Statistical Science Research Articles

Examining Teachers’ Professional Learning in an Online Asynchronous System: Personalized Supports for Growth and Engagement in Learning to Teach Statistics and Data Science

Teachers’ professional learning often includes online components. This study examined how a case of 37 teachers utilized a specific online asynchronous professional learning platform designed to support teachers’ growth in learning to teach statistics and data science in secondary schools in the United States. The platform’s features and learning materials were designed based on effective online learning designs, supports for self-guided learning, and research on the teaching and learning of statistics and data science. We paid particular attention to the features we designed into the platform to support self-regulation and personalizing the experiences to meet their preferred learning goals such as allowing for free choice of learning materials, flexibility of when and how long to engage, providing personal recommendations based on user input, internal systems to track progress, and generating certificates of completion. In this study, we used a case study with both quantitative and qualitative data to examine whether teachers had gains in meeting learning goals related to their development in teaching statistics and data science, had sustained engagement, and found the features for personalization supportive for their learning. Results showed, overall, positive growth towards meeting learning goals and making small changes towards improved classroom practice. Most teachers were generally engaged in sustained ways across the study period, though we found six different patterns of completion that highlight ways in which teachers’ goal-directed and self-regulated learning occurred within the busy schedules of educators. Several personalized features, especially the recommendations and tracking system, were highly utilized and perceived as supportive of teachers’ learning.

Knowledge and Preventive Practice of Urinary Tract Infection among Bangladeshi Female Ready-made Garments Worker

Ready-made garments (RMG) are Bangladesh’s largest export earner sector, with 800,000 workers, including two-thirds of females. Female RMG workers suffer from various illnesses, including urinary tract infections (UTI), but data is scars related it in Bangladesh. Therefore, the study aimed to evaluate the knowledge and preventive practice of UTI among female RMG workers. Method: This cross-section survey was conducted to evaluate the knowledge and preventive practice of UTI among female RMG workers. Using convenient sampling technique, we collected 351 data between February to April 2021 from two RMG. Data were analysed by using Statistical Package Social Science (SPSS) software version 23. Descriptive statistics was conducted using frequencies and proportions. Chi-square test was used to determine the association between dependent and independent variables. Linear regression and Pearson co-relation was used to determine the association between knowledge and practice. Results: Of 351 participants, 22% had good knowledge and 33% were in good practice. The study found a statistically significant correlation between knowledge level of UTI and participants’ age, education, residence, family size, parents’ education and occupation (p<0.05). Similarly, participants’ education, marital status, residence, family size, parents’ education and occupation were significantly correlated with practice level of female RMG workers towards UTI (p<0.05). Furthermore, participants who received health-related training had good knowledge and practice of UTI. Conclusion: Female RMG workers’ sociodemographic variables were significantly correlated with their knowledge and practice of UTI. Training on health-related issues was significantly associated with good knowledge and practice of UTI. It’s urgent to keep female RMG workers aware of health-related issues and easily accessible health care. EWMCJ Vol. 12, No. 1&2, January-July 2024: 50-59

Teaching Statistics and Data Science Collaboration via a Community of Practice

Due to the applied nature of statistics and data science, many educators in these fields recognize the need to teach their students how to be effective interdisciplinary collaborators. Some prior research considers different approaches to teaching interdisciplinary collaboration skills. However, missing from this literature are the connections between teaching collaboration and education theory. Thus, there is a lack of understanding about why the various pedagogical approaches may be effective. In this descriptive study, we describe an approach to teaching interdisciplinary collaboration using a Community of Practice (CoP) and highlight connections between potentially reproducible elements of this approach and education theory that explains why this approach may be effective from the perspectives of both education and collaboration theory. Our results show that students and content-area experts recognize this approach to teaching statistical and data science collaboration to be effective. By grounding our methods for teaching statistics and data science collaboration skills in education theory, we focus attention on which aspects can be replicated in other contexts, why they work well, and how they can be improved. We recommend instructors intentionally create a CoP within their courses, encourage peer mentorship, and emphasize a growth mindset.

