International Journal of Industrial Engineering and Productional Research
Volume:35 Issue: 2, Jun 2024

The incorporation of sustainable practices becomes crucial as firms transition from Industry 4.0 to Industry 5.0. Therefore, this systematic review explores the relationship between the two sustainability approaches; Green Human Resource Management (GHRM) and Green Supply Chain Management (GSCM) using peer-reviewed studies from 2016-2023, retrieved from Scopus and Web of Science databases. 2016 marks the starting point as the first relevant paper emerged in the literature in that year. The PRISMA approach was used to identify relevant studies, resulting in the inclusion of 30 studies for analysis purposes. The study reveals a growing interest in understanding the relationship between GHRM and GSCM practices and their impact on sustainable performance. The majority of reviewed studies utilized quantitative survey methods, suggesting the need for future research utilizing qualitative and mixed methods for gaining deeper insights. The review indicates that most studies are conducted in emerging countries, and there is a significant gap in research on the relationship between GHRM and GSCM practices in other context. Finally, the study provides valuable insights for practitioners and researchers, emphasising the importance of integrating GHRM and GSCM practices for a sustainable competitive advantage.

Over the years, the attention given to corporate social responsibility (CSR) and sustainability topics has received a lot of attention significantly and various new terms have been introduced. This result has sparked a wide-ranging and unspecified discussion, particularly in the fields of economics and business management. The presents of functional CSR and sustainability enable management to make better decisions for the benefit of the entire society.  As a result, understanding the topic of interest and broadening research collaboration are critical for advancing research development.  The purpose of this study is to identify global research trends in CSR and sustainability based on publication numbers, co-authorship, affiliated countries, and keyword co-occurrences. This study used RTools and Prisma for its analysis. The findings indicate a significant rise in the number of articles published in the field of corporate social responsibility and sustainability since 2015. The USA contributed more than half of the publications, with Italy and Spain following closely behind.

These days, industries, individuals and organizations are highly dependent on software. Software plays an important role in our daily life. They use in embedded systems, databases, computers, mobiles etc.  Great demand for ICT cause environmental problem and endanger the future sustainability.  In this case, sustainable development has become a hot research topic in software engineering community. Sustainability as a software quality is a general term. Therefore, there is a chance that software developers mislead about develops sustainable software. Therefore, there are some questions that should be answered to help practitioners to develop sustainable software: how developers could develop green and sustainable software? What requirements should be considered to reach green and sustainable software? Which non-functional requirement has an effect on each sustainability dimension?   In this paper, we selected 20 non-functional requirements out of 60. It was identified the effective non-functional requirements in green and sustainable software development by using Delphi method then via interpretive structural modeling (ISM). The study aimed to pave the way for software eco-labeling and help users to choose the green and sustainable one. Also, provide software developers with guideline to develop green and sustainable software by identifying effective non-functional requirements. This would lead to the sustainable future and green environment.

The patient has a legitimate role in evaluating the healthcare services provided to them; this evaluation can be measured through patient satisfaction, which is considered an effective tool to evaluate the provided services and the quality program for hospitals. This study aims to examine the association between patient satisfaction and hospital accreditation status. Cross-sectional design with a random sampling technique for adult hospitalized patients. The SERVQUAL instrument was utilized to measure the patient's satisfaction. The sample size included 800 patients from the two phases based on the inclusion and exclusion criteria. The obtained data was analyzed using SPSS version 26. The study revealed that patient satisfaction was high both before and after accreditation. The order of the patient satisfaction dimensions was as follows: assurance, reliability, tangibles, responsiveness, and empathy. The highest subscale in this phase was assurance, with a mean of (4.49), and the lowest score was empathy, with a mean of (4.25). In the pre-accreditation phase, reliability was the highest subscale, with a mean of (4.46), and the lowest score was responsiveness, with a mean of (4.13). In addition, the study revealed that there is an association between the satisfaction subscales (tangibles, responsiveness, and assurance) and accreditation status, except for reliability and empathy. The study concludes that the high level of satisfaction in the post-accreditation phase may relate to the high level of patient care standards and safety environment implemented in the hospital as requirements of accreditation, which gives evidence that the hospital accreditation status had a positive impact on patient satisfaction.

