Your supply chain runs on gut feel, last year's numbers, and hope.
AI systems for supply chain teams stuck on spreadsheet forecasts, dead inventory, and reactive supplier management. Custom AI that cuts stockouts by 40-60% for mid-market companies.
The Problem
Your supply chain is the most expensive guessing game in your company.
Your demand forecasts are wrong, and everyone knows it: Your planning team builds demand forecasts from historical averages and seasonal adjustments. Forecast accuracy sits at 55-65%, so you are wrong on a third of your SKUs any given month. Demand spikes trigger emergency procurement at premium prices. Drops leave you sitting on inventory with carrying costs at 18-25% annually. Traditional forecasting cannot pick up non-linear patterns, external signals, or channel-level variations that drive your business.
You have $400K 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 movers pile up. 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.
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 got held at customs. The majority of mid-market companies learn about supplier disruptions after the damage is done. The procurement team monitors a fraction of the risk surface through phone calls and quarterly reviews. Financial health, regulatory exposure, sub-tier dependencies: all invisible until production stops.
Logistics costs keep climbing and nobody can 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 on experience and habit, not data. Partial truckloads go out because dispatchers pick speed over cost. Return trips run empty. For a company spending $500-$750K annually on logistics, route and load optimization typically saves 10-15%.
Our Approach
Supply chain audit to deployed AI in eight weeks We do not sell you a supply chain platform. We study your demand patterns, inventory behaviour, supplier network, and logistics operations, then build AI that fits your business.
Phase 1 — Supply chain diagnostic (Days 1-5): We work with your supply chain, procurement, and warehouse teams. We map every process: how demand plans get built, how reorder decisions happen, how supplier performance is tracked, how logistics are planned. We quantify inefficiency costs: forecast error rates, stockout frequency, carrying costs, logistics spend per unit. Nearly every team finds their supply chain leaks more margin than they assumed. Deliverable: Supply chain process map with cost-of-inaction analysis and AI opportunity scoring by ROI
Phase 2 — Data assessment and 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 exist beyond historical sales (weather, market indices, promotional calendars, channel-level trends). Then we design the model architecture. The 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
Phase 3 — System build and integration (Days 13-45): The output is a working AI system (demand forecasting engine, inventory optimizer, supplier risk monitor, or logistics planner) connected to your existing tools: SAP, Oracle, Tally, custom ERPs, warehouse management systems, transport management software. It trains on your actual data, learns your 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
Phase 4 — Parallel run, training, and 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 checks accuracy. We calibrate the model on live feedback, train your supply chain team to interpret outputs and manage exceptions, and hand over full operational control. By the end, you own the system and know when to trust it and when to override it. Deliverable: Parallel run accuracy report, team training completion, exception handling playbook, and monitoring dashboard
Deliverables
Discovery Phase (Week 1-2)
- Supply chain process map with cost quantification per manual workflow
- Forecast accuracy baseline across SKU categories
- Inventory health analysis: excess, slow-moving, and stockout-prone SKUs identified
- Data quality assessment across ERP, WMS, and logistics systems
- AI opportunity matrix scored by ROI, feasibility, and 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 that replaces static rules
- Live 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 model monitoring and recalibration
- ROI baseline for tracking cost savings and service level gains
Who This Is For
Right for you if: You manage 500+ SKUs and demand forecasting still runs on Excel averages and experience alone. You have at least $1.2M in annual revenue and enough transaction volume to train useful AI models. Your stockout rate exceeds 5% or excess inventory exceeds 20% of total stock value. You run multiple warehouses or distribution points and struggle with allocation decisions. You have had supplier disruptions in the past year that cost you production time or customer deliveries. You expect a working system in eight weeks. Two-year transformation programmes do not fit your timeline..
Not right if: You have fewer than 100 SKUs and a single warehouse. Your complexity does not justify AI; a good 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. Garbage in, garbage out.. You want an off-the-shelf supply chain platform. We create custom systems, not resell SaaS products.. Your supply chain team will not spend time during discovery and validation. We cannot build effective systems without domain input..
Use Cases
Manufacturing: A mid-market auto components manufacturer with $18M revenue and 1,800 SKUs was forecasting demand with three-month rolling averages. Forecast accuracy sat at 58%. This caused chronic overproduction of slow movers and frequent stockouts on high-margin parts. Emergency procurement cost them $35,000 annually in premium freight and spot-market raw materials. Their three warehouses held $960K in inventory, $200K of which was slow-moving stock older than six months. — Built a demand forecasting engine with gradient-boosted models trained on 30 months of order history, overlaid with OEM production schedules, seasonal patterns, and commodity price signals. Connected it to their SAP instance and added dynamic safety stock calculations that varied by SKU criticality, lead time variability, and demand volatility.. Outcome: Forecast accuracy went from 58% to 84%. Stockouts on A-class SKUs dropped 62%. Emergency procurement costs fell $23,000 annually. Inventory carrying cost reduced 22%, freeing $135,000 in working capital within six months.
