Circular economy and sustainability are the two key principles increasingly influencing the modern-day world across various industries. The electronics industry is considered to be the most critical of applying these two principles because of its fast pace and a critical need for sustainable development and climate action. This emphasizes the need to look at the environmental footprint of this specific industry and its responsibilities very critically where packaging is one of the most important environmental impacts within the electronics industry. The concepts of the circular economy specifically focus on the durability or size of the packaging, but also on the materials used, with emphasis put on sustainability. This research will basically seek to determine how electronic packaging can offer direct contribution to the case of sustainability. It essentially pursues the enlightenment of the process under which circular economy can be integrated into electronic packaging design to solve the design conflicts with sustainability by circular design principles. This research will apply the method of Quality Function Deployment (QFD) for the evaluation of consumer expectations with relation to the circular design in electronic packaging—considered to be the (“What’s”)—and derivation of the design criteria that result in meeting such expectations—this being the (“How’s”). It is set to discuss the design of electronic packaging in a new perspective, taking into consideration both customer and technical expectations versus circularity and sustainability. The expected results from this paper are the identification of the most important consumer expectations and then the transformation to design requirements and aims to present useful insights to the managers in the industry regarding the important relationship between the design of electronic packaging and sustainability within the idea of circular economy.

Oil storage is a strategic necessity, but each storage technique—above-ground, underground, and in-ground—has distinct strengths and drawbacks, and growing sustainability demands complicate the choice of the best option. This study, therefore, develops a structured multi-criteria decision-making (MCDM) framework that integrates technical, economic, social, and environmental factors to identify the most appropriate oil storage technique. Using a structured literature review and expert consultation, four main criteria and sixteen sub-criteria were defined. The Fuzzy Analytic Hierarchy Process (F-AHP) was then applied to elicit expert judgments and compute weights, while Fuzzy VIKOR (F-VIKOR) was used to rank the alternatives. The results indicate that the social criterion is the dominant factor, with safety being the most influential sub-criterion, followed by return on investment. The F-VIKOR analysis ranks underground storage as the best option, above-ground storage as second, and in-ground storage as third, with underground storage satisfying both the acceptable advantage and stability conditions. Sensitivity analyses confirm that underground storage remains the top-ranked technique under all weighting scenarios, although the rankings of above-ground and in-ground techniques interchange when economic and social weights change. This study offers decision-makers a robust and evidence-based tool for strategic planning and policy-making regarding strategic oil storage options.

The oil and gas industry, with numerous supply chain partners, significantly contributes to the world economy. This industry's operations involve complex processes and interactions with different stakeholders, leading to many drivers contributing to its complexity. This study identifies seventeen complexity drivers in the oil and gas supply chain based on an extensive literature review and the Pareto principle. The identified drivers were then analyzed using an integrated Analytical Hierarchy Process (AHP) and Decision-Making Trial and Evaluation Laboratory (DEMATEL) approaches. The analysis reveals that the procurement system is the most important driver, followed by process synchronization among supply chain partners. Government regulation is the least influential driver in creating complexity in the oil and gas supply chain. Further analysis indicated that seven of the seventeen identified drivers were classified as causes, while the remaining ones fell under the effect group. The results of this study are expected to help decision-makers devise strategies based on the drivers with significant impact to minimize complexity and mitigate its effects on the oil and gas industry supply chain.

The objective of the study is to create a product-service system (PSS) innovative framework that aligns with a case company's strategy, competencies, and strengths. First, this study shows that the case company's Design Thinking Macro process and PSS fundamentals might serve as the foundation of a suitable PSS innovative framework for the company's upcoming service. Second, it serves as a descriptive stat-of-the-art study-the study investigates the case company's innovation potential and potential for controlling hazards in the direction of servitization. The study aims to understand the current state of the case company and assist it in becoming a more flexible PSS supplier. The results show that the case company is committed to advance a cross-organizational development plan to strengthen its PSSs to capitalize in servitization. Because of its end-to-end capabilities, scientific innovation, and value-adding products that have a tangible component of value, the company's product portfolio is well-positioned in terms of servitization.

The objective of this study is to build a predictive model for the call center of a financial services company using predictive analytics methods. Using this model, the organization would be able to transition from a reactive approach to operational resource allocation to a more proactive one in handling inbound calls. This study uses a quantitative methodology, as it provides a structured, objective, and rigorous framework essential for developing, testing, and validating forecasting models. Quantitative methods’ reliance on numerical data and statistical techniques ensures accuracy, reliability, and the ability to generalize findings. This study contributes valuable insights into the application of time series forecasting models in optimizing resource allocation and enhancing service quality within the call centres, particularly within the financial services industry. By addressing the identified research gap and providing practical recommendations, this research offers an addition to further advancements in call center forecasting methodologies, facilitating more efficient and effective operations within financial organizations.