The retail industry is a vital sector of the world economy and is characterized by fierce competition, tight profit margins, and demanding consumers. Understanding customer buying behavior patterns is essential in devising the best retail strategy to enhance product sales. This research aims to comprehend customer shopping behaviors based on retail sales transactions and formulate the best strategies. By employing multi-level association rules, the dataset is arranged hierarchically into categories, sub-categories, and items. The sales transaction data used comprises 5830 transaction records over a month. The results of this study reveal 24 associations of categories, 49 associations of sub-categories, and 12 associations of product items. Moreover, the proposed marketing strategy offers recommendations including store layout improvement, planogram design, and bundled product offerings. This research addresses the gap in empirical evidence from a previous study and suggests further observation from diverse locations to authenticate the findings, which may yield various outcomes

One of the important problems in managing construction projects is selecting the best alternative for activities' execution to minimize the project's total cost and time. However, uncertain factors often have negative effects on activity duration and cost. Therefore, it is crucial to develop robust approaches for construction project scheduling to minimize sensitivity to disruptive noise factors. Additionally, existing methods in the literature rarely focus on environmentally conscious construction management. Achieving these goals requires incorporating the project scheduling problem with multiple objectives. This study proposes a robust optimization approach to determine the optimal construction operations in a project scheduling problem, considering time, cost, and environmental impacts (TCE) as objectives. An analytical algorithm based on Benders decomposition is suggested to address the robust problem, taking into account the inherent uncertainty in activity time and cost. To evaluate the performance of the proposed solution approach, a computational study is conducted using real construction project data. The case study is based on the wall of the east coast of Amirabad port in Iran. The results obtained using the suggested solution approach are compared to those of the CPLEX solver, demonstrating the appropriate performance of the proposed approach in optimizing the time, cost, and environment trade-off problem.

The world today gives much importance to human resource management which is viewed as the gate towards effective performance. In this respect, the application of intelligent human resources management increases the effectiveness of HRM and brings about effective performance. Given the importance of this topic, this study sought to evaluate the future direction of intelligent human resources management applications in employee performance measurement and data analysis in the industrial sector in Jordan. It specifically evaluated the effect of intelligent human resources management applications (recruitment and talent acquisition, learning and development, benefits and incentives, workforce planning and improvement) on measuring the performance of employees and analyzing data in the Jordanian industrial sector. This research used the descriptive-inferential method (Inductive Descriptive Methodology). The population included all employees in the supervisory authorities of the 33 industrial companies listed on the ASE, while the sample includes 146 participants. This research found that human resources management applications directly affect measuring the performance of employees and analyzing data in the Jordanian industrial sector. The novelty of this research is that, unlike the previous studies like Al-Wakeel and Ibraheem (2020) and Wang (2024) that were applied to samples from different countries other than Jordan, it specifically addresses the effect of HRM practices on measuring the performance of employees and analyzing data in the Jordanian industrial sector. This research provides the Industrial Sector in Jordan with the necessary knowledge of the future directions of intelligent human management. Adopting intelligent human resource management is deemed important for the Jordanian industrial sector.  It is very important to carry out this research to highlight the effect of human resources management applications on measuring the performance of employees and analyzing data in the Jordanian industrial sector.

In today's dynamic and competitive manufacturing landscape, accurate demand forecasting is paramount for optimizing production processes, reducing inventory costs, and meeting customer demands efficiently. With the advent of Artificial Intelligence (AI), there has been a significant evolution in demand forecasting methods, enabling manufacturers to enhance the accuracy of the forecasts. This systematic literature review aims to provide a comprehensive overview of the state-of-the-art on demand forecasting models in the manufacturing sector, whether AI-based models or hybrid methods merging both the AI technology and classical demand forecasting methods. The review begins by establishing an overview on demand forecasting methods, it then outlines the systematic methodology used for the literature search. The review encompasses a wide range of scholarly articles published up to September 2023. A rigorous screening process is applied to select relevant studies. Accordingly, a thorough analysis in the basis of the forecasting methods adopted and data used have been carried out. By synthesizing the existing knowledge, this review contributes to the ongoing advancement of demand forecasting practices in the manufacturing sector providing researchers and practitioners an overview on the advancements on the use of AI models to improve the accuracy of demand forecasting models.

