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# System prepended metadata

title: '[Product] Brief'

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# PTP Automation Brief


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:bulb: This is an automation brief tailored to PTP Metal Recyclers.

## :beginner: Project Info

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Automating operations and streamlining business processes can help your metal recycling company achieve greater accuracy, compliance, and timely decision-making. This is essential when choosing the right software solutions and regularly updating them to keep up with changing business needs and regulatory requirements. By leveraging automation, you can optimize your operations and reduce costs while increasing efficiency.
:::

#### :small_blue_diamond:Project Name: **Automation toolkit**  
#### :small_blue_diamond:Automation Team Leader: **Christopher Butler**  
#### :small_blue_diamond:Company Name: **PTP Metal Recylers**


## :triangular_flag_on_post: Background

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1. **Inventory Management**:
    - Automatic tracking of the types and quantities of metal scraps in the inventory.
    - Predictive analytics to forecast demand and supply, helping in optimizing inventory levels.
    - Real-time notifications for low-stock or over-stock situations.

2. **Purchase and Sales Order Management**:
    - Automated generation of purchase orders based on inventory levels.
    - Digitized sales order processing, from order creation to invoicing.

3. **Supplier and Customer Relationship Management (CRM)**:
    - Database management of suppliers and customers, with history tracking.
    - Automated communication for order confirmations, shipping notifications, and payment reminders.

4. **Import and Export Documentation**:
    - Automated generation of necessary documents like Bills of Lading, Certificates of Origin, and Import/Export Declarations.
    - Compliance checks to ensure that all documentation meets international trade regulations.

5. **Financial Management**:
    - Automated invoicing and payment processing.
    - Integration with accounting software for real-time financial reporting and analysis.

6. **Quality Control**:
    - Integration with sensors and measurement tools to automatically check the quality of metal scraps.
    - Alerts for any deviations from set quality standards.

7. **Logistics and Shipment Tracking**:
    - Real-time tracking of shipments, with notifications for delays or issues.
    - Integration with third-party logistics providers for seamless transportation management.

8. **Pricing and Quotation Management**:
    - Dynamic pricing algorithms based on current market rates, demand, and supply.
    - Automated generation of quotations for potential buyers.

9. **Operational Analytics**:
    - Data analytics tools to provide insights into sales trends, inventory turnover, and profitability.
    - Predictive analytics to forecast future market trends and business opportunities.

10. **Waste Management and Recycling**:
    - Tracking of waste generated during the scrapping process.
    - Automated scheduling of waste pickup and recycling.

11. **Employee Management**:
    - Automated scheduling and time-tracking for employees.
    - Integration with payroll systems for seamless salary processing.

12. **Safety and Compliance Monitoring**:
    - Integration with sensors to monitor safety parameters in the scrapping facility.
    - Alerts and notifications for any safety or compliance breaches.
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## :pencil: Objectives and Challenges

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What does this project want to achieve? Are there any challenges?
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### :small_blue_diamond: Problem Formulation:
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The problem is to design and implement an automated system that can track, manage, and optimize the inventory of metal scraps for a metal recycling company. The system should also integrate with other business processes such as order management, CRM, documentation, financial management, quality control, logistics, pricing, analytics, and waste management.
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### :small_blue_diamond: Expected Techniques:
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1. #### Data collection and analysis: 
    
Collecting and analyzing data from various sources such as sensors, measurement tools, invoices, orders, shipments, etc. to monitor and optimize the inventory levels and quality of metal scraps.

2. #### Machine learning and artificial intelligence:

Applying machine learning and artificial intelligence techniques such as predictive analytics, dynamic pricing, natural language processing, computer vision, etc. to automate and improve various aspects of the inventory management system such as demand and supply forecasting, order generation, communication, document generation, compliance checks, quality checks, shipment tracking, etc.

3. #### Cloud computing and Internet of Things (IoT):

Using cloud computing and IoT technologies to enable real-time data processing, storage, and communication between different devices and systems involved in the inventory management process. This can also enhance the scalability, security, and reliability of the system.

4. #### User interface and user experience (UI/UX): 

Designing a user-friendly and intuitive interface for the inventory management system that can provide relevant information and feedback to the users such as suppliers, customers, employees, managers, etc. The interface should also allow users to interact with the system easily and efficiently.

