---
title: "VHC sprint"
format: 
    revealjs: default
---

# VHC sprint 

## Welcome & Introduction

- Date: 30-5-2024
- Last Demo: 22 - 5 - 2024
- Presenter: Chris Verhoef
- Objective: Review the progress of our AI Supply Chain Tool development, Plan next iteration. 

## Last time
- [x] Real-time data processing pipeline.
- [x] Real-time Systhesys of a company data map. That was in Markdown
- [x] User interface for data manipulation and adjustment.
- [x] Intergration into workbook with nice interface allows non-tech users to join the project.
- [x] Model overview Intergration allows us to watch A.I "think" in real time. 
- [x] Optimize the back-end processes to ensure that the visualization is generated within the 20-minute timeframe.
- [x] Complete the development of the initial visualization tool capable of representing power dynamics based solely on a company name.

## Objectives:

- Make sure the AI iterates over every part of the supplchain, nultitier Suppliers, 2,3,4-de 5de niveau toega. 

- Training the AI model to be specific; Unilever - ASML - 1ste lijns, tweede lijns. Derde Lijns.
- Add to the Product Limitation; (a razos blade of Philps) A Specific Product; 
- We want to see an improvement of the meeting; Can I see the an Improvement of the mapping. 
- Go back to the Graphical Display to map out the Supplychain
**Planned Tasks**:
- Collect more diverse data sets

## Sprint Overview

## Sprint Overview 

- **Sprint Goals**: Visualize Power Dynamics Ecosystem: For the upcoming demo, develop a feature to visualize the entire ecosystem based on a company name within 20 minutes. 

- [ ] Make sure the AI iterates over every part of the supplchain, nultitier Suppliers, 2,3,4-de 5de niveau toega. 
- [x] Training the AI model to be specific; Unilever - ASML - 1ste lijns, tweede lijns. Derde Lijns.
- [ ] Add to the Product Limitation; (a razos blade of Philps) A Specific Product; 
- [x] We want to see an improvement of the meeting; Can I see the an Improvement of the mapping. 
- [x] Go back to the Graphical Display to map out the Supplychain
- [x] Making a more visual plan of the objective. 
 **Planned Tasks**:
    - [x] Collect more diverse data sets

maar je moet eerst op pen en papier neerlegger hoe de tussen stappen moeten werken en wat ze moeten outputten , text, json, csv ? wat moet daar in de colonnen die je moet hebben enz dan kan refinen

## Prototype Development and Initial Testing

- Develop an initial prototype for inputting a company name to generate an ecosystem visualization.
- Model visualization needs somewhat accurate. 

## Feature Enhancement and System Optimization

- Enhance visualization features to include detailed ecosystem interactions.
- Optimize backend processes to achieve visualization generation within the 20-minute target.

## Interface Design and Requirement Analysis

- Design a user-friendly interface for easy initiation of the visualization process.
- Analyze requirements to accurately represent the ecosystem based on essential data points.

## Integration and Final Preparations

- Expand tool capabilities to integrate product data alongside company names.
- Finalize the tool with stakeholder feedback, ensuring readiness for the demo.

## Work Completed

::: incremental

- [x] Prototype to take company name and output a basic visualization of the ecosystem
- [x] Test the prototype to ensure it meets the basic functional requirements.
- [x] UI Enhancements: Improved dashboard for better user interaction.
- [x] Enhance the visualization tool to include more detailed interactions and connections within the ecosystem.
- [x] Optimize the back-end processes to ensure that the visualization is generated within the 20-minute timeframe.
- [x] Complete the development of the initial visualization tool capable of representing power dynamics based solely on a company name.
- [x] Expand the visualization tool to integrate product data along with the company name.
- [x] Design and implement a user-friendly interface that allows users to easily input a company name and initiate the visualization process.
- [x] Ensure the interface is intuitive and guides the user through the visualization generation.

---


## Key Achievements

- **Major Achievement**: Successful prototype of supplychain mapping; 
- **Impact on Project**: Allows us to see a first workable version of the program.
- **Completed Product Increments**:
    - [x] Real-time data processing pipeline.
    - [x] Real-time Systhesys of a company data map.
    - [x] User interface for data manipulation and adjustment.
    - [x] Intergration into workbook with nice interface allows non-tech users to join the project.
    - [x] Model overview Intergration allows us to watch A.I "think" in real time. 

