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This paper aims to analyze the relationship between sustainability practices and the degree of innovation in the service provided by restaurants. The study identifies relevant restaurant segments in relation to sustainable practice-based service innovation so that effective actions to raise awareness and train managers and staff may be developed. Segmentation has been identified as a key tool when designing strategies and proposing actions. Yet, the use of segmentation techniques is still scarce regarding service innovation and sustainability in restaurants.

A segmentation analysis was carried out applying the CHAID algorithm from 300 valid questionnaires completed by restaurant owners or managers from coastal Ecuador, where tourism and gastronomy may be drivers of service innovation.

A typology of restaurants based on the sustainability-service innovation interrelation suggests three final segments: sustainable innovators focused on the value chain, moderate innovators focused on saving resources and restaurants with a low innovative profile.

The three segments derived from the analysis present differences in terms of the degree of implementation of sustainability practices, as well as in terms of the demographic profile of the restaurant manager. These segments are measurable, substantial, accessible and actionable, so that tailored initiatives to raise awareness and boost sustainability-oriented innovativeness among restaurant owners/managers may be targeted to each group of establishments.

The present research provides evidence of the positive relationship between sustainability practices and service innovation in foodservices. The segments of restaurants identified enable the design and implementation of actions that facilitate the transition of less sustainability-oriented restaurants towards more innovative and sustainable business models.

This study aims to address the global food waste problem in restaurants by analyzing customer sales information provided by restaurant tickets to gain valuable insights into directing sales of perishable products and optimizing product purchases according to customer demand.

A system based on unsupervised machine learning (ML) data models was created to provide a simple and interpretable management tool. This system performs analysis based on two elements: first, it consolidates and visualizes mutual and nontrivial relationships between information features extracted from tickets using multicomponent analysis, bootstrap resampling and ML domain description. Second, it presents statistically relevant relationships in color-coded tables that provide food waste-related recommendations to restaurant managers.

The study identified relationships between products and customer sales in specific months. Other ticket elements have been related, such as products with days, hours or functional areas and products with products (cross-selling). Big data (BD) technology helped analyze restaurant tickets and obtain information on product sales behavior.

This study addresses food waste in restaurants using BD and unsupervised ML models. Despite limitations in ticket information and lack of product detail, it opens up research opportunities in relationship analysis, cross-selling, productivity and deep learning applications.

The value and originality of this work lie in the application of BD and unsupervised ML technologies to analyze restaurant tickets and obtain information on product sales behavior. Better sales projection can adjust product purchases to customer demand, reducing food waste and optimizing profits.