CLEVresearch Initiatives

Executive Summary: A Unified Intelligence Platform for Strategic Sourcing

Lidl & Kaufland Asia, supported by the Singapore Fashion Council (SFC), requires a unified intelligence platform to overcome the critical challenge of fragmented supply chain data. Project Carbonite is that solution. It is a causal intelligence platform designed to consolidate Lidl's internal sourcing records with external market data, providing a comprehensive, real-time understanding of sourcing trends and market movements.

By transforming scattered data points into a clear, actionable dashboard, Project Carbonite empowers Lidl's sourcing team to make more informed, data-driven decisions, directly addressing the core problem statement of the challenge. This will enhance efficiency, mitigate risk, and establish a strategic advantage in the "Fashion and Lifestyle" and "Home Textiles" categories.

What is a 'Unified Intelligence Platform for Strategic Sourcing'?

A Unified Intelligence Platform for Strategic Sourcing is a system that solves the core problem of data fragmentation in global supply chains. For a global enterprise, data is scattered across dozens of systems: internal ERPs, supplier databases, shipping manifests, and external market reports. Project Carbonite is our answer to this challenge. It acts as a non-disruptive Causal Governance Layer that overlays these existing systems. Instead of costly integration projects, it uses a Hypergraph Model to find and connect the 'digital threads' between all data points. This creates a single source of truth, allowing sourcing teams to see the full picture—from the cost of raw materials in one country to the geopolitical risk in another—enabling them to make strategic, proactive decisions instead of reactive, fragmented ones.

Phase 1: The Three-Month Causal Viability Study (The Task)

The S$80,000 is the budget for the Causal Viability Study (CCVS), a three-month validation of our architecture on the Textile/Lifestyle category. We will use pre-trained models for rapid deployment, delivering the first actionable pieces of financial and ESG intelligence, which will be validated against the real-world processes of Lidl’s team.

Three-Month Achievable Milestones

Timeline	Milestone	Deliverable
Weeks 1-2	Structural Integrity	ERP to Index Harmonization and Unit Standardization Agent (POC).
Weeks 3-5	Causal Modeling	Hyper Edge Matrix Boundaries defined; Factor Beta Regression engine calibrated.
Weeks 6-8	Risk Quantification	Supply Chain Beta (β_SC) calculation verified.
Weeks 9-12	Asset Quantification	Carbon Credit Beta (β_CC) model calibrated and final report delivered.

High-Level Budget Allocation (CCVS)

Category	Allocation	Justification
Lead AI/ML Engineering (3 Months)	S$45,000	Dedicated expert time for model calibration, architecture validation, and hypergraph implementation.
Cloud Computing & API Costs	S$20,000	Covers GPU time for model training/fine-tuning (Google Cloud) and GenAI API usage for the agentic workflows.
Data Acquisition & Licensing	S$10,000	Licensing for essential external market data feeds (e.g., commodity prices, trade indices) for the study period.
Project Management & Contingency	S$5,000	Overseeing deliverables and providing a buffer for unforeseen technical challenges.

Proposed Project Team & Time Commitment (CCVS)

Role	Source	Estimated Hours (3 Months)
Lead AI/ML Engineer	CLEVresearch	Full-Time
Project Manager / Lidl Liaison	Lidl (e.g., Raj)	4-6 hours/week
Sourcing Data SME	Lidl (e.g., Anika)	5-8 hours/week
Risk Assessment SME	Lidl (e.g., Mei)	3-5 hours/week

An image showing the complexity of a modern supply chain with shipping containers and logistics.

Solution Architecture: The CLEV Causal Intelligence Layer (The Action)

Key Benefits: Project Carbonite as the SFC's Mandate Accelerator

Dive Deeper into Our Solution

Explore the detailed breakdown of how our solution addresses key industry challenges.

View the Solution Accelerator

Strategic Advantage & Results: The Predictive Rebalancing Framework

The entire system is governed by Factor Beta (β), the universal language of distributed control. This framework allows us to computationally solve the challenge using Inverse Design: we start with the desired outcome (e.g., β_SC ≈ 0) and compute the optimal sourcing structure to achieve it.

The Dual Beta Model (Risk vs. Asset)

Metric	Description	Example Persona Use Case
Supply Chain Beta (β_SC) — The Instability Metric	This Factor Beta measures sourcing sensitivity to external factors (commodities, tariffs, FX Risk).	Mei (Risk Officer): A high β_SC on a supplier triggers a risk alert. She can then investigate and recommend hedging strategies.
Carbon Credit Beta (β_CC) — The Stability Metric	This Factor Beta quantifies a sourcing decision's future asset value through verifiable Carbon Insetting.	Raj (Manager): Uses a high β_CC to justify strategic partnerships with sustainable suppliers, knowing it will yield tangible ESG assets.
Combined View	The system uses both Betas to create a complete picture of a supplier's true cost and value.	Anika (Analyst): Sees a supplier with a low (good) β_SC but a high (good) β_CC, identifying them as a highly stable, high-value partner for the preferred program.

