In an increasingly complex and heterogeneous pharmaceutical landscape, understanding patient-specific demand patterns has become critical for effective decision-making. This study introduces a patient-centred segmentation framework tailored to the Moroccan pharmaceutical industry, leveraging a metaheuristic-based clustering approach. By integrating qualitative insights from a Delphi study with quantitative indicators (such as socioeconomic factors, regional disparities, and market dynamics), the proposed methodology identifies five distinct patient profiles that reflect differentiated therapeutic expectations, financial constraints, and consumption behaviours. The segmentation process is driven by an enhanced Eagle Strategy-Slime Mould Algorithm (ES-SMA), which outperforms traditional clustering methods in terms of internal validity and inter-cluster separation. Visual analytics are employed to characterize and compare patient segments along multiple decision axes. Results reveal substantial inter-segment variation, highlighting strategic opportunities for targeted forecasting, marketing adaptation, and regional resource allocation. This work offers a novel data-driven tool to support pharmaceutical stakeholders in aligning supply strategies with patient-specific demand in emerging markets.
Ventures are currently collaborating and forming supply chain relationships under specific strategies, such as a Vendor-Managed Inventory (VMI) system. In a VMI system, the vendor manages all inventory decisions instead of the buyer, enhancing supply chain clarity, reducing costs, and potentially improving its environmental sustainability. Our proposed research examines a two-level supply chain with a single vendor and a single buyer handling constantly deteriorating chemical products, such as ethylenediamine, diethyl oxalate, sodium borohydride, and zirconium tetrachloride, whose demand follows a time-dependent quadratic form. These products require proper preservation to minimize deterioration while also addressing the impact of carbon emissions from transportation as a contributor to climate change. Three cases are discussed for the VMI and non-VMI systems: 1) green investment to reduce carbon emissions, 2) preservation technology to reduce deterioration rates, and 3) a combination of preservation technology and green investment to reduce both deterioration and carbon emissions. The objective is to minimize the total supply chain cost per unit time, considering the decision variables, and the classical optimization method used to derive the optimum solution of objective functions. An inventory cost analysis, both with and without VMI, demonstrates that the VMI system in our proposed model consistently reduces overall supply chain costs and outperforms the conventional approach. The findings indicate that the VMI system, when combined with preservation and green investments, incurs the lowest cost compared to other cases. Numerical examples and a sensitivity analysis are provided to validate the derived results.
This paper investigates the ways in which early childhood physical education programs can become more prioritised and budgeted in a more stringent manner in resource-constrained environments. The paper presents a combination of outcome evidence, expert judgement and decision-support modelling based on a 12-week quasi-experimental program in 15 preschools in Southwest China. The empirical part demonstrates an increase in BMI percentile, TGMD-3 motor subscale, general fitness, emotional well-being, and social skills following the intervention. The analytical element applies AHP to calculate baseline priority weights, fuzzy AHP to check the robustness of the model in cases of uncertainty in judgement and DEMATEL to provide a mapping of directional impact among criteria. Instead of trying to describe the optimisation itself as a universal prescription, the paper revisits the optimisation as a scenario model upon which to compare allocation strategies, in the linear and diminishing-return assumptions. In models, teacher professional development and program structure are the two most prevalent priorities and family engagement serves as an enabling factor of critical importance. The updated value of the paper is thus a clear, context-sensitive framework, which relates intervention evidence and structured resource-prioritisation to policymakers and school leaders.
This study explores the comparative efficacy of parametric and nonparametric statistical tests in analyzing clinical metrics, specifically weight (Peso (Kg)), height (H), and Body Mass Index (BMI (kg/m²)) for Glucagon-like Peptide-1 (GLP1) and Sodium-Glucose Co-Transporter-2 (SGLT2) treatment groups. By applying a range of statistical methods, including the Sign Test, Wilcoxon Signed-Ranks Test, Wilcoxon Rank-Sum Test, Kruskal-Wallis Test, and Chi-Square Test for nonparametric analysis, alongside parametric tests such as the Paired t-test, Independent Samples t-test, and Analysis of Variance (ANOVA), we assess the sensitivity and reliability of these approaches under varying data conditions. Python software was utilized for executing these statistical analyses, ensuring precise and reproducible results. The findings indicate that while nonparametric tests offer robustness against assumptions of normality and are effective for small sample sizes and skewed distributions, parametric tests display greater sensitivity, particularly in detecting differences in weight. Notably, significant differences were observed in BMI across both test types, underscoring its variability between treatments. The comprehensive analysis highlights the importance of selecting appropriate statistical methods based on data characteristics to ensure accurate and reliable findings in clinical research.
The tourism industry contributes significantly to Indonesia’s economy. Yet, its long-term sustainability depends not only on appealing destinations but also on the reliability of logistics systems that coordinate the flow of goods, services, and information. Existing studies have largely emphasized technical capability or digital solutions, providing a limited understanding of how ethical conduct and operational competence jointly shape trust within tourism supply chains. Drawing on the resource-based view and social exchange theory, this study examines how logistics competence and integrity impact trust and whether digitalization enhances these relationships. A quantitative approach using Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed, with data collected from 100 tourism managers and logistics actors across diverse tourism settings. The results show that integrity plays a dominant role in fostering trust, whereas competence and digitalization do not exhibit direct effects. However, digitalization significantly amplifies the influence of both competence and integrity on trust, indicating its role as an enabling mechanism rather than a primary driver. This study contributes by demonstrating that, unlike much of the existing literature that highlights technology and technical skill, trust in community-oriented tourism contexts is fundamentally shaped by integrity, with digital tools enhancing rather than replacing ethical reliability. These findings suggest that managers should reinforce transparency and ethical consistency while adopting digital solutions strategically to support trustworthy and sustainable tourism supply chains.