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### :small_blue_diamond:Expected Goals and Criteria:

The expected goals and criteria for inventory management can vary depending on the specific needs of the company. However, some common objectives of inventory management include:


| **Goal/Criteria** | **Description** |
|:-----------------:|:---------------:|
|       Inventory            |       Ensuring optimal inventory levels          |
|               Cashflow   |        Improving cash flow         |
|               Storage    |        Reducing storage requirements         |
|            Waste management       |     Minimizing waste and shrinkage            |
|           Warehouse Management        |       Reducing product shelf-time          |
|              Supply and Demand     |        Supporting supply and demand planning         |
|           Sales        |         Analyzing sales patterns        |


### :small_blue_diamond: Expected Challenges:

#### Expected Solution:
To achieve these goals, companies can use various techniques such as data collection and analysis, machine learning and artificial intelligence, cloud computing and IoT, user interface and user experience (UI/UX), etc.

In addition to these objectives, there are also several key performance indicators (KPIs) that can be used to measure the effectiveness of inventory management. Some of these KPIs include:

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* Inventory turnover rate
* Days on hand
* Weeks on hand
* Stock to sales ratio

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By monitoring these KPIs, companies can gain insights into their inventory performance and make data-driven decisions to optimize their operations.

:school:
## Case Studies

### [Succeeding in the AU supply-chain revolution:](https://www.mckinsey.com/industries/metals-and-mining/our-insights/succeeding-in-the-ai-supply-chain-revolution)

#### New technology solutions could be transformative—but only if executives properly prepare their organizations.

In recent years, supply chains have become substantially more challenging to manage. Longer and increasingly interlinked physical flows reflect the rising complexity of product portfolios. Market volatility, which has been exacerbated by the COVID-19 pandemic, has elevated the need for agility and flexibility. And increased attention on the environmental impact of supply chains is triggering regionalization and the optimization of flows. As a result, companies and stakeholders have become more focused on supply-chain resilience.

Supply-chain management solutions based on artificial intelligence (AI) are expected to be potent instruments to help organizations tackle these challenges. An integrated end-to-end approach can address the opportunities and constraints of all business functions, from procurement to sales. AI’s ability to analyze huge volumes of data, understand relationships, provide visibility into operations, and support better decision making makes AI a potential game changer. Getting the most out of these solutions is not simply a matter of technology, however; companies must take organizational steps to capture the full value from AI.

#### The changing face of supply-chain management

The supply chain is the web linking together multiple functions, including logistics, production, procurement, and marketing and sales (Exhibit 1). Integrated planning enables companies to balance trade-offs across functions and optimize earnings before interest, taxes, depreciation, and amortization (EBITDA) for the organization as a whole.

![](https://hackmd.io/_uploads/BkLuMGSQ6.png)

The experience of one large building-materials company highlights the evolving nature of supply-chain management. The company recently broadened the mission of its supply-chain function along four dimensions: increase operational sustainability; provide premium service levels and implement demand sensing for short-term changes; integrate its manufacturing and logistics supply chains; and make the business more resilient. As part of this effort, the organization expanded its central supply-chain team and created a chief supply-chain officer role who reports directly to the CEO.

Such approaches have stretched supply-chain functions, which must now operate as a “central cross-functional brain” within large corporations. In many organizations, supply-chain management has shifted to concentrate on dynamically optimizing the company’s global value rather than simply improving the performance of local functions. In several process industries (such as chemicals, agriculture, and metals and mining), sales-and-operations planning has evolved into integrated business planning. The recent supply-chain disruptions and demand triggered by the COVID-19 pandemic have further amplified the need for companies to develop their central-planning muscles.

Enhancing the relevance and size of supply-chain or business-plan teams is not enough to achieve better performance. Companies must tackle several additional challenges:

predicting demand across multiple product segments and geographies
dynamically identifying trade-offs with hundreds or thousands of interlinked variables and innumerable technical constraints
integrating AI solutions (such as processing optimization, predictive maintenance, or master data quality) to manage the wider value chain
ensuring that plans get executed and can adapt to variability effects (such as demand shocks, production stoppages, and transportation disruption) in a timely manner

#### A crowded solution landscape

The good news is that AI-based solutions are available and accessible to help companies achieve next-level performance in supply-chain management. Solution features include demand-forecasting models, end-to-end transparency, integrated business planning, dynamic planning optimization, and automation of the physical flow—all of which build on prediction models and correlation analysis to better understand causes and effects in supply chains. Successfully implementing AI-enabled supply-chain management has enabled early adopters to improve logistics costs by 15 percent, inventory levels by 35 percent, and service levels by 65 percent, compared with slower-moving competitors.

