Recent Submissions

  • Analyzing the causes and impact of essential medicines and supplies shortages in the supply chain of the Ministry of health in Saudi Arabia: A quantitative survey study

    Balfaqih, Hasan; External Collaboration; NA; 0; 0; Supply Chain Management; 0; Balfaqih, Hasan (Elsevier, 2024-02-14)
    Background: Investigating the causes and impact of essential medicines and supplies shortages in the supply chain of the MOH in Saudi Arabia could be the initial step in setting innovative strategies for mitigating this issue. This study aimed to identify the key factors contributing to essential medicines and supplies shortages in the supply chain of the MOH in Saudi Arabia and assess their impact on healthcare delivery. Methods: A structured questionnaire was designed to collect relevant data on the causes and impact of essential medicines and supplies shortages. A representative sample of healthcare professionals, from various healthcare MOH facilities in Saudi Arabia. The Statistical Package for the Social Sciences (SPSS) software version 26 was used for the data analysis. Results: A total of 379 respondents participated in the study, 73.7% were males, 51.2% were aged 36–45 years, 23.5% were supply chain professionals, and 32.9% reported an experience of >15 years. 90.0% of the participants reported that they personally have experienced shortages of essential medicines and supplies in the MOH supply chain in KSA. Inadequate planning, forecasting, and procurement were identified as the most significant contributing factors for shortages by about half (48.5%). At least two-thirds of the participants agreed with all strategies adopted for mitigating the issue of shortages. Conclusions: The impact of shortages on patients and healthcare professionals was found to be substantial. The study also identified several key strategies to reduce shortages that received strong support from the participants.
  • Impact of Colloabroative Planning, Forecasting and Replenishment (CPFR) on Supply Chain Performance

    Aljehani, Raghad; No Collaboration; NA; 0; 0; Supply Chain Management; 0; Aljehani, Raghad (2020-12-01)
  • Artificial Intelligence and Smart Logistics Systems in Industry 4.0

    Balfaqih, Hasan; No Collaboration; NA; 0; 0; Supply Chain Management; 0; Hasan, Balfaqih (2023-03)
    AI applications are essential to smart logistics sector for industrial enterprises and for the fourth industrial revolution (Industry 4.0). The aim of this study is to analyze the variables that affect smart logistics and assess the efficiency of artificial intelligence applications on smart logistics. Further, the applications technology and AI has studied and the variables that influence on the adoption of AI are still unknown in this study. Moreover, the main investigation of this study is to find the impact of efficient factors on AI through integrating technology. The finding indicates the large-scale smart logistics system has a substantial impact on industrial organizations and businesses by utilizing AI applications of communication and information technology. Additionally, AI is able to offer supply chain logistics with technological assistance that combine big data, IoT, and cloud computing.
  • Supply Chain Agility Quantification and Analysis

    Almaktoom, Abdulaziz; No Collaboration; Supply Chain Management; Almaktoom, Abdulaziz (International forum of management scholars (infoms), 2019-03)
    Market fluctuations and harsh business environments are known to affect the performance of supply chain networks. To maintain an acceptable response time while coping with varying market fluctuations and potential business disruptions, agility of supply chain networks is desirable. The purpose of the research described here is to develop a mathematical model to measure the agility of members of any given supply chain network. The model is tested using stochastic simulation and a case study. The importance of this research lies in the fact that there is a huge gap in understanding of the concept of supply chain agility and how it can be measured.
  • Evaluating the Impact of Verified Government Accounts on the Knowledge, Attitudes, and Intentions of Saudi Residents During the COVID-19 Pandemic

    Balfaqih, Hasan; External Collaboration; Supply Chain Management; Kayal, Ghadeer (2022-01-01)
    Governments utilize various media channels to disseminate knowledge pertaining to infectious diseases such as COVID-19. When it was declared a pandemic, society began to depend on the media for critical information and countermeasures that would facilitate tackling the virus. The main objective of this research is to examine the impact of verified Saudi government accounts across various media outlets on respondents’ knowledge, attitudes, and intentions regarding COVID-19. A structured online questionnaire was distributed and 315 responses were used for analysis. The results were analyzed using SPSS. The results indicate that the residents of Saudi Arabia have adequate knowledge concerning the symptoms, precautionary measures, and modes of transmission of COVID-19. The respondents have gained this knowledge from verified Saudi government accounts across various media outlets, most prominently via social media.
  • Saudi Oil Export Forecasting

