Your supply chain manager has twenty years of experience, deep vendor relationships, and is still forecasting demand with last quarter's averages in Excel.
We build AI systems that predict demand shifts before they hit, optimize inventory across every warehouse and SKU, flag supplier risks weeks ahead, and cut logistics costs. Your supply chain team stops firefighting and starts planning.
Your supply chain is the most expensive guessing game in the company.
Your demand forecasts are wrong, and everyone knows it
Your planning team builds demand forecasts using historical averages and seasonal adjustments. Forecast accuracy sits at 55-65%, meaning you are wrong on a third of your SKUs in any given month. Demand spikes trigger emergency procurement at premium prices. Drops leave you sitting on inventory bleeding carrying costs at 18-25% annually. Traditional forecasting cannot pick up non-linear patterns, external signals, or channel-level variations that actually drive your business.
You have $400,000 in inventory and no idea which half is wrong
Your warehouse has too much of what is not selling and too little of what is. Your reorder point model treats every SKU the same: same safety stock formula, same lead time assumption. The result is 8-12% stockout rates on top movers while slow-moving inventory accumulates. Carrying costs run 20-30% of inventory value annually. Your ERP tells you what is in the warehouse. It does not tell you what should be.
Your supplier risks are invisible until they become crises
You found out your sole-source supplier was in financial trouble when they missed a delivery by two weeks. You discovered a compliance violation when a shipment was held at customs. Most mid-market companies discover supplier disruptions after the damage is done. Your team monitors a fraction of the risk surface through phone calls and quarterly reviews. Financial health, regulatory exposure, sub-tier dependencies: all invisible until they become a production stoppage.
Logistics costs are climbing and you cannot explain why
Transport costs are up 22% over two years while shipment volume is up only 12%. Nobody can explain the gap. Your logistics team plans routes based on experience and habit, not data. Partial truckloads go out because dispatchers prioritise speed over cost. Return trips run empty. For a company spending $500-$750K annually on logistics, route and load optimization typically saves 10-15%.
From supply chain audit to deployed AI system in eight weeks
We do not sell you a supply chain platform. We study your specific demand patterns, inventory dynamics, supplier network, and logistics operations, then build AI systems that fit your business, not a generic model.
Supply Chain Diagnostic
Days 1-5
We sit with your supply chain, procurement, and warehouse teams: the people who actually manage purchase orders, inventory counts, and delivery schedules. We map every process. How demand plans get built. How reorder decisions are made. How supplier performance is tracked. How logistics are planned. We quantify the cost of current inefficiencies: forecast error rates, stockout frequency, carrying costs, logistics spend per unit. Most companies find their supply chain is leaking far more margin than they thought.
Deliverable: Supply Chain Process Map with cost-of-inaction analysis and AI opportunity scoring by ROI
Data Assessment & Model Design
Days 6-12
We audit your supply chain data across every source: ERP transaction history, POS data, warehouse management logs, supplier delivery records, logistics tracking feeds. We check what signals are available beyond historical sales (weather, market indices, promotional calendars, channel-level trends). Then we design the model architecture. The type of forecasting, optimization, or risk model fits your actual data, not a theoretical ideal.
Deliverable: Data readiness report, model architecture document, and integration plan for your ERP and WMS
System Build & Integration
Days 13-45
We build the AI system (demand forecasting engine, inventory optimizer, supplier risk monitor, or logistics planner) and integrate it with your existing tools. SAP, Oracle, Tally, custom ERPs, warehouse management systems, transport management software. The system trains on your actual data, learns your specific demand patterns and supply variability, and handles your edge cases: long-tail SKUs, seasonal spikes, regional demand variations, multi-warehouse allocation logic.
Deliverable: Working AI system deployed in your environment with live integration to ERP, WMS, and logistics systems
Parallel Run, Training & Handover
Days 46-56
We run the AI system alongside your existing planning process for two weeks. Your team compares AI recommendations against their manual decisions and validates accuracy. We calibrate the model based on real-world feedback, train your supply chain team on interpreting outputs and managing exceptions, and hand over full operational control. By the end, your team owns the system and understands exactly when to trust it and when to override it.
