Jindal SAW Improving Plant Performance With Better Process Control
The Challenge
Jindal SAW’s Kosi Stainless-Steel Division, with a 30,000 MT annual capacity and 500 employees, was losing nearly ₹3 Cr EBITDA every month. Key issues included:
- Production inefficiencies and poor store management.
- High manpower costs and frequent machine breakdowns.
- Long changeovers, low capacity utilization (<40%), and WIP inventory clogging.
- Reactive processes, customer dissatisfaction, and supplier misalignment.
In short, the plant was busy but not effective, compounding losses each month.
The Approach
PBOPlus conducted a one-month diagnostic study to identify real gaps.
The findings revealed inefficiencies across manpower deployment, machine use, stores handling, and cultural alignment.
To reverse the decline, PBO deployed a structured six-step engagement model:
Leadership Alignment & KPI Baselines
Defined clear KPI-linked targets for efficiency, cost, and throughput.
Process Standardisation
Introduced 3T-SOPs (Test, Target, Tolerance) for machine maintenance and critical processes.
Reliability & Maintenance Discipline
Formed a new reliability team to oversee preventive maintenance and established routines to reduce breakdown frequency and severity.
Changeover & Output Optimisation
Applied SMED methodologies to reduce changeover times.
Optimised mill speed to increase output without new capex.
Low-Cost Automation
Implemented conveyors for auto pipe movement, auto feeding/charging devices, sensor-based length measurement, automated cutting, and angular roller systems — all reducing manual burden and costs.
Manpower & Space Optimisation
Deployed a 3-layered structure with multiskilling, strengthening supervision and clarifying roles.
Introduced 5S practices and space optimisation to unclog
WIP inventory and free production capacity.
The Results
Within six months, Jindal SAW achieved a full turnaround:
₹6.5 Cr EBITDA swing (–₹3 Cr/month to +₹3.5 Cr/month).
35–38% OEE improvement across machines.
90% reduction in inventory, freeing space and unlocking throughput.
Stores cost variation cut to 10% for predictability.
Stores cost variation cut to 10% for predictability.