Given the significant value at stake, multiple solutions have emerged. Both incumbent IT vendors and market disrupters are entering the game. New offerings include demand planning (which has been revolutionized by integrating machine learning and harnessing new sources of data); real-time inventory management, thanks to the IoT and connectivity; and dynamic margin optimization of end-to-end chains with digital twins. For example, retail and e-commerce are at the forefront of demand prediction.

Selecting the right solution is critical. To manage the complexity of today’s supply chain, new solutions need to be smartly designed and adapted to specific business cases. They also need to fit well with the organization’s strategy. This alignment enables companies to tackle key decision-making points with an adequate level of insight while avoiding unnecessary complexity. However, implementation can require significant time and investments in both technology and people—meaning the stakes are high to get it right.

#### Embarking on an AI-driven transformation

Transforming a supply chain is an ambitious undertaking, and companies should be fully aware of the challenges (see sidebar “Navigating implementation challenges”). However, the potential benefits are significant: companies that are able to manage four specific areas in tandem will be positioned to achieve far greater visibility and better decision making—all powered by AI (Exhibit 2).

![](https://hackmd.io/_uploads/r1K7mMS76.png)

#### 1. Value-creation identification, strategy, and road map

As a first step, companies need to identify and prioritize all pockets of value creation across all functions, from procurement and manufacturing to logistics and, ultimately, commercial. Less than one-third of companies perform an independent diagnostic at the outset, but this exercise can ensure companies have an accurate list of all the value-creation opportunities.

Clearly defining a digital supply-chain strategy helps support the company’s business strategy and ensures better alignment with its digital program. In addition, a solution-agnostic assessment enables companies to identify the process redesign, organizational changes, and capabilities required to boost performance as well as create a strategic road map.

#### 2. Design of target solution and vendor selection

The complexity of supply chains—from demand forecasting to planning optimization and digital-execution tracking—means that finding one provider that can meet all of these needs is increasingly unlikely. Executives should recognize that the right answer for their company won’t necessarily be the one recommended by the providers, whose goal is often to push for a single end-to-end solution.

Solution design and vendor selection can help support the digital supply-chain strategy. Often, the best approach is a combination of different solutions from different providers, implemented by different systems integrators. Companies that select a suite of solutions must make integration a top priority.

#### 3. Implementation and systems integration

Many companies haven’t had sufficient experience in implementing organization-wide technology. Once companies select solutions, the risks are falling behind the implementation schedule and coming in over budget while losing focus on the primary objective: to properly address value-creation levers from the first pitfall. Only 25 percent of supply-chain leaders reported feeling their objectives are aligned with the incentives of their systems integrators.

Companies should take a holistic approach to implementation and systems integration. By optimizing the end-to-end value, companies can implement solutions that deliver value in the short term and are more sustainable over the long term (see sidebar “An end-to-end approach to supply-chain optimization”).

#### 4. Change management, capability building, and full value capture

Even while focusing on tech solutions, companies must attend to vital supporting elements, such as organization, change management, and capability building. Our research suggests this task is a common challenge: for example, only 13 percent of executives report that their organizations are sufficiently prepared to address their skills gaps.

To ensure adoption of new solutions, companies must invest in change management and capability building. Employees will need to embrace new ways of working, and a coordinated effort is required to educate the workforce on why changes are necessary, as are incentives to reinforce the desired behaviors.

Supply-chain management has never been more formidable, but help is on the way. AI will be able to provide teams with deeper insights at a much higher frequency and granularity than ever. However, this visibility alone will not be enough to capture more value from AI-based supply-chain solutions. Any sizable technology investment must be matched by organizational changes, business process updates, and upskilling efforts. Only then will companies capture the expected ROI.


### [How technology is changing the scrap metal recycling industry:](https://www.scrapware.com/blog/how-technology-is-changing-the-scrap-metal-recycling-industry/)

There was a time in the scrap metal recycling industry when incoming material was sorted by hand, tickets were written by hand, and bookkeeping was done by hand (with perhaps the help of a calculator.)  Today, the scrap metal recycling industry is on the cutting edge of innovation, utilizing new technologies to process more material in a more efficient, more accurate and faster manner.  Adoption of technology in the recycling industry has been employed to increase recycling rates, keep up with changing materials and products entering the waste stream and improve profitability for scrap metal recyclers. 

