INSIGHT
Industrial Networks, Sustainability, and IntelliGent High-impact Technologies Research Group
(INSIGHT)
Driving intelligent, sustainable, and data-driven solutions that transform industries through applied research and real-world impact.
Our focus is on the development of deployable models and rigorously validated frameworks that advance operational excellence across key sectors, including manufacturing, healthcare, logistics, and energy.
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Our Vision To be a leading applied research hub in Industrial Engineering, advancing intelligent, sustainable, and data-driven systems that enhance organizational performance, resilience, and long-term value in a dynamic global environment. |
Our Objective To serve as a strategic innovation partner, enabling organizations to design intelligent systems, strengthen sustainability outcomes, and enhance competitiveness within increasingly complex and data-driven operating environments. |
Mission and Strategic Alignment
The INSIGHT Research Group advances applied, industry-engaged research that integrates sustainability and data-driven intelligence. In alignment with RIT Dubai’s strategic priorities, the group is committed to translating research into practical, scalable solutions that deliver measurable impact. Our objectives align with the core pillars of the RIT Dubai Strategic Plan:
- Research Innovation with Impact: Translating advanced research into deployable, real-world solutions that address complex industry challenges.
- Economic Diversification: Contributing to the development of data-driven and innovation-led sectors through work in smart infrastructure, logistics, and advanced operations.
- Community Engagement: Collaborating with industry and government partners to co-develop solutions that respond to evolving operational and societal needs.
- Educational Technology: Leveraging RIT Dubai’s advanced laboratory ecosystem to support applied research, innovation, and experiential learning opportunities.
Data-Driven Decision Intelligence and optimization
Efficiency Evaluation of Water Pumping Stations Using Data Envelopment Analysis: A Multi-Model Framework Incorporating Undesirable Outputs
Researcher: Dr. Dua Weraikat
Collaborator: Dewa, Rowdha Alblooshi
Water pumping stations are critical components of water transmission systems, particularly in the Gulf region, where potable water supply is heavily dependent on energy-intensive desalination processes. Despite their importance, pumping station efficiency is often assessed using single-dimensional indicators that fail to capture operational complexity, scale effects, and environmental impacts. This study develops a Data Envelopment Analysis (DEA) framework to evaluate the performance of 16 water pumping stations in Dubai, each treated as a decision-making unit (DMU), by incorporating both operational and sustainability dimensions. A multi-model approach was applied, integrating input-oriented Charnes, Cooper, and Rhodes (CCR) and Banker, Charnes, and Cooper (BCC) models with Slack-Based Measure (SBM) models that treat energy consumption as an undesirable output.
An Integrated Decision Support System (DSS) for Sustainable Supplier Selection, Evaluation, and Benchmarking Using a FIS Approach
Researcher: Dr. Dua Weraikat
Collaborator: Ghannam, S., Khan, S.,
Applications of sustainable supplier selection criteria in supply chain management (SCM) remain underdeveloped compared to other evaluation methods. This study focuses on three main dimensions of sustainable supplier performance: economic, environmental, and social criteria. The research aims to identify significant criteria within each dimension that are crucial for sustainable supplier selection process. These criteria will be utilized to develop a decision support system (DSS) that integrates a fuzzy inference system. The proposed model offers a holistic approach to supplier evaluation by considering economic performance, environmental impact and social responsibility. By incorporating these dimensions, the model ensures that the selection process aligns with broader corporate and environmental goals. The approach enables companies to make informed and sustainable decisions, ultimately contributing to a more resilient and responsible SCM. Furthermore, the fuzzy inference system effectively handles the inherent uncertainty and vagueness in supplier performance data. To enhance the robustness and reliability of the decision-making process, the proposed model can be integrated with multi-objective linear programming models, making it a valuable tool for supply chain managers. The findings of this work revealed that the economic criteria of supplier selection focus on quality, flexibility, cost, lead time, relationships, and technical capability. The environmental criteria include resource consumption, eco-design, recycling, emissions, and sustainability practices. The social criteria emphasize stakeholder involvement, staff training, safety, rights, and accident prevention in supplier selection. In summary, this comprehensive framework evaluates and benchmarks supplier sustainable performance, supporting more resilient and sustainable SCM.
