CapabilitiesSupply Chain

AI-Native Supply Chain

Smarter inventoryMore reliable delivery

What needs replenishing, which supplier may be late, and which order needs attention first? ActelosAI connects business data, supply-chain knowledge and AI to help teams spot changes sooner, coordinate action faster, and balance service with operating costs.

Explore AI use cases Discuss your supply-chain goals

AI-Native in practice

Sales & promotionsAvailable & inbound stockLead times & shelf life

Example question

Which stores need which products tomorrow

Connect sales, stock and inbound supply. Forecasting and replenishment models propose quantities; AI explains stockout risks, recommendations and exceptions that need review.

Metrics to track

Product availability · Inventory turnover

Purchase ordersSupplier confirmationsReceipts & demand

Example question

Which supplier changes could affect this week’s orders

Identify changes in commitments across purchase orders, supplier replies and receipts. Link them to affected demand and prepare follow-up priorities.

Metrics to track

Supplier on-time delivery · Risk warning lead time

Order commitmentsWarehouse eventsTransport & delivery status

Example question

Which order exception needs attention first today

Bring together warehouse events, carrier updates and customer requirements. Identify shortages, missed pickups and handoff gaps, then prioritize responses and track feedback.

Metrics to track

On-time, in-full delivery · Exception resolution time

Business value

See AI’s value in supply-chain results

From machine-learning forecasts and replenishment to AI agents handling logistics exceptions, track changes in planning effort, spoilage and fulfillment coordination.

Reduce planning effort

−20%

Planner workload

Use machine-learning forecasts and replenishment recommendations to focus planners on exceptions that need judgment.

About the figures

A food retail cooperative reported a 20% reduction in planner workload after introducing machine-learning forecasting and replenishment at its distribution centers. This vendor-published customer case does not disclose a full time-measurement method or control design. It is not a headcount reduction or an isolated generative-AI effect.

Reduce spoilage

−49%

Spoilage as a share of sales, year on year

Account for promotions, stock and freshness in forecasting and replenishment to reduce the risk of goods going to waste.

About the figures

An online fresh-food retailer reported a 49% reduction in spoilage as a share of sales between the first halves of two consecutive years, while maintaining product availability. The result involved machine-learning forecasting, replenishment and process changes. It is not a 49% reduction in absolute spoilage value or volume, or a generative-AI effect.

Improve logistics handoffs

−42%

Unnecessary return trips after missed pickups

Let AI agents help verify pickup status and exception causes, reducing repeat dispatches caused by missing information.

About the figures

A logistics company reported 42% fewer unnecessary return pickup trips when using AI agents for missed pickups in less-than-truckload transport. This self-reported result does not disclose a full comparison period or control method. It is not a 42% reduction in total transport costs, mileage or missed pickups.

Figures come from specific cases and studies and are for reference only. Actual results vary with business and data conditions.

AI applications

Six ways to connect planning with execution

From demand forecasting and replenishment to procurement, warehouses and transport. These are AI-Native applications we can build around your business, data and existing systems.

Bring demand changes into the plan sooner

How will promotions, seasons and changing orders affect demand

Use forecasting models to analyze demand changes and flag gaps against supply and capacity constraints. AI explains drivers, compares plan versions and prepares scenarios and adjustments for business review.

Connected data: Sales and order history, promotion calendars, stockout records, supply and capacity constraints
What it enables: Demand forecasts, supply-demand gaps and proposed plan adjustments

Improvement goals

Forecast error ↓
Planning preparation time ↓

Put inventory where it is needed

Why are some stores out of stock while other products pile up

Combine demand, available stock, inbound supply and shelf life to compare replenishment and transfer options. AI explains recommendations and highlights slow-moving, expiring and unusual stock for review.

Connected data: SKU and store sales, stock and inbound supply, lead times, ordering constraints and shelf life
What it enables: Replenishment and transfer recommendations, inventory risk list

Improvement goals

Product availability ↑
Inventory turnover ↑

Get purchasing comparisons ready faster

Are those supplier quotes really comparable

Extract terms from quotations, specifications and emails, using consistent units and current versions. Check costs and differences against rules, then prepare source-backed comparisons and clarification questions.

