Mathnal

06/07/2026

The future of Supply Chain belongs to professionals who can combine domain expertise with AI, analytics, and data-driven decision making.

The good news?

You don't need an expensive international certification to build these skills.

At Mathnal Academy, we've created an affordable online learning platform designed specifically for Supply Chain professionals, students, and managers who want practical, job-ready skills.

Why choose Mathnal Academy?

✅ Learn Supply Chain Analytics from industry experts

✅ Master AI for Supply Chain Decision Making

✅ Build skills in Demand Forecasting

✅ Learn Inventory Optimization

✅ Work with Python, SQL, Power BI & Excel

✅ Solve real business case studies

✅ Weekend online classes

✅ Hands-on projects you can showcase to employers

Who should join?

✔ Supply Chain Professionals

✔ Demand Planners

✔ Procurement Specialists

✔ Inventory Managers

✔ Operations Managers

✔ MBA Students

✔ Engineering Graduates

✔ Data Analysts

What makes us different?

We don't just teach software.

We teach how to solve real Supply Chain business problems using Analytics and Artificial Intelligence.

Whether your goal is:

📈 Career Growth

💼 Better Job Opportunities

🤖 AI Upskilling

📊 Data-Driven Decision Making

📦 Supply Chain Optimization

Mathnal Academy provides a structured learning path at a cost that is accessible to working professionals and students.

🎯 Start building the skills that companies are hiring for today—not the skills that were relevant yesterday.

🌐 Explore our programs at www.mathnal.tech

Mathnal Academy – Decisions from Evidence.

08/05/2026

Most middle managers in Supply Chain are about to face a brutal reality.

The industry is changing faster than their skills.

AI is no longer “future technology.”
Machine Learning is no longer optional.
Optimization is no longer only for data scientists.

Yet thousands of experienced professionals are still managing:
❌ Inventory on Excel
❌ Procurement without predictive analytics
❌ Logistics without optimization models
❌ Forecasting without ML
❌ Decisions based on intuition instead of evidence

And the uncomfortable truth is this:

The next generation of leaders in Supply Chain will not just understand operations…

They will understand:
✅ Machine Learning
✅ Optimization Modeling
✅ Network Design
✅ Risk Simulation
✅ Python-based Decision Systems
✅ Data-Driven Supply Chains

Companies are actively looking for professionals who can bridge BUSINESS + ANALYTICS + OPERATIONS.

That’s exactly why we created the CSCOP Program (Certified Supply Chain Optimization Professional).

This is not another theoretical certification.

This is hands-on training for working professionals:
✔ Python for Supply Chain
✔ Optimization using PuLP & OR-Tools
✔ ML Forecasting
✔ Inventory & Transportation Optimization
✔ Real Industry Case Studies
✔ Capstone Projects
✔ Career-Focused Learning

Designed specifically for:
• Supply Chain Managers
• Procurement Professionals
• Inventory Analysts
• Logistics Leaders
• Consultants
• Mid-level Operations Professionals

📅 Starting: 3rd July 2026
📆 Fri, Sat & Sun
⏰ 8:30 PM – 10:30 PM IST

If you feel the industry is changing faster than your current skillset…

That feeling is not wrong.

But you still have time to become future-ready.

Comment “CSCOP” and we’ll share the complete brochure & program details.

Or connect with us directly:
📧 [[email protected]](mailto:[email protected])
🌐 [www.mathnal.tech](http://www.mathnal.tech)
📱 +91 7993651356

01/05/2026

https://mathnal.tech/forecasting_diagnostic_tool.html - Link to test your SKU's forecast using a diagnostic tool. This tool will further improve week over week, The total number of SKU's you can forecast is 10, and you may enter SKU's manually or upload an excel or csv file. Test and Gamify your inventory management

24/04/2026

21/04/2026

The supply chain career that got you here will not get you there.

Let me explain.

I analysed which supply chain roles face the highest automation risk from AI — and the results are uncomfortable:

Inventory Clerk — 90% risk
Production Planner — 85% risk
PO Processing — 85% risk
Freight Coordinator — 75% risk
Purchasing Agent — 70% risk
Demand Analyst — 50% risk
SC Manager — 20% risk

See it?

The more your role is defined by a task, the higher the risk.
The more your role is defined by an outcome, the lower the risk.

AI does not replace people.
AI replaces tasks.

The person who processes POs? Replaced.
The person who redesigns the procurement workflow with AI? Promoted.

Same function. Completely different future.

Here is the data that should keep every SC professional awake:

• 92M jobs displaced by 2030
• 170M new ones created
• 39% of today's skills will be outdated
• 94% of SC workers are open to AI
• Only 36% know how to use it
• Junior logistics roles dropped 25% in one year
• AI-skilled professionals earn 56% more

That last number is the one that matters.

The market is already paying a premium for people who can:
→ Use AI tools (Claude, ChatGPT, Copilot) to accelerate analysis
→ Write basic Python for data cleaning and forecasting
→ Interpret ML model outputs — not blindly trust them
→ Design AI-augmented workflows
→ Lead digital transformation, not just participate in it

I have published a complete survival playbook:

✅ Role-by-role risk matrix
✅ 5-level AI literacy roadmap
✅ 6 critical skills to build
✅ Salary benchmarks for AI-literate vs. traditional roles
✅ A 30-day action plan starting with one free course

📰 Read the full newsletter → www.mathnal.tech
(Newsletter section → Issue #4)

You have two choices:

1. Learn to use AI and command a 56% premium.
2. Compete against AI and watch your role disappear.

There is no third option.

—
Krish Naidu
Mathnal Analytics | Decisions from Evidence
www.mathnal.tech

20/04/2026

Your supply chain KPIs are about to get destroyed.

Not might. Will.