    Almaktoom, Abdulaziz; Bajafar, Lama; Beran, Tasnim; Shafaamri, Asmaa; Department Collaboration; Lama Bajafar, Tasnim Beran, Asmaa Shafaamri; Supply Chain Management; Bajafar, Lama (Proceedings of the International Conference on Management and Social Sciences (ICMSS-2022), Bangkok, Thailand, December 28-29, 2022., 2022-12-29)
    This study aims to show how the oil industry may affects the Saudi oil exports. The oil price has been uneven in recent years. In this paper, several forecasting methods been evaluated to help augmenting oil export prediction accuracy. In this paper, several forecasting methods were applied to the data collected and errors been measured. As a result, based on Mean Forecast Error (BIAS), the linear regression method was the best because it has the lowest error value. This indicates that there will be variation in the demand which means the accurate prediction will help on oil production demand planning. The data used was collected from Aramco's annual report, the Saudi General Authority for Statistics, and online newspapers.
  • Composite Exponential Reaching Law Based SMC with Rotating Sliding Surface Selection Mechanism for Two Level Three Phase VSI in Vehicle to Load Applications

    Almaktoom, Abdulaziz; University Collaboration; External Collaboration; Supply Chain Management (MDPI, 2022-12-28)
    Voltage source inverters (VSIs) are an integral part of electrical vehicles (EVs) to enhance the reliability of the supply power to critical loads in vehicle to load (V2L) applications. The inherent properties of sliding mode control (SMC) makes it one of the best available options to achieve the desired voltage quality under variable load conditions. The intrinsic characteristic of robustness associated with SMC is generally achieved at the cost of unwanted chattering along the sliding surface. To manage this compromise better, optimal selection of sliding surface coefficient is applied with the proposed composite exponential reaching law (C-ERL). The novelty of the proposed C-ERL is associated with the intelligent mix of the exponential, power, and difference functions blended with the rotating sliding surface selection (RSS) technique for three phase two level VSI. Moreover, the proposed reaching law along with the power rate exponential reaching law (PRERL), enhanced exponential reaching law (EERL), and repetitive reaching law (RRL) were implemented on two level three phase VSI under variable load conditions. A comparative analysis strongly advocates the authenticity and effectiveness of the proposed reaching law in achieving a well-regulated output voltage with a high level of robustness, reduced chattering, and low %THD.
  • Supply Chain in Relation to Tourism and Crowd Management

    Almaktoom, Abdulaziz; Shami, Atheer; Department Collaboration; Atheer N. Shami; Supply Chain Management; Shami, Atheer (international Business Information Management Association, 2022-11-24)
    The tourism industry is one of the fastest growing economic sectors in the world, and the developments in the industry are linked to the operation of tourism enterprises and the products offered by them. Being one of the most competitive industries in the business world, the management of supply and demand is essential aspect of successful management in the tourism sector. A major issue that arises in the tourism sector is the management of crowds in many of the beautiful and renowned cities in the world. The massive flow of visitors is not only an issue for the travelers, but also imposes risks on the supply chain that operates within the tourism industry. The aim of this article is to gain an understanding of supply chain applications in the tourism industry and exploration of crowd management with regards to tourism industry. The article also explores the advantages of using supply chain for crowd management in tourism industry, particularly with reference to the Saudi Arabia’s tourism industry, along with suggestions to integrate crowd management strategies in supply chain.
  • Assessing supply chain performance through the interplay among success drivers

