Dynamics in the food and beverage (F&B) industry have become increasingly complex and abrupt because of consumer expectations regarding health issues, nutrition facts, functionality, sensory attributes, sustainability, and lifestyles. Companies are in a constant state of experimentation, under pressure from a post-pandemic business environment to accelerate product innovation. In this context, Research & Development (R&D) processes have changed over the past years to accelerate new product launches and display a high probability of success through a deep understanding of product design, formulation, raw materials, processing, packaging, storage, distribution, shelf life, and regulatory restrictions. This chapter tackles recent and innovative R&D practices in the F&B industry from a practical perspective and a graphic model based on innovation capabilities, innovation activities, and product innovation. The results illustrate the importance of considering the technical factors involved in formulation and production and market pull requirements to adapt swiftly to market trends and even fads that consumers demand.

Around the world, innovation strategy is becoming necessary for companies of all sizes. In any case, a solid innovation strategy results from a planned examination of the competitive environment. The selection of the relevant factors to consider for a suitable competitive analysis that supports the decision-making process to establish an innovation strategy could be a challenge for firms, particularly for small and medium-sized enterprises (SMEs). This chapter develops two main elements to fulfill a meaningful competitive analysis oriented to the innovation of prevalent conditions in the Mexican dairy industry: A sectorial competitive analysis focused on innovation conditions and a benchmarking of innovation efforts of a leader in the market. The study enlightens the opportunities for innovation in the business model of SMEs in the dairy industry in Mexico, with practical managerial implications transferable to other sectors.

This study presents an integrated approach for sustainable food supply chain design by evaluating how sourcing geography and logistics network structure influence Global Warming Potential (GWP) in a multi-echelon Mexican cold chain integrating Life Cycle Assessment (LCA) and Linear Programming (LP) network optimization. Three soy products are evaluated: edamame from China, tofu from the U.S., and textured vegetable protein (TVP) modeled as a soy-based alternative. Results are calculated using a cradle-to-retailer system boundary, normalized to 100 g of delivered protein. Four network configurations are evaluated, varying sourcing geography, port selection, and warehouse allocation. Distribution-stage emissions are minimized through LP optimization, while upstream emissions are incorporated as exogenous LCA parameters. Sourcing geography, distribution-network design, and protein density significantly affect GWP per functional unit, with domestic sourcing yielding the lowest impacts for all products and network configurations. Tofu under the baseline configuration exhibits the highest GWP (1.2236 kg CO₂e/100 g protein), whereas TVP with domestic sourcing exhibits the lowest (0.1146 kg CO₂e/100 g protein), representing a 90.64% difference. The integrated approach provides a decision-support framework for lower-emission sourcing and distribution in emerging-economy food supply chains.