Deliverable: Parallel run accuracy report, team training completion, exception handling playbook, and monitoring dashboard
Supply Chain Diagnostic
Days 1-5
We sit with your supply chain, procurement, and warehouse teams: the people who actually manage purchase orders, inventory counts, and delivery schedules. We map every process. How demand plans get built. How reorder decisions are made. How supplier performance is tracked. How logistics are planned. We quantify the cost of current inefficiencies: forecast error rates, stockout frequency, carrying costs, logistics spend per unit. Most companies find their supply chain is leaking far more margin than they thought.
Deliverable: Supply Chain Process Map with cost-of-inaction analysis and AI opportunity scoring by ROI
Data Assessment & Model Design
Days 6-12
We audit your supply chain data across every source: ERP transaction history, POS data, warehouse management logs, supplier delivery records, logistics tracking feeds. We check what signals are available beyond historical sales (weather, market indices, promotional calendars, channel-level trends). Then we design the model architecture. The type of forecasting, optimization, or risk model fits your actual data, not a theoretical ideal.
Deliverable: Data readiness report, model architecture document, and integration plan for your ERP and WMS
System Build & Integration
Days 13-45
We build the AI system (demand forecasting engine, inventory optimizer, supplier risk monitor, or logistics planner) and integrate it with your existing tools. SAP, Oracle, Tally, custom ERPs, warehouse management systems, transport management software. The system trains on your actual data, learns your specific demand patterns and supply variability, and handles your edge cases: long-tail SKUs, seasonal spikes, regional demand variations, multi-warehouse allocation logic.
Deliverable: Working AI system deployed in your environment with live integration to ERP, WMS, and logistics systems
Parallel Run, Training & Handover
Days 46-56
We run the AI system alongside your existing planning process for two weeks. Your team compares AI recommendations against their manual decisions and validates accuracy. We calibrate the model based on real-world feedback, train your supply chain team on interpreting outputs and managing exceptions, and hand over full operational control. By the end, your team owns the system and understands exactly when to trust it and when to override it.
Deliverable: Parallel run accuracy report, team training completion, exception handling playbook, and monitoring dashboard
Working systems, not consulting decks
Discovery Phase (Week 1-2)
- Supply chain process map with cost quantification for every manual workflow
- Forecast accuracy baseline measurement across SKU categories
- Inventory health analysis: excess, slow-moving, stockout-prone SKUs identified
- Data quality assessment across ERP, WMS, and logistics systems
- AI opportunity matrix scored by ROI, feasibility, and implementation speed
Build Phase (Weeks 3-6)
- Custom AI system built and deployed: forecasting, inventory optimization, or risk monitoring
- Integration with your ERP, warehouse management, and logistics systems
- SKU-level demand forecasting model trained on your historical and external data
- Dynamic safety stock and reorder point engine replacing static rules
- Real-time dashboard for supply chain KPIs and AI-generated alerts
Validation Phase (Weeks 7-8)
- Two-week parallel run with documented accuracy comparison against manual planning
- Supply chain team training on system usage, interpretation, and exception handling
- Operations playbook for ongoing model monitoring and recalibration
- ROI baseline for measuring ongoing cost savings and service level improvements
Scope boundaries
This engagement focuses on AI systems for your supply chain function. For broader analytics, automation, or operational AI needs, see our related services.
Enterprise-wide BI dashboards and reporting
We build supply chain AI systems that flag what needs attention and recommend what to do about it. If you need company-wide business intelligence, cross-departmental dashboards, or a data warehousing strategy, that is our Data Analytics practice.
Data AnalyticsProcurement and ERP workflow automation
We optimize supply chain decisions with AI: what to order, how much, and when. If you need end-to-end automation of purchase order generation, vendor onboarding, invoice processing, or approval workflows, that falls under our Automation practice.
AutomationOngoing AI model monitoring and retraining
We build and deploy the system, train your team, and hand it over. If you want us to manage ongoing model performance, retraining cycles, drift detection, and system health on a monthly retainer, that is our AI Operations service.
