Intelligent Circular Economy Orchestration Engine

The currently adopted reclycling models lack many attributes that limit economic viability and the ability to truly execute the intent at scale. This is because current systems and value chains are not globally cross-industrialized, interconnected, collaborative, or networked. Participating public and private sector entities are also lacking a mapping of intelligence knowledge such as raw material need, storage, transportation, and processing to name a few. The result is an suboptimal system.   

The capability to create knowledge and subsequently deliver it to buyers, suppliers, and other participants in the Circular Economy will be a catalyst for new value creation. The resulting knowledge and awareness of the interconnection opportunity will drive greater adoption and will serve as a positive force towards a more regenerative and globally distributed environmental and economic structure.

IoT, collaboration, AI and BlockChain based Inter-cluster knowledge creation and orchestration methodology for implementing a total Circular Economy.

The Circular Economy often refers to an economic construct and value chain in which previously used or excess material is collected and utilized in the production of new goods in support of existing value chains.  Looking at this definition, it is possible to identify what we believe is the main shortcoming of the current CE initiatives. These shortcomings severely limit the realization of CE’s potential in for-profit industry, particularly industry verticals with thin operating profit margins.

We believe that there are no efficient ways (nor real cases) of a methodology that can address the maximum achievable efficiency Circular Economy across industries.  This is what we consider the Inter-Cluster Circular Economy model we aim to implement.  The lack of an economically efficient, optimized model supporting intra-cluster Circular Economy-based commerce is a fundamental barrier to sustainable adoption of a closed-loop regenerative economic system.

In summary, the problem we identify is a fundamental lack of a real-time AI based knowledge empowered self-adapting match-making mechanism and of a common methodology and platform capable of enforcing a high-efficiency Circular Economy model that facilitates interaction of multiple players, working in completely different verticals (i.e. O&G, Fashion,  Food, Packaging, Chemical, Cities, etc.).  

Essentially, we are aiming at a global solution, impacting supply chain, enterprises, government, waste management, and indirectly citizens to build a more regenerative ecosystem. In this video from the Pitkin town dump the need to sort and find the match is key. 

https://youtu.be/PMUFDDVlgl0

For a full description of the solution please see this defensive publication describing the solution in detail. https://www.tdcommons.org/dpubs_series/1532/

In few words, our solution targets the development of all the block needed to create a AI powered inter-cluster adaptive marketplaces. That includes:

- A knowledge mining block. This is necessary to develop the intra and inter cluster knowledge to identify possible usage of used and regenerated materials. In this block rules to understand where and how materials can be used within and across industry verticals are mined applying AI to multiple including social media, email, unified communication solutions, public interviews, online courses, direct interviews, scientific papers, etc. Last, but not least this block evaluates the real time optimization function of available resources across partecipant industries

- A smart resource model notification mechanism. It takes care of recommending to relevant industries about potential existing matches between supplies and demands, based on the knowledge mining effort performed in previous block.

- A blockchain enabled intercluster smart-transaction system. It enables a traceable "marketplace" to enforce and track transactions. We are not restricting to our own blockchain system but we are platform agnostic.

The integration of these block provides a real time, knowledge discovery based, inter-cluster optimization exchange platform for used materials into a regeneration cycle.

This innovation combines both human and technology. Designers are creatively looking at the knowledge and noting what could be a match, combined to extend the life of the resources. AI is scourging for matches from online information. While ML is learning from the designs humans enter. Information on the "things" is being garnered automatically from all packaging, goods, devices, etc. where it is on the edge already existing on the network.

One key aspect of our solution is that it is designed to be "self-learning". In other words we believe that methodologies existing today to reuse materials will be improved/reinvented as soon as more knowledge about materials will be discovered. So a key element of our solution is a inter-cluster knowledge mining engine that is key to evaluate the most efficient use of resources across cluster. With this approach we believe the best optimization is going to be achieved at any time.

A full description of our solution is given at Invention description.

