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To become a sustainable lean manufacturing (LM) system, an organization must be first distinctly cognizant of the terms “lean” and “sustainability” as they relate to the state of affairs of their particular industry and business. Next, the organization must identify and acquire the necessary qualities it needs to become sustainable in lean philosophy and its practices in the organization. The LM paradigm has been a top priority for many businesses; thus, this article is based on actual research done in Indian small and medium scale organizations to see how widely it is understood and implemented.

A framework was developed based on literature review and academic research. A preliminary analysis of a small number of small and medium-sized enterprises (SMEs) that, conceptually, summarizes and demonstrates the concerted efforts that a company may undertake to increase its leanness. This conceptual model was employed to create a questionnaire that was administered to survey the SMEs of India. The information gathered through this questionnaire was analyzed using the model developed by the researchers. Then fuzzy logic and systems approach were used to find out the effectiveness index (EI) of the organization.

The EI for system leanness at different organizational levels within an organization is determined using fuzzy logic and systems approach for 48 SMEs in different clusters. The average EI of the system was found to be 0.336 on a scale of 0–1 which indicates that the current state of lean implementation and its sustainability is very low and poor in Indian SMEs. This article outlines the key model components and describes how they were applied to analyze the data gathered from an industry study.

The research aims to make lean continuously sustainable by surfacing and eliminating the wastes in the Indian SMEs whenever it appears rather than using it as a cleaning tool. The present study was focused on India’s southern industrial areas and it was difficult to gather the information around the country due to its diverse industrial culture and geography. Hence, more research and the comparative study of the same that takes into account the various regions of the nation’s industrial lean behavior can be conducted.

The generalized sustainable lean framework analyzed using fuzzy logic and systems approach gives the current effectiveness of the leanness in SMEs of south India. This model can be effectively implemented in other areas of the nation to identify the scenario of lean and its sustainability and a final comprehensive model can be developed.

There is a dearth of comprehensive studies on the assessment of sustainability of the lean philosophy in Indian SMEs. With the help of combined fuzzy logic and systems approach, the model developed in this study evaluates the sustainability of the lean methodology using the EI used in SMEs by taking into account both the lean and sustainability factors as well as enablers like customer satisfaction, ethics, innovation and technology.

The short/fast-food and perishable food supply chains (PFSC) have similar characteristics of lower lifespan and variable demand, leading to significant waste. However, the global population surge and increased health awareness make it impossible to continue wasting food because it is responsible for the loss of economy, resources, and biodiversity. A sustainable transition in short and PFSC is necessary; thus, addressing challenges is critical to explore the best strategy for redesigning PFSC.

An extensive literature review helped to identify 40 challenges, while a Delphi study highlighted 21 critical challenges. The fuzzy decision-making trial and evaluation laboratory method establishes a causal relationship between sustainable development (SD) challenges to help redesign PFSC.

From a strategic development perspective, frequent transportation disruption is the main critical challenge. Lack of supplier reliability is the most substantial cause of independence, with a causal value of 2.878. Overhead costs and lack of green maintenance strategies are part of the performance-oriented challenges. As it belongs to the driving zone, the second quadrant requires control while transforming PFSC for better sustainable development.

The study has several implications, such as lack of supplier reliability and frequent transportation disruption, which have the most robust causal value used as short-term strategy development. For short- and fast-food supply chains, it is necessary to study market and consumer behavior patterns to optimize inventory and customer service. Combating transportation disruption and supplier reliability challenges is vital in both PFSC and short and fast-food supply chains to reduce waste and promote sustainability.

The study’s findings are unique and put value toward the sustainable transition of PFSC by revealing critical challenges and their impact.

This study aims to assess AI literacy and attitudes among medical students and explore their implications for integrating AI into healthcare practice.

A quantitative research design was employed to comprehensively evaluate AI literacy and attitudes among 374 Lusaka Apex Medical University medical students. Data were collected from April 3, 2024, to April 30, 2024, using a closed-ended questionnaire. The questionnaire covered various aspects of AI literacy, perceived benefits of AI in healthcare, strategies for staying informed about AI, relevant AI applications for future practice, concerns related to AI algorithm training and AI-based chatbots in healthcare.

The study revealed varying levels of AI literacy among medical students with a basic understanding of AI principles. Perceptions regarding AI’s role in healthcare varied, with recognition of key benefits such as improved diagnosis accuracy and enhanced treatment planning. Students relied predominantly on online resources to stay informed about AI. Concerns included bias reinforcement, data privacy and over-reliance on technology.

This study contributes original insights into medical students' AI literacy and attitudes, highlighting the need for targeted educational interventions and ethical considerations in AI integration within medical education and practice.