Inizio Technologies Pvt Ltd
Inizio Technologies Pvt Ltd
Inizio Technologies Pvt Ltd
How Inizio Technologies Pvt Ltd reduced firefighting using a single system of record

How Inizio Technologies Pvt Ltd reduced firefighting using a single system of record

How Inizio Technologies Pvt Ltd reduced firefighting using a single system of record

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

53%

Reduction in daily coordination time for electrical work

53%

Reduction in daily coordination time for electrical work

16%

Reduction in excess inventory across store and production

16%

Reduction in excess inventory across store and production

5→2 days

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

5→2 days

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

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About Inizio Technologies Pvt Ltd

About Inizio Technologies Pvt Ltd

Inizio Technologies Pvt Ltd is a micro electrical manufacturing unit based in Ghaziabad. 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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Electrical

Industry

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Electrical

Industry

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Micro

Company Size

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Micro

Company Size

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Ghaziabad, India

Location

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Ghaziabad, India

Location

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Sales enquiry to dispatch workflow with approvals

Use Cases

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Sales enquiry to dispatch workflow with approvals

Use Cases

Challenges

Priority orders jumped the queue; other jobs got half-started and left in between.

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

Payment follow-ups depended on WhatsApp threads; disputes re-opened invoices at month-end.

Stabilising planning with live WIP and material signals

Stabilising planning with live WIP and material signals

Stabilising planning with live WIP and material signals

Earlier, the daily plan was decided in fragments—by urgency, not by capacity and material signals. This broke down as soon as urgent orders came in; jobs were half-started and WIP piled up across stages. We implemented a single system of record connecting sales, purchase, production and dispatch so everyone worked off the same status. Planning moved to a simple daily schedule linked to orders, capacity and material, reducing last-minute reshuffles. For electrical operations in Ghaziabad, this reduced dependence on a single person for updates. The practical outcome was smoother handovers between departments and more predictable daily execution.

Earlier, the daily plan was decided in fragments—by urgency, not by capacity and material signals. This broke down as soon as urgent orders came in; jobs were half-started and WIP piled up across stages. We implemented a single system of record connecting sales, purchase, production and dispatch so everyone worked off the same status. Planning moved to a simple daily schedule linked to orders, capacity and material, reducing last-minute reshuffles. For electrical operations in Ghaziabad, this reduced dependence on a single person for updates. The practical outcome was smoother handovers between departments and more predictable daily execution.

Cleaning masters to make reports match reality

Cleaning masters to make reports match reality

Cleaning masters to make reports match reality

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. 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 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. 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.

Implementation: Centralised workflow with clear owners

Implementation: Centralised workflow with clear owners

Implementation: Centralised workflow with clear owners

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 single system of record connecting sales, purchase, production and dispatch so everyone worked off the same status. Collections follow-ups moved to a single list with dispute tagging so teams stopped chasing the wrong invoices. Over time, the team built confidence because the numbers matched what they saw on the floor. The practical outcome was that teams stopped re-entering the same data and acted on exceptions early.

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 single system of record connecting sales, purchase, production and dispatch so everyone worked off the same status. Collections follow-ups moved to a single list with dispute tagging so teams stopped chasing the wrong invoices. Over time, the team built confidence because the numbers matched what they saw on the floor. The practical outcome was that teams stopped re-entering the same data and acted on exceptions early.

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