Over the past twenty years, service organizations have adopted total quality management to enhance their service quality, significantly impacting business performance, customer satisfaction, and profitability. This study delves into policy development of sustainable quality management theory, benefits, and various service components, while reviewing its implementation in services industries and policy innovation. The concept of Sustainable Quality Management 4.0 (SQM 4.0) integrates sustainable management, traditional quality management, and Quality 4.0 principles to optimize resources, reduce environmental impacts, and enhance decision-making through Industry 4.0, IoT, AI, and big data analytics. The findings offer valuable framework and policy insights for managers and practitioners on quality management and service systems, providing an implementation framework for Sustainable Quality Management in the service sector. The paper outlines comprehensive elements and strategies for implementation as a SQM framework for attaining sustainable quality management in the services industry.

This study, drawing on the Knowledge-Based View (KBV) and Contingency Theory, explores how analyzer strategic orientation, learning capability, technical innovation, administrative innovation, and SME growth and learning effectiveness are interrelated. Analyzing cross-sectional data from 407 founders, cofounders, and managers of trade and service SMEs in Vietnam’s Southeast Key Economic Region through PLS-SEM, the research demonstrates that analyzer orientation positively impacts both technical and administrative innovation, thereby bolstering SME growth and learning effectiveness. However, learning capability does not significantly impact technical innovation or growth and learning effectiveness. Instead, learning capability negatively affects administrative innovation. Notably, technical and administrative innovations act as mediators between analyzer orientation and SME growth and learning effectiveness. The study provides practical insights tailored for SMEs navigating dynamic market environments like Vietnam, enriching theoretical understanding of SME strategic management within the trade and service sector.

Given its insular geographic location, Taiwan inherently benefits from a natural advantage in developing its shipping industry, positioning it as a critical sector for the nation’s economic advancement. The shipping industry operates within a highly competitive maritime market, wherein ocean freight forwarders provide services on a global scale, thus classifying them within the international transportation and logistics industry. The global competition from logistics peers renders the services highly substitutable. This study breaks new ground by integrating the SERVQUAL scale with advanced methodologies such as the Analytic Hierarchy Process (AHP) and Decision-Making Trial and Evaluation Laboratory (DEMATEL) to assess and enhance service quality in the shipping industry. By segmenting the five dimensions of SERVQUAL, the study delineates 19 specific evaluation indicators. The expert questionnaires developed and analyzed through AHP and DEMATEL reveal a previously unidentified link between specific service quality dimensions and customer satisfaction. The findings from this analysis offer crucial insights into the critical success factors (CSFs) of service quality and their causal interrelationships, thereby establishing a model for service standards. By leveraging the identified CSFs and understanding the causal relationships among these key factors, ocean freight forwarders can enhance and optimize their value propositions and resources. This proactive approach is expected to significantly improve service quality, fortify core competitiveness, and elevate customer support and satisfaction levels, ultimately leading to an increased market share and ensuring sustainable business operations.

The study explores improving opportunities of forecasting accuracy from the traditional method through advanced forecasting techniques. This enables companies to optimize inventory management, production planning, and reducing the travelling time thorough vehicle route optimization. The article introduced a holistic framework by deploying advanced demand forecasting techniques i.e., AutoRegressive Integrated Moving Average (ARIMA) and Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM) models, and the Vehicle Routing Problem with Time Windows (VRPTW) approach. The actual milk demand data came from the company and two forecasting models, ARIMA and RNN-LSTM, have been deployed using Python Jupyter notebook and compared them in terms of various precision measures. VRPTW established not only the optimal routes for a fleet of six vehicles but also tactical scheduling which contributes to a streamlined and agile raw milk collection process, ensuring a harmonious and resource-efficient operation. The proposed approach succeeded on dropping about 16% of total travel time and capable of making predictions with approximately 2% increased accuracy than before.