TOO GOD TO GO[1]

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1 TOO GOOD TO GO Final project Gsb business innovation and design Bansari Patel

2 Executive Summary The Executive Summary summarizes the report's main points. We discovered key issues in the foundation report, which shaped this document's novel solutions. We developed two innovations to meet the identified demands using Design Thinking and Lean Startup methods. The following sections describe these innovations. We align with market expectations and maximize resources by proactively examining customer segments and existing technologies. Too Good To Go's planned improvements claim to fix problems and boost its market position. This paper emphasizes the need to seamlessly integrate these innovations into the organization's strategy to capitalize on possibilities and overcome obstacles. The proposed measurement and testing methodologies use class notions to quantify success. In conclusion, this research diagnoses major organizational issues and proposes novel ways to boost Too Good To Go's competitiveness and growth.

3 Needs or Problems Summary The initial research found Too Good To Go's operational efficiency and client involvement lacking. Further research has shown that the company's offers do not match changing consumer expectations, causing these issues. The findings suggest a more tailored and interactive approach to food waste reduction (Aurigi & Odendaal, 2022). The foundational research accurately recognized these concerns, but additional data has shown the necessity of real-time data analytics in anticipating customer preferences and maximizing surplus food distribution. This update stresses the importance of technology in solving challenges. The continuous study has revealed another layer to the foundation report's issues. Beyond operational challenges, consumer environmental awareness says Too Good To Go could improve its sustainability efforts. Thus, the updated needs summary incorporates this new knowledge and emphasizes the company's need for eco-friendly operations. Sustainable packaging, local sourcing, and eco-friendly transportation are examples. This larger approach coincides with industry trends and portrays Too Good To Go as a socially and environmentally responsible company. Ideation and Innovation Processes Too Good To Go Ideation and Innovation Processes discuss how to produce and develop innovative ideas into goods. Our Design Thinking-based brainstorming technique prioritized end-user empathy. An empathetic study revealed customer food waste reduction desires. We improved our concepts via feedback loops using Design Thinking's iterative nature to meet Too Good To Go's diverse customer base's shifting demands. Design Thinking and Lean Startup were essential to our innovation strategy. Lean Startup's rapid experimentation and verified learning enabled us to test and improve our ideas

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4 while optimizing outcomes and reducing resource use. We launched our thoughts rapidly and learned from customer feedback via an MVP. Too Good To Go requires agility to respond to market and customer demands in the ever-changing food industry. TRIZ and Behavioral Design influenced our innovation process. TRIZ, famed for its rigorous problem-solving, uses disagreements to develop innovative solutions. We employed TRIZ to solve the foundation report's problems unconventionally yet effectively (Vargo et al., 2020). Instead, Behavioral Design led our approach to changing client behaviors to meet developments. We developed sustainable and waste-reducing remedies by understanding psychological decision-making. Demand for comprehensive and dynamic innovation fueled these methods and procedures. Design Thinking and Lean Startup enabled agile, user-centric development to address changing consumer and market needs. TRIZ and Behavioral Design helped us solve problems by organizing new ideas and analyzing customer behavior. This comprehensive approach mix enables Too Good To Go's strategic and customer-focused innovation. Description of Innovations 1. Customized Surplus Prediction: A revolutionary Personalized Surplus Prediction System is the first innovation. It will analyze client preferences, historical purchase data, and real-time market trends using powerful data analytics and machine learning algorithms. Too Good To Go uses predictive analytics to estimate which surplus food items each customer will prefer. This tailored method improves user experience and lowers food waste by matching surplus products to consumer preferences. Continuous learning lets the system adapt to changing trends and user preferences. 2. Eco-Friendly Packaging and Sourcing Mission:

5 Sustainable sourcing and eco-friendly packaging are the second innovation. This project will revamp Too Good To Go to reflect the rising significance of environmental responsibility. Compostable packaging and less plastic reduce excess food distribution's ecological effects. Working with local eco-friendly farmers and producers supports sustainable sourcing. Sustainable practices help Too Good To align with customer values and promote brand image. Every innovation solves foundation report concerns. The Personalized Prediction System optimizes excess food distribution, eliminates waste, and enhances customer experience to boost operational efficiency. Customized goods increase brand loyalty and engagement. Additionally, the Eco-Conscious Packaging and Sustainable Sourcing Initiative tackles environmental effects. Too Good To Go must be an excellent corporate citizen to meet demand for sustainable business practices. Eco-friendly packaging and ethical sourcing lessen the ecological impact of excess food distribution and position Too Good To Go as a socially responsible company, appealing to more ecologically concerned customers. These solutions address Too Good To Go's operational and social concerns holistically and strategically. Competitive Positioning and Strategic Fit The Competitive Positioning and Strategic Fit section analyze how the suggested innovations will affect Too Good To Go's market position and organizational strategy. Competitive Position Effect: The Personalized Surplus Prediction System, Eco-Conscious Packaging, and Sustainable Sourcing Initiative make Too Good To Go an innovation and sustainability leader. By offering a personalized client experience, the Personalized Surplus Prediction System differentiates the organization from competitors who distribute surplus food generically. Predictive analytics decrease operational inefficiencies and boost consumer loyalty with customized products.

