Retailers frequently face stockouts and overstocking due to inaccurate demand forecasting, leading to financial losses and reduced customer satisfaction. This study proposes a data-driven framework to improve weekly sales forecasting at both aggregate and store levels using Walmartâs historical sales data. A hybrid methodology integrating time series models, regression techniques, deep learning, and a hierarchical structure is developed to capture temporal patterns and external demand factors. The proposed approach achieves high predictive accuracy, with a Mean Absolute Error (MAE) of 306,361.11, Root Mean Square Error (RMSE) of 528,096.34, and an R² of 0.99, outperforming traditional models. Beyond accuracy, the study emphasizes the role of forecasting as a decision-support tool. The results demonstrate that improved forecasts enable better operational decisions such as replenishment planning and safety stock optimization, while also supporting tactical and strategic decisions related to distribution, workforce planning, and supply chain design. Overall, the findings highlight that integrating hybrid forecasting models with decision-making processes can reduce inventory costs, enhance service levels, and support more efficient and sustainable retail operations.
This study examines Smart Tourism Technologies (STTs) as a structured, decision-relevant system within sustainability-oriented contexts, where multiple interacting factors shape tourism outcomes under conditions of complexity and uncertainty. A PRISMA-guided systematic review of 78 peer-reviewed studies (2015â2025) is conducted to synthesise how STTs-related attributes, multi-level mechanisms, and contextual conditions influence travel experience outcomes. The analysis organises existing literature into a multi-level framework that connects core technological dimensions with mediating and moderating mechanisms and broader contextual enablers. Within this structure, these elements jointly determine how cognitive, affective, behavioural, and well-being outcomes emerge across different tourism settings. The evidence indicates that STTs operate through interdependent processes rather than isolated technological effects, involving factors such as security, personalisation, technology readiness, perceived value, and digital well-being. These factors can be understood as implicit decision variables shaping experience quality, satisfaction, and sustainable behavioural responses. The review also identifies a gradual shift in the literature from technology adoption perspectives toward more integrated analytical interpretations that combine experience evaluation, sustainability considerations, and decision-relevant reasoning. By reorganising fragmented findings into a coherent analytical structure, the study provides a basis for further modelling, comparative evaluation, and structured decision analysis in sustainability-oriented tourism systems.
Choosing optimal materials for cryogenic storage systems is a challenging intelligent decision-making task with several competing requirements. To determine the best material for producing cryogenic tanks used in the transportation of liquid nitrogen, this study proposed an integrated multi-criteria decision-making (MCDM) framework that combined the Criteria Importance through Intercriteria Correlation (CRITIC) method with the Combinative Distance-based Assessment (CODAS) method. Seven technical performance criteria, including toughness index, yield strength, density, Youngâs modulus, thermal expansion, thermal conductivity, and specific heat were adopted to assess seven potential materials. By considering both contrast intensity and intercriteria correlation, the CRITIC technique could scientifically establish criteria weights while reducing subjective bias. The options were ranked using the CODAS approach according to their Euclidean and Taxicab distances from the negative ideal solution. The findings demonstrated that density had the greatest management weight when it came to sustainable design. Therefore, aluminium 5052-O was the most appropriate material for cryogenic tank applications out of all the alternatives under investigation. The proposed CRITICâCODAS framework, a dependable intelligent decision-support tool for strategic material selection in advanced manufacturing and engineering management contexts, exhibits robustness, transparency, and computing efficiency.
Rapid expansion of the construction industry in Bangladesh has been accompanied by substantial contributions to economic development, while simultaneously intensifying environmental pressures. In response to these challenges, the adoption of circular economy principles has been widely recognized as a viable pathway toward sustainable development. However, despite growing global attention, the effective implementation of a circular economy within the construction industry of emerging economies remains limited and insufficiently structured. In this study, the key enablers facilitating circular economy implementation in the construction industry of an emerging economy were systematically identified and analyzed. Initially, a comprehensive set of enablers was derived through an extensive literature review and subsequently refined through expert validation to ensure contextual relevance. The total interpretive structural modeling methodology was then employed to develop an interpretive structural model, through which hierarchical relationships and contextual interdependencies among the identified enablers were established. The robustness and practical applicability of the proposed model were further validated through expert assessment. In addition, Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) analysis was conducted to classify the enablers based on their driving power and dependence. The results revealed a six-level hierarchical structure, in which âgovernment support and policy framework,â âtop management commitment,â and âadvanced knowledge and awareness of the circular economyâ were identified as dominant driving enablers exerting significant influence over other factors. These findings provide a comprehensive and structured understanding of the systemic interactions among circular economy enablers and offer actionable insights for policymakers and industry practitioners in emerging economies. The study contributes to the existing body of knowledge by advancing a theoretically grounded and empirically validated framework that supports strategic prioritization and facilitates the transition toward a circular construction paradigm.
Pharmaceutical cold chain warehousing (PCCW) systems operate in highly regulated environments where maintaining product integrity and ensuring continuous operation are critical. In recent years, increasing exposure to systemic disruptions has made it necessary to reconsider how sustainability and resilience criteria are prioritised in warehouse configuration and management. This study aims to investigate how the relative influence of decision criteria evolves under different disruption conditions and to develop a structured analytical framework for evaluating such changes. A decision-analytic framework based on the Decision Criteria Influence (DCI) model was developed. The framework integrated a dual-dimension evaluation of sustainability performance and system reliability with a scenario-based sensitivity adjustment. A structured assessment was conducted across three representative disruption contexts, including energy supply instability, pandemic-induced demand fluctuations, and war-related systemic disruptions. The results showed that under stable conditions, sustainability-oriented criteria, particularly energy efficiency and monitoring-related factors, exerted dominant influence. However, as disruption intensity increased, criteria associated with infrastructure redundancy, inventory buffering capacity, and system reliability became progressively more significant. In extreme scenarios, such as war-related disruptions, resilience-oriented determinants clearly dominated the decision structure, indicating a substantial reordering of strategic priorities. The findings indicate that decision criteria in pharmaceutical cold chain systems exhibit strong context dependency and cannot be treated as static evaluation factors. The proposed framework provides a structured decision-analytic approach for capturing dynamic priority shifts under uncertainty and offers methodological support for designing adaptive and resilient cold chain infrastructures.
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