Motivation is a key element for successful project execution. However, different people are motivated by various means in different sectors. Hence, this work aims to study, analyze and define the main motivational factors in project execution in the oil and gas industry. To achieve this, a dedicated survey is prepared and distributed among employees working in the oil and gas industry in Oman. To ensure diversifications and minimize biases, the survey is distributed randomly to people working in projects in different organizations in the oil and gas industry. Altogether, 86 respondents completed all the survey questions. The study revealed that, in general, external (extrinsic) factors have a significant motivational influence on employee performance in which money is considered as a powerful motivator. Moreover, task achievement is found to be the major intrinsic motivational factor influencing the employee’s performance. The study also reveals that irrespective of years of experience, organizational type and the level at which employee works, lack of management support will have a significant influence in lowering team motivation in the Oil and gas project. The study can serve as a guideline for the Oil and gas industry to target the specific factor which helps enhance the level of motivation of a specific segment of employees

The facility management (FM) unit of an organization struggles with the sustainable selection of contractors for the maintenance of critical facilities. This paper aimed to optimally allocate maintenance contractors to public buildings by incorporating cost and other criteria into a matching objective that ensures sustainability. Sustainability criteria related to contractors and buildings were identified through an extensive literature review and expert opinion. These criteria were analyzed using integrated AHP and TOPSIS techniques, and the result was then utilized as input for a bi‐objective optimization model designed for contractor allocation to mechanical, electrical, and plumbing and Fire assets. The optimization model reduces costs, increases matching between buildings and contractors, and respects FM strategy to limit the number of contractors. The quality of the solutions obtained and their contribution to achieving the United Nations Sustainable Development Goals (SDGs) is discussed. A case study is presented based on real data from the FM department responsible for maintaining 146 governmental buildings in the United Arab Emirates. The results show that the proposed approach outperforms the current solution, potentially reducing the number of contractors to three, reducing maintenance costs by 23.01%, and significantly improving sustainability by 50.02%. The study demonstrates the practical application of integrating SDGs into FM operations, contributing to achieving global sustainability targets. To show the robustness of the proposed approach, the sensitivity analyses of AHP weights on the ranking of contractors and buildings, as well as the sensitivity analysis of the number of buildings and contractors on the efficiency of the optimization model, have been conducted.

This study proposes a Supply Chain Operations Reference (SCOR®) based performance prediction model for the Make-to-Order job shop facility. The model uses Artificial Neural Network, which is fed with the real data collected from automotive job shop to predict cost and customer response using feed forward back error propagation learning algorithm with nonlinear activation function. The model was implemented using the MATLAB program and the correlation coefficient results demonstrated a high positive correlation between the expected and projected performance values, which supports SCOR® level 1 metrics for all ANN models. The average percentage error and the percentage standard deviation of the best cost model are found to be 0.75 and 1.28 respectively. Similarly, for the response model, they are found to be 0.13 and 0.25 respectively. These results highlight the quality of the developed model and its expected positive impact for improving supply chain performance.

As the third largest emirate in the UAE, Sharjah is a fast-urbanizing region that has grown into a principal cultural, commercial, and educational center of the country. Among the major economic activities in Sharjah, masonry work provides the necessary economic lifeline for the Emirate. Nonetheless, some workers are often unaware of the detrimental effects of their manual work activities on their health. Accordingly, this research seeks to explore the existence and extent of occupational health hazards among workers in the masonry industry in Sharjah. A cross-sectional research design has been carried out to gather relevant data from occupational safety and health (OSH) experts, contractors, and consultants, using semi-structured interviews and surveys. The result shows that masonry workers in Sharjah face physical and chemical hazards, which are caused by high temperatures, dust, vibrations, repetitive weight lifting, as well as the loading and unloading of materials. The findings from the physical hazard show that low lights and high sounds are the highest in terms of frequency, whereas falls from heights are the highest in terms of severity. Further, the findings show that dust is the major chemical hazard faced by the masonry workers in terms of frequency, whereas asphyxiation is the highest in terms of severity. The survey that was conducted verifies the analytic hierarchy process results. For instance, asphyxiation is the most severe factor, accounting for 69% average weight, while dust account for 6% average weight which makes it the least severe chemical hazard, despite being the most common chemical factor in masonry work across Sharjah. The high rates of chemical and physical hazard exposures demonstrate that the current OSH regulations in Sharjah are insufficient in addressing the most prior masonry works hazards.

