Kshitij Polyline Limited
Kshitij Polyline Limited
Kshitij Polyline Limited
How Kshitij Polyline Limited reduced firefighting using a single system of record

How Kshitij Polyline Limited reduced firefighting using a single system of record

How Kshitij Polyline Limited reduced firefighting using a single system of record

A practical shift from WhatsApp/Excel coordination to trackable daily execution.

38%

Reduction in daily coordination time for packaging work

38%

Reduction in daily coordination time for packaging work

28%

Reduction in excess inventory across store and production

28%

Reduction in excess inventory across store and production

5→1 days

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

5→1 days

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

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Inside Kshitij Polyline Limited

Inside Kshitij Polyline Limited

Kshitij Polyline Limited is a micro packaging manufacturing unit based in Dadra & Nagar Haveli. 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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Packaging

Industry

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Packaging

Industry

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Micro

Company Size

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Micro

Company Size

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Dadra & Nagar Haveli, India

Location

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Dadra & Nagar Haveli, India

Location

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Order-to-dispatch control for job work stages

Use Cases

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Order-to-dispatch control for job work stages

Use Cases

Challenges

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

Vendor committed dates were verbal; delays were discovered only after repeated calls.

Receivable status changed daily because part-payments were not tagged to specific invoices.

Cleaning masters to make reports match reality

Cleaning masters to make reports match reality

Cleaning masters to make reports match reality

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 packaging operations in Dadra & Nagar Haveli, this reduced dependence on a single person for updates. The practical outcome was fewer escalations, faster decisions, and dates that changed less often.

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 packaging operations in Dadra & Nagar Haveli, this reduced dependence on a single person for updates. The practical outcome was fewer escalations, faster decisions, and dates that changed less often.

Dispatch readiness built into the daily routine

Dispatch readiness built into the daily routine

Dispatch readiness built into the daily routine

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. 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. The practical outcome was that teams stopped re-entering the same data and acted on exceptions early.

Controlling rework through traceability and holds

Controlling rework through traceability and holds

Controlling rework through traceability and holds

Earlier, collections follow-ups were scattered and disputes reopened older invoices. This broke down on part-payments; the receivable view changed daily and escalations increased. We implemented a centralised workflow where each order, material requirement and update had a clear owner and timestamp. Collections follow-ups moved to a single list with dispute tagging so teams stopped chasing the wrong invoices. It also made owner reviews easier because key exceptions were visible without chasing the team. The practical outcome was smoother handovers between departments and more predictable daily execution.

Earlier, collections follow-ups were scattered and disputes reopened older invoices. This broke down on part-payments; the receivable view changed daily and escalations increased. We implemented a centralised workflow where each order, material requirement and update had a clear owner and timestamp. Collections follow-ups moved to a single list with dispute tagging so teams stopped chasing the wrong invoices. It also made owner reviews easier because key exceptions were visible without chasing the team. The practical outcome was smoother handovers between departments and more predictable daily execution.

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