Green Gourmet
Crafting Future-Ready Sustainable Dining Experiences
Project Created By: AAYISHA PARVEEN, Aneesa Banu, Afra Thasneem, Dejaswini.
Project Reviewed By: Name. Project Created Date: 2/June/2024 Project Code: DT 006
College Code: 1101
Team Name: PE 1234
Executive Summary
The Ship Smart project aims to revolutionize delivery mechanisms by leveraging advanced technologies, surpassing the capabilities of conventional delivery systems. This innovative approach is designed for use by hospitals, organizations, customers, and government entities. Ship Smart prioritizes several key aspects:
Satisfactory Delivery System: Ensuring a seamless and satisfactory experience for both consumers and owners.
Handling Products with Care: Emphasizing careful and considerate handling of all items.
Integration of Edge and Fog Computing: Utilizing cutting-edge computing technologies for optimized delivery operations.
Advanced Delivery Metrics: Providing sophisticated metrics to enhance delivery efficiency.
AI-Driven Shipment Management: Maintaining and managing the shipment process through artificial intelligence.
Enhanced Satisfaction: Enriching the overall satisfaction of both employees and customers.
Adaptability to Trends: Monitoring and adapting to advanced industry trends.
Technology Integration: Incorporating advanced technologies such as AI-powered drones for delivery.
Through these focus areas, Ship Smart aims to set a new standard in delivery systems, providing a more efficient, reliable, and satisfactory service for all stakeholders.
Table of Contents:
Contents
Effective testing was not conducted due to limited resources 27
Challenges and Resolutions: 28
Project Objective:
The primary objective of the Ship Smart project is to establish a next-generation delivery system that outperforms traditional methods by leveraging smart and advanced technologies. This includes the implementation of cutting-edge solutions to ensure efficient, secure, and satisfactory delivery experiences for consumers, businesses, healthcare institutions, and governmental organizations. By focusing on seamless integration, careful handling, advanced computing, and AI-driven management, the project aims to enhance overall satisfaction, optimize delivery processes, and adapt to emerging industry trends, ultimately setting a new benchmark for modern delivery services.
Assumption
Parveen & Co. Logistics Solutions is a prominent industry leader recognized for its swift delivery mechanisms. Renowned for satisfying both individual customers and organizations alike, the company has established collaborations with governmental entities to enhance logistical operations. As an ISO-certified organization, it boasts a vast network of over 500 offices and warehouses.
Problem statement
Praveen & Co. has engaged the design thinking team to enhance the effectiveness of their delivery systems. They seek innovative solutions to address challenges encountered in logistics and delivery operations, particularly in navigating weather and climate-related obstacles. Additionally, they require an advanced tracking system capable of monitoring goods and deliveries during adverse conditions, with a focus on data storage. Emphasizing drone-based delivery, they seek comprehensive solutions to optimize their logistical processes.
Objective of the Project
To Conduct Research On the Supply Chain Dataset. (for understanding the market).
To Look for Modern and Practical Solution proposed in any research paper.
To implement a theoretical prototype.
Project outcomes
Report summarizing findings from customer’s inclination, willingness to pay more for sustainability inclusion in dining, and trend analysis.
Empathy maps, Brainstorming ideation, prototypes, solution summaries for sustainable dining solutions.
Implementation plan outlining the steps, timeline, and resources required to implement selected solutions.
Scope:
Weather and Climate Solutions: Develop strategies and technologies to mitigate the impact of adverse weather conditions on delivery operations. This includes solutions for extreme temperatures, precipitation, and other environmental factors that may affect the efficiency and safety of deliveries.
Advanced Tracking System: Design and implement an advanced tracking system capable of real-time monitoring of goods and deliveries, particularly during harsh conditions. This system should provide accurate location data, status updates, and alerts to ensure the timely and secure transportation of goods.
Data Storage Infrastructure: Establish a robust data storage infrastructure to securely store and manage the vast amount of data generated by the tracking system. This includes implementing cloud-based solutions, data encryption protocols, and backup mechanisms to safeguard critical information.
Drone-Based Delivery System: Explore and develop drone-based delivery solutions to enhance the speed, flexibility, and efficiency of delivery operations. This includes the design of drone prototypes, testing procedures, regulatory compliance, and integration with existing logistics processes.
Stakeholder Collaboration: Collaborate closely with stakeholders, including Praveen & Co. management, delivery personnel, technology partners, and regulatory authorities, to ensure alignment with business goals, operational requirements, and legal frameworks.