#### Outlined below are some examples of how technology is impacting the recycling industry.

##### Sorting and Processing Material

A host of technological advances in recent years have been put in place in scrap metal recycling yards to help sort and process material.  This technology has been implemented to speed up the task of sorting everything by hand, to address the changing nature of complexity of materials now in the waste stream and the complexity of products such as electronics.  The goal has been to speed up processing and increase the amount of recycled material.  Overall, better sorting of incoming scrap can help companies produce “cleaner” batches of material for onward processing.

Today, sensors and sorting machines do much of the work today that was at one time done by hand.  Companies are experimenting with x-ray technology and infrared scanning to sort non-magnetic metals.  Laser object detection (LOD) is being used to identify non-metals so they can be removed before processing. Laser technology for scrap metal processing can result in significant savings by retaining more recyclable material in a faster period.

The use of XRF, or x-ray fluorescence, creates immense value for scrap metal recyclers.  This technology can positively identify numerous alloy grades and rapidly analyze their chemical composition at material transfer points and thus help guarantee the quality of the product.  Handheld XRF analyzers provide accurate and reliable material identification.  These and other handheld devices using x-rays and/or lasers detect metals, alloys and contaminants.  They can even be equipped with technology to improve sorting by identifying the chemical composition of most scrap material in a few seconds.   This is an important technological development because there are many different types and grades of metals today than there were in the past. Subsequently, more sophisticated processes are needed to identify the material.

The volume of electronic devices entering the waste stream poses additional challenges requiring new technology.  Although these devices are made with precious metals, they contain such minute amounts that recovering them is not cost effective without new technology.  The separation of e-waste devices requires more technologically advanced and sophisticated equipment.  Some large recyclers today are using infrared and x-ray technology to sort out valuable metals in the e-waste stream.

#### Software

[Industry-specific software designed for scrap metal recyclers](https://www.scrapware.com/product-overview/) implements many technological advances that have improved many facets of a recycler’s operations.  Overall, software helps companies maximize efficiency, while helping improve integration and productivity. 

#### Here are just a few areas software has enabled technological advances for recycling companies.

- **Routing** – Routing software for scrap metal recyclers has enabled them to optimize their truck fleet routing.  It can arrange driving routes to optimize the use of resources in the most productive way possible.  Some fleet and container technology allows customer and vendor information to be collected, helping to improve quality of service.  Utilizing this technology decreases energy consumption, increases efficiency, and improves client interaction.

- **Inventory management** – Software programs can help scrap metal recycling companies manage, analyze and optimize their inventory flow.  For most companies, this is one of the most significant elements of profitability. The right software program allows a recycler to know what material is on hand, at what location, at any given time.  It will indicate the price or value of the material, given its stage of processing and what is available for sale.  A scrap metal recycling software system inventory module should allow you to:  print and scan barcodes, define categories of material and track its pricing, allow for accurately entering data and integrate with other modules of the software for even greater efficiencies.

- **Anti-theft compliance** – A good recycling software will utilize technology to facilitate accurate and complete compliance with state and local anti-theft laws.  Scrap metal recyclers can use technology such as cameras, digital signature and thumbprint capture pads and other scrap yard hardware that will interface with programs that create reports and can be uploaded to the authorities for compliance.

- **Document signing** – A premium scrap metal recycling software will allow companies to obtain digital signatures instead of relying on outdated paper documents.  ScrapWare Corporation, which provides software to the scrap metal recycling industry, last year teamed up with DocuSign, the national leader in e-signatures, to make this technology available to its customers.  ScrapWare’s e-signature product, ScrapScribe™, is technology that will streamline the business process, reduce user errors that can occur with paper documents, and remove bottlenecks associated with moving pieces of paper.  ScrapScribe increases security and flexibility when obtaining signatures.  This proprietary system facilitates e-signatures throughout all aspects of a company’s business and enables users to seamlessly send ScrapWare documents to DocuSign for signature.  This includes dispatch tickets, purchase quotes, purchase contracts, packing lists and more.  Documents can be easily viewed on any device and can be printed at any time, whenever necessary. 