The supply chain of many industries, including Oil and Gas, was significantly affected by the disruption caused by the Covid pandemic. This, in turn, had a knock-on effect on other industries around the globe. Sustaining the impact of the disruption posed a major challenge for the industry. This study contributes to the existing literature by identifying and analyzing the most significant drivers that affected the sustainability of the Oil and Gas supply chain during the Covid pandemic. Fifteen drivers were identified based on an extensive literature review and a survey conducted with experts working in the Oil and Gas industry. Multi-criteria decision-making methodologies were used to analyze these drivers. The analysis from the fuzzy analytical hierarchy process found that the most important drivers for the sustainability of the Oil and gas supply chain during the pandemic were “Risk management capacity”, “Government regulation” and “Health and safety of employees”. On the other hand, the driver “Community Pressure” was found to be of the least importance. Furthermore, the study integrated the results of the fuzzy analytical hierarchy process with the fuzzy technique for order of preference by similarity to ideal solution to calculate the supply chain sustainability index. A case example was demonstrated to rank the industries based on such calculations. This study can support the governmental institutions in benchmarking the Oil and Gas industry based on its sustainability index. Additionally, the outcomes of the study will help industrial decision makers prioritize the drivers the company should focus and devise strategies based on the priority to improve the sustainability of their supply chain during severe disruption. This will be crucial as the World health organization has cautioned that the world may encounter another pandemic in the near future.

In the healthcare system, advanced medical devices such as ventilators, defibrillators, and dialysis machines are essential. Effective maintenance strategies are critical to ensure the continuous and safe operation of these devices. This study explores the use of the Quality Function Deployment (QFD) method to identify suitable maintenance strategies for ventilator devices in UAE intensive care units. A survey of healthcare professionals was conducted in the UAE, results identified nine key customer requirements, in which safety (5), and efficiency (4) prioritized higher than remaining attributes. These requirements were then mapped to technical maintenance solutions such as scheduled maintenance, spare parts management, emergency repair protocols, predictive analytics, and energy efficiency. The QFD results ranked spare parts management (138), and emergency repair protocols (96) as the first and second important technical requirements respectively. These requirements fall under preventive and corrective maintenance strategies. Therefore, the possible strategies should be implemented to ensure the efficiency and safety of ventilators in healthcare system. Prioritizing preventive maintenance protocols can enhance the operational efficiency and reliability of ventilator devices, which is crucial for patient safety in UAE healthcare facilities.

The supply chain of many industries, including Oil and Gas, was significantly affected by the disruption caused by the Covid pandemic. This, in turn, had a knock-on effect on other industries around the globe. Sustaining the impact of the disruption posed a major challenge for the industry. This study contributes to the existing literature by identifying and analyzing the most significant drivers that affected the sustainability of the Oil and Gas supply chain during the Covid pandemic. Fifteen drivers were identified based on an extensive literature review and a survey conducted with experts working in the Oil and Gas industry. Multi-criteria decision-making methodologies were used to analyze these drivers. The analysis from the fuzzy analytical hierarchy process found that the most important drivers for the sustainability of the Oil and gas supply chain during the pandemic were “Risk management capacity”, “Government regulation” and “Health and safety of employees”. On the other hand, the driver “Community Pressure” was found to be of the least importance. Furthermore, the study integrated the results of the fuzzy analytical hierarchy process with the fuzzy technique for order of preference by similarity to ideal solution to calculate the supply chain sustainability index. A case example was demonstrated to rank the industries based on such calculations. This study can support the governmental institutions in benchmarking the Oil and Gas industry based on its sustainability index. Additionally, the outcomes of the study will help industrial decision makers prioritize the drivers the company should focus and devise strategies based on the priority to improve the sustainability of their supply chain during severe disruption. This will be crucial as the World health organization has cautioned that the world may encounter another pandemic in the near future.

All countries need to store petroleum products, and different oil storage techniques are available for this purpose. With the increasing interest in sustainability issues, it is needed to compare these different oil storage alternatives. Thus, this research presents high-volume oil storage techniques and identifies the criteria and sub-criteria that can be used to compare them. By conducting a thorough literature review, 16 sub-criteria were identified and then verified by experts. These sub-criteria were clustered into four different criteria, namely, economic, social, environmental, and technical. Using the analytic hierarch process (AHP) method, weights were given to the different criteria and sub-criteria. The AHP results show that the social criterion has the highest weight among the identified criteria, followed by the economic criteria. At the global level, sub-criteria safety and security have the highest weight. Conversely, the sub-criteria potential for corrosion / damage has the least weight. This research will help decision-makers, such as governments, oil industry professionals, or environmentalists, make informed choices regarding the storage of oil based on their impact on environmental and social and economic sustainability.

