Use case
Exceptions that find the right owner before the fire drill
Planners live in inbox triage: late shipments, short picks, ASN mismatches, and sudden demand spikes. Arcloops designs AI-assisted exception detection and routing so supply chain teams work a prioritised queue — not a wall of undifferentiated red.
The problem: every exception looks equally urgent
ERP exception reports dump hundreds of lines daily. Planners sort by habit. Critical customer orders wait behind noise. Carrier ETA updates never reach the buyer who can expedite. Warehouse short-ships create downstream production stops that sales discovers from the customer.
Signals are fragmented across TMS, WMS, supplier portals, and email. Duplicate tickets open for the same PO. Root causes repeat — chronic late suppliers, bad master lead times — but the organisation only fights symptoms. Night shifts escalate without context daylight teams already knew.
Network complexity multiplies the blast radius. Multi-echelon inventory, cross-border delays, and promotion spikes interact. Hiring more expeditors scales cost without improving prioritisation logic.
Supply planning owns network exceptions; procurement owns supplier performance; logistics owns in-transit; customer service owns promise dates. Anti-patterns include auto-expediting everything, hiding exceptions in private spreadsheets, and alerting without an owner or playbook.
Network complexity multiplies the blast radius. Multi-echelon inventory, cross-border delays, and promotion spikes interact. Hiring more expeditors scales cost without improving prioritisation logic. Night shifts escalate without context daylight teams already knew, and duplicate tickets open for the same PO across email, ERP, and chat.
Root-cause discipline separates firefighting from improvement. If every late container is closed as “carrier delay” without codes that feed supplier scorecards and lead-time updates, the same exceptions return next month. Playbooks must distinguish one-off weather events from chronic supplier underperformance and master-data errors.
AI exception handling should detect material deviations, score business impact, and route with context — leaving resolution actions with humans who own trade-offs between cost, service, and risk. A healthy queue is short, owned, and ageing-visible — not a wall of undifferentiated red that planners learn to ignore.
AI approach
Unify exception signals across systems
PO confirms, ASN, inventory breaches, ETA slips, and quality holds enter a common model keyed to order, SKU, and node. Deduplication prevents triple tickets for one late container.
- 02
Prioritise by impact and recommend playbooks
Scoring considers customer criticality, revenue at risk, shelf life, and alternative supply. Suggested next actions come from your playbooks (expedite, reallocate, substitute, notify). What good looks like: a morning queue sorted by impact with owners already assigned.
- 03
Route, collaborate, and close with evidence
Exceptions go to procurement, logistics, or planning with documents and contact context. Closures capture root cause codes for supplier scorecards. Customer promise updates can feed service teams when policy allows.
- 04
Feed planning improvements upstream
Chronic patterns update lead times, buffers, and supplier reviews — linking to demand and inventory programmes. Failure modes: treating every delay as one-off, and flooding Slack with low-severity noise.
How Arcloops delivers this
Exception handling is delivered under /solutions/ai-in-supply-chain, with plant and network execution often paired to /solutions/ai-in-operations. Upstream buying automation links to /use-cases/ai-purchase-order-automation. When data and process maturity are unclear, /ai-consulting/ai-readiness-assessment frames an honest starting point.
Delivery starts with a high-pain exception family (late inbound, ASN mismatch, or stockout risk), ownership maps, and a pilot lane or category. Integration notes cover ERP/TMS/WMS events and case tools. We track time-to-owner, exception ageing, and whether critical orders were actioned before customer impact — we do not invent OTIF or freight ROI percentages.
Related offerings
Related pages & industry combinations
FAQ
Recommendations can include expedite options, but commercial and cost approval stay human unless you explicitly design a narrow straight-through band with owners.
Severity thresholds, deduplication, and playbook gating are designed with planners. Low-value exceptions are batched or suppressed rather than page-worthy.
Not necessarily. Many pilots start by unifying a few critical signals into a queue. Broader tower ambitions can follow once ownership works.
Exception ageing, time-to-owner, repeat root causes, and whether critical orders were actioned before customer impact. We do not invent OTIF or freight ROI figures.
Pilot one exception family
Pick late inbound, ASN gaps, or stockout risk. Arcloops will outline detection, prioritisation, and routing under AI in Supply Chain.