International Journal of Industrial Engineering and Productional Research
Volume:34 Issue: 1, Mar 2023

Palm oil is a commodity whose demand continues to increase, requiring proper risk management in the supply chain. This study aims to develop a hybrid method that integrates probability impact matrix, analytical network process, and house of risk to mitigate strategies in the palm oil supply chain. The Probability Impact Matrix (PIM) method is used to map the priority risk agents and determine the occurrence value of the risk agents, and Analytical Network Process (ANP) is used to determine the severity value of the risk event. Furthermore, the House of Risk (HOR) is proposed to determine the priority of the mitigation strategy. The proposed method was applied in a case study on the palm oil supply chain in Indonesia. The research results show that ten priority risk agents and 6 mitigation strategies were obtained based on the proposed method to overcome risk agents in palm oil supply chain

Product Service System (PSS) is a developing field of examination in in industry practices in todays’ global economy, which significantly affect both production and utilization of resources. In order to enable the applicability of a product service system in an industry. It is important to assess the system using a metrics SERVQUAL model, which characterizes the quality as difference between the customer’s expectation and perception. And a complex examination device intended to gauge quality by catching the hope affirmation worldview which proposes the customers saw nature of how well a given conveyance measures up to their assumptions of that conveyance. This study analysis the adoption of KMS approach, significantly enhances the employee’s knowledge on tangibility and reliability thus influences the customer’s perceptions and expectations of the quality. The findings of the study revealed that KMS approach served as a facilitated tool for sharing and utilizing knowledge in different organizations.

Venture capital (VC) financing is associated with the challenges of double-sided moral hazard, and uncertainty, which leads to the difficulty in estimating the venture's value accurately and consequently the impossibility of determining the optimal equity sharing between the entrepreneur and investor. Traditionally, convertible preferred equity mechanisms used to be implemented as an incentive to decline moral hazard. However, despite the emphasis on investor risk-taking, such mechanisms transfer the investor risk to the entrepreneur and do not mitigate the incentive of opportunistic behaviors. Furthermore, according to the literature review, and to the best of the authors’ knowledge, there has not been developed any practical mechanism for equity sharing in VC financing up to now. This paper proposes a fair equity sharing mechanism, which alleviates the above-mentioned deficiencies. It adjusts both parties' share during the equity dilution in each stage of financing, regarding the difference between the venture's ex-ante and ex-post values. Moreover, it manages uncertainty by applying staged financing and the option of abandonment at the end of each stage. The proposed mechanism has been verified by using the mathematical tools and drawing its curves for a case study.

The main objective of this research is effective planning as well as greener production and distribution of mineral products in supply chain network. Through a case study in cement industry, it considers the design of the mining supply chain network including several factories with a number of production lines and multiple distribution centers. It leaves part of the transportation operation to contractor companies so as to enable the core company to better focus on its products’ quality and also create job opportunities to local people. It employs a multi-period and multi-product mixed integer linear programming model to both maximize the profit of the factory as well as minimize its carbon dioxide gas emissions which are released during cement production and transportation process. Due to the uncertainty of its cost parameters, fuzzy logic has been used for the modeling and solved via a novel fuzzy multi-choice goal programming approach. Sensitivity analysis has also been done on some key parameters. Comparing results of the model with those from the single-objective models, shows that the model has good efficiency and can be used by managers of mining industries such as cement. Although leaving part of the transportation operations to contractor companies increases the number of vehicles used by the contractor companies, its associated decrease in the number of required factory vehicles, improves both objectives of the model. This should be considered by the managers since on top of profit maximization, it can help them build an eco-friendly image. Mining industries generally generate significant amount of pollutions and companies that pay attention to different dimensions of their social responsibilities can remain stable in the competitive market.

Abu Dhabi National Oil Company (ADNOC), being a significant role player the actualization of the Abu Dhabi Economic Vision of 2030, would need to improve and upgrade its performance level and its competitive advantage during this Covid-19 pandemic period. Therefore, this study examined the effects of technology management, innovation and training on organization performance in ADNOC United Arab Emirates (UAE). It also examined the moderating effect of work environment on the relationships between technology management and organizational performance, between innovation and organizational performance, and between training and organizational performance. This study has followed the quantitative method to collect the data and smart-PLS has been used to examine the nexus among the understudy variables. The overall findings signified a positive effect of technology management, innovation, and training on organization performance in ADNOC UAE. The result also indicates the moderating effect of working environment on the relationship between technology management and organizational performance. The overall findings imply that technology management, training and innovation are crucial drivers of enhanced performance, but conducive environment should be ensured to stimulate endured performance.

Providing skills training is an essential need of different societies. Considering the significance of the role of skill training in empowerment and employment of individuals through the training of skilled labor required by the labor market and industry, in this study the supply chain of skills training has been designed. In the proposed supply chain, according to the skill training aspects, a network structure is conceptualized to include appropriate factors in different layers of the supply chain. Evaluating the performance of the supply chain is handled applying a network data envelopment analysis. Network Data Envelopment Analysis (NDEA) is an efficient method analyzing all the factors included in the evaluation network. Among the NDEA models, the output-oriented BCC model was selected due to the importance of the output of the supply chains of the skills training. In addition to efficiency, the concept of complementary loss is also introduced to validate the results. The research findings show the efficiency of various factors in the stages of the designed network. On the other hand, unlike the classic DEA method, which shows the maximum efficiency of a factor, in the proposed network model, the efficiency priority is calculated and the efficiency is determined at each stage of the supply chain.

