Operations & Logistics AI Solutions

Because most planning is built on static rules (min/max, reorder points) and slow human cycles (weekly spreadsheets). Reality moves daily: promotions, supplier variability, lead time drift, substitution, channel shifts.

AI helps by recalculating risk continuously—then recommending replenishment and safety stock per SKU/location based on your service level goals and constraints (MOQ, case packs, capacity, expiry).

Route plans degrade when new constraints appear: changing time windows, urban restrictions, failed deliveries, traffic patterns, and driver rule changes. Manual dispatching tends to “patch” yesterday’s plan rather than optimise.

AI route optimisation uses constraints explicitly (capacity, windows, driver rules, priorities) to generate plans that reduce kilometers and protect on-time performance. It can also re-plan safely when exceptions occur.

Customers ask when tracking is unclear, ETAs shift, or exceptions aren’t communicated. Humans end up copying data from systems into emails and tickets—work that scales linearly with volume.

A logistics AI agent can answer from your shipment events, predict ETA risk early, and trigger proactive updates— with approvals and logging so you stay in control.

ETA prediction combines historical shipment patterns with live events (scans, handoffs, GPS where available) to estimate the probability of delays before they happen. Exception management then turns predictions into workflows: notify the customer, propose re-routing, escalate to a carrier, or reschedule within SLA rules.

• Outputs: risk scoring per shipment, early delay alerts, proactive notification templates, escalation logic.
• KPIs: SLA breaches avoided, exception resolution time, inbound “WISMO” contact volume reduction.

Warehouse improvements often fail when they ignore how people actually work. We focus on decision points that operators can adopt fast: slotting recommendations, wave suggestions, bottleneck detection, and labour forecasting that respects constraints. The goal is higher throughput with less congestion and fewer late orders.

• Outputs: slotting plans, pick path improvements, wave/picking recommendations, shift forecasts.
• KPIs: lines per hour, order cycle time, pick accuracy, labour cost per order.

If a decision depends on visual evidence—damage, labels, packaging compliance, missing items—vision models can standardise inspection, capture evidence automatically, and reduce disputes. Vision becomes even stronger when it connects to claims, supplier scorecards, and rework workflows.

• Outputs: pass/fail checks, defect tagging, evidence capture, trend reports by SKU/supplier/site.
• KPIs: claims rate, rework time, supplier quality score, defect detection consistency.

The highest ROI is in repetitive, policy-driven work: answering order status and ETA questions, classifying tickets, drafting responses grounded in real data, generating incident summaries, and routing to the right team. “Safe automation” means strict permissions, logging, and human approval where needed.

• Outputs: self-service resolutions, ticket triage, response drafts, knowledge base suggestions.
• KPIs: first-contact resolution, average handle time, cost per ticket, CSAT.

The core issue is volatility: campaigns, seasonality, marketplace shifts, and substitutions. AI forecasting plus dynamic replenishment helps you decide what to buy, where to store it, and when to replenish—while tracking confidence.

The most effective rollout is usually category-by-category (start with high movers, then long tail) with clear guardrails: approvals for high-impact SKUs, and automated proposals for low-risk replenishment.

Multi-warehouse operations suffer when rules don’t reflect location-specific behaviour. AI optimises safety stock and reorder policies per site using actual lead times, variability, and service level goals—then proposes transfers or purchase orders with explainable logic.

This works best when you connect the model to the same approval points your planners already use, so adoption is fast.

Route optimisation is the obvious win, but the biggest impact usually comes from coupling it with ETA risk detection. When you predict late drops early, you can re-plan, reassign, or proactively communicate—preventing failed deliveries and repeated attempts.

This reduces wasted kilometres and support load at the same time, because fewer exceptions reach the customer.

Claims often fail because evidence is missing or inconsistent. Computer vision makes inspection consistent and captures evidence automatically at inbound/packing/dispatch points. Over time you get defect trends by supplier, site, SKU, and operator.

The real advantage is operational learning: you don’t just reject defects—you identify root causes earlier.

We use role-based access, explicit “allowed actions”, and approval steps for sensitive changes (e.g., big purchase orders, customer-impacting notifications). Every action can be logged with inputs and outputs.

For predictive models: monitoring, drift checks, bias detection, and fallback logic. For copilots/agents: grounded answers (retrieval from your sources), strict citation to internal data, and safety checks.

Documentation, runbooks, versioning, and dashboards for both business KPIs and technical health. If something changes, your team knows where it changed and how to respond.