I spent the last month analysing the 10 converging risks that will reshape global supply chains between 2026 and 2030 — and mapped their impact against 15 critical supply chain metrics.

The findings are sobering:

→ OTIF: 92–96% today → 72–85% under disruption

→ Inventory turns: halved

→ Cash-to-cash cycle: doubled

→ Freight costs: +25–80%

→ Lead time variability: 3x–5x worse

→ Forecast accuracy: dropping below 50%

And the scariest part? These risks are not independent.

They compound.

Geopolitics triggers chokepoint closure → lead times extend → safety stock inflates → warehouses overflow → carrying costs surge → cash-to-cash stretches → investment capacity shrinks → AI adoption delays → competitors widen the gap.

One disruption creates a cascade across all 15 KPIs.

The 10 risks I mapped:

🔴 Geopolitical fragmentation & tariff wars

🔴 Maritime chokepoint disruptions

🔴 Climate-driven extreme weather

🔴 Critical mineral & semiconductor shortages

🔴 Cyberattacks on logistics

🟡 Labour shortages & skills gaps

🟡 Energy volatility & grid failures

🟡 Infrastructure decay & port congestion

🟢 Regulatory & ESG compliance

🟢 AI disruption of planning

For each one, I have calculated:

• The probability (from High to Certain)

• The specific KPI degradation

• The cost implication

• The primary metric it destroys

Plus a 5-year timeline showing exactly when each risk peaks, and a 5-point resilience playbook for what to do about it.

This is not a 500-word opinion piece.

This is a 15-minute strategic briefing with data, benchmarks, and a full metric impact matrix that belongs on the wall of every S&OP room.

📰 Read the complete newsletter → www.mathnal.tech

(Head to the Newsletter section — Issue #3)

The question is no longer "What could go wrong?"

It is: "Which of these 10 risks should inform my next decision — and which KPIs will they destroy first?"

To read the full article visit our website https://lnkd.in/grmwjZD4

&OP

17/04/2026

🚀 Excited to launch my new book Transport Optimization!
If you're in logistics or supply chain, this book will help you move from intuition to data-driven decision-making.
✔ Practical
✔ Structured learning approach
✔ Real-world relevance
💰 Price: $20
📩 DM me to get your copy
🌐 https://lnkd.in/g3Wwb8bN

13/04/2026

Stop Looking for a Crystal Ball—Start Building an Architecture 🏗️📈

In the world of data science, "forecasting" often feels like a black box. Stakeholders ask for a number, and the model spits one out. But if you can't explain why the line is moving, the forecast is just a guess with a fancy title.

The most robust forecasting models aren't magic; they are modular architectures.

Here is how we turn chaos into a predictable trajectory:

1. The Foundation: Local Trend 🧱

This is your "north star." It captures the overall trajectory and growth of your business. Is the baseline rising or falling? Without a solid grasp of the underlying trend, your model will succumb to short-term noise.

2. The Heartbeat: Seasonality 💓

Markets breathe. Whether it’s the "Monday Blues," the "Holiday Peak," or the "Summer Slump," capturing periodic rhythms is essential. Seasonality tells you what is expected, so you can spot what is truly exceptional.

3. The Weight of History: Auto-regression 🔄

Data has memory. Auto-regression captures immediate past momentum. If you were growing at 10% for the last three days, that inertia matters for what happens on day four. It’s the model’s way of saying, "History doesn't repeat, but it often rhymes."

4. The Wildcard: Dynamic Regressors ⚡

This is where the real world enters the math. Marketing spend, competitor price drops, or global supply chain shocks—these "exogenous shocks" aren't part of the internal data pattern, but they change everything. A great model allows these external variables to "dock" into the stack.

The Result: The "Glass Box" Forecast

When you combine these four layers, you don't just get a prediction—you get interpretability. Instead of saying, "Sales will be up 5%," you can say:

"We expect a 5% increase: 2% from our organic Local Trend, 4% from Seasonality, offset by a 1% dip because we aren't running the same Dynamic Regressor (promo) as last year."

That is the difference between a data point and a strategy.

What’s your "Layer 5"? In your industry, what is the one variable that always throws off your forecast? Let’s talk about it in the comments! 👇

05/04/2026

Welcome to IntelligentSupply Chain!🚀 Supply Chain Analytics Case Study | Conditional Probability for Stockout OptimizationStruggling with stockouts, supplie...

05/04/2026

Are you making supply chain decisions based on incomplete data? 📦📊

In a recent case study, we analyzed a 72% unconditional stockout probability. On the surface, that number is terrifying. But the "unconditional" view rarely tells the whole story.

By applying Conditional Probability, we discovered that the risk wasn't uniform. By shifting orders to Supplier S3, we could immediately optimize that probability down to 50%.

Key Takeaways from the Optimization Session:

The "Reverse" Forecasting Shift: Stop splitting monthly forecasts into equal weekly buckets. The most accurate approach is "Bottom-Up": Start with daily demand $\rightarrow$ Aggregate to weekly $\rightarrow$ Finalize monthly.

Identifying High-Risk Zones: Using SUMPRODUCT and COUNTIFS, we isolated SKUs where Lead Time (> 15days) and Forecast Error (> 30%) overlapped. This revealed a 75% stockout risk for specific items (SKUs 2, 3, and 5) that required immediate intervention.

Data-Driven Prescription: Don't just identify the problem—solve it. For high-risk SKUs, the mandate is clear: tighten lead-time controls or diversify to more reliable suppliers.

Technical Tip: If you're still using nested IF statements for complex conditions, try the SUMPRODUCT method with double-unary (--) conversion. It’s faster, cleaner, and much easier to audit.

How are you using probability to guide your procurement strategy this year? Let's discuss in the comments. 👇