The United Arab Emirates is one of the nations that is highly renowned for its rapid advancement in a variety of industries. The UAE's healthcare sector is thought to be in a reasonable status, although it can be strengthened by utilizing its resources and advanced technology. One of the well-known technologies that, when incorporated into the healthcare industry, can enable advancement is IR4.0. This paper identifies and examines the barriers the nation will encounter in implementing IR4.0. Systematic literature review was conducted to understand the generic barriers of introducing the technology. Altogether, nineteen generic barriers were identified, which is further screened into eight barriers based on a survey. The identified barriers are analyzed using Fuzzy DEMATEL technique. The outcome of this research work hopes to help government and decision makers in the healthcare industry when deciding to introduce IR4.0.

This study investigates the previous studies on successful digital transformation initiatives in government organizations and deduces the tangible and intangible benefits to showcase some real-life examples and evidence. This article provides a thorough evaluation of the available literature on successful digital transformation initiatives. It analyzes 53 important success elements grouped across seven dimensions, giving a conceptual framework for executing digital transformation in government organizations. The research identifies key success elements that are crucial for digital transformation, emphasizing the importance of clear planning, flexibility, agility, and robust data security measures. This study provides practical insights for organizations aiming to undertake digital transformation initiatives, highlighting strategies to overcome hurdles and maximize benefits. This study contributes a proposed conceptual framework and empirical evidence to guide academics, professionals, and decision-makers in effectively navigating and leveraging digital transformation in a rapidly evolving digital landscape.

Emad Summad has a PhD in Industrial Engineering.He is specializing on policy issues

Bone drilling is a universal procedure in orthopaedics for fracture fixation, installing implants, or reconstructive surgery. Surgical drills are subjected to wear caused by their repeated use, thermal fatigue, irrigation with saline solution, and sterilization process. Wear of the cutting edges of a drill bit (worn drill) is detrimental for bone tissues and can seriously affect its performance. The aim of this study is to move closer to minimally invasive surgical procedures in bones by investigating the effect of wear of surgical drill bits on their performance. The surface quality of the drill was found to influence the bone temperature, the axial force, the torque and the extent of biological damage around the drilling region. Worn drill produced heat above the threshold level related to thermal necrosis at a depth equal to the wall thickness of an adult human bone. Statistical analysis showed that a sharp drill bit, in combination with a medium drilling speed and drilling at shallow depth, was favourable for safe drilling in bone. This study also suggests the further research on establishing a relationship between surface integrity of a surgical drill bit and irreversible damage that it can induce in delicate tissues of bone using different drill sizes as well as drilling parameters and conditions.

The tourism supply chain aims at satisfying the needs of the tourists based on their preferences. However, the preference of each tourist may be different. Some tourists prefer to optimize a single criterion, while others prefer to optimize conflicting multiple-criteria. The tourism service provider can hardly offer the tourists with the itinerary according to their precise preferences. This paper proposes a multi-objective optimization framework based on which tourists can generate itineraries according to their preferences. A mathematical model is presented, which is multi-objective and NP-hard. Consequently, four meta-heuristic algorithms, namely none-dominated sorting genetic algorithm versions II (NSGA-II) and III (NSGA-III), multi objective grey wolf optimization, and multi objective imperialist competitive algorithm are developed. The proposed method helps the tourists to compare different combinations of activities and select the one that best suits their preferences. The model is tested on a small-scale real case pertaining to the Sultanat of Oman. Thereafter, the performances of the proposed algorithms were evaluated on large scale problems. The result shows that NSGAs outperformed other algorithms. NSGA-II outperformed its NSGA-III counterpart in small instances. Surprisingly, as the size of the problem increases, the efficiency of NSGA-II decreases while that of NSGA-III increases. In large instances, NSGA-III outperformed NSGA-II.

This study examines the ability to machine a type of bio-composite material made up of polypropylene reinforced with date palm fibers. The focus is on how drilling affects the delamination of the material at the entry and exit points of the hole. The impact of three drilling parameters (spindle speed, drill size, and feed rate) on the quality of the holes, as measured by the delamination factor, is analyzed. The research was conducted experimentally based on the design of experiment method, and data were analyzed using statistical techniques such as analysis of variance (ANOVA), response surface modeling (RSM), fuzzy logic (FL), and artificial neural network (ANN). The design expert software was also used to identify the optimal values for the machining parameters that minimize delamination. The results indicate that delamination caused by drilling is primarily influenced by drill bit diameter, with a threefold greater impact than feed rate. This bio-composite material may have potential for use in industrial applications where joining parts is necessary for product design and assembly.