Learn to diagnose, model, optimize and simulate inventory decisions — from safety-stock science to multi-echelon network optimization — using Excel, Python, SQL and Power BI on real inventory data.
Cut stockouts and excess at the same time. Master ABC/XYZ classification, demand-driven replenishment, safety-stock science and multi-echelon optimisation with Excel, Python and Power BI — on real inventory decisions.
Who should attend
Learn to hold exactly the right stock — not the safe excess that buries working capital.
Safety stock sized to a real service level. Reorder points based on demand variability — not spreadsheet habit.
EOQ, ABC/XYZ segmentation, (s,S)/(R,Q) policies, multi-echelon positioning — the full analytical toolkit.
Excel for modelling, Python for simulation, Power BI for live KPI dashboards. The stack every planning team needs.
Every exercise uses anonymised real inventory: FMCG, spare parts, retail and manufacturing datasets.
Build an inventory health dashboard that tracks turns, days-on-hand, fill rate and GMROI — updated automatically.
Replace static reorder rules with dynamic, data-driven replenishment policies.
Close the loop between forecast accuracy and safety stock sizing.
Understand working capital trade-offs without needing a data science team.
You know Python or SQL — this teaches the inventory science those tools should be solving.
The most practical first course — every formula touches money directly.
Each module: live instruction · real dataset exercise · graded assignment · recording.
ABC/XYZ + turns + DOH + SLOB
Forecast + variability + service level
(s,Q) / (s,S) / (R,Q) compared
LP/MILP using PuLP / Pyomo + HiGHS
Monte Carlo + SimPy scenarios
Power BI + DAX
Management recommendation
M10 isn't a spreadsheet exercise. You formulate and solve a real inventory-optimization model in Python.
Your Monte Carlo + SimPy work in M10 isn't a one-off exercise — it's a repeatable simulator you run against named disruption scenarios.
What happens to inventory, service level and cost?
How much safety stock does that actually require?
Where does the network break first?
What does that cost in working capital?
What's the knock-on effect on cycle stock?
Can the network absorb it, and at what cost?
A realistic enterprise dataset — SKU, category, location, demand, unit cost, lead time and its variability, current inventory, open PO, MOQ, order/holding/stockout cost, service target, supplier, DC, store. Ten steps, one deliverable.
These are teaching cases — scenarios built on published industry practice and patterns from named companies' known operating models, used to teach the method. They are not Mathnal client engagements. For Mathnal's own anonymised client work, see Case Studies →.
4 core tools — Excel · Python · SQL · Power BI. Underneath Python, a full analytics and optimization ecosystem.
Business Tools
EOQ, policy models
Inventory dashboards
Turns, DOH, Fill Rate
Optimization
LP/MILP + HiGHS solver
Analytics & Simulation
pandas, SciPy
Stock & movement
Monte Carlo
Variability models
Analysis environment
Salary varies by experience, location, industry and employer — figures below are industry sectors, not placement commitments.
* Indicative pricing. GST charged additionally at the applicable rate. Early-bird and multi-seat discounts on request.
Enrolment in 3 steps
1. Choose your payment method (UPI / PayPal / Wise) below.
2. Pay, then enter your transaction ID / reference in the form.
3. Submit — you'll get confirmation from Mathnal shortly after.
Mathnal Analytics is a supply chain AI and analytics firm based in Hyderabad. Our instructors apply inventory analytics and optimization to real supply chain problems — every concept is grounded in production use, not theory.
Free up cash. Protect service levels. Build the inventory process your team deserves.
To earn CIOP™, you must:
Minimum passing requirement: 70% in each of the theory, quantitative and Python assessments, plus successful completion of all practical deliverables and the capstone defense.
Core inventory theory and Excel-based policy design.
Python, multi-echelon optimization and the full capstone.
The LP/MILP model, simulation and network design in depth.
Seats per cohort are limited to keep capstone reviews personal. Fill in the form to secure your place or request a corporate proposal.
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