a maintenance approach towards benchmark effective...

A maintenance approach towards

Benchmark Effective Operating Hours at

Blast Furnaces in Tata Steel,

Jamshedpur

2

Aspiration to achieve 96% EOH (350 Days) in next 2 years

An additional 0.5 MTPA (300+ Cr to Steel Business)

Benchmarking for Effective Operating Hours/Days

EOH – Effective Operating Hours

1% increase in EOH results in significant value creation

Effective

Operating

Hours

Utilization Losses

Planned Losses

Quality Losses

Unplanned Losses

Speed Losses

Selling losses

Logistic losses

No input material

No orders

Equipment Failures

Breakdown

Operator Failures

Unscheduled events

Planned Maintenance

Scheduled Operation Downtime

Performance Loss

Process disturbances

HM loss due to Si >1.3%

HM loss due to S> 0.1%

EOH (in hrs) = God Hours- A-B-C-D-E

A

B

C

D

E

EOH

Measuring Effective Operating Hours

SCOPE OF WORK

EOH loss analysis (MTPA)

Blast furnace EOH basis best performance

Unplanned

Losses

0.172

Planned

Losses

0.193

Utilization

Loss

0.183

Maximum

Production

9.05

Availability Quality Loss

0.108

Performance

7.968

Performance

Loss

8.5

EOH

7.861

FY14

Maximum

Production

EOH

8.1

Quality Loss

0.073

8.959

8.387

Unplanned

Losses

0.136

Planned

Losses

0.25

Utilization

Loss

0.186

Performance

8.172

Performance

Loss

0.215

Availability

EOHMaximum

Production

9.091

Unplanned

Losses

0.133

Planned

Losses

0.252

Utilization

Loss

0.173

8.293

Quality Loss

0.042

Performance

8.336

Performance

Loss

0.199

Availability

8.535

86.86%88.04%93.92%100%

FY15

FY16

543.1 hr 551.2 hr 510.3 hr

549 hr 734 hr 410hr

486 hr 724 hr 376 hr

90.41%91.21%93.61%100%

91.25%91.67%93.88%100%

600 hr 89.67 hr

638 hr 223 hr

539 hr 124 hr

Maintenance strategy to increase EOH

5

Major shutdown

Two main value pools to target

Value pool 1: Zero delays

Ideal maintenance planning

Intelligent inputs, monitoring

(digital)

Smarter execution/ work

processes

Value pool 2: Efficient

shutdowns

Higher MTBS

Lower planned shutdown

time

Higher execution

compliance, effectiveness

Monthly

shutdown

MTBS1

0

5

10

15

20

25

30

35

mTBS2

Delays/

Interruptions

1

2

MTBS – Mean Time Between Shutdowns


6

Major shutdown





(digital)


processes


shutdowns

Higher MTBS


time

Higher execution


Monthly

shutdown

MTBS1

0

5

10

15

20

25

30

35

mTBS2

Delays/

Interruptions

1

2

Blast furnace: Key Unplanned Losses – Chronic Soft

Spots

62%

GCP

12%

MB 17%

Tuyere

2%

Stoves 7%

BLT

Key breakdown

issues(FY14)

1

13

18

15

26

Effective

availability loss

(# of hours)

Frequency

(# of times)

8%

BLT 13%

Stoves

GCP 1%

MB

16%

Tuyere 62%

Key breakdown

issues(FY15)

6

32

29

10

29

Effective

availability loss

(# of hours)

Frequency

(# of times)

55%

1%

GCP 6%

MB

BLT

9%

Stoves 29%

Tuyere

Key breakdown

issues(FY16)

4

5

26

14

19

Effective

availability loss

(# of hours)

Frequency

(# of times)

8

Strategy for Addressing Chronic Soft Spots

Tuyeres Conveyors BLT

Charging

EXTENSION OF CONDITION DIAGNOSTICS

– SETTING UP OF AMDC

(Asset Management & Diagnostics Centre)

STANDARDIZATION OF INSPECTION &

MAINTENANCE PROCEDURES ACROSS

BFs

RETURN TO BASIC OPERATING

CONDITIONS –

-- MODEL WORKPLACE CAMPAIGN

-- MODEL CONVEYOR CAMPAIGN

FORUM FOR TECHNICAL EXCHANGE

WITH OEM CREATED

Condition Diagnostics for Blast

Furnaces

9

• Temperature Trends

• Vibration Trends

• Thickness Checks

• Weld Joint Checks

• Replacement Strategies

Model Conveyors

10

Data Analytics for Blast Furnaces

Real Time Data Capturing &

Monitoring

11

Competency Building

programmes

Standardization of Technical Specifications/Training

• Drawing standardization of all conveyor pulleys to ensure

higher reliability.

