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Supply Chain AI & Analytics Services

Services engineered for your industry's supply chain.

Retail, e-commerce, manufacturing, agri-industry, food & beverage, fashion & textiles — six specialised services delivered by practitioners who write the code, own the models with you, and prove ROI in weeks.

🏭 Find your industry ↓ 💬 Free 30-min diagnostic
Built for your industry

Six industries. Six playbooks. One diagnostic-first method.

Generic supply chain advice fails because retail demand doesn't behave like a harvest, and a garment size-curve is nothing like a production schedule. Pick your industry — see the exact pains we solve, what we deploy, and the numbers it moves.

🛒

Retail

Stores · DCs · Omnichannel

😖 The pains we hear

  • Promo-driven demand spikes that break the baseline forecast
  • Store-level stockouts while DCs sit on excess
  • Margin lost to markdowns and emergency transfers

⚙️ What we deploy

  • Demand sensing with promo, price & holiday features (XGBoost/LSTM)
  • Store–DC multi-echelon replenishment with dynamic safety stock
  • Assortment & availability dashboards for category teams (Power BI)
96%+Availability
−30–50%Forecast MAPE
−15–25%Markdown loss
📦

E-commerce

Marketplaces · D2C · Q-commerce

😖 The pains we hear

  • Long-tail SKU explosion that no planner can forecast manually
  • Split shipments and delivery costs eating unit economics
  • Returns flowing back with no analytics on why

⚙️ What we deploy

  • Long-tail & intermittent demand forecasting (Croston, ML ensembles)
  • Automated safety stock & reorder engine across FCs
  • Last-mile routing optimisation and returns analytics
98%+Fill rate
−20–35%Split shipments
−8–15%Cost per order
🏭

Manufacturing

Discrete · Process · Auto-components

😖 The pains we hear

  • Production schedules churned daily by material shortages
  • Raw-material buying on gut feel, WIP piling between lines
  • No single view connecting sales plan to plant capacity

⚙️ What we deploy

  • Material requirement forecasting tied to the demand plan
  • Production scheduling optimisation (OR-Tools, changeover-aware)
  • Plan-adherence, OEE & shortage dashboards; S&OP cadence build-out
+15–25 ptsSchedule adherence
−20–30%Inventory days
−40%Expediting spend
🌾

Agri-Industry

Agri-inputs · Commodities · Farm-to-fork

😖 The pains we hear

  • Supply driven by weather and harvest windows, not by plans
  • Perishable produce losing value at every handling point
  • Mandi/commodity price volatility wrecking procurement budgets

⚙️ What we deploy

  • Harvest & supply forecasting with weather and seasonality features
  • Procurement planning & price analytics for volatile commodities
  • Cold-chain and dispatch routing to cut wastage in transit
−15–30%Post-harvest wastage
−5–12%Procurement cost
↑ measurablyFreshness on arrival
🍽️

Food & Beverage

FMCG foods · Dairy · QSR supply

😖 The pains we hear

  • Short shelf life turning every forecast error into waste
  • Freshness SLAs from modern trade you can’t consistently hit
  • Distributor returns and claims with no root-cause visibility

⚙️ What we deploy

  • Daily demand sensing at SKU–depot level
  • Expiry-aware (FEFO) inventory & production sequencing
  • Route-to-market and dispatch optimisation; returns analytics
−20–40%Expiry waste
97%+OTIF
+1–3 daysFresh-on-shelf days
👗

Fashion & Textile

Apparel · Footwear · Home textiles

😖 The pains we hear

  • Season buys committed months before demand is known
  • Size-curve errors: broken sizes on winners, full runs on losers
  • End-of-season markdowns destroying the margin plan

⚙️ What we deploy

  • Pre-season demand planning with attribute-based ML forecasting
  • Size-pack & allocation optimisation across stores/channels
  • In-season replenishment triggers and markdown analytics
+8–15 ptsFull-price sell-through
−15–25%Markdown value
↑ on top sellersSize availability
📊 Analytics
📈 Forecasting
🤖 Agentic AI
🧠 ML Engineering
⚙️ Optimisation
📋 S&OP
AnalyticsPower BI

SC Analytics & Dashboards

Turn raw ERP data into strategic clarity. We build executive dashboards that track 15+ KPIs in real-time — OTIF, fill rate, inventory turns, forecast accuracy, supplier performance — with SKU-level drill-down and automated threshold alerts.

