Supply Chain Optimisation: Cut Costs & Boost Efficiency With AI Tools
Ever wonder why your favourite product suddenly vanishes from shelves during festive seasons? Or why do fresh vegetables cost twice as much after a good rain? That's your supply chain speaking, or rather, screaming for help.
Here's what keeps supply chain managers awake at night: India wastes ₹1.52 lakh crore worth of agricultural produce annually. Meanwhile, 86% of executives globally plan to invest in AI and analytics for cost reduction across their supply chains in 2025. So the question is, how fast can you optimise your supply chain process before your competition does?
But where do you even start? With inventory piling up in one warehouse while another runs empty? With transport costs eating into margins?
Let's cut through the complexity and show you exactly how supply chain optimisation can give your company a competitive advantage.
What is supply chain optimisation?
Supply chain optimisation is the strategic process of maximising efficiency and minimising costs across your entire network. Right from raw material sourcing through final delivery.
Think of it as fine-tuning every moving part in your business engine to work in perfect harmony.
But what is optimisation? It means finding the sweet spot where service quality meets cost efficiency.
You're orchestrating a complex balance of inventory, transportation, warehousing, and information flow. When done right, businesses see stockouts drop, working capital shrink, transport costs plummet, and forecast accuracy improves.
In India's context, where the supply chain market is projected to reach $5.55 billion by 2032[1] (growing at 11.12% CAGR), SCM optimisation is crucial.
Why do you have to optimise the supply chain process?
Why should you care about supply chain process improvements? Because the numbers don't lie. Companies implementing comprehensive optimisation strategies see transformative results across multiple dimensions.
First, let's talk benefits that matter to your bottom line:
- Stockouts decrease: Real-time visibility and demand forecasting mean you actually have what customers want when they want it.
- Working capital reduces: Less dead inventory sitting in warehouses equals more cash for growth.
- Transport and handling costs drop: Better routing, consolidation, and load optimisation make every rupee count.
- Waste and spoilage reduce: Particularly crucial in India, where cold chain gaps cause massive losses.
- Forecast accuracy jumps: AI-driven models beat gut feelings every single time.
If these sound familiar, start by diagnosing your supply chain bottlenecks before layering advanced models.
Is your logistics optimisation working though?
But how do you know if your optimisation efforts are working? Track these KPIs religiously:
- Fill Rate: Percentage of customer demand met from available stock (target: >95%)
- OTIF (On-Time In-Full): Orders delivered complete and punctual (benchmark: 90%+)
- WAPE/WMAPE & Bias: Weighted forecast accuracy metrics that reveal planning precision
- Inventory Turns: How often you sell and replace inventory annually (higher is better)
- Dwell Time: How long products sit idle in your supply chain
- Cost per Order: Total fulfilment cost divided by order volume
Use real-time analytics to surface stockout risk, dwell spikes, and WAPE drift as they happen.
The 7C's of supply chain management
The 7C's of supply chain management provide a practical framework that maps perfectly to modern challenges. It's a proven approach used by leading logistics optimisation experts globally.
1. Connect: Integration across your entire network. How well do your suppliers talk to your warehouses? Can your transport providers see inventory levels? Connection means breaking silos, whether between departments, systems, or partners.
2. Create: Innovation and value addition throughout the chain. This means developing new solutions for old problems, like using AI for route planning.
3. Customise: Segmentation and personalisation for different customer needs. Your B2B clients need different service levels than D2C customers. One-size-fits-all approach doesn’t work anymore, and today's supply chain optimisation demands tailored approaches.
4. Coordinate: Orchestration of all moving parts. Production schedules must sync with transport availability, warehouse capacity needs to match seasonal demand. Without coordination, you're just managing chaos.
5. Consolidate: Pooling resources for efficiency. Whether it's combining shipments, centralising optimal procurement, or merging supplier contracts, consolidation eliminates redundancy and drives scale benefits.
6. Collaborate: Partnerships across the ecosystem. Collaboration between states, suppliers, and logistics providers is crucial.
7. Contribute: Sustainability and social impact. Modern supply chains need to go green and balance profit with purpose, reducing carbon footprint while ensuring farmer welfare and fair wages.
Supply chain optimisation: Core models and techniques
These supply chain optimisation models are tested approaches delivering real results across different industries.
Network design and flow optimisation
Multi-echelon planning and facility location problems sound complex, but they answer simple questions: Where should you place warehouses? How should products flow through your network?
Mixed-integer programming helps solve these puzzles. Take the classic warehouse location problem - you need to balance fixed costs (opening facilities) against variable costs (transportation).
For transport mode mix, the question becomes: When to use rail versus road? Air versus sea? Optimisation algorithms factor in speed requirements, costs, and capacity constraints to find the perfect blend.
Inventory optimization
Safety stock calculations, reorder points, and multi-stage optimisation keep products flowing without tying up working capital. But here's where it gets interesting - the newsvendor model[2], perfect for perishable goods and uncertain demand.
Let’s say you're selling fresh produce with a one-day shelf life. Order too much, you lose money on spoilage. Order too little, you miss sales.
