Ray Enterprises - TranZact AI

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Ray Enterprises

Ray Enterprises tightened shop-floor control with a centralised workflow

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

52%

Reduction in daily coordination time for electrical work

52%

Reduction in daily coordination time for electrical 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

Inside Ray Enterprises

Ray Enterprises is a micro electrical manufacturing unit based in Ambala. 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.

Electrical

Industry

Electrical

Industry

Micro

Company Size

Micro

Company Size

Ambala, India

Location

Ambala, India

Location

BOM-based production for panels/wiring kits

Use Cases

BOM-based production for panels/wiring kits

Use Cases

Challenges

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

No single schedule linked orders, material availability and machine loading.

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

Controlling rework through traceability and holds

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 structured stage-wise tracking and checklists so work moved forward only when prerequisites were met. Masters were cleaned once, and daily reporting pulled from the same transactions instead of manual consolidation. 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.

Cleaning masters to make reports match reality

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 centralised workflow where each order, material requirement and update had a clear owner and timestamp. Planning moved to a simple daily schedule linked to orders, capacity and material, reducing last-minute reshuffles. 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.

Reducing month-end surprises with traceable transactions

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 centralised workflow where each order, material requirement and update had a clear owner and timestamp. 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.

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