Request a Demo →See Dashboard Product →
DASHBOARD — KPI OVERVIEWOTIF94.2%FILL RATE97.8%INV TURNS8.4xWEEKLY OTIF TRENDDecision Latency3 days (was 12)Reports Consolidated23 Excel → 1
80%
Faster Reporting
15+
KPIs Tracked
3 days
Decision Time Saved
23→1
Reports Consolidated
Problems We Solve → How We Solve Them
No real-time visibility — teams make decisions on data that is days or weeks old. By the time reports arrive, the opportunity has passed.
23+ disconnected Excel reports taking 3–5 days to compile monthly. Manual copy-paste, formula errors, no version control.
Cannot find root cause of OTIF failures. "We missed target" — but which SKU? Which supplier? Which region? No drill-down capability.
Live Power BI dashboards updated daily. OTIF, fill rate, inventory turns, forecast accuracy — all in one view. Threshold alerts notify before problems escalate.
Automated data pipeline pulls from ERP/WMS, transforms, and publishes. Zero manual effort. Reports appear in seconds, not days.
SKU-level drill-down with root cause waterfall: forecast error? Lead time? Production? Supplier? The dashboard shows exactly where to act.
📊
Power BI Dashboard Design

KPI cards, trend lines, heatmaps, drill-through pages, mobile-responsive layouts.

🔌
ERP/WMS Data Integration

SAP, Oracle, D365, custom databases. SQL-based ETL pipelines with Power Query.

🔔
Automated Alerting

Threshold-based alerts via email/Teams when KPIs breach targets.

🔒
Row-Level Security

Multi-region access control. Each manager sees only their data.

Case Study

Pharmaceutical Distributor

Replaced 23 manual Excel reports with a single live Power BI dashboard. Decision latency dropped from 12 days to 3 days. OTIF improved 6 percentage points within first quarter as teams could see and act on exceptions in real time.

12→3 days
Decision Speed
+6pp
OTIF Gain
23→1
Reports

Still running on Excel reports?

See a live demo built on your KPI structure. Free.

Book Live Demo →
AI / MLDemand Planning

SC Forecasting & Demand Sensing

Full ML forecasting pipelines — from ARIMA through XGBoost, LSTM, Prophet, and foundation models. Automated bias correction, demand sensing with external signals, tracking signal monitoring, and uncertainty quantification.

Request a Demo →See Forecasting Product →
FORECAST ACCURACY — BEFORE vs AFTERSKU-ASKU-BSKU-CSKU-DBefore (38% MAPE)After (19% MAPE)
35%
Avg MAPE Reduction
6x
Faster Reforecast
22%
Safety Stock Savings
Zero
Undetected Bias
Problems We Solve → How We Solve Them
High MAPE (30–55%) inflating safety stock and locking working capital. Every 5% error = 10–15% excess cost.
Undetected forecast bias compounding monthly. Over-forecast bias of 12% creates $120K excess/month on a $1M SKU.
Management overrides degrade accuracy in 60–70% of cases. No FVA measurement to prove it.
Ensemble ML models auto-select best performer per SKU. XGBoost, LightGBM, Prophet, LSTM compete — accuracy improves 20–40%.
Automated tracking signal monitors CFE and triggers recalibration when |TS|>4. Bayesian bias correction applied monthly.
FVA analysis measures every process step's contribution. Steps that degrade accuracy are flagged and eliminated.
Case Study

FMCG Distributor — 14,000 SKUs

Reduced MAPE from 38% to 19% in 4 months. Bias-corrected forecasting eliminated $2.4M in annual excess inventory. Auto-recalibration triggers when tracking signal exceeds ±4.

38→19%
MAPE
$2.4M
Capital Freed
4 months
Time to Value

Where is your forecast bias hiding?

Get a free accuracy audit — we'll show you exactly where the errors are.

Request Free Audit →
Agentic AIAutomation

Supply Chain Agentic AI

Multi-agent systems for procurement automation, inventory replenishment bots, supplier risk agents and S&OP copilots. Built on LangGraph, AutoGen & CrewAI with human-in-the-loop governance.

Request a Demo →See Copilot Product →
EXCEPTION HANDLING — COPILOT IMPACT55%Auto-ResolvedAuto-Resolved (55%)Copilot-Assisted (25%)Human Required (20%)PLANNER TIME FREED40%
55%
Auto-Resolved
4x
Faster Response
40%
Planner Time Freed
24/7
Continuous Monitoring
Problems We Solve → How We Solve Them
Planners spend 70% of time firefighting — chasing exceptions, expediting, responding to alerts manually.
Decision latency 10–21 days because exceptions queue. By the time someone acts, the disruption has cascaded.
200+ alerts/day — planners can't distinguish critical from noise. Important signals get buried.
55% of exceptions auto-resolved by pre-approved playbooks. Reorders, escalations, alerts — handled autonomously.
Response in minutes, not days. Copilot detects anomaly, generates action, executes or routes for one-click approval.
Priority-ranked exception queue with revenue impact scoring. Top 10 critical actions, not 200 noise alerts.
Case Study

Electronics OEM — 6,200 SKUs, 4 Regions

55% of supply exceptions auto-resolved. Planner capacity freed 40%, redirected to strategic sourcing. Decision latency dropped from 12 days to under 4 hours for critical alerts.