Fostering Nurses’ Support in the Workplace: Addressing Job Burnout Among Malaysian Medical Doctors Working in Public Hospitals

Job burnout in employees is caused by individual factors, including demographic characteristics and nurses’ support. This study analyzed the relationship between nurses’ support and job burnout among medical doctors in Malaysian public hospitals. This research was a correlational study with a cross-sectional design. The research sample was 471 employees, using the non-probability sampling method with the purposive and quota sampling techniques. Data collection used the Abbreviated Maslach Burnout Inventory (aMBI) and nurses’ support items. The data in this study were analyzed using the Statistical Package Social Science (SPSS) and Partial Least Squares Structural Equation Modelling (PLS-SEM) software. Findings suggest that 77 medical doctors who participated in the study were from Hospital Pulau Pinang, from the Emergency Department, mostly female, ranging from 24-56 years old, single, and have an MBBS education level. The study assessed the measurement model’s loading, average variance extracted (AVE), and composite reliability (CR) where the loadings were above 0.60. AVEs were greater than 0.50 and CRs were greater than 0.708. Additionally, the bootstrap BCI LL (-0.079) and UL (-0.002) for nurses’ support and job burnout were not straddled a 0 in between. Job burnout had a negative association with nurses’ support. In particular, nurses’ support towards medical doctors lowers their risk of job burnout, which has a substantial impact on medical doctors’ overall well-being. The findings indicated that medical doctors who had complete support from their nurses performed their jobs more efficiently, displayed good task performance, and experienced less job burnout.

Recursive Computation of Rayleigh- Rayleigh Distribution via Ordered Random Variates

Ordered random variables (ORVs) are of great importance in statistical science. These random variables are organized in increasing order called generalized order statistics (GOS). It has tremendous applications in engineering and science due to the inclusion of ordered random variables. This article addresses recursive moments of Rayleigh-Rayleigh distribution using order random variables. Such moments are applicable in studying the characteristics of random variables in increasing order such as time to failure of an electronic devices. The characterization result is also obtained by simple moments.

Robin Dunbar: The neurobiology of human sociality

Robin Dunbar is an eminent evolutionary psychologist and anthropologist whose pioneering work has permanently redefined how we think about human social relationships. Dunbar, who is an emeritus Professor of Evolutionary Psychology at the University of Oxford, became world-renowned for, among other contributions, his formulation of “Dunbar's number,” a theoretical limit to the number of stable social relationships an individual can maintain, typically cited as about 150. That idea, which he developed as he studied the relationship between brain size and social group size in various primate species, has since had broad influences across areas from social media design to organizational management. Over time, Dunbar's number has become more widely known and discussed, particularly with the advent of social media and online social networks. This has led to renewed interest in its implications for digital social interactions. Dunbar's illustrious career spans multiple prestigious institutions, including, prior to joining Oxford in 2007, the University of Cambridge, the University of Stockholm (Sweden), University College London and the University of Liverpool. He is a fellow of the British Academy and the Royal Anthropological Institute, a Foreign Member of the Finnish Academy of Science and Letters, and an elected Honorary Member of the Hungarian Academy of Sciences. His interdisciplinary expertise is reflected in his professorships in psychology, evolutionary biology, and anthropology at institutions such as Liverpool and Oxford Universities. He has held a visiting chair in statistical physics and computer science at Aalto University, Finland, which has also awarded him an honorary doctorate. Dunbar studies the evolution of social processes in primates and humans, with work that blends neurobiology, cognitive science, and the social dynamics of how we communicate via friendship networks. His insights into friendships and community cohesion continue to shape our understanding of human social behaviour in the digital age. It is an absolute pleasure that Professor Dunbar answered the Genomic Press Interview as we celebrate the extraordinary story of his scientific odyssey with our readers.