The newsvendor formula finds that critical fractile - the exact quantity balancing overage costs against underage costs.
Production and scheduling
Finite-capacity planning ensures you're not promising what you can't deliver. Sequence setups to minimise changeover waste, which is critical when machine downtime costs thousands per hour.
Promotion/price uplift
Causal models separate baseline demand from promotional lift.
Why does this matter? Because without it, you can't tell if that Diwali sale actually drove incremental sales or just shifted timing.
Risk and resilience
Scenario planning for lead-time variance, dual-sourcing strategies, strategic buffers - these aren't nice-to-haves anymore.
Recent surveys[3] show 90% of executives view supply chain flexibility as critical. Models help quantify trade-offs: How much extra does dual-sourcing cost versus the risk of single-supplier disruption?
How to use AI for supply chain optimisation?
What changes when AI enters your supply chain process? Everything and nothing. The goals remain the same - efficiency, cost reduction, service improvement. But the scale and speed change with supply chain optimisation technologies.
Planning and inventory: AI reduces inventory through superior segmentation, demand forecasting, and parameter tuning. Machine learning models catch patterns humans miss.
Dynamic execution: Real-time routing, dock scheduling, exception handling - AI makes thousands of micro-decisions hourly. When a truck breaks down, AI instantly recalculates routes for the entire fleet.
Decision intelligence: This combines optimisation algorithms, ML predictions, and business rules into recommendation engines. Should you accept that rush order? Which supplier should fulfil it? What's the optimal price point? AI provides answers with confidence scores and reasoning.
Companies seeing real ROI from AI report organisational changes were as important as the technology itself.
Check out how AI is changing supply chains across the globe
Logistics and supply chain optimisation examples
Here are supply chain optimisation examples with real numbers and actionable tactics.
Amul: Federated excellence at 35 million litres daily
India's dairy giant collects from 3.64 million farmers across 18,600 villages daily. How? Through federated collection with standardised intake QA at village-level cooperatives plus AI-driven route planning that reduced collection time.
They measure what matters: chilling lead time (target: <4 hours from milking) and rejection rate (maintained below 0.5% through real-time quality testing).
Flipkart: Solving peak surge through kirana networks
During festive peaks when orders jump 10-20x, Flipkart pilots kirana hand-off nodes - converting neighbourhood stores into temporary delivery points.
This supply chain optimisation innovation cuts last-mile costs. They track cost per drop religiously (target: ₹25 in metros, ₹40 in tier-2) and delivery promise kept percentage by PIN cluster (maintaining 94%+ during Diwali).
SleepyHug: From chaos to ₹100 crore
GrowthJockey's case study of SleepyHug shows the brand faced 20% return rates and inventory stuck with 3PL partners.
The turnaround? Strategically placed warehouses, real-time inventory sync using logistics and supply chain optimisation tools, and predictive analytics for demand forecasting.
Result: Returns dropped to 5%, delivery TAT improved to 6 days, and they scaled from ₹50 lakh loss to ₹100 crore ARR in 13 months.
ITC e-Choupal: Changing SCM in agriculture
ITC's procurement kiosks serve 4 million farmers across 35,000 villages. They forecast arrivals using weather data and historical patterns, achieving 85% accuracy in predicting daily procurement volumes.
Real-time price discovery through these hubs reduced price volatility by 40%. Most impressive? Shrink (post-harvest losses) dropped from 15% to 7% through better handling and immediate quality grading.
Blinkit/Zepto: 10-minute magic through micro-optimisation
How do Blinkit and Zepto deliver in 10 minutes profitably? SKU-zone assortment ensures each dark store stocks only items with >3 orders daily in that 2km radius. Pick-path algorithms reduce picker movement by 40%.
They gate new delivery zones ruthlessly - no launch until on-time percentage exceeds 95% and picks per hour hit 30+. This disciplined supply chain optimisation approach helped them achieve positive unit economics in 12 months.
Pick the right supply chain optimisation model for your business
Supply chain optimisation is a journey that changes how you compete. We've covered the frameworks, models, and technologies that turn supply chain chaos into competitive advantage.
At GrowthJockey - a full-stack venture builder, we've helped businesses across sectors implement these supply chain optimisation strategies.
Our Intellsys.ai platform brings together the Connect, Create, and Coordinate elements of modern supply chains, delivering real-time visibility and AI-powered recommendations that drive measurable results.
Let's discuss how our proven frameworks and technologies can deliver 20-30% efficiency gains for your business. Because when supply chains work smarter, businesses grow faster.
FAQs on supply chain optimisation models
Q1. What are the three types of supply chain optimisation models?
The three main types are descriptive models (understanding current state), predictive models (forecasting future scenarios), and prescriptive models (recommending optimal actions).
Q2. What is the first step of supply chain optimisation?
Map your current state completely - document all processes, measure baseline KPIs, and identify the biggest pain points before implementing any changes.
Q3. What is SCO and SCM?
SCO (supply chain optimisation) focuses on improving efficiency and reducing costs through mathematical models and analytics, while SCM (supply chain management) encompasses the broader practice of overseeing the entire flow of goods and services.
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