Connected data: Purchase requirements, supplier quotes, specifications, fees and commercial terms
What it enables: Quotation comparison, missing-information flags and clarification drafts

Improvement goals

Quote comparison preparation time ↓
Quote verification error rate ↓

Spot supplier delivery risks earlier

A supplier changed its delivery date. Which orders or workstations are affected

Extract dates, quantities and changes from supplier replies. Check purchase orders and receipts, link affected demand, and prepare evidence, follow-up actions and recovery options for the owner’s review.

Connected data: Purchase orders, supplier confirmations, receipts, linked demand and work orders
What it enables: Supply risk list, affected demand and follow-up recommendations

Improvement goals

Risk warning lead time ↑
Supplier on-time delivery ↑

Act before delays spread

Where is the order stuck, and who needs to act next

Connect order commitments, inventory, warehouse and transport events to find shortages, missed pickups and conflicting statuses. AI brings together evidence, prioritizes proposed actions, drafts communications and tracks confirmed outcomes.

Connected data: Orders and promised dates, inventory, warehouse and transport events, delivery receipts and communications
What it enables: Order exception list, response recommendations and handoff records

Improvement goals

On-time, in-full delivery ↑
Exception resolution time ↓

Keep warehouse work moving

What comes first today: picking, replenishing pick locations or loading

Use rules and optimization to compare task priorities against dispatch deadlines, stock locations, labor, equipment and dock schedules. AI explains queues and missing information, prepares task suggestions and flags handoff issues.

Connected data: WMS tasks and stock, dispatch deadlines, labor and equipment capacity, docks and pickup bookings
What it enables: Task priority recommendations, dispatch readiness and handoff exceptions

Improvement goals

Outbound cycle time ↓
Handoff error rate ↓

Arrows show intended improvement directions. Set targets against your business baseline and assess service, cost and errors together. Responsible owners confirm purchasing, inventory changes, schedules and customer commitments; system actions stay within agreed permissions.

Data & metrics

Better data for more reliable decisions

Align products, quantities, times and statuses so recommendations have a sound basis and the business can verify improvements.

Demand & inventory data

Align SKUs, stores, units and shelf-life definitions. Account for lost sales during stockouts and distinguish available, inbound and blocked stock.

Support forecasting and replenishment while balancing availability, stock and spoilage

Inventory record accuracy · Forecast error · Product availability · Spoilage rate

Purchasing & supplier data

Align material specifications, supplier identities, order versions and promised dates. Link email changes to purchase orders and receipts.

Improve comparisons and identify supply risks earlier

Key-field completeness · Timely commitment updates · Supplier on-time delivery

Order & logistics events

Connect order lines, batches and shipments. Preserve commitment versions, event times and owners, and identify late updates or conflicting statuses.

Locate fulfillment gaps and speed up exception coordination

Key-event completeness · Status update delay · Exception resolution time · On-time, in-full delivery

Experience

Relevant experience and expertise

Partner case · AI replenishment & store execution

Hero Market · Malaysia

AI replenishment, connected to execution

An AI demand-forecast agent turns sales, inventory and store requests into daily replenishment recommendations for procurement review and ERP approval. Warehouse, delivery and store-receipt data feed back into planning across five warehouses and 35+ stores in Malaysia.

Case study presented in collaboration with Opxiq.

Explore the partner case

Relevant expert

Hans Cheng

Expert Partner, Agentic Supply Chain Solutions & Delivery Lead

Connecting supply-chain knowledge, AI agent product architecture and WMS/TMS integration—from solution design to operational adoption, human review and cross-border delivery.

Meet Hans

Before we begin

Define a planning or fulfilment trial

Supply Chain connects planning with purchasing, stock and delivery work. A first engagement should make the decision, constraints and exception owner clear.

What should the first trial include

Select a bounded planning, replenishment, procurement or fulfilment task. Bring its demand, inventory, commitments and relevant constraints, with a team able to check the records. Forecasting models, AI explanations and system actions have different roles; agree which output is advisory and which actions require approval.

How do we avoid moving the cost elsewhere

Assess service, inventory, waste and total coordination effort together where they apply. Compare the same business scope and retain exceptions and errors, not just successful recommendations. A forecast or faster review does not establish better availability, lower total cost or permission to change orders automatically.

Which supply-chain measure would you most like to improve

Start with one business goal: fewer stockouts and overstocks, more reliable supplier deliveries, or faster exception handling. Together, we can find the AI application worth investing in.

Discuss your supply-chain goals

Actelos Delta supports use-case discovery, capability building and team adoption.

How we work together