Assessing the Level of Knowledge Regarding Medication Errors among Nurses in Karachi, Pakistan

One of the key elements influencing hospital service quality and decreasing patient safety in healthcare systems is medical error. In the healthcare industry, medication errors are a frequent source of harm and fatalities. Objective: To assess the level of knowledge among nurses regarding medication errors. Methods: This cross-sectional study was conducted in a tertiary care hospital in Karachi among 120 nurses from January 2024 to March 2024. Participants were selected through a convenient sampling technique. The sample size was calculated by open EPI softer by taking into consideration a 95% confidence level and a 5% margin of error. The data were entered and analyzed by Statistical Package Social Sciences software version 26. Results: According to the study results, 14 (12%) of the participants had a low level of knowledge, and 37 (31%), had a moderate level of knowledge. while 69 (57%) had a high level of knowledge regarding medication errors. Conclusion: This study concluded that most of the nurses had a high level of knowledge regarding medication errors.

Wkład ośrodków statystycznych w Krakowie i we Wrocławiu w rozwój metod klasyfikacji i analizy danych

The year 2022 marked the 10th anniversary of the passing of Prof. Kazimierz Zając, the long-standing head of the Department of Statistics and later the director of the Institute of Statistics, Econometrics and Computer Science at Krakow University of Economics, while 2023 marked the 10th year of the passing of Prof. Zdzisław Hellwig, the long-standing head of the Department of Statistics and director of the Institute of Economic Cybernetics at Wroclaw University of Economics. Both professors were the scientific leaders of statistical centres at the above-mentioned universities. The aim of the article is to present the contribution of Polish statisticians to the development of the theoretical foundations and empirical research in the field of numerical taxonomy and methods of data classification and analysis. Particular emphasis is placed on the achievements of Professor Zając and Professor Hellwig and their students and associates. According to the authors of this work, recalling the achievements of these scholars is especially important as most commonly cited sources on data classification and analysis are mainly English-language.

Linear Programming and Product Mix of Paint Manufacturing Firms in Port Harcourt

This study examined the relationship between linear programming and product mix of paint manufacturing firms in Port Harcourt. This study adopted a correlational survey research design. The population of the study was 10 selected paint manufacturing firms in Port Harcourt as retrieved from Nigerian business directory (2024). 3 managers were selected from each firm multiplied by 10 firms gives us a total of 30 respondents. Structured questionnaire was used in data collection. The result of the Cronbach's Alpha reliability test indicates .804 which is above .70 which implies that the items are reliable. Pearson product moment correlation was used to test the hypotheses using SPSS (statistical package social sciences). The study revealed that there is a significant relationship between Ellipsoid Linear Programming and Product Mix of paint manufacturing firms in Port Harcourt. There is a significant relationship between Graphical method Linear Programming and Product Mix of paint manufacturing firms in Port Harcourt. There is a significant relationship between Simplex Linear Programming and Product Mix of paint manufacturing firms in Port Harcourt. In conclusion, Linear programming is a powerful mathematical tool that can be effectively utilized by paint manufacturing firms in Port Harcourt to optimize their product mix and maximize profits. The paint manufacturing firms should conduct a thorough analysis of market demand for different types of paint products to identify profitable opportunities for product mix optimization.

Time series forecasting of price of the agricultural products using data science

Purpose. The purpose of our article is to research and forecast prices for agricultural products using the example of potato prices based on the most effective models using data science techniques. Methodology / approach. Various forecasting models are explored, starting from baseline models like decomposition and exponential smoothing models to more advanced techniques such as ARIMA, SARIMA, as well as deep learning models including neural network. The data is split into training and testing sets, and models are validated using cross-validation techniques and optimised through hyperparameter tuning. Model performance is evaluated using metrics such as MAE, MSE, RMSE, and MAPE. The selected model is then used to generate future price forecasts, with uncertainty quantified through confidence intervals. Results. The study successfully applied advanced data science techniques to forecast potato prices, leveraging a range of effective models. By analysing historical price data and using various forecasting methods, the research identified the most accurate models for predicting future price trends. The results demonstrate that the selected models can provide reliable forecasts. In particular, the results showed that the model could achieve good forecast results when applied to real problems and, thus, can be effectively used for forecasting tasks especially considering seasonality. In addition, it should be noted that the model has a higher prediction accuracy at the time intervals closest to the original data. The obtained results support using both models simultaneously for forecasting, which can compensate for the shortcomings of each of them. The models can be used separately, to more accurately predict the values for the required period, or a combination of them is also possible. Originality / scientific novelty. The study’s originality lies in development of methods for effectively accounting for seasonality in agricultural price data, such as using seasonal decomposition techniques or more advanced techniques that combine statistical and data science approaches. The novelty implies the implementation of real-time data processing and forecasting system allows for the timely prediction of price changes, enabling stakeholders to make more informed decisions. Practical value / implications. Forecasting potato prices holds significant practical value for various stakeholders. For farmers, accurate forecasts enable informed decisions on the optimal times to plant, harvest, and sell their crops, thereby optimising their profits. In the supply chain, distributors and retailers can use these forecasts to manage inventory more effectively and plan contracts, reducing waste and avoiding shortages. Policymakers benefit from forecasts by anticipating market fluctuations and stabilising prices, which supports both consumers and producers. For consumers, stable pricing ensures better budgeting and helps avoid sudden price spikes, making essential foods more affordable. Overall, accurate price forecasting enhances market efficiency by reducing uncertainty and aiding investors in managing risk.

