International Journal of Industrial Engineering and Productional Research
Volume:36 Issue: 1, Mar 2025

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%).

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.

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.

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.

Food supply chain management has become a crucial issue due to increasing food waste caused by globalization and population growth, which not only harms the environment but also social and economic aspects. The circular model has proven to be a powerful solution to overcome this, but its implementation is quite challenging due to the involvement of many stakeholders along the supply chain. So, it is important to understand the driving factors of a circular economy in the food supply chain (FSC) which can stimulate the development of a circular food supply chain, the barrier factors that can cause the failure of circular practices in the FSC, as well as strategies to overcome and mitigate the barriers that arise. Therefore, this study conducted a systematic literature review by analyzing 43 articles to answer specific research questions related to drivers, barriers, and circular food supply chain (CFSC) strategies. The results present nine main drivers, main barriers, and strategies, of which there are 47 sub-drivers, 50 barriers, and 47 strategies. Out of all the strategies identified, 24 greatest strategies using Pareto and SWOT analysis can be adopted for CFSC practice in Indonesia. This research contributes to the existing literature with the strategies, along with the responsible FSC stakeholders.

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.

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.

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.

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.

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

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.

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.

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.

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