AI OperationsIs this right for you?
Right for you if
- You manage 500+ SKUs and your demand forecasting still runs on Excel averages and sales team gut feel
- You have at least $1.2M in annual revenue and enough transaction volume to train meaningful AI models
- Your stockout rate exceeds 5% or your excess inventory exceeds 20% of total stock value
- You operate multiple warehouses or distribution points and struggle with allocation decisions
- You have experienced supplier disruptions in the past year that cost you production time or customer deliveries
- You want a working system in eight weeks, not a two-year digital transformation programme
Not right if
- You have fewer than 100 SKUs and a single warehouse. Your complexity does not justify AI; a good planning spreadsheet will serve you better.
- You do not have at least 18 months of clean transaction data in your ERP. We need data to train models, and garbage in means garbage out.
- You are looking for an off-the-shelf supply chain platform. We build custom systems, not resell SaaS products.
- Your supply chain team is not willing to spend time during the discovery and validation phases. We cannot build effective systems without domain input.
What these engagements look like in practice
Problem
A mid-market auto components manufacturer with $18M revenue and 1,800 SKUs was forecasting demand using three-month rolling averages. Their forecast accuracy sat at 58%. This drove chronic overproduction of slow movers and frequent stockouts on high-margin parts. Emergency procurement was costing them $35,000 annually in premium freight and spot-market raw material pricing. Their three warehouses held $960K in inventory, of which $200K was slow-moving stock older than six months.
What we did
Built a demand forecasting engine using gradient-boosted models trained on 30 months of order history, overlaid with OEM production schedules, seasonal patterns, and commodity price signals. Integrated the system with their SAP instance and added dynamic safety stock calculations that varied by SKU criticality, lead time variability, and demand volatility.
Outcome
Forecast accuracy improved from 58% to 84%. Stockouts on A-class SKUs dropped by 62%. Emergency procurement costs fell by $23,000 annually. Overall inventory carrying cost reduced by 22%, freeing up $135,000 in working capital within the first six months.
Problem
A D2C beauty and personal care brand doing $7M in annual revenue across their own website, Amazon, Nykaa, and Flipkart was managing inventory allocation across four sales channels manually. Each channel had different demand patterns, return rates, and lead time requirements. Their planning team spent 12 hours a week in spreadsheets trying to figure out where to allocate limited stock. Stockouts on hero SKUs during sale events were costing them an estimated $55,000 in lost revenue per quarter.
What we did
Built a multi-channel demand forecasting and inventory allocation system that predicts demand at the channel-SKU level, accounts for promotional events and marketplace sale calendars, and automatically generates allocation recommendations across all four channels and two warehouses. The system pulls real-time sell-through data from marketplace APIs and adjusts allocation daily.
Outcome
Channel-level forecast accuracy reached 81%, up from 54%. Stockouts during sale events dropped by 71%. Allocation planning time went from 12 hours to 45 minutes per week. Revenue recovery from reduced stockouts contributed an estimated $150,000 in the first two quarters.
Problem
An FMCG company with $24M revenue distributing through 4,500 retail touchpoints was experiencing a 9% stockout rate at distributor level while simultaneously carrying 35 days of excess inventory on slow-moving variants. Their demand planning relied on distributor-reported secondary sales data that arrived two weeks late and was often inaccurate. Regional sales managers were overriding forecasts based on personal targets, inflating demand estimates by 15-20% consistently.
What we did
Built a demand sensing system that combines primary sales data from the DMS, secondary sales approximations from retailer POS data, weather data for seasonal categories, and regional event calendars. Added a bias correction layer that detects and adjusts for systematic forecast inflation by region. Integrated with their distribution management system to generate daily replenishment recommendations at the distributor level.
Outcome
Distributor-level stockout rate dropped from 9% to 3.4%. Excess inventory reduced by 28%, releasing $350,000 in working capital. Forecast bias from regional overrides was eliminated, improving plan accuracy from 61% to 83%. Distributor fill rates improved to 96%, up from 88%.