Open Case Studies: Statistics and Data Science Education through Real-World Applications

With unprecedented and growing interest in data science education, there are limited educator materials that provide meaningful opportunities for learners to practice statistical thinking, as defined by Wild and Pfannkuch, with messy data addressing real-world challenges. As a solution, Nolan and Speed advocated for bringing applications to the forefront in undergraduate statistics curriculum with the use of in-depth case studies to encourage and develop statistical thinking in the classroom. Limitations to this approach include the significant time investment required to develop a case study—namely, to select a motivating question and to create an illustrative data analysis—and the domain expertise needed. As a result, case studies based on realistic challenges, not toy examples, are scarce. To address this, we developed the Open Case Studies (opencasestudies.org) project, which offers a new statistical and data science education case study model. This educational resource provides self-contained, multimodal, peer-reviewed, and open-source guides (or case studies) from real-world examples for active experiences of complete data analyses. We developed an educator’s guide describing how to most effectively use the case studies, how to modify and adapt components of the case studies in the classroom, and how to contribute new case studies (opencasestudies.org/OCS_Guide).

New alpha power transformed beta distribution with its properties and applications

The main purpose of this paper is to introduce a new alpha power transformed beta probability distribution that reveals interesting properties. The studuy provide a comprehensive explanation of the statistical characteristics of this innovative model. Various properties of the new distribution were derived, using the baseline beta distribution, statistical techniques, and probabilistic axioms. These include the probability density, cumulative distribution, survival function, hazard function, moments about the origin, moment generating function, and order statistics. For parameter estimation, the maximum likelihood estimation method using Newton Raphson numerical technique is employed. To evaluate the performance of our estimation method, the mean squared errors of the estimated parameters for different simulated sample sizes are used. In addition simulation studies of the new distribution are conducted to demonstrate the behavior of the probability model. To demonstrate the practical utility and flexibility of the alpha power transformed beta distribution, it is fitted to two real-life datasets and compared to commonly known probability distributions such as the Weibull, exponential Weibull, Beta, and Kumaraswamy beta distributions. It offers a superior fit to the data considered. The distribution reviales of the microbes reveald a wide range of shapes of probability density functions and flexible hazard rates. The distribution is a new contribution to the field of statistical and probability theory. The findings of the study can be used as a basis for future research in the area of statistical science and health.

Decision Making Under Cumulative Prospect Theory: An Alternating Direction Method of Multipliers

