Shree Khandelwal Traders
Shree Khandelwal Traders
Shree Khandelwal Traders
Shree Khandelwal Traders tightened shop-floor control with a centralised workflow

Shree Khandelwal Traders tightened shop-floor control with a centralised workflow

Shree Khandelwal Traders tightened shop-floor control with a centralised workflow

Turning daily follow-ups into a repeatable workflow across departments.

58%

Reduction in daily coordination time for fabrication work

58%

Reduction in daily coordination time for fabrication work

14%

Reduction in excess inventory across store and production

14%

Reduction in excess inventory across store and production

6→3 days

Improvement in order-to-dispatch lead time with fewer last-minute follow-ups

6→3 days

Improvement in order-to-dispatch lead time with fewer last-minute follow-ups

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Inside Shree Khandelwal Traders

Inside Shree Khandelwal Traders

Shree Khandelwal Traders is a small fabrication manufacturing unit based in Indore. Their day-to-day work involves coordinating orders, material availability, production execution and dispatch commitments. Earlier, updates were shared through a mix of Excel sheets, calls and WhatsApp, which made it hard to keep everyone aligned. By moving to a centralised workflow, the team standardised handovers, reduced rework in reporting, and improved execution predictability.

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Fabrication

Industry

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Fabrication

Industry

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Small

Company Size

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Small

Company Size

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Indore, India

Location

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Indore, India

Location

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Raw material cutting plan + WIP visibility

Use Cases

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Raw material cutting plan + WIP visibility

Use Cases

Challenges

PO approvals got stuck with managers, leading to last-minute buying and frequent expedite.

Month-end reporting became a rework exercise because data lived in multiple files and chats.

Job-work material sent out was tracked in registers; return quantity and shortages were unclear.

Implementation: Centralised workflow with clear owners

Implementation: Centralised workflow with clear owners

Implementation: Centralised workflow with clear owners

Earlier, vendor follow-ups were call-driven and depended on whoever remembered to push. This broke down during peak load—approvals got delayed and committed dates slipped without a simple exception view. We implemented a single system of record connecting sales, purchase, production and dispatch so everyone worked off the same status. The purchase team used one follow-up queue, logged committed dates, and escalated exceptions early. Over time, the team built confidence because the numbers matched what they saw on the floor. The practical outcome was smoother handovers between departments and more predictable daily execution.

Earlier, vendor follow-ups were call-driven and depended on whoever remembered to push. This broke down during peak load—approvals got delayed and committed dates slipped without a simple exception view. We implemented a single system of record connecting sales, purchase, production and dispatch so everyone worked off the same status. The purchase team used one follow-up queue, logged committed dates, and escalated exceptions early. Over time, the team built confidence because the numbers matched what they saw on the floor. The practical outcome was smoother handovers between departments and more predictable daily execution.

After: Predictable execution, fewer escalations

After: Predictable execution, fewer escalations

After: Predictable execution, fewer escalations

Earlier, masters and transactions were split across Excel, accounting exports and WhatsApp updates. This broke down at month-end; teams rebuilt the same report multiple times and still debated the numbers. We implemented a single system of record connecting sales, purchase, production and dispatch so everyone worked off the same status. Masters were cleaned once, and daily reporting pulled from the same transactions instead of manual consolidation. For fabrication operations in Indore, this reduced dependence on a single person for updates. The practical outcome was smoother handovers between departments and more predictable daily execution.

Earlier, masters and transactions were split across Excel, accounting exports and WhatsApp updates. This broke down at month-end; teams rebuilt the same report multiple times and still debated the numbers. We implemented a single system of record connecting sales, purchase, production and dispatch so everyone worked off the same status. Masters were cleaned once, and daily reporting pulled from the same transactions instead of manual consolidation. For fabrication operations in Indore, this reduced dependence on a single person for updates. The practical outcome was smoother handovers between departments and more predictable daily execution.

Vendor coordination with structured PO follow-ups

Vendor coordination with structured PO follow-ups

Vendor coordination with structured PO follow-ups

Earlier, job-work material movement was tracked in registers and return status was unclear. This broke down on returns; shortages and rate differences led to repeated reconciliations. We implemented a single system of record connecting sales, purchase, production and dispatch so everyone worked off the same status. Material sent out and received back was logged against job-work orders, making shortages visible immediately. It also made owner reviews easier because key exceptions were visible without chasing the team. The practical outcome was that teams stopped re-entering the same data and acted on exceptions early.

Earlier, job-work material movement was tracked in registers and return status was unclear. This broke down on returns; shortages and rate differences led to repeated reconciliations. We implemented a single system of record connecting sales, purchase, production and dispatch so everyone worked off the same status. Material sent out and received back was logged against job-work orders, making shortages visible immediately. It also made owner reviews easier because key exceptions were visible without chasing the team. The practical outcome was that teams stopped re-entering the same data and acted on exceptions early.

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