D2C / E-commerce: A D2C beauty brand doing $7M in annual revenue across their own website, Amazon, Nykaa, and Flipkart was allocating inventory 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 put limited stock. Stockouts on hero SKUs during sale events cost them roughly $55,000 in lost revenue per quarter. — Built a multi-channel demand forecasting and allocation system that predicts demand at the channel-SKU level, accounts for promotional events and marketplace sale calendars, and generates allocation recommendations across all four channels and two warehouses. It pulls live 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 71%. Allocation planning went from 12 hours to 45 minutes per week. Revenue recovery from reduced stockouts contributed roughly $150,000 in the first two quarters.
FMCG / CPG: An FMCG company with $24M revenue and 4,500 retail touchpoints had a 9% stockout rate at distributor level while carrying 35 days of excess inventory on slow-moving variants. Demand planning relied on distributor-reported secondary sales data that arrived two weeks late and was often wrong. Regional sales managers overrode forecasts based on personal targets, inflating demand estimates by 15-20% consistently. — 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. Connected it to 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 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%.
Results
What supply chain AI delivers
Manufacturing / Auto Components: 84% forecast accuracy (up from 58%), $135,000 working capital freed in six months. An auto components manufacturer with 1,800 SKUs across three warehouses and $18M in revenue ran demand planning on three-month rolling averages in Excel. Forecast accuracy was 58%. Overproduction tied up warehouse space while stockouts triggered $35,000 in annual rush orders. We built a demand forecasting engine with gradient-boosted models trained on 30 months of order history, layered with OEM production schedules and commodity price indices. Connected directly to SAP. Replaced static safety stock with a dynamic model that adjusts by SKU. Within three months, forecast accuracy reached 84%. Stockouts on top 200 SKUs dropped 62%. Inventory carrying costs reduced 22%, freeing $135,000 in working capital.
Frequently Asked Questions
How much historical data do we need for AI forecasting to work?
18-24 months of clean transaction data is ideal. That gives us enough signal to capture seasonality, trends, and demand patterns. If you have less, we can still build useful models, but we will be upfront about accuracy limitations and may supplement with external data. Clean, consistent data matters more than volume.
Will this work with our existing ERP?
Yes. Everything runs on top of your existing systems. We have integrated with SAP Business One, Oracle NetSuite, Tally, Microsoft Dynamics, Epicor, Infor, Siemens Opcenter, and several custom-built ERPs common in regional manufacturing. If your ERP has an API or can export structured data, we can connect to it. Nothing changes about how people work day to day.
How is this different from the forecasting module in our ERP?
ERP forecasting modules use basic statistical methods (moving averages, exponential smoothing) applied uniformly across all SKUs. They cannot pull in external signals like commodity prices, weather, or market trends. They do not learn from their own errors or adapt when demand patterns change. Our models train on your data, handle non-linear patterns, and typically improve accuracy by 20-35 percentage points over standard ERP forecasting.
What kind of ROI can we realistically expect?
For a mid-market company with $6-25M in revenue: 15-25% reduction in inventory carrying costs, 40-60% reduction in stockouts, 30-50% reduction in emergency procurement costs, and 8-15% savings on logistics. Most clients recover the investment within four to six months. We give you an ROI estimate during the diagnostic based on your actual numbers.
Can AI really predict demand better than our experienced planners?
Your experienced planners understand context, customer relationships, and market dynamics that no model captures alone. AI takes the grunt work off their plate: baseline forecasts, consistent methodology across thousands of SKUs, data signals no human can track manually. The best results come when planners review and adjust AI forecasts rather than build them from scratch. Their time shifts from data processing to decisions.
How long does it take to see measurable results?
Deployment takes eight weeks. Measurable improvements in forecast accuracy and inventory metrics typically appear within the first month. Cost savings (fewer stockouts, lower carrying costs, fewer rush orders) show up by month three. Full ROI usually lands within four to six months.
What happens if our demand patterns change significantly?
The models adapt. They retrain on new data continuously and detect when underlying patterns shift, whether from new product launches, market disruptions, or channel mix changes. We put in drift detection that alerts your team when accuracy drops so you can trigger recalibration. If you want us to manage this ongoing, our AI Operations service covers model monitoring and retraining on a monthly retainer.
What is the typical cost of an AI for Supply Chain engagement?
Engagements range from $12-$40K depending on scope. A single-function build (demand forecasting or inventory optimization alone) sits at the lower end. Multi-function engagements covering forecasting, inventory optimization, and supplier risk sit at the higher end. Most clients recover the investment within six months through lower carrying costs and fewer stockouts.
Do you support on-premise deployment and data residency requirements?
Yes. For manufacturing environments with strict data residency, air-gapped networks, or compliance constraints, we deploy on-premise or in your private cloud. Many manufacturing clients keep production and supply chain data within their own infrastructure, and we design for that from the start. It connects to your on-site ERP, WMS, and shop-floor systems without data leaving your network.