This study develops warranty cost models for repairable products subject to Lemon Laws, encompassing Critical and Non-Critical components forming a multi-component system. Failures can arise naturally or be induced by other components (i.e., failure interaction), defining a lemon if recurrent failures reach a threshold (k) during the warranty period. A lemon declaration triggers a refund or replacement by the manufacturer. Four warranty cost models are proposed from the manufacturer's standpoint, considering failure mechanisms. Increasing failure thresholds in the warranty scheme substantially decreases warranty cost rates. For instance, a threshold (k) of 5 in refund and replacement schemes yields the lowest cost rates of 33.7159 and 25.8249, respectively. Failure interactions escalate total warranty costs; for instance, in a refund scheme (k = 5), costs with failure interaction reach 31.0169 compared to 28.7603 without. Similar trends apply to replacement schemes. Moreover, a lower warranty cost rate will extend the period, indicating regulation fulfillment due to a closer warranty period to the Lemon period. Sensitivity analysis also underscores the role of higher reliability in reducing warranty costs and complying with Lemon Laws. Finally, maintenance strategies and product reliability are emphasized to fulfill Lemon Laws with minimal costs, i.e., fewer warranty claims.

Supply chain agility (SCA) has emerged as a significant focus for industries and businesses, serving as a cornerstone for gaining a competitive edge and playing a pivotal role in supply chain management. This importance is further underscored in the context of Product–Service Systems (PSS), which involve the development of both products and services. Despite the existing body of research on SCA and PSS, there has been a notable dearth of empirical studies examining the readiness of PSS SCA. This study makes a substantial contribution by developing a valid and reliable framework to assess the readiness of PSS for supply chain agility. The process involves defining domains, generating items, analyzing agreement among raters, testing for response bias, and conducting exploratory and confirmatory factor analyses. Using structural equation modeling, the model's validity and reliability were evaluated through an online survey with 405 participants from official motorcycle service partners. The findings identify six key capability constructs: collaboration, knowledge transfer, service partner development, information sharing, logistic integration and supply chain agility. This examination of PSS SCA readiness and its constructs provides a validated tool for industry practitioners to enhance their supply chain agility.

Ethiopia has made enormous efforts in the leather industry to gain manufacturing capabilities that can be scaled up to other sectors. Those efforts have resulted in the industry shifting its role from raw material supplier to producer of value-added products for the global supply chain (GSC). However, the industry has faced severe challenges in generating the expected revenue, utilizing capacity, and finally coping with the global competitive environment. Studies reveal that manufacturing firms tackle similar challenges by improving their supply chain performance (SCP). The challenges that appeared in the leather industry of Ethiopia could also be solved by improving its SCP. Nonetheless, there is a lack of study on the basic characteristics and SCP of the industry after it has shifted its role. The main objective of this study is, therefore, to measure the SCP to know where it stands using a bench mark and identify the elements that contribute considerably to the low overall SCP in order to lay the foundation for subsequent improvement. To achieve the research objective, data was collected from primary and secondary sources through a questionnaire, survey, observation, and focus group discussion. The data is analyzed using the supply chain operations reference model (SCOR version 12.0). Accordingly, the overall SCP is found to be 67.33%, suggesting an average rating as per the set benchmark. The source process is identified as the most influential element for the overall low SCP, with a percentage gap of 17.23%. Taking corrective action on the identified elements could help the industry overcome the existing challenges by improving its SCP.

The area coverage of machines on the production line to address the scheduling and routing problem of autonomous guided vehicles (AGV) is an innovative way to improve productivity in manufacturing enterprises. This paper proposed a new model for the optimal area coverage of machines in the production line by applying a single AGV to minimize both the transfer costs and the number of breakpoints of AGV. One of the unique advantages of the area coverage employed in the present study is that it minimizes transfer costs and breakpoints, and makes it possible to provide service for several machines simultaneously since the underlying assumption was finding a path to ensure that every point in a given workspace is covered at least once. Since rail AGV is used in this study, AGV can only pass horizontal and vertical distances in the production line. The reversal of the AGV path in vertical and horizontal distances implies failure and breakpoint in the present paper. The simulation results confirm the feasibility of the proposed method.