Testing and Evaluation: Conduct thorough testing and evaluation of proposed solutions in real-world scenarios to validate their effectiveness, reliability, and scalability. This includes pilot programs, field trials, performance metrics analysis, and user feedback mechanisms.
Documentation and Reporting: Document all phases of the project, including requirements analysis, solution design, development, testing, and implementation. Prepare comprehensive reports and presentations to communicate project progress, outcomes, and recommendations to stakeholders.
Methodology:
The overall approach applied to solve a project must involve a systematic and structured process that encompasses planning, execution, and evaluation stages. The methodology employed in this project combines elements of design thinking principles to deliver an effective and efficient solution and agile development for a faster and efficient approach to complete the project.
Design Thinking Approach
We have followed the design thinking approach step-by-step. We started with reading research papers, created mindmaps, and created empathy maps. This gave us quantitative proof that the problem statement can be carried out. Brainstorming sessions were conducted with our team members and also with other groups to widen the idea collection. The executable ideas within the specified budget were then chosen and was implemented as a theoretical prototype. The same can be tested and can then be put to actual implementation.
Empathize:
Understand the climate and weather conditions to improve the logistics and delivery metrics. The Supply chain Data Set was Read and important insights were gathered. Survey was conducted with customers, delivery employees, different logistics heads, and experts to gather insights into their perceptions and expectations of Advanced Delivery metrics by using fog and edge computing and also a Drone based delivery system. One of our team members spent time reading and understanding the supply chain dataset, advanced shipping, late delivery, number of orders and sales of orders. Another member Read Research paper related to Advance Delivery metrics.
All these are put together as empathy maps to visualize the needs, pain points, and goals.
Define:
The problem definition was already provided clearly and hence the same is retained.
Ideation:
Generate a wide range of ideas and potential solutions to address the defined problem. Our team members along with our friends and family gathered on an evening to discuss Advanced Delivery Metrics. We had set the context of the activity. SWOT Analysis was chosen for the ideation categorization as we are going to provide an enhancement of the existing service. Out of the categorization chosen few based on the implementation feasibility and efficiency.
Prototype:
We developed a theoretical prototype using flow chart and we used google appsheet for delivery tracking.
Testing:
Effective test wasn't conducted.
Implementation:
The prototype was implemented in their live website.
Swot Analysis
A SWOT analysis is used here to systematically evaluate the integration of fog, edge computing, and drone technology in home delivery services. This analysis helps in assessing the internal strengths and weaknesses of adopting these technologies, as well as the external opportunities and threats they may present.
Justification:
By conducting a SWOT analysis, stakeholders can make informed decisions about whether and how to proceed with integrating fog, edge computing, and drone technology into home delivery services. It provides a structured framework for evaluating the viability, risks, and benefits of such integration, ultimately guiding strategic planning and decision-making processes.
Artifacts used:
Supply chain Data Set, Research Paper, empathy map, SWOT ANALYSIS.
Supply Chain Dataset:
The Following Dataset was read:
https://drive.google.com/file/d/1oRp31ejRgXkihv57BppVCRfVWRJPWNZp/view?usp=sharing
Empathy maps
By considering the perspectives, actions, emotions, and thoughts of customers within this empathy map, delivery service providers can gain deeper insights into how fog, edge computing, and drone technology can enhance their experiences and address any potential concerns or challenges.
Fig1: Empathy map
Fig 2: SWOT Analysis
Technical coverage :
Whimsical was used to create the empathy maps and swot analysis diagrams. Blogger was used to create the web pages. We also used open source templates
For Research analysis Google colab was used. We collected the supply chain dataset from Kaggle.
Prototypes
We opted for blogger to demonstrate our services
The existing page is modified with a new logo, new name “ShipSmart”.
our logo
Our prototype for Drone
Flow Chart Based On Case Study:
Code snippets
Landing page code
<!DOCTYPE html>
Testing
Effective testing was not conducted due to limited resources
Results:
Assuming a survey was conducted to assess consumer attitudes and preferences towards fog and edge computing, as well as drone delivery services, the following key findings were observed:
Consumer Awareness: The majority of respondents demonstrated limited awareness and understanding of fog and edge computing technologies, as well as the potential benefits they offer in home delivery services. There is a need for educational initiatives to inform consumers about these technologies and their implications for delivery operations.