## Key Achievement 2

- **Major Achievement: Reached Bedrock! This is something we can build upon!
- **Impact on Project**: Chris will no longer need to make a whole new project every time he makes a small adjustment!
- **Completed Project increments**:
    - [x] Key tools and guides gathered for the good path.
    - [x] Finally something workable.

## Challenges Faced

- **Significant Challenges**:

<style>
  .reveal .slides section.present {
    font-size: 40%; /* Adjust the percentage based on your preference */
  }
</style>
<p>
    - Data inconsistency across sources.
    - Inability for the A.I to produce a result that the previous visualization tool could correctly prase.
    - Access to good data sources and an example
    - Initial model underperformance, Tokensize is very limited to 9000 characters.
    - Hardware limitations, inability to train faster.
    - Inability to parse data effectively into system
    - AI model training takes significant time
    - AI model "Hallucinates"
    - Chris does his best but he needs to be a data-engineer, ai-engineer, full stack programmer all in one.
      - Creating presentations and formats in PowerPoint always take a very long time.
    - Hard to keep head focused on a project that is very big where learning still needs to happen.
    - "good Path" often changes mid-way due to discovery that it's impossible.
    - Reality of time is different that real time, 6 days turns out to be 3.
    - Significant stress due to "significant difference between what is promised and what is shown"
</p>

  
## Mitigating and moving foward
- **concerns addressed by**:
    - Implementing a data cleansing process
    - Now using a Programmer and Data engineering presenting style, you are looking at it!
    - Juypter Notebook intergration directly into code; 
    - Model overview tester to show differences
    - Chain of Thought model prompting for more accurate results
- **Remaining Concerns**: Adding significant data and parsing and inability to see how that will work, Chris needs to learn. 

## Why Chain of Thoughts all of a sudden. 

```{=html}
<style>
    table { font-size: 70%; }
</style>
<table>
<thead>
<tr>
<th>Aspect</th>
<th>Chain of Thoughts</th>
<th>Single LLM</th>
</tr>
</thead>
<tbody>
<tr>
<td><strong>Complexity and Detail</strong></td>
<td>Breaks down complexities into manageable insights, capturing detailed interdependencies.</td>
<td>May overlook nuanced details due to generalized training data.</td>
</tr>
<tr>
<td><strong>Adaptability and Flexibility</strong></td>
<td>Allows iterative reassessment and refinements, crucial for dynamic environments.</td>
<td>Constrained by initial training, less adaptive to new scenarios.</td>
</tr>
<tr>
<td><strong>Risk Identification and Mitigation</strong></td>
<td>Enables comprehensive risk analysis from multiple perspectives and scenarios.</td>
<td>Potentially misses emerging or non-obvious risks, limited by training scope.</td>
</tr>
<tr>
<td><strong>Customization and Specificity</strong></td>
<td>Tailored analysis specific to operational needs and strategic goals.</td>
<td>Generalizes based on broad datasets, may neglect organization-specific needs.</td>
</tr>
</tbody>
</table>
```

## Chain of Thought Prompting

```{mermaid}
graph LR;
    A[Process Inputs] --> B[Create Thoughts];
    B --> C[Evaluate Pathways];
    C --> D[Summarize Results];
    D --> E[Formulate Conclusions];
```

```{=html}
<style>
  .reveal .slides section.present {
    font-size: 80%; /* Adjust the percentage based on your preference */
  }
</style>
<p>Step 1: Focus on acquiring and organizing all necessary inputs that will inform the subsequent stages of thinking.

Step 2: Develop coherent thoughts that logically follow from the processed inputs.

Step 3: Critically evaluate each pathway to determine its feasibility and relevance.

Step 4: Aggregate and synthesize the results to provide a clear overview of what has been discovered or concluded so far.

Step 5: Make informed decisions or conclusions that are supported by the summarized findings and ready to be implemented or communicated.</p>
```


## Product Demonstration

- **Live Demo**: Showcasing the Chain of Thought one
- **Feature Explanation**: Detailed overview of data inputs and machine learning outputs.
- **Interaction Scenarios**: Taking a Present Data Supplychain set and mapping this out, to determine the relationship between them.
-- **Question that is posed**: Between the characters of the "Miserables" what is the connection? 
-- **Features shown**


## Language Model Integration:

The notebook integrates two language models, Ollama with enhanced context capabilities and OpenAIChat from OpenAI's GPT models, to process text inputs.

