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Quantified outcomes · 9 case studies

Supply Chain AI in the Real World — Quantified ROI Across 9 Engagements

Every Mathnal Analytics engagement is measured against a baseline. Below are nine anonymised case studies covering forecasting, inventory, transport, procurement, S&OP, agentic AI and risk — with hard numbers, not anecdotes.

What ROI to expect

Mathnal Analytics supply chain AI engagements have delivered 12-38% reductions in core supply chain costs or errors: MAPE down 25-38%, safety stock down 18-28%, transport cost down 8-12%, RFQ cycle 60-70% faster. ROI is typically realised within 6-9 months of go-live. All cases below are anonymised by industry and scale to respect client confidentiality.

38%
MAPE cut (best)
₹14Cr
Working capital freed
70%
Faster RFQ cycle
9
Industries covered
📂 9 case studies

Hard numbers, not anecdotes

Each case study lists the client challenge, the Mathnal approach, the technology stack used and the quantified outcomes after go-live.

FMCG · ₹1,200Cr · India

From Spreadsheet Forecasts to ML-Driven Demand Sensing

Challenge

A multi-state FMCG distributor was running monthly Excel-based forecasts with MAPE hovering at 42%. Stockouts were 8% in fast-moving SKUs, while slow-movers carried 11 months of dead stock.

Approach

Mathnal deployed a hierarchical ML stack: ETS & SARIMA baselines, XGBoost with calendar + price features, LSTM ensemble for top-50 SKUs. Forecast Value Added (FVA) used to audit planner overrides.

-38%
MAPE
-65%
Stockouts
XGBoost LSTM FVA Python
Pharma · ₹3,400Cr · Multi-site

Bayesian Safety Stock Across 4,200 SKUs

Challenge

A pharma manufacturer carried safety stock based on rule-of-thumb (2× average demand × LT). With volatile API supply and 4,200 SKUs, working capital had ballooned by ₹38Cr in 18 months.

Approach

Multi-echelon inventory optimisation (MEIO) with Bayesian posterior P(stockout) per SKU. (s,S) policies for A-items, (R,Q) for B/C, lean make-to-order for slow-Z. Service-level differentiation by ABC class.

-28%
Safety stock
+4pp
Service level
Bayesian MEIO ABC-XYZ
3PL Logistics · 220 vehicles · India

VRPTW Routing & Live Dispatch Optimisation

Challenge

A 3PL operator was dispatch-planning manually with 220 trucks across 5 hubs. OTIF was 81%, route utilisation 64%, empty backhaul 27%.

Approach

VRPTW formulation with OR-Tools, time-windows, vehicle-capacity and driver-hour constraints. Live re-optimisation every 2 hours via ETA feedback. Backhaul matching engine for return-trip loads.

-12%
Transport cost
+9pp
OTIF
VRPTW OR-Tools Live ETA
Auto Parts · OEM Tier-1 · India

Agentic AI for RFQ Negotiation & Award

Challenge

A Tier-1 auto-parts manufacturer was running 1,400+ RFQs/month with 3-supplier-quote rules. RFQ cycle averaged 11 days, supplier scoring was subjective, award decisions inconsistent.

Approach

LangGraph-based agentic AI for RFQ orchestration: agents handle supplier outreach, quote parsing, scoring on 7 dimensions, scenario simulation. Human-in-the-loop approval on awards > ₹10L.

-70%
RFQ cycle
-6.2%
Purchase cost
LangGraph Agentic AI Procurement
Apparel Retail · ₹680Cr · 142 stores

End-to-End S&OP Transformation

Challenge

An apparel brand ran disconnected demand, supply and finance plans. Markdown losses were 14% of revenue, end-of-season stock 31% of starting inventory.

Approach

Built unified S&OP cadence with consensus demand (ML + commercial overrides), supply allocation MIP, financial reconciliation. Power BI executive S&OP dashboard with scenario simulation.

+24pp
Forecast accuracy
-9pp
Markdown losses
S&OP MIP Power BI
E-Commerce · 4 DCs · India

Warehouse Slotting & Pick-Path Optimisation

Challenge

Peak-season pick times at 4 fulfilment centres hit 11 minutes per order. SLA breaches were costing ₹2.4Cr/month in escalations and refunds.

