International Journal of Research in Industrial Engineering

From realizing the technology-development nexus and the climate challenge, this study aims to identify the correlation between ISO 50001, Sustainable Development Goals (SDGs), and Industry 4.0. The study analyzes the Pearson Correlation Coefficient among the implementation benefits of ISO 50001, SDG scores, and the factors of the Network Readiness Index (NRI), etc. Denmark, Indonesia, UAE, India, USA, Argentina, and Saudi Arabia have been chosen for the study because they comprehensively represent ISO 50001-based energy management practices, economic profiles, stages of industrial development, etc., for various regions. The study highlights the multifaceted nature of the relationships among ISO 50001 certification, energy management outcomes, organizational aspects, and SDG alignment. The survey's insights will support policymakers on policy formulation for sustainable energy transitions. In addition, the study will offer practical guidance to industries as well as stakeholders on sustainability with technological advancements and strategic planning. Furthermore, it will stimulate further research by fostering efforts toward a sustainable future.

The increase in the number of tourists around the world has increased the necessity of planning for the promotion and development of tourism in order to minimize the environmental effects of tourism and maximize the social and economic benefits of tourism destinations. According to the current necessity, in this article, the identification and evaluation of the cultural performance of the eco-tourism residences of Mazandaran province from the point of view of tourists has been discussed. The data of the present study is from the narratives related to the cultural strategies of the eco-tourism centers of Mazandaran province in online social media. Narratives and data were obtained by searching tourism websites. In this article, the efficiency value of 39 ecotourism centers in Mazandaran province of Iran has been obtained with CCR input nature models and ranked with Andersen-Petersen super efficiency model. These ecotourism centers were evaluated with three entrances and three exits, and the inputs were the total land area of eco-tourist the residence (square meters), the number of rooms in the ecotourism residence, and the maximum capacity of the ecotourism residence (persons). Also, the overall cleanliness of the ecotourism residence, the way of hosting the ecotourism residence personnel and staff, and the services of the ecotourism residence personnel and staff from the point of view of tourists were considered as the outputs of this study. The ranking of Decision-Making Units (DMUs) is also given. And at the end, some solutions have been proposed to improve the situation of ineffective eco-tourism resorts.

Nowadays, online shopping plays a vital role in providing services and delivering goods to customers in the context of business intelligence and e-commerce. This research analyzes the customer purchase data of an Iranian online shopping company in Tehran. Among the available data sets provided by the company, 200 thousand records of one week of transactions have been selected for the present study. Several classification methods (i.e., Random Forrest, gradient-boosted trees, K-Nearest Neighbor (KNN), Naive Bayes, Kernel Naive Bayes, and Neural Networks) and clustering approaches have been applied to discover the knowledge and patterns. The results show that before balancing the dataset, the KNN algorithm with K=5 is the best classification method among the existing methods. However, after balancing, gradient-boosted trees outperform the other classification methods. For clustering methods, the results show that the K-Means algorithm with K=3 is more efficient regarding the average within centroid distance for each cluster. Finally, concluding remarks and suggestions for future studies are stated.

The supplier is a crucial component of the supply chain, with the supply chain encompassing vendor management. Selecting an industry-appropriate supplier involves assessing the supplier's performance. This selection process not only satisfies customer requirements and generates profit for the organization but also ensures compliance with all defined supplier attributes. This paper presents a new supplier evaluation method for Relative Reliability Risk (R3I) assessment for new suppliers specifically to reduce uncertainty and increase the accuracy of the evaluation. Thus, the proposed method includes the Analytical Hierarchical Process (AHP), entropy method, and alternative functionality charts (AFGs). Empirical case studies provided evidence of the superiority of this model compared to classical methodology in considering multiple criteria such as cost, reliability, buyer-supplier. The objectives of the supply chain include enhancing productivity, reducing costs, and meeting the demands of emerging markets. This thesis introduces the concept of relative reliability risk assessment, particularly for new suppliers. Measuring performance is critical for vendor selection, as failure to do so can negatively impact the organization's reputation. The paper employs a four-tier method: establishing a functional structure, applying the Analytical Hierarchical Process (AHP), utilizing the entropy method, and organizing alternative functionality graphs. The model's effectiveness is demonstrated through empirical case studies and comparisons with traditional supplier selection methods, showcasing its capability to manage uncertainty and improve decision accuracy. Multiple alternatives and criteria are considered to facilitate the decision-making process for selecting suitable vendors. The AHP is utilized for multi-attribute decision-making among various vendors, effectively addressing the inherent biases, vagueness, and subjectivity in human decision-making. The alternative functionality graph displays the strengths and weaknesses of all alternatives concerning multiple attributes. A real-life case study demonstrating the application of Multiple Attribute Decision Making (MADM) with the proposed method is presented in this paper.

Maturity models are valuable ways to help manufacturing organizations develop their collaboration. However, there is a lack of empirical work on developing a strategic alliance maturity model with clear guidelines. So, there is no model to measure the maturity of a strategic alliance with an evaluation tool that can deal with the inaccuracy caused by human judgment and the inherent uncertainty of the evaluation. This research created a strategic alliance maturity model based on a weighted fuzzy inference system through a clear and precise procedure and developed a multi-method approach, including literature review, interviews, focus groups, and case studies, from model design to model evaluation. In this research, to obtain the weight of the model’s dimensions, the fuzzy data envelopment analysis technique has been used, which has suitable capabilities for this purpose. The proposed model has been evaluated and validated through a case study. The results of the present study show that this approach provides a strong and practical diagnostic tool based on a set of strategic alliance maturity indicators. By using the results of this model and analyzing the gaps for the first time, an action plan can be prescribed to increase the maturity level of the strategic alliance.