6 However, the Eco-Conscious Packaging and Sustainable Sourcing Initiative meets consumer demand for eco-friendly methods, boosting Too Good To Go's social responsibility. Organizational Strategy Integration: Both ideas fit Too Good To Go's objective to reduce food waste and promote sustainability. By optimizing surplus items matching client preferences, the Personalized Surplus Prediction System helps the organization reduce food waste (Vargo, Akaka & Wieland, 2020). The Eco-Conscious Packaging and Sustainable Sourcing Initiative aligns with Too Good To Go's environmental duty, indicating a purposeful change toward sustainable corporate practices. These solutions solve urgent operational issues and strengthen the company's beliefs and mission, integrating with its overall strategy. Opportunities, Obstacles; The innovations provide Too Good To Go possibilities. By offering a customized experience to attract and retain customers, the Surplus Prediction System may improve market share—data insights from predictive analytics impact company choices. The Eco-concerned Packaging and Sustainable Sourcing Initiative satisfies consumer demand for sustainability, works with eco-friendly suppliers, and promotes Too Good To Go. Possible barriers must be considered. Implementing costs may be problematic for the complicated Personalized Surplus Prediction System technologyised. Customer adoption of the personalized system may need effective marketing and communication (McPhearson et al., 2021). The Eco-Conscious Packaging and Sustainable Sourcing Initiative may need help to locate inexpensive, scalable, sustainable materials. Compliance with rules and customer education are also required to overcome these programs' problems. Measurement and Testing of Innovation

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7 The Measurement and Testing of Innovation section provides a detailed plan for assessing the suggested innovations' efficacy. Proposed Measurement and Testing Methods: We will use A/B testing and user feedback analysis to evaluate the Personalized Surplus Prediction System. In this iterative process, selected user groups will test multiple versions of the system, compare their experiences, and refine the system based on observations. For the Eco- Conscious Packaging and Sustainable Sourcing Initiative, we will experiment with sustainable packaging and sourcing in certain regions to determine feasibility and acceptance. Estimated Times: To ensure testing and improvement, the Personalized Surplus Prediction System prototype will take six months to roll out. The Eco-Conscious Packaging and Sustainable Sourcing Initiative will pilot in select markets for 12 months to gather consumer preferences, supplier relationships, and operational changes. MVP with A/B Testing: The Personalized Surplus Prediction System MVP will focus on important features for speedy deployment and user input. A/B testing will evaluate user engagement, conversion rates, and satisfaction between the old system and the tailored approach in the early phases. The MVP for the Eco-Conscious Packaging and Sustainable Sourcing Initiative will test customer response and supply chain sustainability in a small product range before broader implementation. Success Criteria: KPIs connected with innovation goals will measure success. Increased customer involvement, less surplus food waste, and good user feedback are Personalized Surplus Prediction System success measures. The Eco-Conscious Packaging and Sustainable Sourcing

8 Initiative will thrive if it reduces environmental impact, makes sustainable sourcing cheaper, and improves brand impression. It will also evaluate both innovations' impact on business growth and market share. Other Relevant Aspects Other class subjects influenced Too Good To Go's recommended improvements' creation and research. The curriculum emphasized stakeholder cooperation and involvement during innovation. We brainstorm, test, and refine with clients, suppliers, and internal teams. Collaboration enables stakeholder ownership and a thorough knowledge of innovations' effects. The choices were also affected by class talks on ethics and business operations. The Eco- Conscious Packaging and Sustainable Sourcing Initiative promotes ethical sourcing, local supplier connections, and sustainable agriculture. The class encouraged flexibility in solving unanticipated issues. We incorporated contingency plans in implementing both technologies as part of our risk management approach since adaptability is critical to success (Biesta, 2019). Lessons from class enhance our Too Good To Go innovation approach with theoretical frameworks, practical problems, and ethical and responsible business practices. Conclusion Finally, Too Good To Go helps overcome the challenges by integrating class materials for sustained development and competitive advantage. The Design Thinking and Lean Startup-based Personalized Excess Prediction System aims to enhance excess food distribution by matching offers to customer preferences, increasing consumer involvement, and minimizing food waste. The Eco-aware Packaging and Sustainable Sourcing Initiative promotes Too Good To Go's ethical and responsible business practices as a socially aware brand. These solutions use iterative testing, including A/B testing and stakeholder interaction, to address operational issues and

9 promote sustainability and food waste reduction. The suggested innovations position Too Good To Go for long-term success in the dynamic food sector by recognizing market dynamics, consumer expectations, and ethical business practices. They match class discussions on ethics, teamwork, and flexibility.

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10 References Aurigi, A., & Odendaal, N. (2022). From "smart in the box" to "smart in the city": Rethinking the socially sustainable smart city in context. In Sustainable Smart City Transitions (pp. 53- 68). http://dx.doi.org/10.1080/10630732.2019.1704203 Biesta, G. (2019). What kind of society does the school need? Redefining the democratic work of education in impatient times. Studies in Philosophy and Education , 38 (6), 657-668. https://doi.org/10.1007/s11217-019-09675-y McPhearson, T., Gulsrud, N., Albert, C., Coles, N., Fagerholm, N., Nagatsu, M., Olafsson, A. S., Soininen, N., & Vierikko, K. (2021). Radical changes are needed for transformations to a good Anthropocene. Npj Urban Sustainability , 1 (1), 1-13. https://doi.org/10.1038/s42949-021-00017-x Vargo, S. L., Akaka, M. A., & Wieland, H. (2020). Rethinking the process of diffusion in innovation: A service-ecosystems and institutional perspective. Journal of Business Research , 116 , 526-534. https://doi.org/10.1016/j.jbusres.2020.01.038

TOO GOD TO GO[1]

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