55%
Auto-Resolved
12d→4h
Response Time
40%
Capacity Freed

Let AI handle the exceptions.

See how the Copilot resolves your top 10 supply chain exceptions.

Request Copilot Demo →
EngineeringMLOps

SC ML Engineering & MLOps

Production-grade ML systems purpose-built for supply chain. Feature engineering on transactional data, model deployment, monitoring, retraining. MLflow, Prefect, Docker and FastAPI pipelines that deliver value in production — not just notebooks.

Talk to Us →
ML PIPELINE — SUPPLY CHAINData IngestionERP / WMS / CSVFeature EngineLag, rolling, calendarModel TrainingXGBoost / LSTM / ProphetModel RegistryMLflow trackingMonitoringDrift / accuracy alertsAPI ServingFastAPI / DockerPrefect Orchestration · CI/CD · Auto-Retrain on Drift · 99.5% Uptime
10x
Faster Deployment
99.5%
Pipeline Uptime
CI/CD
Auto-Retrain
0
Manual Retraining
Case Study

Logistics Company — 3 ML Models in Production

Deployed demand forecasting, ETA prediction, and carrier allocation models via FastAPI + Docker. MLflow tracks 50+ experiments. Prefect orchestrates daily retraining. Models serve 15,000 predictions/day with 99.5% uptime. Zero manual intervention for 8 months.

15K
Predictions/Day
99.5%
Uptime
8 months
Zero Manual

Your ML models should run in production, not in notebooks.

Let us build the pipeline. You focus on the business.

Discuss Your ML Pipeline →
OptimisationOperations Research

Supply Chain Optimisation

Mathematical and AI-driven optimisation across inventory, transport, production, warehouse and procurement. OR-Tools, PuLP, Pyomo for large-scale combinatorial problems.

Request a Demo →See Inventory Product →
OPTIMISATION IMPACT ACROSS DOMAINSInventory SS-28% (optimal)currentTransport Cost-12% (VRP optimised)Production OEE+18% (scheduling)Warehouse Pick+25% (slotting)Procurement-15% spend (allocation)
28%
SS Reduction
12%
Transport Cost Down
$3.2M
Capital Freed
5
Domains Optimised
Case Study

Auto Parts + FMCG — Multi-Domain Optimisation

Inventory: 31% SS reduction ($3.2M freed), fill rate 93%→98.5%. Transport: 12% cost reduction ($890K/year) across 8 DCs. Production: changeover time cut 22%, plan adherence 81%→94%. All using OR-Tools, PuLP, and custom Python optimisation.

$4.1M
Total Savings
5
Domains
6 months
Implementation

How much working capital is trapped in your inventory?

Free inventory health assessment with savings quantified.

Request Free Assessment →
PlanningConsulting

S&OP Analytics & Planning

End-to-end S&OP process redesign: demand review, supply review, pre-S&OP, executive S&OP. Integrated with AI-driven scenario planning, consensus forecasting, and FVA analysis.

Talk to Us →
S&OP CYCLE — MONTHLY CADENCEWeek 1DemandReviewWeek 2SupplyReviewWeek 3Pre-S&OPAlignmentWeek 4ExecutiveS&OPAI-POWERED SCENARIO PLANNINGBest CaseBase CaseDisruption Case
15%
Accuracy Gain
6pp
OTIF Improvement
30%
Planning Time Saved
70%
Ad-Hoc Decisions Reduced
Case Study

Industrial Manufacturer — Multi-Site

Implemented structured S&OP across 3 manufacturing sites. Forecast accuracy improved 15% through FVA-driven cleanup. Executive S&OP reduced ad-hoc decision-making by 70%. OTIF improved from 88% to 94% in two quarters.

88→94%
OTIF
-70%
Ad-Hoc Decisions
2 quarters
Time to Value

Is your S&OP process driving decisions or just generating slides?

We'll assess your current S&OP maturity and design a roadmap.

