You predict supply in industrial gases with AI by turning 3 things into one system:

(Real-time data) × (AI demand & usage forecasts) × (operations constraints) → Daily Supply Plan & Alerts.

Let’s build it step by step, case: Industrial Gases (bulk, cylinders, on-site, medical O₂).

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1️⃣ Define the Supply Questions First (Not the Algorithm)

Before any AI model, be brutal on what you want to decide every day:

Typical industrial gas supply questions:

1. How much product to produce today / this week?
   • By plant, by product: O₂, N₂, Ar, CO₂, H₂, special gases.
2. Where to position stock?
   • Bulk depots, cylinder depots, hospital buffer tanks.
3. Which trucks to send where, when?
   • Routing + load optimization.
4. Where are we at risk?
   • Low tank levels, plant outage, peak demand (heatwave, flu, big shutdowns ending).

Write these as AI questions:

• “Given current and forecast usage, what is the fill schedule per customer tank for the next 7 days?”
• “Given all orders + forecasts, what production plan minimizes overtime & emergency trips?”

These become your AI outputs.

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2️⃣ Build the Data Spine: What AI Needs for Industrial Gases

For industrial gases, AI works only if the data spine is solid:

🔹 Core data sources

1. Historical deliveries (5+ years if possible)
   • Customer, product, delivered quantity, date/time, route, special conditions.
2. Tank / telemetry data
   • Level/pressure readings, time between fills, min/max levels allowed.
   • Hospitals: strict min safety level, ICU loads, backup systems.
3. Production plant data
   • Daily capacity by product, maintenance windows, energy cost profile (hourly prices).
4. Customer & segment info
   • Sector: hospital, steel, welding, food, pharma, electronics.
   • “Criticality index”: Can they stop? Or must you always supply (medical O₂)?
5. External signals
   • Calendar (weekends, holidays, shutdowns).
   • Weather (heatwaves → O₂/N₂ usage, CO₂ logistics).
   • Macroeconomic/market trends (automotive/steel cycles).

Make a simple table in your mind:

Layer	Example
Demand	Historical orders, tank levels, customer forecasts
Supply	Plant capacity, production schedule, maintenance
Logistics	Fleet capacity, depots, route times
Constraints	Safety stocks, contracts, service levels (e.g. 99.95% uptime for O₂)

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3️⃣ Choose the AI Models for Supply Prediction

You don’t need exotic AI. For industrial gases, 3 families are practical:

3.1 Time-series forecasting per customer or segment

Use AI to predict daily/weekly usage:

• For stable customers: simple models (ARIMA, Prophet-style, gradient boosting).
• For volatile or “event-driven” customers: models with exogenous variables (events, campaigns, shutdown schedules, weather).

Output:

Forecast usage per customer tank / depot / segment for next 7–30 days.

3.2 Classification models for risk & refill events

AI predicts events like:

• “Will this hospital need an emergency refill in the next 48 hours?” (Yes/No, probability)
• “Is this depot at risk of stock-out in the next 3 days?”

Inputs: current and past tank levels, consumption trend, delivery patterns, constraints.

3.3 Optimization layer: turning forecasts into supply plans

Once you have forecasts, you solve:

• How much to produce where?
• How much to ship from which plant/depot?
• Which routes to use?

Here you can mix:

• Heuristics + optimization (linear / mixed-integer programming)
• AI-assisted dispatch: model proposes candidate plans ranked by cost, risk, service level.

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4️⃣ The Industrial Gas AI Supply Loop (Daily/Weekly)

Think in a simple loop you run every day:

1. Ingest & Update
   • Pull tank levels, orders, plant status, truck availability.
2. Forecast
   • Predict demand per customer & location for next 7 days.
3. Simulate
   • Simulate plant loading, depot stocks, logistics load.
4. Optimize
   • Generate a recommended plan:
     • Production per plant
     • Transfers between depots
     • Truck routes & delivery sequence
5. Alert
   • Highlight: stock-out risks, overloaded plants, underused assets, CO₂/ESG deviations.
6. Learn
   • Compare forecast vs reality → retrain models.

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5️⃣ Example: Medical Oxygen Supply Prediction (Mini Case)

Context: 50 hospitals, 10 homecare O₂ depots, 2 ASU plants.

Step A – Define targets

• No hospital below 30% tank level.
• Minimize emergency deliveries.
• Balance production across two plants (energy cost vs load).

Step B – Run AI demand forecast

For each hospital:

• Input:
  • Historical O₂ usage (daily/hourly), occupancy, ICU beds, local COVID/flu data, seasonality.
• Output:
  • Daily O₂ consumption for next 14 days.
  • Confidence band (pessimistic/optimistic).

Step C – Translate into tank trajectories

For each tank:

• Start from current level.
• Subtract forecast consumption day-by-day.
• Add planned deliveries (if any).
• AI flags breach days (when level < safety threshold).

Step D – Optimization

Given all predicted tank levels:

• AI proposes:
  • Which tanks to refill on which days
  • The volume per delivery
  • Optimal routing (minimize km & overtime)
  • Check plant capacity constraints

Dispatcher gets a ranked plan:

• Plan A – Lower km, higher plant load
• Plan B – Balanced plant load, slightly more km
• Plan C – High resilience (extra safety stock before holidays).

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6️⃣ How to Start Practically (Without Overkill)

If you wanted to start within weeks, I’d structure it like this:

Phase 1 – Pilot on one segment

Pick one high-impact segment, e.g.:

• Hospitals in one region
• Cylinder welding customers in one country
• Bulk nitrogen for steel customers

Do only:

• Tank-/order-based demand forecasting
• Simple stock-out risk alerts

Phase 2 – Add production & depot layer

Layer in:

• Plant capacities
• Depot inventory
• Simple what-if:

“What if customer X increases demand by 20%?”
“What if we shut down Plant A for 3 days?”

Phase 3 – Add routing & cost (full AI Supply Cockpit)

Now connect:

• Routing optimizer
• Cost per trip / per ton
• CO₂ emissions per route

Dashboard outputs:

• Tomorrow’s Supply Plan
• Top 10 Risk Points
• Cost & CO₂ impact of different scenarios.

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7️⃣ AI Supply KPIs for Industrial Gases

To keep this from becoming a “tech toy”, define hard KPIs:

• Stock-out incidents per year (target → near 0 for hospitals).
• Emergency deliveries (frequency & cost) ↓.
• Plant utilization vs target window (avoid wild swings).
• Kilometers per ton delivered ↓.
• CO₂ emissions per ton delivered ↓.
• Forecast accuracy (% MAPE) → gradual improvement.

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8️⃣ One-Sentence Formula for You

AI Supply in Industrial Gases =
(Demand forecast per tank & segment) + (Plant & logistics constraints) → Optimized daily plan + early risk alerts.