Demand Forecasting with Real Case Analysis for Effective Retail Decision-Making
Researcher: Dr. Dua Weraikat
Collaborator: Safika Kabir, Caroline Zaidan, Moh’d Ishtiaq, Adolf Acyaqe
Supply chain decisions involve different entities and activities such as sourcing of material, production of goods, and logistics. Through the efficient management of these activities, firms can gain a competitive edge by beig responsive in maximizing customer value and enhance its efficiency by minimizing operating costs. Inadequate management results in poor replenishment processes which can, in turn, lead to lack of product availability in stores, lost sales, or oversupply of products, etc. Accurate demand forecasting plays a crucial role in reducing these problems by providing precise quantities required to order and store to satisfy the customer demands, reduce inventory and production costs, reduce safety stocks and transportation, and improve replenishment processes. However, demand forecasting for products, mainly perishable goods can be challenging for firms. Many companies, such as retailers, have struggles in accurately predicting future demand. Suppliers usually have a bulk shipment process that can cause inventory differences for the retailer's desires. Furthermore, without proper information sharing between a retailer and its suppliers, mismanagement of order quantities and production can occur. Recently, these problems are tackled through the adoption of collaboration models between supplier and retailer. More precisely, companies are encouraged to integrate inventory management models to order when required. The integrated model enhances the demand planning, inventory control and replenishment processes in the supply chain. Nonetheless, the investments in such methodologies are costly for both suppliers and retailers alike. Moreover, the benefits from implementing these models are not clear in monetary terms and are not widely discussed in the literature. This research aims to improve inventory management of a retailer located in the United Arab Emirates that is facing issues with its replenishment and forecasting processes. To achieve this purpose, forecasting models such as ARIMA, exponential smoothings and machine learning models such as Artificial Neural Networks (ANN) were built and tested for the highest accuracy. The results show that forecast models ARIMA and average of ARIMA and triple exponential smoothing achieve the best accuracy, i.e., the least forecasting error value. Implementing the forecast model needs coordination and collaboration between the supply chain entities. Therefore, collaborative planning, forecasting and replenishment collaborative, CPFR, was suggested for the retailer’s supply chain through a specific framework. Finally, the benefits of the collaboration framework in terms of transportation costs were displayed using discrete-event simulation. Several scenarios and parameters were tested. Adopting the CPFR collaborative framework could reduce the transportation cost for some retail items by 42% evident in the simulation.
AI-Driven Quality Monitoring for Additive Manufacturing
Researcher: Kosanoglu, F.
Collaborator: Pervaiz, S. (RIT Dubai) and Beyca, O. F. (Istanbul Technical University)
Our research explores the use of artificial intelligence, computer vision, and machine learning to enable real-time quality monitoring in extrusion-based additive manufacturing. We focus on detecting and predicting printing defects during the manufacturing process, with the goal of improving part quality, reducing material waste, and increasing process reliability. The research also investigates adaptive AI models that can generalize across different printers, materials, and operating conditions, supporting the development of intelligent and autonomous additive manufacturing systems.
A Fuzzy-AHP (FAHP)-based model for evaluating blockchain adoption in the UAE
Researcher: Dr. Karam Al-Assaf
Collaborator: Alshaikh, R., Aldoukhi, S., & Ndiaye, M.
Blockchain, a decentralized digital ledger, enables secure and traceable online transactions, fostering a shared economy and digital currencies while protecting user privacy. Governments worldwide, including Canada, Switzerland, and Denmark, are increasingly adopting blockchain technology to address inefficiencies and enhance operations. However, its adoption remains limited in some governmental sectors, underscoring the need to understand the factors influencing this process. This research identifies critical factors affecting the adoption of blockchain technology in government and private sectors across technological, environmental, and organizational domains. Utilizing Multi-Criteria Decision Making (MCDM) methods, the study reveals key determinants that influence adoption, providing valuable insights for policymakers and agencies to make informed decisions on implementing blockchain technology, thereby enhancing operational efficiency and security.
Customers' perception of residential photovoltaic solar projects in the UAE: A structural equation modelling approach
Researcher: Dr. Haneen Abuzaid
Collaborator: L. A. Moeilak, and A. Alzaatreh
Photovoltaic solar systems are widely used as a renewable energy resource worldwide due to the numerous negative impacts of the conventional-depleted energy resources. In the context of the Middle East, the United Arab Emirates has been a pioneer in regulating renewable energy and setting annual targets for the dependency on and market share of renewable energy and carbon dioxide emissions. The purpose of this paper is to define the main factors that affect customers' perception of photovoltaic solar projects for the residential sector as an alternative renewable source of electricity in the United Arab Emirates. Reliably collected responses were used to build a hypothetical model using confirmatory factor analysis and structural equation modeling. The findings showed that financial and environmental aspects are the main contributors to customers' perception in the United Arab Emirates. Furthermore, it was apparent that different nationalities in the United Arab Emirates have similar average perception of photovoltaic systems projects and finally, we suggest that perception may positively impact the intention towards adopting residential photovoltaic systems. These findings can be utilized by governmental related authorities, photovoltaic systems contractors, photovoltaic systems leasing companies, and energy consultants to enhance the perception of customers and increase the tendency to install such projects in the residential sector.
