Barriers and enablers to food mass customization Calegari, Luiz Philipi; Marianne Costa Avalone; Diego Castro Fettermann
Journal of agribusiness in developing and emerging economies,
08/2020, Letnik:
10, Številka:
4
Journal Article
Recenzirano
PurposeThis study is to propose a procedure to support decisions on which enablers should be employed to minimize the impact of barriers to implementing mass customization strategies in food ...companies.Design/methodology/approachThrough interpretive structural modeling, the authors analyzed the relationships between barriers. Then, with an approach similar to the quality function deployment technique, commonly used in general product and process development, the authors clarified the relationships between barriers and enablers.FindingsThe results revealed 19 barriers and 17 enablers for implementing food mass customization. The analysis indicates that most of the barriers (16) present strong associations with each other. The barrier “products with non-customizable features” depends on the whole chain of associations and causes a minor impact on the other barriers. In turn, the barrier “ingredient incompatibility” causes impact over the whole chain, and its dependence on other barriers is very low.Research limitations/implicationsThe results were tested in a single Brazilian company in the food sector.Practical implicationsThe findings can allow food manufacturing companies to focus their efforts on the improvement of enabling technologies, such as smart packaging, Internet of Things and additive manufacture.Social implicationsThis study would help food companies to improve their business and provide better products to society.Originality/valueThere are few recommendations in the literature to how to implement mass customization strategy in companies from the food sector. This study fills in this gap presenting a procedure to guide managerial staff to develop this promising approach for food companies.
The article discusses the problems of creating Digital Twin for use in neural network technology for personalizing food products for people with a genetic predisposition to diabetes. When designing ...an information system for personalizing food products, it is proved that the main direction of development is the modeling of a Digital Twin of the product and the consumer, as well as the determination of the technologies that form the basis of the personalized food model to create an accurate, correctly functioning system.
This chapter discusses the challenges of moving to reduced meat consumption, while looking ahead to innovation and future needs for a sustainable planet. Critical choices loom as we ponder ...planet-friendly, plant-based diets to feed the global populace, while deciding whether meat consumption is still a viable option. Developments of meat substitutes are drawing investments, while commercialization of new protein sources moves ahead. Shifting from a primarily meat-based diet to a plant-based diet will require some consumers to adjust to different dietary habits and lifestyles. Intensification, yield enhancements, and improved seeds will be important agricultural factors to feed more people. Food start-ups disruptive ability will face off against conventional food manufacturers to decide the next frontier in product development. The human microbiome’s effect on neural systems may identify approaches to influence changes to people’s dietary habits. Individuals, organizations, and governments around the globe will need to resolve several policy issues and taking no action is not an option.
Modern computational techniques offer new perspectives for the personalisation of food properties through the optimisation of their production process. This paper addresses the personalisation of ...beer properties in the specific case of craft beers where the production process is more flexible. Furthermore, this work presents a
that could be suitable for more complex, industrial setups. An evolutionary computation technique was used to map brewers' desired organoleptic properties to their constrained ingredients to design novel recipes tailored for specific brews. While there exist several mathematical tools, using the original mathematical and chemistry formulas, or machine learning models that deal with the process of determining beer properties based on the predetermined quantities of ingredients, this work investigates an
approach. The process, which was applied to this problem for the first time, was investigated in a number of simulations by "cloning" several commercial brands with diverse properties. Additional experiments were conducted, demonstrating the system's ability to deal with on-the-fly changes to users' preferences during the optimisation process. The results of the experiments pave the way for the discovery of new recipes under varying preferences, therefore facilitating the personalisation and alternative high-fidelity reproduction of existing and new products.
Beer organoleptic optimisation al-Rifaie, Mohammad Majid; Cavazza, Marc
Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion,
07/2020
Conference Proceeding
This paper addresses the personalisation of beer properties in the specific case of craft beers where the production process is more flexible. The problem is investigated by using three swarm ...intelligence and evolutionary computation techniques that enable brewers to map physico-chemical properties to target organoleptic properties to design a specific brew. The process is illustrated by a number of experiments designing craft beers where the results are investigated by "cloning" popular commercial brands based on their known properties. The proposed approach allows for the discovery of new recipes, personalisation and alternative high-fidelity reproduction of existing ones.
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