The research is devoted to the theoretical and applied   organizational bases to held of the comparative analysis of the economic development of the regions of the Black Sea region. The main purpose of the article is the process of comparative analysis of economic development of the Black Sea region. The article tests the authors' hypothesis about the adequacy of the indicators defined for evaluation through the proposed number of relative indicators, which in the complex will characterize the achievements of the region in ensuring the economic stability of the regional system, quality of transformation processes and indirectly the conditions created by public authorities for economic development. There is confirmed the dependence of the use of the proposed methodological approaches and the constructed comparative profile of the regions of the region, which can be useful for identifying the strengths and weaknesses of the region, outlining key issues and developing regional development plans and programs. It is determined that the largest vector length in the Mykolaivska region, which indicates that in the region on a number of economic indicators achieved higher results than in other regions of the Black Sea region and on average in other regions of Ukraine during the study period.

Researchers in many fields, such as operations research, computer science, AI engineering, and mathematical engineering, extra, are increasingly adopting nature-inspired metaheuristic algorithms because of their simplicity and flexibility. Natural metaheuristic algorithms are based on two essential terms: exploration (diversification) and exploitation (intensification). The success and limitations of these algorithms are reliant on the tuning and control of their parameters. When it comes to tackling real optimization problems, the Gorilla Troop Optimizer (GTO) is an extremely effective algorithm that is inspired by the social behavior of gorilla troops. Three operators of the original GTO algorithm are committed to exploration, and the other two operators are dedicated to exploitation. Even though the superiority of GTO algorithm to several metaheuristic algorithms, it needs to improve the balance between the exploration process and the exploitation process to ensure an accurate estimate of the global optimum. For this reason, a Novel Enhanced version of GTO (NEGTO), which focuses on the correct balance of exploration and exploitation, has been proposed. This paper suggests a novel modification on the original GTO to enhance the exploration process and exploitation process respectively, through introducing a dynamic controlling parameter and improving some equations in the original algorithm based on the new controlling parameter. A computational experiment is conducted on a set of well-known benchmark test functions used to show that NEGTO outperforms the standard GTO and other well-known algorithms in terms of efficiency, effectiveness, and stability. The proposed NEGTO for solving global optimization problems outperforms the original GTO in most unimodal benchmark test functions and most multimodal benchmark test functions, a wider search space and more intensification search of the global optimal solution are the main advantages of the proposed NEGTO.

Diagnosis of diseases is a critical problem that can help for more accurate decision-making regarding the patients’ health and required treatments. Machine learning is a solution to detect and understand the symptoms related to heart disease. In this paper, a logistic regression model is proposed to predict heart disease based on a dataset with 299 people and 13 variables and to evaluate the impact of different predictors on the outcome. In this regard, at first, the effect of each predictor on the precise prediction of the outcome has been evaluated and analyzed by statistical measurements such as AIC scores and p-values. The logit models of different predictors have also been analyzed and compared to select the predictors with the highest impact on heart disease. Then, the combined model that best fits the dataset has been determined using two statistical approaches. Based on the results, the proposed model predicts heart disease with a sensitivity and specificity of 84.21% and 90.38%, respectively. Finally, using normal probability density curves, the likelihood ratios have been established based on classes 1 and 0. The results show that the likelihood ratio classifier performs as satisfactorily as the logistic regression model.

Previous work has highlighted the need to apply stochastic modeling to understand the dynamics of phenomena occurring in the insurance industry. In this paper, for life insurance and applying a stochastic approach under efficient markets, we use survival probabilities and stochastic differential equations to model the actuarial reserve, changes in the constituted actuarial reserve, and estimated income over time. We present an application, sensitivity analysis, and discussion of the results using United States life tables.

The COVID-19 pandemic forced Malaysian Higher Education Institutions to pursue online and distance learning. This study aimed to gain insight into the pre-university students’ acceptance and intention to use the Microsoft Teams (MS Teams) application for online learning platforms during the pandemic. This group of students was chosen because they had just finished high school and their transition from the school system to the university system with online learning will pose many difficulties. The theoretical framework for this study was developed using the Technology Acceptance Model (TAM) with additional facilitating conditions and computer self-efficacy as the external elements. The participants were 180 pre-university students from Universiti Kuala Lumpur Malaysian Institute of Information Technology who had experience using MS Teams during their first semester. With SPSS, the predictive factors on the acceptance of students toward online learning have been explained. The findings also indicate that the proposed TAM-based scale successfully explained the factors predicting intention to use MS Teams during the pandemic. The findings assist researchers and practitioners in developing a more comprehensive view of pre-university students’ acceptance and intention to use MS Teams. Finally, several recommendations have been made, including the implications and limitations of the study at the end of this paper to reference future research.

The problem of family businesses in Saudi Arabia is noteworthy due to the large scope of family enterprises in Arab countries. Most organizations established in the Arab world are either state-owned or family owned. Many of these companies were formed by forefathering merchant families that dealt in vital ports along the Gulf and Red Sea coastlines. The current study's primary aim was to determine the challenges and benefits of family business success in Saudi Arabia. The purpose is to discover relevant literature on this critical issue to understand the barriers to the growing family businesses in Saudi Arabia and the potential advantages of family enterprises to the country's economy. The current study used the PRISMA statement 2020 to include and exclude records from the well-known databases Scopus and Web of Science to include high-quality documents. Additionally, the documents were examined via content analysis to determine the classifications of the study. VOS Viewer software analyzes the published literature' content—data clusters created on the text established to group the related ideas. The results indicate that three main themes were identified during the key term's occurrence research: entrepreneurship, family business, and performance. The findings of the study shows that family businesses are an essential aspect of the Saudi economy, contributing heavily to it. Such firms have lately received attention because of their critical role and contribution to job creation and employment, as well as the promotion and encouragement of entrepreneurship in their operations. Furthermore, results showed that it is commonly accepted that the involvement of a family in the business differentiates the family business. It is recommended that family companies be distinguished by their behaviour, which is based on the firm's objective, vision, and mission.