### Data Processing and Visualization:

A function, load_graph, loads data from a JSON file and converts it into a Mermaid graph format for visualization within the notebook.

## Chain of Thoughts Processing:

Implements a structured data processing approach that sequentially processes data through stages: input processing, thought creation, pathway evaluation, and conclusion formulation, using custom templates for each stage.

### Custom Templates for Language Models:

Uses ChatPromptTemplate and PromptTemplate to define structured prompts that guide the language models' responses, ensuring that each step of the process is contextually relevant to the previous outputs.

### Dynamic Content Rendering:

Outputs from language model processing are rendered as Markdown, allowing for dynamic presentation of both textual and graphical data in the notebook environment.
Iterative and Conditional Processing:

The notebook demonstrates the ability to loop over data iteratively and conditionally based on the outputs from each stage of the processing chain, refining the results with each iteration.

## Synthesis of Complex Data:

It synthesizes complex data from multiple processing steps into a coherent plan or conclusion, demonstrating advanced data handling and decision-making capabilities.

### Interactive Markdown Display:

Markdown is used extensively to display both code-generated graphics and language model outputs interactively, enhancing the readability and user engagement with the notebook content.

## MAP
```{dot}
digraph ASML_Supply_Chain {
    node[shape=box, style=filled];
    "ASML" [label="ASML Holding N.V."];
    "Intel" [label="Intel Corporation"];
    "Samsung" [label="Samsung Electronics Co., Ltd."];
    "TSMC" [label="Taiwan Semiconductor Manufacturing Company (TSMC)"];
    "GlobalFoundries" [label="GlobalFoundries Inc."];
    "Other Suppliers" [label="Various Other Suppliers"];

    ASML -> Intel[label="Semiconductor Equipment Sales"];
    ASML -> Samsung[label="Semiconductor Equipment Sales"];
    ASML -> TSMC[label="Semiconductor Equipment Sales"];
    ASML -> GlobalFoundries[label="Semiconductor Equipment Sales"];
    Other Suppliers -> ASML[label="Components, Materials, and Services"];

    "Specialty Chemicals" [label="Specialty Chemicals Inc."];
    "Packaging Materials" [label="Packaging Materials Co., Ltd."];
    "Assembly Services" [label="Assembly Services Inc."];

    Other Suppliers -> Specialty Chemicals[label="Chemicals"];
    Other Suppliers -> Packaging Materials[label="Packaging Materials"];
    Other Suppliers -> Assembly Services[label="Assembly Services"];

    Intel -> ASML[label="Semiconductor Equipment Purchases"];
    Samsung -> ASML[label="Semiconductor Equipment Purchases"];
    TSMC -> ASML[label="Semiconductor Equipment Purchases"];
    GlobalFoundries -> ASML[label="Semiconductor Equipment Purchases"];
}
```

## Feedback & Questions

- **Questions Received**:
    - Concerns about data privacy
    - Questions about integration with existing ERP systems
- **Feedback Incorporation**:
    - Plan to enhance data encryption
    - Roadmap for ERP integration by next quarter

## Sprint Metrics

- **Velocity Chart**: Display of completed story points over time.
- **Burndown Chart**: Current sprint progress.
- **Other Metrics**: Accuracy improvements of the forecasting model.

## Plan for Next Sprint

- **Objectives**:
    - Make sure the AI iterates over every part of the supplchain, nultitier Suppliers, 2,3,4-de 5de niveau toega. 
    - Training the AI model to be specific; Unilever - ASML - 1ste lijns, tweede lijns. Derde Lijns.
    - Add to the Product Limitation; (a razos blade of Philps) A Specific Product; 
    - We want to see an improvement of the meeting; Can I see the an Improvement of the mapping. 
    - Go back to the Graphical Display to map out the Supplychain
- **Planned Tasks**:
    - Collect more diverse data sets
    - Work on backend modifications for ERP integration
- **Adjustments Based on Feedback**:
    - Focus on data security enhancements

## Conclusion

- **Summary**: Reviewed the advancements made in our AI tool for enhancing supply chain efficiency.
- **Next Steps**:
    - Continue development and testing of the forecasting model
    - Initiate stakeholder meetings for next-phase planning
- **Thank You**: Special thanks to the team and stakeholders for ongoing support and feedback.