Order SLA Protection & Overtime Minimization During Demand Spikes

A commercial HVAC equipment manufacturer producing custom rooftop air handling units with high seasonal demand surges during pre-summer months.

on-time delivery maintained across pre-summer demand spike: 100%on-time delivery maintained across pre-summer demand spike
reduction in overtime labor spend by restricting overtime: 62%reduction in overtime labor spend by restricting overtime

How the work runs

The pressure that made this worth automating, the steps the system runs, and what came out the other side.

Pressure & Trigger Points

A 45% seasonal surge in customer orders threatened contract delivery SLAs, risking heavy liquidated damage penalties.
Plant managers authorized uncoordinated weekend overtime across all departments, driving labor costs up by 35% without resolving specific assembly bottlenecks.
Planners could not determine which specific departments actually required overtime to protect delivery SLAs.

The run · 5 operational steps

Click any step to inspect telemetry signals, model reasoning, and governance gates.

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Niyojan models incoming order volumes against finite plant capacity, identifying exact delivery SLA breach risks.

Input Signal:

Real-time operational telemetry & queue

Reasoning Pattern:

MCP grounded vector inference

Governance Gate:

Policy constrained with audit write-back

Verified Business Outcomes

100% on-time delivery maintained across pre-summer demand spike season with zero SLA penalty fines.
62% reduction in overtime labor spend by restricting overtime exclusively to identified bottleneck workstations.
Preserved project gross margins while absorbing a 45% volume surge.

Capabilities this relies on

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