    Balfaqih, Hasan; External Collaboration; Supply Chain Management; Alsmairat, Mohammad (2022-04-24)
    This study examines the success drivers of Supply Chain Performance (SCP). This study aims to examine the interrelationships among various proposed success drivers of SCP that were analyzed individually or collectively in some previous studies. Four proposed forces have been identified in this study including organizational culture, SC relationships, SC integration, and SC innovation. SC integration is seen as a mediating factor between SC relationships and SCP. Using a deductive and quantitative approach that is based on collecting data from (17) companies in the field of logistics using an online survey, the study focuses on four success drivers including organizational culture, SC innovation, SC relationships, and SC integration. Smart PLS 3 software was applied to analyze the data based on the use of Structural Equation Modelling (SEM). The findings confirm that organizational culture and SC innovation have a direct significant impact on SCP. Regarding the mediating role of SC integration, the finds confirmed that SC integration mediates the relationship between SC relationships and SCP. A set of implications and recommendations for decision-makers and researchers are proposed based on the empirical findings of the current study. SC managers must first consider organizational culture and try to create positive supportive cultural attributes. A culture that encourages openness to change, sharing of knowledge, and collaboration seems a necessity to further improve SCP. Additionally, as the study findings emphasized the significant impact of SC innovation, SC managers should encourage innovative initiatives and behavior.
  • Analyzing Optimal Battery Sizing in Microgrids Based on the Feature Selection and Machine Learning Approaches

    Almaktoom, Abdulaziz; Khan, Hajra; Mian Qaisar, Saeed; Waqar, Asad; Krichen, Moez; Nizami, Imran; University Collaboration; External Collaboration; Supply Chain Management; Khan, Hajra (MDPI, 2022-10-24)
    Microgrids are becoming popular nowadays because they provide clean, efficient, and lowcost energy. Microgrids require bulk storage capacity to use the stored energy in times of emergency or peak loads. Since microgrids are the future of renewable energy, the energy storage technology employed should be optimized to provide power balancing. Batteries play a variety of essential roles in daily life. They are used at peak hours and during a time of emergency. There are different types of batteries i.e., lithium-ion batteries, lead-acid batteries, etc. Optimal battery sizing of microgrids is a challenging problem that limits modern technologies such as electric vehicles, etc. Therefore, it is imperative to assess the optimal size of a battery for a particular system or microgrid according to its requirements. The optimal size of a battery can be assessed based on the different battery features such as battery life, battery throughput, battery autonomy, etc. In this work, the mixed-integer linear programming (MILP) based newly generated dataset is studied for computing the optimal size of the battery for microgrids in terms of the battery autonomy. In the considered dataset, each instance is composed of 40 attributes of the battery. Furthermore, the Support Vector Regression (SVR) model is used to predict the battery autonomy. The capability of input features to predict the battery autonomy is of importance for the SVR model. Therefore, in this work, the relevant features are selected utilizing the feature selection algorithms. The performance of six best-performing feature selection algorithms is analyzed and compared. The experimental results show that the feature selection algorithms improve the performance of the proposed methodology. The Ranker Search algorithm with SVR attains the highest performance with a Spearman’s rank-ordered correlation constant of 0.9756, linear correlation constant of 0.9452, Kendall correlation constant of 0.8488, and root mean squared error of 0.0525.
  • Heuristic dynamic approach to perishable products in presence of deterioration effect

    Almaktoom, Abdulaziz; Bahebshi, Raghad O.; Supply Chain Management (2019)
    oining warehouses and suppliers facilities to deliver the finished product to the end customer is a complex process that requires extensive consideration. The resulting chain is an integration of such entities as the supplier, manufacturer, distributor, warehouse, retailer, and end customer. A perishable product is any product that can rot, spoil, or deteriorate rapidly and, soon after manufacture, may become unusable or obsolete. Perishable products thus have special nutritional characteristics that necessitate care and unique treatment for them. Such products can be anything that becomes outdated a short time after production or harvest, such as fruits, vegetables, meat, certain drinks, blood, and pharmaceuticals. The objective of this study is to find the best heuristics for distributing multiple perishable products as early as possible to maximize profit. Case studies involving featuring perishable products at different rates of degradation with multiple retailers and limited transportation capacity were carried out to demonstrate the effectiveness of the proposed method.