• Development of SMPs(Standard Maintenance Practices) for all

critical equipment.

• Formulation of different CCTs( Core Competency Team) for

quality assurance of spares

12

The Bottom-Up Approach

12

Shop-floor driven Campaigns for Blast Furnaces

• MASS

• SGA Circles


13

Major shutdown





(digital)


processes


shutdowns

Higher MTBS


time

Higher execution


Monthly

shutdown

MTBS1

0

5

10

15

20

25

30

35

mTBS2

Delays/

Interruptions

1

2

Shutdown Optimization: Analysis of Planned Losses

A Planned Outage i.e. Furnace Shutdown affects production under following

buckets

1. Ramp-Up & Ramp-Down Time – Fixed in nature

2. Isolation Time - Fixed in nature

3. Net Maintenance Time - Varies according to the Shutdown Plan

4. Isolation Removal & Trials - Fixed in Nature

Shutdown Optimization: Reduction in Number of Planned Outages will result

in :

• Net Gain in total Maintenance time keeping Planned Outage hour

as constant in a year

• Net Gain in EOH

• An advantage in the planning of resources & subsequently, cost

Following levers identified for optimizing shutdown

planning & execution process

15

Shutdown

optimization

Increase

MTBS

Reduce

duration/

sd

Increase

eff./sd

Identify max. possible

MTBS basis life, ops.

constraints

De-risk b/d between s/d

Effective planning &

scheduling

Improve productivity in

HoT activities

Streamline NVA

activities

1

2

3

16

Increase in MTBS - Risks & Challenges

.If strategies are not implemented rigorously,

• Chances of an increase in Unplanned Outages

• De-Bottlenecking of one equipment shifts the constraint to

another equipment

• Chances of bigger failures

Approach for Deployment - The increase in MTBS was

first implemented in a smaller furnace

Trend1: MTBS has steadily increased from 45 days to 75 days while delay is also

in decreasing trend.

Trend2: Total shutdown hours has decreased from 200 hours to 150 hours range &

in last 3 years shutdown hours more or less are same. While no. of Shutdowns

goes down from 8 to 4.

Deployment of Learning in the Larger Furnaces

0

20

40

60

80

100

120

FY12 FY14 FY15 FY16 FY17

Delay v/s MTBS

8

6

5 44

0

1

2

3

4

5

6

7

8

9

0

50

100

150

200

250

FY12 FY14 FY15 FY16 FY17

Total SD hours v/s No. of SD

Total SD Hours No. of SD

An Experiment in a Small Furnace

BF MTBS Journey

18

76 DAYS

59 DAYS

54 DAYS

FY 14 FY 15 FY 16 FY 17

41 DAYS

90 DAYS

FY 18

Increasing Planning &

Resourcing capabilities

Entire value chain of shutdown management being

optimized & digitized

19

Shut down

strategy

Scope definition

& planning

Resource

planning

Scheduling,

execution &

governance

Contractor mgmt./

coordination

Contractor skills

Resource mgmt./

coordination

Contractor bidding/

strategy

Production planning

to minimize losses

Sequencing and

clustering

Reliability/ risk

assessment

Scope challenging

process

Target benchmarks

(cost/duration)

First time right

execution KPI

NVA activity

identification

Equipment, job

clustering

Distributed

maintenance

Clear decision roles &

responsibilities

Efficient handover/

start-up procedures

Optimized duration

of critical path

Detailed pre-shutdown

walkthroughs

Efficient permitting

process

Tight execution

monitoring

Look-backs

Safety monitoring

Shutdown preparedness index being developed as a

lead indicator for checking readiness

20

9 parameters to define S/D readiness

100

89

0

20

40

60

80

100

# of days to s/d

3715

54

30

19

Preparedness %Planning

Spare mgmt.

Document.

Contractor mgmt.

Prework

Facility maint.

Resource mgmt.

Scheduling

Coordination

Desirable readiness at each point of time

Shutdown Execution Control &

Measurement of Shutdown Performance

21

Improvement in Shutdown

Effectiveness

- Zero Rework

- No breakdowns after SD

- Time Compliance to Plan

- Job Compliance to Plan

- Unanticipated Jobs

- Jobs that took longer than

plan

22

Shutdown Duration Vs Delay

MTBS Vs Blast Furnace Availability

24

Questions

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