NJA INDUSTRIES PRIVATE LIMITED
NJA INDUSTRIES PRIVATE LIMITED
NJA INDUSTRIES PRIVATE LIMITED
How NJA INDUSTRIES PRIVATE LIMITED reduced firefighting using a single system of record

How NJA INDUSTRIES PRIVATE LIMITED reduced firefighting using a single system of record

How NJA INDUSTRIES PRIVATE LIMITED reduced firefighting using a single system of record

Standardising approvals and handovers so teams work off the same numbers.

40%

Reduction in daily coordination time for engineering work

40%

Reduction in daily coordination time for engineering work

19%

Reduction in excess inventory across store and production

19%

Reduction in excess inventory across store and production

9→1 days

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

9→1 days

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

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Inside NJA INDUSTRIES PRIVATE LIMITED

Inside NJA INDUSTRIES PRIVATE LIMITED

NJA INDUSTRIES PRIVATE LIMITED is a medium engineering manufacturing unit based in Vadodara. 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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Engineering

Industry

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Engineering

Industry

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Medium

Company Size

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Medium

Company Size

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Vadodara, India

Location

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Vadodara, India

Location

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Quotation-to-order conversion + dispatch coordination

Use Cases

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Quotation-to-order conversion + dispatch coordination

Use Cases

Challenges

Dispatch dates slipped because packing list, invoice and transporter booking were not in sync.

Batch/lot traceability gaps made it hard to isolate which raw material caused a quality issue.

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

Dispatch readiness built into the daily routine

Dispatch readiness built into the daily routine

Dispatch readiness built into the daily routine

Earlier, dispatch readiness depended on people coordinating invoice, packing and transporter on calls. This broke down when transporter slots moved; finished goods were ready but paperwork and packing lagged behind. We implemented a single system of record connecting sales, purchase, production and dispatch so everyone worked off the same status. Dispatch checklists were run a day ahead so invoicing, packing and transporter booking stayed aligned. For engineering operations in Vadodara, this reduced dependence on a single person for updates. The practical outcome was that teams stopped re-entering the same data and acted on exceptions early.

Earlier, dispatch readiness depended on people coordinating invoice, packing and transporter on calls. This broke down when transporter slots moved; finished goods were ready but paperwork and packing lagged behind. We implemented a single system of record connecting sales, purchase, production and dispatch so everyone worked off the same status. Dispatch checklists were run a day ahead so invoicing, packing and transporter booking stayed aligned. For engineering operations in Vadodara, this reduced dependence on a single person for updates. The practical outcome was that teams stopped re-entering the same data and acted on exceptions early.

Before: Too many moving parts, no single view

Before: Too many moving parts, no single view

Before: Too many moving parts, no single view

Earlier, QC holds and rework notes were kept on paper and were not visible to planning. This broke down during rejections; without traceability, isolating root cause took days and blocked dispatch. We implemented a single system of record connecting sales, purchase, production and dispatch so everyone worked off the same status. QC marked holds against lots, and planners saw only cleared stock as available for production. It also made owner reviews easier because key exceptions were visible without chasing the team. The practical outcome was fewer escalations, faster decisions, and dates that changed less often.

Earlier, QC holds and rework notes were kept on paper and were not visible to planning. This broke down during rejections; without traceability, isolating root cause took days and blocked dispatch. We implemented a single system of record connecting sales, purchase, production and dispatch so everyone worked off the same status. QC marked holds against lots, and planners saw only cleared stock as available for production. It also made owner reviews easier because key exceptions were visible without chasing the team. The practical outcome was fewer escalations, faster decisions, and dates that changed less often.

After: Predictable execution, fewer escalations

After: Predictable execution, fewer escalations

After: Predictable execution, fewer escalations

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 structured stage-wise tracking and checklists so work moved forward only when prerequisites were met. The purchase team used one follow-up queue, logged committed dates, and escalated exceptions early. 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, 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 structured stage-wise tracking and checklists so work moved forward only when prerequisites were met. The purchase team used one follow-up queue, logged committed dates, and escalated exceptions early. 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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