In terms of main technologies we plan to use:

- Artificial Intelligence/Machine Learning. This is going to be applied in several parts of the solution. The cross cluster and inter cluster knowledge  mining blocks are based on a AI/ML implementation to define all possible use of specific materials across industrial clusters. The smart recommendation knowledge block and the optimization block use AI/ML to create and advertise matches (e.g. supply-material-use-demand) based on the knowledge mining block.

- IoT and edge computing. This is necessary to track the full path of "things" in order to get real time info about availability and traceability of resources. More IoT data can be essential in providing the base to enable new knowledge discovery.

- Collaboration. Interaction among people and people and "things" will be managed by Unified Communication solution. That will enable not only the enforcement of transaction, but also new knowledge discovery.

- Blockchain based smart contracts. This is essential to enforce  trusted transactions and also to enable a tracking system for the traceability of used materials. As a matter of fact, industries will likely have to provide evidence of recycling efforts they're putting in place and traceability and reportability  will be relevant at that scope as well.

We've conducted over 2000 interviews and this is what people note is the crux of the problem. Our innovation helps make the match to ensure resources always have a next life. 

More, the Knowledge discovery approach makes this solution "open ended" in the sense that as soon as new knowledge is available about reusing materials, it will be immediately applicable and will be matched to a smart marketplace to make it also economically viable for all participants.

Since we are in prototype we are not serving any large populations currently.

Our approach will start addressing key players (e.g. TerraCycle) and help them to enable a ecosystem of matches. Next step, for the five year period, will be to expand to entire verticals. 

From the individual business owners needing materials for their products to the large enterprises we will help make the recommended match to the demand.

We hope to have a working instance of our solution within the next year, ideally applied to key specific supply verticals initially (e.g. plastic, textile, etc.). Within next five years, we foresee our solution hitting many more verticals and expanded to major countries worldwide.

- Cultural: Create momentum to bring suppliers on the platform, 

- Reach out and engage with large and local entities (such as companies to use/reuse materials) proposed on our inter-cluster optimization exchange platform.

- Legal: Our data platform is meeting GDPR laws, we want to ensure that our service is reputable. The supplier must commit to meet local codes and requirements to repurpose.

- Financial: We are looking to provide an as a service model that allows us to scale as we grow the business. The most important part of our business model is to ensure the commitment of large multinationals to use the platform.

- The potential engagement of Cisco and MIT, two of the most relevant players in technology space, can be the spark triggering a bigger ignition of many more players in the industry and in governments as incentive to adopt the solution.

- We plan to advertise the solution, once ready, in major events in order to generate interest and traction.

We currently have 5 people actively committed to develop the solution, we also have teams of engineers, architects that provide ad hoc advisory capabilities.

We believe that we could make a significant impact by leveraging technology.  Our leaders are gaining momentum to take action and encourage us to do the same. As Chuck Robbins our CEO notes, "Do the right thing for the sake of doing the right thing." Our leaders are creating consciousness for employees to create change.  Now we are empowered to innovate around the circular economy. To make an impact. Now we can apply what we do best to solving the problem that many have presented to us. We know it is our responsibility to take action. Our team committed its business acumen and technical knowledge as well as experience to create the orchestration engine. 

With the additional leverage of having access to a majority of the world's cities and companies networks we can bring the right data together in a secure and anonymized way. We have built relationships where we are invited to the table to help solve for the Circular Economy. People are excited to pilot with us starting this year. 

Cisco was one of the first companies to be supportive of the Ellen MacArthur Foundation. Cisco was Barron's #1 Most Sustainable Company last year. Relationships are key because we know we can't do this alone. In collaboration with government, cities, and organizations: Aspen Institute, The Recycling Partnership, ReMade, etc. effective, comprehensive piloting can occur. Through conscious outreach as well as organically we are building working relationships with the stakeholders and those that can execute. Brands, manufacturers, and resource groups seeking solutions for waste as well as with Waste Management, Walmart, TerraCycle,and others we understand it's about bringing the ecosystem together that will make this work.

