinezhad,
Volume 36, Issue 1 (3-2025)
Abstract
Today, data mining and machine learning are recognized as tools for extracting knowledge from large datasets with diverse characteristics. With the increasing volume and complexity of information in various fields, decision-making has become more challenging for managers and decision-making units. Data Envelopment Analysis (DEA) is a tool that aids managers in measuring the efficiency of the units under their supervision. Another challenge for managers involves selecting and ranking options based on specific criteria. Choosing an appropriate multi-criteria decision-making (MCDM) technique is crucial in such cases. With the spread of COVID-19 and the significant financial, economic, and human losses it caused, data mining has once again played a role in improving outcomes, predicting trends, and reducing these losses by identifying patterns in the data. This paper aims to assess and predict the efficiency of countries in preventing and treating COVID-19 by combining DEA and MCDM models with machine learning models. By evaluating decision-making units and utilizing available data, decision-makers are better equipped to make effective decisions in this area. Computational results are presented in detail and discussed in depth.
Ahmad Padhil, Hari Purnomo, Hartomo Soewardi, Imam Djati Widodo,
Volume 36, Issue 1 (3-2025)
Abstract
Occupational safety and health (OSH) challenges in the Micro, Small, and Medium Enterprises (MSMEs) sector are serious issues that Warrant significant attention. This study aims at developing an integrative model investigation of Occupational Safety and Health for SMEs that use Job Shop production floors with a macro-ergonomics approach and Human Factors Analysis and Classification System (HFACS) to reduce the number of work accidents in this sector. Firstly, the organizational structure and management system of UMKM Job Shop are analyzed, including work procedures, training, and Occupational Health and Safety (OHS) policies. Then, HFACS is used to identify human factors that contribute to incidents and accidents, including human error, organizational factors, and environmental factors. Finally, the relationship between macroergonomic variables and HACFS variables is tested using the SEM-PLS (Structural Equation Modelling - Partial Least Squares) method. The results show that the resulting model can improve OHS in the MSME sector including key variables including Physical Environment, Good Supervision, Good Organization, Balanced Division of Tasks, Use of Technology that is in accordance with needs and Human resources will reduce the occurrence of Unsafe Action in MSMEs with the Job Shop Layout model
Maryam Ghasemi, Mehdi Seifbarghy, Nezir Aydin, Wichai Chattinnawat,
Volume 36, Issue 1 (3-2025)
Abstract
One of the most important issues regarding community health is animal health, followed by the health of animal products. Providing a sustainable environment for production facilities like livestock centers is essential. In this study, we have proposed designing four fuzzy inference systems for managing the sustainability of livestock centers. The first, second, and third systems are applied for the economic, social, and environmental dimensions. The fourth is for a system whose output is the sustainability level while its inputs are the three addressed sustainability dimensions. The data source was experts' judgment, and the major limitation of this research was access to a limited number of experts in making system rules. The validation is made by cross-checking with other experts. Considering a maximum of 10 points for each sustainability dimension and supposing that the economic dimension is 5.05, the social dimension is 7.77 and the environmental dimension is 8.12, the sustainability level turns out to be 7.92
Khalil Abbal, Mohammed El Amrani, Youssef Benadada,
Volume 36, Issue 1 (3-2025)
Abstract
In this paper, we study the Multi-Level Multi-Capacitated Facility Location Problem (ML-MCLP), which was first introduced in 2022 as a double generalization of the Capacitated P-Median Problem (CPMP). The objective of this problem is to determine the optimal facilities to open at each level, and their appropriate capacities to meet customer demands, while minimizing assignment costs. We adopt the Benders Decomposition exact approach, complemented by modern acceleration techniques to enhance convergence speed. The performance of the accelerated BD algorithm is evaluated using a dataset generated based on justified difficulty criteria and data generation methods from the literature. The results showed that hybridization of acceleration techniques, such as subproblem reformulation and cut selection, significantly improves convergence. However, decomposition-based technique proved to be inefficient, particularly due to the structure of the ML-MCLP, and was therefore excluded.
Atef Fakhfakh, Amr Noureldin, Mohamed Aboueldahab, Basem Nabil,
Volume 36, Issue 1 (3-2025)
Abstract
This paper focuses on mobile telecommunication companies (MTCs) in Egypt to investigate the impact of digital leadership (DL) on sustainable performance (SP). The mediating role played by digital organizational culture (DOC) in the relationship between DL and SP is also examined. The survey method is employed to conduct this research, and data is collected from 331 respondents. The proposed hypotheses are tested using structural equation modeling and analyzed using structural equation modeling Smart PLS V.4. The results indicate that DL directly influences DOC. SP and DOC partially mediate the relationship between DL and SP. Previous research has not extensively examined the mediating role of DOC in the relationship between DL and SP. This research is one of the first studies to demonstrate that DL positively impacts the SP of Egyptian MTCs through the mediating role of DOC.
Dwi Kurniawan, Sabila Rafa Budiyanto,
Volume 36, Issue 1 (3-2025)
Abstract
This paper studied the impact of relationships and past positive experiences on the dimensions of trust (ability, benevolence, integrity) and the influence of these trust dimensions on customer purchase intention. The measurement instrument was developed based on the literature. The study was conducted using a questionnaire completed by two hundred customers of an Indonesian Marketplace in Bandung and its surrounding areas. The data were then processed using Structural Equation Modeling (SEM). The results showed that ability and integrity affect customer purchase intentions, while benevolence does not. Additionally, we found that relationships and positive experiences in the past significantly affect ability and integrity.