This paper proposes a novel numerical method for solving the problem of decision making under cumulative prospect theory (CPT), where the goal is to maximize utility subject to practical constraints, assuming only finite realizations of the associated distribution are available. Existing methods for CPT optimization rely on particular assumptions that may not hold in practice. To overcome this limitation, we present the first numerical method with a theoretical guarantee for solving CPT optimization using an alternating direction method of multipliers (ADMM). One of its subproblems involves optimization with the CPT utility subject to a chain constraint, which presents a significant challenge. To address this, we develop two methods for solving this subproblem. The first method uses dynamic programming, whereas the second method is a modified version of the pooling-adjacent-violators algorithm that incorporates the CPT utility function. Moreover, we prove the theoretical convergence of our proposed ADMM method and the two subproblem-solving methods. Finally, we conduct numerical experiments to validate our proposed approach and demonstrate how CPT’s parameters influence investor behavior, using real-world data. History: Accepted by Antonio Frangioni, Area Editor for Design & Analysis of Algorithms: Continuous. Funding: This research was supported by the National Natural Science Foundation of China [Grants 12171100, 71971083, and 72171138], the Natural Science Foundation of Shanghai [Grant 22ZR1405100], the Major Program of the National Natural Science Foundation of China [Grants 72394360, 72394364], the Program for Innovative Research Team of Shanghai University of Finance and Economics [Grant 2020110930], Fundamental Research Funds for the Central Universities, and the Open Research Fund of Key Laboratory of Advanced Theory and Application in Statistics and Data Science, Ministry of Education, East China Normal University. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0243 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0243 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Probabilistic Inventory Models and Productivity of Selected Manufacturing Firms in Port Harcourt

This study examines the relationship between probabilistic inventory models and productivity of selected manufacturing firms in port Harcourt. Correlational survey research design was adopted for this study as this study seek to determine the relationship between the two variables. The population of this study is thirty-two (32) manufacturing companies in Rivers State which are registered with the Rivers State branch of Manufacturers Association of Nigeria (MAN). This gave us a total of ninety-two (92) for the study. Structured questionnaire instrument title” Probabilistic inventory models and productivity of selected manufacturing firms in Port Harcourt. questionnaire was developed on five- point likert scale. The result of the Cronbach's Alpha reliability test indicates .800 which is above .70 which implies that the items are reliable. Pearson product moment correlation was used to test the hypotheses using SPSS (statistical package social sciences). The study revealed that there is a significant relationship between Continuous Review Models and Productivity of manufacturing firms in Port Harcourt, Rivers State. There is a significant relationship between Periodic Review Models and Productivity of manufacturing firms in Port Harcourt, Rivers State. There is a significant relationship between Single-Period Models and Productivity of manufacturing firms in Port Harcourt, Rivers State. The study concluded that Probabilistic inventory models play a crucial role in optimizing inventory management for manufacturing firms. The study recommended that Manufacturing firms in Port Harcourt should consider implementing probabilistic inventory models such as continuous review, periodic review, and single-period models to optimize their inventory management processes.

Research on Difficulties Associated with Webcam Application in Virtual Medical Training during Covid-19

The COVID-19 pandemic has forced an immediate change in medical education toward online platforms, which include webcams for remote training and teaching. The shift has created a number of challenges for students that are considering about taking part in online medical education. To maximize virtual learning opportunities and ensure the continuous provision of excellent healthcare instruction during the pandemic, it is vital to comprehend the challenges related to webcam applications in such an environment. The research was carried out in Indian medical students, received the survey on social media between July 8, 2022 and October 25, 2023 by the Google form. Total is 3 550 respondents, women made up 47 % (n =1 682) and males made up 52,7 % (n =1 868) responses. The study highlights challenges faced by medical students using webcams in virtual medical teaching due to the Covid-19 pandemic, technological issues and privacy concerns and data analyzed using statistical package social sciences (SPSS). Males prefer blended learning, while females prefer social media. The study emphasizes the need to improve webcam application is privacy and the COVID-19 pandemic; increase the technological support infrastructure for online medical education.

Policy-induced air pollution health disparities: Statistical and data science considerations.

Air pollution causes premature death and disease and disproportionately harms non-white and lower-income groups in the United States. Government policies are responsible for the racial disparity in air pollution exposure and related health outcomes. Investigating complex relationships between policies, air pollution, and health requires (i) harmonized data connecting policies, environmental exposures, socioeconomic characteristics, and health at the individual and area level; (ii) interpretable estimands accounting for the complex interplay between policies and disparities in exposures and health outcomes; and (iii) data science approaches that can elucidate direct and indirect policy effects on disparities to identify effective interventions. We review statistical considerations and new data science approaches needed to scrutinize the policy impacts on disparities in air pollution exposure and health outcomes.

