Aasha Planters
Aasha Planters
Aasha Planters
Bringing order-wise control to Aasha Planters

Bringing order-wise control to Aasha Planters

Bringing order-wise control to Aasha Planters

Daily reviews got shorter because teams worked off the same status and dates. during vendor delays.

3 days

Order lead time improvement

3 days

Order lead time improvement

31%

Faster month-end reconciliation

31%

Faster month-end reconciliation

16%

Reduction in rework/rejection loops

16%

Reduction in rework/rejection loops

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About Aasha Planters

About Aasha Planters

Aasha Planters operates from Gurgaon and builds ducting for OEM customers. As a medium-scale fabrication unit running two shifts, they work with a mix of make-to-stock and make-to-order model and single stores. They needed a centralised workflow to reduce daily follow-ups and keep commitments predictable.

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Fabrication

Industry

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Fabrication

Industry

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Medium

Company Size

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Medium

Company Size

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Gurgaon

Location

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Gurgaon

Location

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Material planning for steel/consumables and outside processing control

Use Cases

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Material planning for steel/consumables and outside processing control

Use Cases

Challenges

Inspection status was sequenced via WhatsApp-based follow-ups, so WIP clarity broke during two shifts.

Fasteners shortages showed up late, causing emergency buys and delays on tanks.

Dispatch dates were promised before checks for job sequencing, causing reshuffles (bulk dispatch at month-end).

Workcentre progress captured at source

Workcentre progress captured at source

Workcentre progress captured at source

Earlier, Aasha Planters relied on WhatsApp-based follow-ups and calls to confirm cutting progress. When priorities changed, different teams carried different versions of the plan. A centralised workflow captured stage updates at source, so WIP moved with clear ownership. The daily review shifted from collecting updates to resolving exceptions. Supervisors started closing handovers on time, improving predictability during extended shifts during peak weeks.

Earlier, Aasha Planters relied on WhatsApp-based follow-ups and calls to confirm cutting progress. When priorities changed, different teams carried different versions of the plan. A centralised workflow captured stage updates at source, so WIP moved with clear ownership. The daily review shifted from collecting updates to resolving exceptions. Supervisors started closing handovers on time, improving predictability during extended shifts during peak weeks.

Materials linked to live orders

Materials linked to live orders

Materials linked to live orders

Earlier, buying for pipes was triggered after shortages hit the shopfloor. Follow-ups lived in personal reminders, so receipts slipped during busy days. Requirements were linked to live orders and ducting, creating a daily shortage list. Vendors were followed up against dates, not memory, and approvals became faster. The team reduced emergency purchases and avoided excess on slow movers across separate RM and FG stores.

Earlier, buying for pipes was triggered after shortages hit the shopfloor. Follow-ups lived in personal reminders, so receipts slipped during busy days. Requirements were linked to live orders and ducting, creating a daily shortage list. Vendors were followed up against dates, not memory, and approvals became faster. The team reduced emergency purchases and avoided excess on slow movers across separate RM and FG stores.

Fewer last-minute dispatch changes

Fewer last-minute dispatch changes

Fewer last-minute dispatch changes

Earlier, dispatch dates were committed before checks on outside job-work. Packing, QA clearance, and paperwork were tracked in different places. Readiness gates were added so jobs were released only when documents were complete. Customers got clearer dates, and escalations reduced because holds had visible reasons. Dispatch planning became steadier even with a mix of make-to-stock and make-to-order model and project-based customers.

Earlier, dispatch dates were committed before checks on outside job-work. Packing, QA clearance, and paperwork were tracked in different places. Readiness gates were added so jobs were released only when documents were complete. Customers got clearer dates, and escalations reduced because holds had visible reasons. Dispatch planning became steadier even with a mix of make-to-stock and make-to-order model and project-based customers.

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Become the
AI-Run Factory

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Built by IIT & IIM founders

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Become the
AI-Run Factory

Born in India. Building for the world.

Built by IIT & IIM founders