Nor Hasrul Akhmal Ngadiman, Nur Syahirah Mustafa, Izman Sudin, Denni Kurniawan,
Volume 36, Issue 1 (3-2025)
Abstract
Bone tissue scaffolds that closely mimic the mechanical and biological properties of natural bone is critical for enhancing the outcomes in treatment of bone tissue damages. This study introduces an optimisation approach to designing bone tissue engineering scaffolds using Triply Periodic Minimal Surface (TPMS) structures, evaluated through a Full Factorial Design methodology. Finite Element Analysis was applied to simulate the TPMS scaffolds under mechanical loading. The influence of key factors of strut thickness, unit cell configuration, and TPMS type, on the scaffold’s mechanical performance, specifically targeting Young's modulus was evaluated. By employing Full Factorial Design, this study generates empirical models of Young’s modulus as a function of those key factors. Primitive and Gyroid TPMS structures emerged as optimal, achieving Young's modulus values of 4912.3 MPa and 4666.7 MPa, respectively, with configurations of 0.01 mm strut thickness in a 3-unit cell construct. These results demonstrate that optimised TPMS scaffolds can meet the mechanical demands of bone tissue while providing adequate porosity for cell proliferation and nutrient transport, essential for effective bone regeneration.
Atef Fakhfakh, Salaheldin Salaheldin, Amr Noureldin, Mohamed Aboueldahab, Neama Elwakeel,
Volume 36, Issue 1 (3-2025)
Abstract
This study investigates the interplay between manufacturing ambidexterity, Industry 4.0 readiness, and sustainable excellence in Egypt's food and beverage sector. It explores how Industry 4.0 readiness mediates and moderates the relationship between ambidexterity and sustainability outcomes. A quantitative research design was employed, utilizing a survey of 308 professionals in Egypt's food and beverage industry. Structural equation modeling (SEM) was used to analyze the relationships among manufacturing ambidexterity, Industry 4.0 readiness, and sustainable excellence. The results reveal that Industry 4.0 readiness fully mediates and significantly moderates the relationship between manufacturing ambidexterity and sustainable excellence. While manufacturing ambidexterity alone does not directly impact sustainable excellence, its effect becomes significant through Industry 4.0 readiness, highlighting the importance of digital transformation. This study focuses on a single sector in Egypt, limiting generalizability. Future research could explore other industries and regions or examine specific dimensions of Industry 4.0 readiness. The findings emphasize the need for organizations to invest in digital infrastructure and foster ambidextrous capabilities to achieve sustainability goals. Policymakers are encouraged to support Industry 4.0 adoption through incentives and training programs to enhance competitiveness and sustainability in emerging markets. This study contributes to the limited research on the application of manufacturing ambidexterity and Industry 4.0 technologies in developing economies, offering insights into achieving sustainable excellence through digital transformation.
Mohsen Nourizadeh, Moharram Habibnejad Korayem, Hami Tourajizadeh,
Volume 36, Issue 1 (3-2025)
Abstract
The purpose of this paper is to optimal control a dual-stage cable robot in a predefined path and to determine the maximum load-carrying capacity of this robot as a tower crane. Also, to expand the workspace of the robot two stages are employed. Today, cable robots are extensively used in load handling. Positive cable tension and collision-free cable control are the most important challenges of this type of robot. The high ratio of transposable loads to weight makes these robots very attractive for use as tower cranes. Dynamic Load Carrying Capacity (DLCC) is the maximum load that can be carried along a predefined path without violating the actuators and allowable accuracy constraints. State-Dependent Riccati Equation (SDRE) is employed to control the end-effector within the path to achieve the maximum DLCC. This approach is chosen since it can optimize the required motors' torque which consequently leads us to the maximum DLCC. In addition, the constraint of cables’ collision together is also checked along the predetermined path using the non-interference algorithm. The correctness of modeling is verified by comparing the results with previous research and the efficiency of the proposed optimal controlling strategy toward increasing the DLCC is investigated by conducting some comparative simulations. it is shown that the proposed cable robot by the aid of the designed optimal controller can increase the load carrying capacity successfully along any desired path using the allowable amount of motors' torque.
Muh Syarif, Ismie Roha Mohamed Jais, Iffan Maflahah, Ihsannudin Ihsannudin,
Volume 36, Issue 1 (3-2025)
Abstract
The research focuses on improving the performance of the corn supply chain in Madura Island, Indonesia. The purpose of the study is to identify, evaluate, and prioritize risks that have the potential to disrupt the smooth operation of the corn supply chain. The research method uses Failure Mode and Effects Analysis (FMEA) to identify risk levels and Root Cause Analysis (RCA) approach for mitigation strategies. Risk level assessment is based on severity, probability, and detectability at the level of farmers, middlemen, processing industries, and distributors. Based on the analysis, it shows that the risks are a priority in handling and prevention as well as proposals that can be made to improve the root cause of the occurrence of risks with the highest category based on the RPN value at the farmer level are the occurrence of pest and disease attacks (648), the middleman level is when the amount of corn is abundant (336), the processing industry level is the price of corn is unpredictable (252), and the level of distributors is a limitation in product promotion (324). To improve the efficiency and quality of the corn supply chain, namely increasing storage capacity, using more efficient processing technology, flexible production planning, and more innovative marketing strategies. The managerial implications of corn-supply chain risk assessment are the need to improve product quality, corn supply stability, price management, and strengthen partnerships and mutual benefits between all parties in the supply chain. Every element of the supply chain needs to encourage the adoption of modern technologies in maize cultivation, processing, and distribution to increase productivity and reduce risks associated with manual processes. It is necessary to establish mitigation strategies to address environmental risks, including the implementation of sustainable agricultural practices and early warning systems.