Target Customers: Our approach is to create a circular partnership model across companies more than a supplyer-customer one.

Customer Value Proposition: Customers will benefit this solution in multiple ways: they'll have the chance to get access to a large set of suppliers, with lower cost of materials (e.g. renewed plastic costs less than new plastic). More, enterprises will gain access to CE credits issued by government and public institution for the implementation of a sustainable supply chain. Last but not least they'll gain support from conscious final users.

Value Proposition: The creation of new material and match making knowledge to enable a sustainable marketplace.

Key Partnerships: Governments, PPPs (public-private partnerships), NGOs, haulers, waste management, design firms, schools, and transformers of materials.

Channels & Relationships:

• "Transformation" enterprises (e.g. Ecosistem in Italy), that are companies converting used material in new resources.
• Material marketplace companies (e.g. Path21 in US) that today acts as material repository, and playing a manual identification of matches.

Key Activities and Resources: Building the engine, Integrating technologies, Refining design, Testing and regional PoC, Creating partnerships,Advertising the solution, Expanding to multiple geographies

Cost Elements: R&D, Marketing, Infrastructure, Legal

Revenue Models: Multiple models and/or a combination apply. That can be a transaction fee approach and/or a subscription model and/or a revenue share model (revenue share with transformation companies) and/or a "green certificate" fee for companies responsible for proper reuse of materials they produce (e.g. plastic).

Reference: https://www.ellenmacarthurfoun...

The market linkage model of social enterprise facilitates trade relationships between the target population or “clients,” small producers, local firms and cooperatives, and the external market. The social enterprise functions as a broker connecting buyers to producers and vice versa, and charging fees for this service. Selling market information and research services is a second type of business common in the market linkage model. Unlike the market intermediary model, this type of social enterprise does not sell or market clients' products; rather it connects clients to markets.

We selected this model since our solution aims to create a new partnership model, where companies will benefit in building circular relationships among them, endorsing a sustainable model for renewed goods. Our solution, being the engine of a completely new sustainable market share, can get financial stability by either charging fees (and)or get a small percentage of the transaction.

We feel that MIT has business and technical expertise, as well as labs that could help us enhance the solution. Some of the areas are:

- Gained knowledge on chemical and mechanical composition 

- Additional AI to create new materials use cases

- Additional AI to create inter-cluster knowledge

- Additional AI to create the inter-cluster optimization engine

- Identify additional data sets and data sources to expand the knowledge mining

- Linkage to chemical/material database to enable better material knowledge for more efficient match-making

Design firms and schools (MIT, VSA, Parsons, etc)

Blockchain companies that have the composition of the item data

Material sorters

Retail digital identity companies

Governments

Transformers (ability to extract components and make them into other items)

Other public-private partnerships

AI is an essential enabler for our solution for many reasons and in multiple application areas. In particular we foresee AI application in:

- Material knowledge discovery. That is the process to identify from sparse sources (e.g. publications, papers, news, etc.) possible source of knowledge related to the use of re-conditioned material

- NLP for interaction among people and systems. This is generally applicable to the use of ChatBot to have human level interaction with bots

- Optimization engine. This is the heart of the solution and can be solved with "traditional" approaches or with a AI enabled approach. This is under investigation now.

As previously described, Artificial Intelligence is the key enabler of our solution and it is only via an extensive application of AI that we'll be able to create a business opportunities for a vaste set of companies in US. In particular we foresee AI application in:

- Material knowledge discovery. That is the process to identify from sparse sources (e.g. publications, papers, news, etc.) possible source of knowledge related to the use of re-conditioned material

- NLP for interaction among people and systems. This is generally applicable to the use of ChatBot to have human level interaction with bots

- Optimization engine. This is the heart of the solution and can be
solved with "traditional" approaches or with a AI enabled approach. This
is under investigation now.