The Challenge

With material coming in and going out for its trading business, alongside ongoing tarpaulin and pipe production, Ruby Polymers had several operational activities happening simultaneously across the facility.

A mismatch in a material consignment could mean time spent tracing and reconciling what was actually received or dispatched.

Trucks could remain inside the premises longer than necessary without a clear view of where time was being lost.

On the shop floor, machine performance depended on whether the machine was attended and running consistently, while unsafe behaviours such as mobile phone usage could go unnoticed during busy shifts.

Supervisors therefore had to rely heavily on manual observation to keep track of activities that were happening continuously across the facility.

Kalydo was deployed on the existing CCTV infrastructure to turn this activity into real-time, measurable operational data.

The Solution

LiveOps for Material & Logistics

• Gunny bags received and dispatched

• Inbound vs outbound material counts

• Loading and unloading duration

• Vehicle arrival, departure and dwell time

• Material handling rate and movement frequency

LeanOps for Production

• Machine cycle count per shift

• Average cycle time

• Working vs idle time

• Shift-wise and process-wise efficiency

SafeOps for Shop-floor Safety

• PPE compliance and missing PPE

• Mobile phone usage on the shop floor

• Behavioural violations by area and shift

• Restricted-zone access

Impact

Material counting accuracy
Automated counting created a reliable record of material movement, eliminating missing inventory and delivery errors. The team could identify discrepancies at the point of loading or unloading rather than during reconciliation.

Reduction in vehicle dwell time
Visibility into waiting, loading and unloading durations helped identify where delays were occurring. Management could allocate the right resources to delayed processes and plug operational gaps, reducing unnecessary vehicle dwell.

Improvement in machine utilization
Cycle and idle-time data highlighted underutilised production periods and variations across shifts. Management could identify the causes of idle time and adjust operations to improve machine utilisation.