Approach

Slotting optimisation by velocity + correlation. S-shape and largest-gap pick routes via shortest-path on aisle graph. Cluster picking for multi-item carts. WMS rule engine refresh.

-31%
Pick time
+18%
Throughput
Slotting VRP WMS
Specialty Chemicals · ₹1,800Cr

Multi-Echelon Inventory Optimisation

Challenge

Specialty chemicals manufacturer with 3-tier network (plants → CFAs → distributors). Tier-level inventory was being managed independently, with 5.8 months of total system inventory.

Approach

End-to-end MEIO with stochastic lead times. Joint optimisation of plant-CFA-distributor safety stock. Pyomo + Gurobi solver, weekly refresh. Service-level guard rails per business segment.

₹14Cr
WC freed
-1.6mo
Inv days
MEIO Pyomo Gurobi
Electronics · ₹4,500Cr · Multi-country sourcing

Bayesian Supply Chain Risk & VaR

Challenge

An electronics OEM faced semiconductor lead-time spikes (12 → 38 weeks). With 11 single-source critical components, exposure was material but unquantified.

Approach

Bayesian risk modelling per supplier × component, Monte Carlo simulation for portfolio VaR, mitigation optimiser (dual-source, safety stock, contracts) under cost cap. Quarterly refresh.

-42%
Portfolio VaR
-31%
CVaR (tail)
Bayesian Monte Carlo VaR
Higher Education · CSCOP Cohort #3

CSCOP Capstone — 14 Industry Projects in 12 Weeks

Challenge

A B-school partnered with Mathnal to upskill 32 working executives on supply chain optimisation. Programme had to combine theory, Python labs and industry-grade capstones.

Approach

12-week CSCOP curriculum: LP/MIP/NLP, VRP, network design, Bayesian risk. 4 capstone tracks (FMCG distribution, airline spares, e-commerce fulfilment, supplier risk), each presented to industry panel.

14
Industry projects
94%
Placement / promotion
CSCOP Training Capstone

Aggregate ROI across these 9 engagements

Average across all cases — not best-in-class. Your mileage will vary by data quality and change-management capacity.

26%
Avg cost reduction
5.4x
Avg ROI multiple
7 mo
Avg payback
100%
Knowledge transfer

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Frequently Asked Questions

Everything you need to know
What ROI can I expect from a Mathnal supply chain AI engagement?
Typical Mathnal engagements have delivered 12-38% reductions in core supply chain costs or errors: MAPE down 25-38%, safety stock down 18-28%, transport cost down 8-12%, RFQ cycle time down 60-70%. ROI is usually realised within 6-9 months of go-live.
Does Mathnal publish detailed client names?
Mathnal anonymises all case studies by industry, geography and scale to respect client confidentiality. We are happy to introduce qualified prospects to a relevant reference client under mutual NDA.
What industries has Mathnal worked with?
Mathnal Analytics has delivered supply chain AI projects across FMCG, pharma, 3PL logistics, automotive, apparel, e-commerce, chemicals, electronics, and higher education capstone partnerships. We work with companies of all sizes — from ₹50Cr regional brands to ₹5,000Cr+ enterprises.
What results has Mathnal delivered for clients?
Mathnal's case studies document quantified outcomes across nine engagements — including forecast accuracy improvements (MAPE reductions), inventory reductions of 15-30%, and network and transport cost savings — each tied to a specific supply chain problem and solution.
How does Mathnal measure project ROI?
ROI is measured against baseline metrics agreed at project start — forecast error, inventory holding cost, service level, transport cost, or schedule adherence — and tracked through to post-deployment results, expressed as both percentage improvement and financial value.
Can Mathnal share references from past clients?
Mathnal can discuss relevant engagement experience and, where client permission allows, connect prospective clients with references. Reach out via WhatsApp or email to arrange a conversation.
How long before a project shows measurable results?
Many engagements show measurable improvement within the first pilot phase (weeks), with full benefits realised as models are deployed and adopted across the organisation over subsequent months.
What makes Mathnal's approach different?
Mathnal combines machine learning with mathematical optimisation and a 'Decisions from Evidence' philosophy — building solutions on each client's actual data and constraints rather than generic templates, and quantifying every recommendation.