#### What the future holds

Looking into the future, scrap metal recyclers can anticipate more complicated products and materials entering their waste stream, but with more intelligent product design.  Manufacturers of consumer goods like electronics are looking at innovative ways to improve product design so that when an item is at the end of its useful life, it can more adequately be broken down and disassembled for easier recycling.

Finally, the use of artificial intelligence (AI) is expected to play a larger role in the recycling industry.  AI is now already being used at some larger solid waste services companies, where AI guided robots can pull recyclable materials from waste streams.  In the future AI machines are expected to be able to recognize materials, trucks and even drivers with optical technology.

Recyclers and researchers are continuing to develop and implement more innovations to make recycling more efficient, more prevalent and less expensive.  The result will be more recyclable material available to manufacturers and less material inadvertently ending up in a landfill.  This translates to more profitability for scrap metal recyclers and more benefits for the environment.


### [Metal Recycling: Opportunities, Limits, Infrastructure:](https://www.recyclingtoday.com/news/recycling-robots-ai-sorting/)

#### How robots are revolutionizing recycling

##### In the quest for upgraded recyclable materials, research and investment in automation seem destined to keep growing.

Consumers of secondary commodities, including plastic, paper, metal and wood scrap, all maintain specifications related to the purity and the quality that these materials must possess, as they are key ingredients in the goods they produce.

Many of these consumers advocate for collection that is as source-separated as possible. That is, if a plant is using clear PET (polyethylene terephthalate) scrap as feedstock, the owner of the plant wants only that specific feedstock shipped to the facility without contamination.

Managers of waste and recycling companies often reply, however, that to obtain sufficient volumes of a given material—including clear PET bottles—using only pinpoint targeted collection methods will come up short.

This disparity between what is able to be easily collected and what materials are desired by a given consumer has helped lead to one of the priciest research and development (R&D) efforts in the waste and recycling sector this century: the ongoing quest to deploy automation to separate commingled recyclables from one another.

The effort has a long history of industry stakeholders bridging mechanical, magnetic and laser-optical techniques to achieve this end goal. Some of the latest technology deployed fits into the artificial intelligence (AI) or machine learning categories with a healthy side order of robotics included.

#### Defining the tactics

Operators of material recovery facilities (MRFs) and other recycling plants face the risk of being overwhelmed with technical terms as they sift through pitches and proposals from technology and machinery vendors.

To understand the basics of these technologies, distinctions need to be made regarding the differences between AI and machine learning. AI is considered a broader category of computer systems developed to perform tasks normally requiring human intelligence, including visual perception and follow-up decision-making.

Within AI, machine learning is defined by [ExpertSystems.com](http://expertsystems.com/) as “an application of AI that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.”

When it comes to identifying and separating materials in a commingled recycling application, machine learning is a segment of AI that is playing a starring role.

Companies including Europe-based TOMRA Sorting Recycling; Eugene, Oregon-based Bulk Handling Systems (BHS); Canada-based Machinex; and Finland-based ZenRobotics are among those focusing on tying machine learning into devices and systems to offer thorough automated sorting options for recyclers.

#### Learning to sort

Companies such as BHS with its MaxAI systems, Machinex with its SamurAI line of sorting machinery and ZenRobotics with its Recycler have all gained attention and investments from recycling plant operators who have calculated that such advanced technology will yield a healthy return on investment (ROI).

Even before machine learning became an integral part of commingled recycling sorting systems, investments in automation focused on two priorities: improving the quality of shipped secondary commodities and decreasing labor costs on the sorting line.

BHS marketing plays heavily into both. “Max,” the spokes-robot for MaxAI, states on BHS’s website, “I don’t get sick. I don’t need breaks, lunches or days off. I work harder, longer and better than anyone else.” Regarding quality, Max adds, “I’m more accurate and more efficient than anyone could be.” And when it comes to machine learning capabilities, Max states, “Thanks to my intelligent neural network, I’m capable of learning on the job so I can adapt to changing conditions and variables.”

Jonathan Ménard, an executive vice president with Machinex, describes the company’s SamurAI product line as a “self-aware sorting robot [that] answers a worldwide need for increased automation.”

Ménard says the SamurAI was unveiled at the April WasteExpo event in Las Vegas, and then internationally a month later at the IFAT trade fair in Germany. He says the MRF market has responded positively to it. “Machinex has officially sold eight SamurAI units, including three of them that will be running before the end of 2018,” he says.