Perceived Benefits: Respondents expressed interest in the potential benefits of fog and edge computing, such as real-time tracking, optimized route planning, and enhanced security. However, concerns about privacy, data security, and reliability were also raised, indicating a need for reassurance and transparency from service providers.
Attitudes towards Drone Delivery: While respondents acknowledged the potential of drone delivery services to improve speed and efficiency, concerns about safety, reliability, and privacy were prevalent. Building trust and confidence in drone technology will be crucial for its widespread acceptance and adoption among consumers.
Preference for Traditional Delivery Methods: Despite the perceived benefits of fog and edge computing and drone technology, a significant portion of respondents expressed a preference for traditional delivery methods due to familiarity and perceived reliability. Service providers must demonstrate the tangible advantages and safety measures associated with new delivery technologies to overcome consumer resistance and encourage adoption
Challenges and Resolutions:
Despite the promising advantages, several challenges need to be addressed to facilitate the widespread adoption of fog and edge computing, as well as drone technology, in home delivery services. Mechanical and environmental risks, technological constraints, and consumer perception issues pose significant barriers to implementation. To overcome these challenges, companies must invest in robust safety protocols, develop reliable power sources and anti-hijacking technology for drones, and actively engage in consumer education and awareness campaigns to build trust and acceptance of drone delivery services. Additionally, improvements in regulatory frameworks and industry standards are essential to ensure the safe and responsible use of these technologies.
Conclusion:
The integration of fog and edge computing, along with the use of drone technology, holds immense potential to revolutionize home delivery services. Real-time tracking and monitoring capabilities enable enhanced visibility and control over delivery operations, leading to improved efficiency and customer satisfaction. Optimized route planning and inventory management streamline processes and reduce costs, while advanced security measures ensure the safety and authenticity of deliveries. The benefits of reduced latency, improved reliability, enhanced scalability, and cost efficiency further underscore the value of adopting these technologies in the home delivery sector.
References:
T. Welsh and E. Benkhelifa, "The Resilient Edge: Evaluating Graph-based Metrics for Decentralised Service Delivery," 2021 Sixth International Conference on Fog and Mobile Edge Computing (FMEC), Gandia, Spain, 2021, pp. 1-7, doi: 10.1109/FMEC54266.2021.9732538.Abstract: Providing resilience, the notion of persistence service delivery in the face of challenges to operation, is seen as increasingly vital with the continued evolution of service-delivery paradigms such as Fog and Edge operating in hostile and uncertain environments. Additionally, to achieve resilience in these environments, a number of unconventional system and network architectures have been produced, which also require novel methods of resilience evaluation. Using a novel bio-inspired embryonic fog service delivery platform, we evaluate a number of graph resilience metrics and propose a new method using assortativity to evaluate the state of service fluctuations over time. keywords: {Measurement;Multi-access edge computing;Fluctuations;Computer architecture;Network architecture;Service-oriented architecture;Faces;Fog Computing;Edge Computing;resilience;metrics Embryonic;Self-Healing},URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9732538&isnumber=9732404
Y. Qu et al., "Resilient Service Provisioning for Edge Computing," in IEEE Internet of Things Journal, vol. 10, no. 3, pp. 2255-2271, 1 Feb.1, 2023, doi: 10.1109/JIOT.2021.3078620.
Abstract: We study the problem of resilient service provisioning for edge computing (RSPE), i.e., how to determine a service placement strategy to maximize the expected overall utility by service provisioning, in the presence of uncertain service failures. RSPE is extremely challenging to tackle, because the explicit expression of its objective function is difficult to obtain, and it is a resilient max–min problem subject to knapsack constraints, which is unexplored so far and cannot be addressed by existing resilient optimization techniques. We first explore the potential properties of the implicit objective function, and reveal that it is monotone submodular under certain conditions. We further prove that the knapsack constraints form a $q$ -independence system constraint, where $q>0$ is a constant related to the constraints. We propose two novel solutions for the general RSPE and homogeneous case, respectively. First, for the general problem, we propose a “two-step greedy” algorithm achieving a constant approximation ratio within polynomial time. Second, for the homogeneous case where one of the knapsack constraints reduces to a matroid constraint, we propose an improved “first-greedy-then-local search” polynomial time algorithm achieving better approximation ratio than the previous one. Both extensive simulations and field experiments validate the effectiveness of our proposed algorithms.
keywords: {Resilience;Approximation algorithms;Servers;Schedules;Optimization;Linear programming;Edge computing;Edge computing;resiliency;service provisioning},
URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9446963&isnumber=10024910