The current landscape of academic statistical and data science collaboration units with examples

The current landscape of academic statistical and data science collaboration units with examples

Exceedance statistics based on bottom-k\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$k$$\\end{document}-lists

Similar to usual lower records, Bottom-k\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$k$$\\end{document}-lists (Kozan and Tanil, İstatistik J Turk Stat Assoc 13:73–79, 2013) have a wide range of practical applications in meteorology, hydrology, sports, etc. Also, exceedance statistics can be viewed as a close relative to tolerance limits—an important field of statistical science. In this study, an idea of combining these two important subjects together is studied and an exceedance statistic is defined based on bottom-k\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$k$$\\end{document}-lists in an independent and identically distributed (iid) continuous random sequence. Probability mass function (pmf) of a selected exceedance statistic is obtained. Also, an illustrative application of the exceedance statistic is given.

Pengaruh Persepsi Kemudahan Penggunaan, Literasi Keuangan, Efektivitas, Dan Kemampuan Finansial Terhadap Minat Penggunaan Qris Dalam Transaksi Pembayaran (Studi Kasus Mahasiswa S1 Universitas Amikom Yogyakarta)

This research aims to test empirically the perceived ease of use has a positive and significant effect on interest in using QRIS in payment transactions among undergraduate students at Amikom University, Yogyakarta, financial literacy has a positive and significant effect on interest in using QRIS in payment transactions among undergraduate students. Amikom University Yogyakarta, effectiveness has a positive and significant effect on interest in using QRIS in payment transactions among undergraduate students at Amikom University Yogyakarta, financial capability has a positive and significant effect on interest in using QRIS in payment transactions among undergraduate students at Amikom University Yogyakarta, perception ease of use, financial literacy, effectiveness, and financial capability simultaneously have a significant influence on interest in using QRIS in payment transactions among undergraduate students at Amikom University, Yogyakarta. This research is quantitative research using a quantitative approach method. The population in this study were undergraduate students at Amikom University Yogyakarta and used primary data as a data collection method by distributing questionnaires and getting 100 respondents using purposive sampling techniques. The analysis technique used in this research is multiple linear regression analysis with the help of the Statistical Package Social Science (SPSS) vs 29 program. The results of this research show that perception of ease of use has a positive and significant effect on interest in using QRIS in payment transactions, financial literacy has a positive and insignificant effect on interest in using QRIS in payment transactions, effectiveness has a positive and insignificant effect on interest in using QRIS in payment transactions, financial ability has a positive and significant effect on interest in using QRIS in payment transactions, perception of ease of use, financial literacy, effectiveness and financial capability simultaneously have a significant influence on interest in using QRIS in payment transactions.

Global Supply Chain Management and The Performance of Multi-National Companies in Rivers State of Nigeria

This study examined the relationship between global supply chain management and the performance of multi-national companies in rivers state of Nigeria. This study adopted a correlational research design. The population of the study consisted of 10 multinational companies operating in Rivers State as retrieved from Nigerian business directory search engine. The study adopted a census study, 3 respondents were selected from each firm multiplied by 10 firms give us a total of 30 respondents. Structured questionnaire instrument title” Global supply chain management and performance questionnaire was developed on five point likert scale. The result of the Cronbach's Alpha reliability test indicates .800 which is above .70 which implies that the items are reliable. Pearson product moment correlation was used to test the hypotheses using SPSS (statistical package social sciences). The study revealed that there is a significance relationship between global sourcing and profitability of multi-national companies in Rivers State of Nigeria. There is a significance relationship between outsourcing and operational7process efficiency of multi-national companies in Rivers State of Nigeria. There is a significance relationship between global production and distribution and customer satisfaction of multi-national companies in Rivers State of Nigeria. In conclusion, global supply chain management is a critical aspect of business operations for multi-national companies in Rivers State of Nigeria. By investing in infrastructure, building strong relationships with local partners, implementing effective communication and coordination processes, and utilizing technology, MNCs can improve the performance of their supply chains and gain a competitive advantage in the region. The study recommended that MNCs should consider investing in the development of infrastructure in Rivers State, including transportation and logistics systems, to improve the efficiency and effectiveness of their supply chains.