Ranking risk attitudes using an integrated AHP-TOPSIS approach
Researcher: Dr. Haneen Abuzaid
Collaborator: L. Obaid, D. Dalalah, S. Alhassani, F. Albastaki, and T. E. Khalil
Due to the ambiguity between risk attitudes, this study aims at ranking the different risk attitudes considering the factors that affect the behaviour of the decision-makers. Both the technique for order preference by similarity to ideal solution (TOPSIS) and analytical hierarchy process (AHP) are employed to address the characteristics of risk attitudes aiming to highlight the criteria significance and finally to rank the most impactful risk attitude. It was found that regret aversion and risk aversion attitudes have higher impact in real life decision-making problems. In contrast, the maximin and maximax risk attitudes have the lowest importance. Risk seeking and regret aversion attitudes demonstrated the highest importance using TOPSIS of equal-weights while the importance of loss aversion and regret aversion have the highest for the AHP-TOPSIS approach. The results of this study can be beneficial for decision-makers who encounter a variety of risk attitudes in their decision problems.
Driving towards Sustainability: A Neural Network-Based Prediction of the Traffic-Related Effects on Road Users in the UAE
Researcher: Dr. Haneen Abuzaid
Collaborator: R. Almashhour, and G. Abu-Lebdeh
Transportation is fundamental, granting access to goods, services, and economic opportunities. Ensuring sustainable transportation, especially in vehicular modes, is crucial for the pillars of social, economic, and environmental sustainability. High-traffic countries, like the United Arab Emirates (UAE), grapple with significant challenges to this end. This study delves into the repercussions of traffic-related incidents on UAE road users and their intricate links to the social and economic dimensions of sustainability. To achieve this, this work examines the influential demographic factors contributing to incidents, utilizing artificial neural network models to predict the likelihood of individuals experiencing traffic tickets and accidents. Findings reveal associations between gender, driving frequency, age, nationality, and reported incident frequency. Men experience more accidents and tickets than women. Age exhibits a negative linear relationship with incident occurrence, while driving experience shows a positive linear relationship. Nationalities and cultural backgrounds influence road users’ adherence to traffic rules. The predictive models in this study demonstrate their high accuracy, with 93.7% precision in predicting tickets and 95.8% in predicting accidents. These insights offer valuable information for stakeholders, including government entities, road users, contractors, and designers, contributing to the enhancement of the social and economic aspects of road sustainability.
A Structural Equation Modeling of Customer Attitudes towards Residential Solar Initiatives in Jordan
Researcher: Dr. Haneen Abuzaid
Collaborator: L. A. Moeilak, and A. Alzaatreh
Photovoltaic (PV) systems have gained global significance as a sustainable and renewable energy resource, playing a pivotal role in mitigating climate change and meeting growing energy demands. Like many other nations, Jordan recognizes the potential of PV systems and has been actively pursuing their adoption across various sectors, particularly in the residential sector. This paper aims to investigate the factors influencing residential customers' perceptions of PV systems and their intentions to adopt these systems in Jordan by identifying key determinants of perception and adoption and developing a comprehensive Structural Equation Model (SEM) to quantify the relationships between identified factors and their effect on the intention to adopt residential PV systems. A theoretical model was developed to achieve the study's objectives, employing confirmatory factor analysis and structural equation modeling. Data for this study was collected through a comprehensive survey targeting the residential segment. The findings indicate that social, technological, governmental, and financial factors positively and statistically significantly impact residential customers' perceptions of PV systems, fostering their intention to embrace this technology. Surprisingly, the environmental factor was deemed insignificant in influencing residential customers' decision-making processes. This research offers valuable insights to various stakeholders, including PV contractors, consultants, governmental entities, policy, and decision-makers. By understanding the key drivers and barriers shaping residential PV adoption, stakeholders can devise more effective strategies to promote sustainable energy practices, foster economic growth, and contribute to the global transition towards cleaner energy sources.