Buyers in the MRF segment are attempting to garner their ROI on the commingled container front, and they’ve turned to AI-enabled technologies to fast-track improvements in lowering contamination rates.

“The majority of our applications are currently for the recovery of different types of recyclables on the reject quality control line, which mainly allows the recovery of natural and colored HDPE [high-density polyethylene], PET [polyethylene terephthalate], metals, Tetra Pak and other types of plastics otherwise missed by the previous sorting equipment,” Ménard says.

#### Advances in robotics

Learning-enabled robots are also gaining a presence in the sorting of mixed construction and demolition materials (C&D), where objects in a commingled stream can be picked in either a negative or positive sort. Recently, robots programmed by ZenRobotics have made an impact on how operators in this space are able to sort incoming materials.

Operators of mixed C&D recycling facilities face labor cost and quality control issues similar to those encountered by MRF operators. As have many MRF operators, C&D recycler Walter Biel of Austin, Texas-based Recon Services has invested in machine learning and robotics to address both of those issues.

In 2017, Biel and his staff worked with ZenRobotics and its U.S. distributor Plexus Recycling Technologies, Denver, to become the first C&D recycler in the country to deploy ZenRobotics robot sorting arms. (A profile of Recon’s overall operations can be found on the Construction & Demolition Recycling magazine website at www.CDRecyler.com.)

At the Recon plant in Austin, two robot arms with “smart grippers” have been programmed and deployed to pick 12 different kinds of materials, and they can separate plastics based on polymer, color, shape and size.

Recon Services says the robots can make roughly 2,000 picks per hour, selecting objects with market value in a positive sort process. By comparison, according to Recon and ZenRobotics, humans can make approximately 800 such picks per hour.

“The robots have added a positive piece to our overall concept,” Biel told Construction & Demolition Recycling magazine earlier in 2018. “Being the first to implement something always has its good and bad, but it never affected any decision we made. It was something we saw value in and decided to add into our operation.”

Whether to be a pioneer of a new technology or wait and see if early adopters benefit will be an ongoing decision-making process for recyclers of all commingled materials as AI and machine learning continue to be configured to work along with sorting devices.

One thing is for sure: These technologies aren’t going away. Sorting technology companies have committed to AI, machine learning and robotics as an integral part of their future. The question is how advanced these machines can become.

According to Ménard, this is an ongoing process.

“We are still working with our partner [Colorado-based] AMP Robotics to further develop enhanced identification technologies and capture rates for plastics applications,” he comments.

When it comes to robotic arm sorting, Ménard states, “In reality, the robot is a tool powered by the AI. Once the neural network of the AI is well-developed, the technology can be inserted in many existing sorting technologies to enhance their performances (recognition, purity, maintenance requirements, auto adjustments, etc.).”

Technology providers such as Machinex are not standing still with their current AI-related product lines, says Ménard. Beyond the sorters themselves, he says a “focal point of development” at the company right now involves “technologies needed to design a connected smart facility,” referred to as “Industry 4.0.”

“We are currently establishing the foundation needed to collect and analyze essential data that will be available and useful to track for the MRF operator,” he says. “This data would help any operator in his or her decision-making process and would be supported by clear indicators of what is currently going on in the plant. Ultimately, this information would also lead to automatic adjustments of certain equipment, assuring complete system performance optimization.”

When it comes to investing in machine learning and AI sorting technology, recyclers clearly will continue to have plenty of vendors and options to choose from. By examining variables such as the type of incoming feedstocks, desired purity rates, speed, accuracy, manpower and automation, operators can help narrow their search and find the sorting solution that best fits their needs.

## :timer_clock: Timeline

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Schedule the timeline, including essential dates and deadlines.
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|        **Item**        | **Date** | **Note** |
|:----------------------:|:--------:|:--------:|
|     Submit Proposal    |          |          |
|    Review References   |          |          |
| Propose   Architecture |          |          |
|        Implement       |          |          |
|       Experiment       |          |          |
|       Make Slide       |          |          |
|      Present/DEMO      |          |          |


## :book:Reference

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List the reference you use in this project.
:::

1. [Simpfi](https://app.clarity.so/simpfi)
2. [Blockchain Initiative](https://app.bounce.finance/auction/fixed-price/ETH/32)
          