Interactive Causal Viability Study

A "what-if" engine demonstrating how different events impact overall supply chain stability.

Toggle Risk/Opportunity Factors:

New Tariffs (15%)-15 Points

Port Delays-10 Points

Demand Spike+20 Points

FX Hedging+5 Points

Initial Stability100

Net Stability

100

The Causal Viability Framework

A deep-dive into the modeling of the Causal Viability Study.

Causal Viability Study Dashboard

This mockup demonstrates how the system translates stability scores (like those from the Causal Viability Study) into actionable, persona-specific insights for the entire team.

Overall Supply Chain Beta (β_SC)

0.82

Lower is more stable. Goal: < 1.0

Projected Carbon Credit Beta (β_CC)

1.15

Higher means more ESG asset potential.

Avg. Supplier Risk Score

6.5

Based on β_SC, location & dependency.

Persona-Driven Action Center

The system provides tailored alerts and next steps for each team member based on changes in Beta factors and stability scores.

Anika (Analyst)'s Action Items

New Low-Risk Supplier Identified

Bangladesh Apparel shows a stable Beta of 0.9. Recommend for expedited qualification process.

Performance Review Triggered

Vietnam Textiles Co. has maintained a low Beta for 3 consecutive quarters. Initiate performance review for preferred status.

Supplier Risk Analysis

Live Beta (β_SC) scores for key suppliers.

Supplier	Beta (β_SC)	Risk Level
Vietnam Textiles Co.	0.75	Low
Global Garments Ltd.	1.1	Medium
Shenzhen Style Inc.	1.35	High
Bangladesh Apparel	0.9	Low

The Project Carbonite Advantage

Causal Linking vs. Brute-Force Data Merging

The Old Way: Brittle Data Merging	Our Solution: The Causal Governance Layer
The Problem: High-Maintenance, Brittle Data Traditional data pipelines force non-standardized schemas from disparate sources into a single table. This process is fragile, requires constant maintenance for data deduplication, and is a major source of the time gaps and failures that halt modern machine learning pipelines.	The Result: Resilient, Linked Intelligence We use a Causal Hypergraph to form Hyper Edges that semantically link disparate IDs into one queryable entity. Our Factor Beta (β) model doesn't need the raw data to be merged or deduplicated, only causally linked. It operates at a higher level of abstraction, making the entire brittle deduplication layer obsolete.

The Old Way: Brittle Data Merging

Our Solution: The Causal Governance Layer

The Problem: High-Maintenance, Brittle Data

Traditional data pipelines force non-standardized schemas from disparate sources into a single table. This process is fragile, requires constant maintenance for data deduplication, and is a major source of the time gaps and failures that halt modern machine learning pipelines.

The Result: Resilient, Linked Intelligence

We use a Causal Hypergraph to form Hyper Edges that semantically link disparate IDs into one queryable entity. Our Factor Beta (β) model doesn't need the raw data to be merged or deduplicated, only causally linked. It operates at a higher level of abstraction, making the entire brittle deduplication layer obsolete.

A Case Study in Systemic Failure

Our Causal Governance System, reframed for Megan Khung.

Key Concepts

Hypergraph Model

A data structure used in advanced mathematics and computer science that goes beyond a simple graph. While a normal graph connects two nodes with an edge, a hypergraph connects multiple nodes with a single "hyperedge." This is essential for modeling complex, multi-party relationships, like those in a supply chain where an event (e.g., a port closure) simultaneously affects multiple suppliers, shippers, and buyers.

Factor Beta (β)

Borrowed from financial engineering (Capital Asset Pricing Model), Beta (β) is a measure of volatility or systemic risk. We adapt this concept to supply chains. A Beta of 1 means a supplier's cost moves exactly with the market. A Beta > 1 is more volatile. A Beta < 1 is more stable. Our goal is to use this to quantify and minimize supply chain risk.

Inverse Design

A problem-solving approach where you define the desired outcome first and then work backward to find the input parameters that produce it. Instead of asking "What happens if we switch to this supplier?", we ask "What is the optimal supplier mix to achieve a target cost and risk profile (a target Beta)?"

Matrix Boundaries

In our system, this is a conceptual container for a set of related data. Think of it as a clearly defined "box" in the hypergraph that holds all information related to one specific entity, like a single supplier or a single shipment. This allows the system to treat complex groups of data as a single, manageable unit.

Foreign Keys

A standard database term for the "digital threads" that connect different data tables or boundaries. For example, a "Supplier ID" acts as a foreign key that links a specific supplier in one table to all of their corresponding shipments in another table, creating a relational link.

Agentic Workflow

A series of automated, intelligent processes (agents) that perform specific tasks to support human experts. For example:

For Anika (Analyst): An agent automatically pre-processes and scores new supplier data based on established criteria, presenting her with a prioritized list for review.
For Raj (Manager): An agent continuously monitors market data and competitor movements, automatically generating a weekly "Strategic Opportunities" brief for his attention.
For Mei (Risk Officer): An agent monitors geopolitical and climate news, flagging events that could impact high-risk suppliers and automatically triggering a re-calculation of their Beta score.

Project Carbonite