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The study aims to identify the perceived authenticity of cheeses by consumers of different sociological generations in Mexico.

An online questionnaire was applied to 1,204 consumers. A Free Word Association (FWA) tool was used to determine the perceived authenticity of cheeses creating categories and dimensions. The sample was segmented into sociological generations. A chi-square test and a correspondence analysis were used to identify differences in the perception between generations. The information was complemented with word clouds of the cheeses mentioned and consumer testimonials about cheese consumption.

A total of 29 categories and ten dimensions revealed consumers' perception of cheese authenticity, most important of which were hedonic, rurality and new consumption. Authenticity is a mix of the pleasure of consuming the product, the link to rural life and new consumer values. Perceived authenticity is different in each sociological generation according to the dimensions of raw material, identity, market, new consumption and distrust, since it is related to the experiences of each generation. The results made it possible to contextualise another vision of the reality of the cheeses in the search for a quality seal.

The information contributes to the typification and promotion of cheeses in the process of patrimonialisation, by creating differentiated marketing tools that allow their valorisation.

This work contributes to the knowledge of the perceived authenticity of cheeses in the sociological generations, due to their differentiation by age, sociocultural, ethical, political and consumer aspects. It enables the knowledge of the consumer's perspective on these products.

This study aims to discuss the state-of-the-art digital factory (DF) development combining digital twins (DTs), sensing devices, laser additive manufacturing (LAM) and subtractive manufacturing (SM) processes. The current shortcomings and outlook of the DF also have been highlighted. A DF is a state-of-the-art manufacturing facility that uses innovative technologies, including automation, artificial intelligence (AI), the Internet of Things, additive manufacturing (AM), SM, hybrid manufacturing (HM), sensors for real-time feedback and control, and a DT, to streamline and improve manufacturing operations.

This study presents a novel perspective on DF development using laser-based AM, SM, sensors and DTs. Recent developments in laser-based AM, SM, sensors and DTs have been compiled. This study has been developed using systematic reviews and meta-analyses (PRISMA) guidelines, discussing literature on the DTs for laser-based AM, particularly laser powder bed fusion and direct energy deposition, in-situ monitoring and control equipment, SM and HM. The principal goal of this study is to highlight the aspects of DF and its development using existing techniques.

A comprehensive literature review finds a substantial lack of complete techniques that incorporate cyber-physical systems, advanced data analytics, AI, standardized interoperability, human–machine cooperation and scalable adaptability. The suggested DF effectively fills this void by integrating cyber-physical system components, including DT, AM, SM and sensors into the manufacturing process. Using sophisticated data analytics and AI algorithms, the DF facilitates real-time data analysis, predictive maintenance, quality control and optimal resource allocation. In addition, the suggested DF ensures interoperability between diverse devices and systems by emphasizing standardized communication protocols and interfaces. The modular and adaptable architecture of the DF enables scalability and adaptation, allowing for rapid reaction to market conditions.

Based on the need of DF, this review presents a comprehensive approach to DF development using DTs, sensing devices, LAM and SM processes and provides current progress in this domain.