Equipment & infrastructure

Whether your labs and workshops can support the training volumes local demand requires.

Equipment readiness

74%

Weighted across all course areas

Critical gaps

Blocking planned intake

Total units short

Across all equipment types

Fully equipped areas

No shortfall recorded

Equipment readiness

Overall readiness across labs and workshops.

74% readiness. Two critical shortfalls — EV diagnostic kits and GPU workstations — must close before the recommended intake increases can proceed.

Equipment counts by type.

Shortfalls that limit practical training hours.

Full inventory with condition and gap.

			Condition		Criticality
EV Diagnostic Kits EV Technician	20	11	Good	9	Critical
CNC Machines CNC Operator	8	8	Good	0	Low
GPU Workstations AI / ML	30	18	Good	12	Critical
PLC Training Kits Industrial Automation	20	15	Fair	5	High
Battery Analysers EV Technician	10	6	Fair	4	High
Developer Laptops Full Stack	120	108	Good	12	Medium
EV Charging Test Bench EV Technician	4	1	Fair	3	Critical
Metrology Instruments CNC Operator	24	22	Good	2	Low

Each recommendation is tied to a planned intake change. Costs are illustrative demo estimates.

Critical

Reason: AI/ML student intake is expected to increase from 40 to 120 seats.

Expected impact: Support 120 additional students per year · ₹42.0 Lakh (indicative)

Critical

Reason: EV Technician intake is recommended to rise from 60 to 100 seats.

Expected impact: Enable 2 parallel EV practical batches · ₹18.5 Lakh (indicative)

High

Reason: Charging infrastructure maintenance is the fastest growing employer request.

Expected impact: Adds charging infrastructure module to curriculum · ₹11.2 Lakh (indicative)

Medium

Reason: Industrial automation demand is steady at +21% with ageing kits.

Expected impact: Reduces lab waiting time by an estimated 30% · ₹7.4 Lakh (indicative)

Closing the GPU and EV diagnostic kit gaps would support an estimated 220 additional trained students per year.