Request S&OP Assessment →

⚙️ CSCOP — Certified Supply Chain Optimisation Professional

12 weeks · 96 hours · Python-powered optimisation (LP, MIP, VRP) · 15+ case studies from Amazon, P&G, DHL, Maersk · Capstone + industry panel

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📰 Latest from Mathnal Insights

Supply Chain Intelligence You Can't Afford to Miss

Issue #10 · Crisis Analysis

Iran-Israel-USA War: Quantified Supply Chain Impact & 12 Mitigation Strategies

Hormuz closure disrupts 20% oil, 34% helium, 46% urea. Brent +55%, freight +50%. Every route, cost & mitigation quantified.

Issue #9 · ESG & Scope 3

ESG Compliance & Scope 3: Genuinely Compliant or Exposed to Greenwashing Risk?

EU CSRD fines 5% revenue, UK CMA 10% turnover, 150+ US lawsuits. 6 regulations, 8 warning signs, 6-pillar compliance framework.

Free Tool · Interactive Simulator

Supply Chain Risk & Resilience Simulator (SCRRS)

Bayesian risk engine, 45 scenarios, Monte Carlo simulation, VaR/CVaR — simulate the Hormuz crisis on your supply chain.

View all 10 newsletters →  |  Free diagnostic tools →  |  CSCOP Certification →

Frequently Asked Questions

Everything you need to know
What supply chain AI services does Mathnal Analytics offer?
Mathnal offers six core services: SC Analytics and Dashboards (Power BI, Python, SQL), SC Forecasting and Demand Sensing (XGBoost, LSTM, Prophet), Supply Chain Agentic AI (LangGraph, AutoGen, CrewAI), SC ML Engineering and MLOps, Supply Chain Optimisation (inventory, transport, production, warehouse, procurement using OR-Tools and PuLP), and S&OP Analytics and Planning.
What is supply chain agentic AI?
Agentic AI in supply chain refers to autonomous AI agents that monitor exceptions, generate corrective actions, and execute pre-approved responses without human intervention. Applications include automated reorder triggers, supplier escalation bots, and S&OP copilots. Companies deploying agentic AI report 55% of supply exceptions auto-resolved and 40% of planner time freed for strategic work.
What supply chain services does Mathnal offer?
Mathnal offers ML demand forecasting and demand sensing, inventory and network optimisation, agentic AI systems and automation, ML engineering and MLOps, S&OP transformation, and Power BI analytics dashboards — each combining machine learning with mathematical optimisation.
How does Mathnal price its consulting services?
Pricing is scoped per engagement based on complexity, data readiness and duration. Mathnal offers focused pilots (single category or process) as well as enterprise-wide programs. Contact the team via WhatsApp or email for a tailored quote.
How long does a typical consulting engagement take?
A focused pilot — such as a demand-forecasting model for one product category — can deliver results in a few weeks. Enterprise optimisation or S&OP transformation programs run longer, scoped around your integration and change-management needs.
Does Mathnal work with companies outside India?
Yes. Mathnal is based in Hyderabad but works with clients internationally, with payment options including UPI, PayPal and Wise. Engagements are delivered remotely with regular video collaboration.
What industries does Mathnal serve?
Mathnal focuses on six industries: retail, e-commerce, manufacturing, agri-industry, food & beverage, and fashion & textile — each with a dedicated playbook covering demand forecasting, inventory, logistics and S&OP. Adjacent sectors like pharma and auto-components are also served.
Does Mathnal work with retail and e-commerce supply chains?
Yes — retail and e-commerce are core focus industries. Mathnal deploys promo-aware demand sensing, store/FC multi-echelon inventory optimisation, last-mile routing and availability dashboards, with typical results of 30–50% forecast error reduction and 96%+ availability.
Does Mathnal work with manufacturing supply chains?
Yes. For manufacturers Mathnal builds material requirement forecasting, changeover-aware production scheduling (OR-Tools), procurement optimisation and monthly S&OP — typically improving schedule adherence 15–25 points and cutting inventory days 20–30%.
Does Mathnal work with agri-industry and food & beverage supply chains?
Yes — agri and F&B supply chains are a specialisation. Mathnal deploys weather-aware harvest/supply forecasting, perishability-aware (FEFO) inventory, cold-chain routing and freshness-first production sequencing, typically cutting wastage 15–40%.
Does Mathnal work with fashion and textile supply chains?
Yes. For apparel and textiles Mathnal builds attribute-based pre-season demand planning, size-pack and allocation optimisation, and in-season replenishment/markdown analytics — typically lifting full-price sell-through 8–15 points.