An SLA problem should surface before the customer reports it.

SLA breaches often surface in a weekly review, or when the customer calls, instead of the moment a job first goes at risk.

Trigger

A job’s status changes in a way that affects its SLA timeline.

End state

At-risk jobs are flagged in real time with enough lead time to intervene, not discovered after the breach.

People involved
Operations leadCustomer serviceAccount managers for affected customers
Typical systems
TMSSLA/contract terms (often in a spreadsheet or contract system)Customer notification tool
Common failure modes
  • →SLA status is reviewed weekly instead of continuously.
  • →The first sign of a breach is the customer calling to ask what happened.
  • →SLA terms live in contracts nobody has connected to live job data.
  • →Breach patterns by lane, customer, or vendor go unnoticed because nothing aggregates them.
What can be automated
  • →Continuously checking job status against SLA terms and flagging at-risk jobs.
  • →Alerting the operations lead with enough lead time to intervene.
  • →Aggregating breach patterns by lane, customer, or vendor for review.
What stays human
  • →Deciding how to communicate a breach to the customer.
  • →Judgment calls on service recovery.
Exception handling

Jobs affected by circumstances outside the SLA’s control (documented force majeure, customer-caused delay) are flagged separately rather than counted as a standard breach.

Source of truth

SLA terms are centralized against the TMS job record, so monitoring reads live status against agreed terms instead of a separate spreadsheet.

Implementation pattern

Centralize SLA terms against job records in the TMS, build continuous status checks, and alert operations before the SLA window closes.

ROI framework

Estimate using: SLA breaches per month currently caught late x average cost per breach (credits, expedite fees, account risk) x the portion that earlier warning would have prevented.

Run the numbers →
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