✅ 1. Airbus & Palantir

– Stopping Supplier Surprises

In Plain Terms:
Airbus was building aircraft, but

supplier problems kept blowing up

timelines and budgets. They couldn’t

see where things were going

wrong—until they partnered

with Palantir.

Palantir’s Decision Intelligence

stitched together supplier, parts,

and production data into a real-time

model. It let Airbus simulate

“what if this supplier delays?” or

“what if this part price spikes?”

—before problems hit the floor.

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What Changed:

• Reduced production delays by 30%

• Renegotiated smarter supplier contracts

• Gained real-time control over cost risks

 

ScrapDI Equivalent:
ScrapDI connects supplier pricing, route profitability, and buyer behavior just like Palantir did—but for scrap. You see which vendor or quote is silently eroding margin and act before it hits your books.

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📌 Source: Palantir + Airbus Case Study

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✅ 2. UK National Grid & DeepMind – Predicting Costly Power Spikes
 

In Plain Terms:
The UK’s power grid was overproducing electricity because operators couldn’t predict real-time demand. That waste cost millions. Google DeepMind created an AI tool that accurately forecast energy demand 48 hours ahead, letting the grid fine-tune output and avoid overproduction.

 

What Changed:

• Forecasting error cut by 25%

• Massive savings in load balancing

• Operators could act ahead of time—not after loss

 

ScrapDI Equivalent:
ScrapDI forecasts when margin will disappear—based on market shifts, supplier slippage, or route drag. You don’t wait for losses. You fix them before they appear.

 

📌 Source: DeepMind AI Energy Forecasting

 

✅ 3. Berkshire Hathaway Energy & Uptake – Fix It Before It Breaks
 

In Plain Terms:
Utility companies under

Warren Buffett’s

Berkshire Hathaway

were losing money

when key equipment

failed unexpectedly.

Uptake’s AI watched

machines in real time

and predicted, “This unit

will fail in 6 weeks.” Teams

fixed small problems

before they became

million-dollar disasters.

 

What Changed:

• 13% less unplanned downtime

• $10M+ in loss avoidance

• Shifted from reacting to predicting failures

 

ScrapDI Equivalent:
ScrapDI watches buyer performance, pricing behavior, and route economics like a predictive mechanic. If a quote or buyer is trending toward loss, it tells you early—before your margin gets crushed.

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📌 Source: Uptake Customer Case Studies

 

✅ 4. Enel & C3.ai – Catching Hidden Margin Leaks
 

In Plain Terms:
Enel, one of Europe’s biggest utilities, knew it was losing money—but had no idea where. C3.ai deployed an AI system that merged billing, usage, pricing, and contract data. The AI flagged errors, leaks, and pricing mismatches in real time. The result: €40 million recovered.

 

What Changed:

• €40M in hidden margin recovered

• 20% improvement in operational efficiency

• AI enforcement of pricing and contract rules

 

ScrapDI Equivalent:
ScrapDI flags pricing errors, quote slippage, bad route economics, and buyer non-compliance instantly. You catch the margin leak as it’s happening—not when it’s too late.

 

📌 Source: C3.ai + Enel Case Study

 

✅ 5. McKinsey QuantumBlack – Quoting Without Losing
 

In Plain Terms:
Sales reps at a large manufacturer were pricing quotes below minimum margin—but no one knew. McKinsey’s QuantumBlack deployed an AI agent that reviewed each quote live. If it wouldn’t meet target profit, the AI flagged it before it was approved. Teams adjusted quotes in real time.

 

What Changed:

• 12% improvement in deal margin

• 20% quoting compliance boost

• $50M in margin protected in one year

 

ScrapDI Equivalent:
ScrapDI does this for your buyers. It reviews every quote and flag if it misses target margin, benchmark pricing, or haul economics. You don’t lose money because of guesswork.

 

📌 Source: McKinsey: Next-Gen Revenue Growth

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