SUSTAINABILITY IN MANUFACTURING PROCESS IN STEEL RE ...

SUSTAINABILITY IN MANUFACTURING PROCESS IN STEEL RE-ROLLING MILLS

A synopsis

Submitted to

Gujarat Technological University

For the award of

Doctor of Philosophy

In

Mechanical Engineering

By

Rita Kishorkumar Jani

[149997119024]

Under the supervision of

Dr. Jeetendra A. Vadher

GUJARAT TECHNOLOGICAL UNIVERSITY

CHANDKHEDA, AHMEDABAD.

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INDEX

Sr.No. Topic Page no.

1. Title Page 1

2. Index 2

3. Abstract 3

4. State of art of research 4

5. Problem definition 4

6. Objective of work 5

7. Scope of work 5

8. Original contribution by the thesis 5

9. Research Methodology 6

10. Achievements with respect to objectives 20

11. Conclusion 21

12. Future scope 21

13. Details of research paper published 21

14. References 22

15. Appendix 25

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ABSTRACT

The traditional manufacturing processes resulted in depletion of natural resources as well as created

environmental pollution issues which were directly affecting the society. Sustainable development in

manufacturing industries leads the way towards clean and green manufacturing. Sustainable manufacturing

is emerging out as one of the most important area for environmental, economic, societal, and technological

changes in an industry. Steel Re-Rolling mill is the second most important steel forming industry in India.

In terms of specific energy consumption, the steel re-rolling process largely contributes to the total energy

input required for the smooth operation of the plant. The manufacturing process of steel re-rolling from

ingots to finished products has high energy consumption and it directly affects productivity and

manufacturing cost. Measuring the productivity of the process can unleash the low productive process,

which leads towards rectification for increasing the productivity of lean area. Bhavnagar district of Gujarat

state is having one of the maximum steel re-rolling mills. The productivity of Steel Re-Rolling mill of

Bhavnagar cluster is considered for research. Initially energy productivity and energy productivity ratio of

the whole manufacturing process was also calculated with the current data. Later on Performance

Objective – Productivity model is developed and applied to the mills of Bhavnagar Cluster. The Key

Performance Indicators were identified by intensive survey and process of prioritization was done by

Analytical Hierarchical Process. The actual values of the Key Performance Indicators of the system were

compared with the objectivated values Key Performance Indicators of the system in the developed model.

The outcome of the model lead to Productivity Index of the system, sub-system, and key performance areas

of the process revealing the areas with low-performance index which have the highest impact on

productivity of the process. This calculation showed that energy subsystem is having the lowest

performance index and is the main source of loss leading to low productivity. Rectifications and

recommendations are made to increase the productivity of energy sub-system. The developed model was

applied in the mills which gave positive output and better insight regarding the productivity of the

manufacturing process. The empirical relation between raw material consumed and coal fuel used is

developed which was used in industry to give out positive result in fuel saving.

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STATE OF ART OF RESEARCH

Steel industry is the spine of any nation. India being 3rd

largest producer of steel in world after

China and European Union produces 168.2 Million MT annually. These steel industries are equally

supported by secondary steel industries which comprise privately owned small and medium sized re-rolling

mills (SRRMs). There are nearly 1200 working re-rolling mills contributing nearly 57% of total India’s

steel outputs (bars, sections, industrial products, etc.).

Steel re-rolling is the process of converting raw/unprocessed steel into finished products by rolling and re-

rolling them in their hot state into desired shapes such as bars, sectional products, and wires. A bulk of this

finished products are utilized in construction sector. Steel re-rolling process is energy and waste intensive

process. Direct energy use in the SRRM sector consists of thermal energy used for heating the billets/ingots

and electric energy used for giving the shape by passing the material through rollers. Indirect energy usage

is accounted by the use of energy intensive raw materials. The SRRM units are characterized by the use of

outdated and low investment technologies and practices. In general, there is low awareness about energy

efficiency and many companies lack the in-house engineering and technical man power to absorb energy

efficiency measures in their process and to operate high end-technologies.

The manufacturing activities are the main source of consuming natural resources often detrimental to

environment affecting the sustainability of the process. Probing the low productive area in the

manufacturing system can lead to a sustainable manufacturing process. An attempt is made to develop a

sustainable manufacturing process of steel re-rolling mills.

DEFINITION OF PROBLEM

Sustainability in steel re-rolling mills is the most prominent issue considering at the current

requirement of the world. The steel re-rolling mills working currently are having very low productivity

which affects the economic, environmental, and social parameters.

The issues which are hindrance to the attainment of sustainability are:

The mill owners were not aware of energy productivity.

High Energy consumption due to low productive process.

Variable Fuel cost and grade of coal.

Inconsistency in availability of raw material.

Loss of massive amount of heat from furnace by flue gases and hot metal.

High material loss due to inaccurate heating profile in furnace.

Electric energy loss due inefficient working of gear box and idleness in rolling process.

Unskilled labour.

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OBJECTIVE OF WORK

To derive and prioritize the key performance indicators of steel re-rolling mills of Bhavnagar

Cluster for manufacturing evaluation.

To develop a model for computing productivity of the steel re-rolling mill, this will lead to a

sustainable manufacturing process.

Probing low productive area and rectification to increase productivity in SRRM.

To validate the developed productivity measuring model.

Implementation of developed productivity model in steel re-rolling mills.

SCOPE OF WORK

Steel re-rolling mills from Bhavnagar Cluster were taken into account out of which two of them

were semi-automatic and two were manually operated mills having coal fired furnace. The names of

the mills consulted are:

- Sachdeva Steel Products Pvt. Ltd. [Semi-automatic]

508/GIDC, Phase-2, Sihor, Bhavnagar.

- Triveni Rolling Mills Pvt. Ltd. [Semi-automatic]

F/28, Ruvapari Road, Bhavnagar.

- JR Steels Pvt. Ltd. [Manual]

40, Vadia, Sihor, Bhavnagar.

- Vinubhai Steels Pvt. Ltd. [Manual]

F-8, Ruvapari Road, Bhavnagar - 364001

ORIGINAL CONTRIBUTION BY THESIS

Referring the literature and consulting the personnel of re-rolling mills, it was concluded that the

mills where concentrating on energy efficiency rather than considering productivity of the mill. The main

objective of the research was to develop a productivity model for steel re-rolling mills.

The key contributions of the research are:

Derivation of key performance indicators of steel re-rolling mills and prioritizing them in

accordance to their weightages in manufacturing process.

Development of model for calculating the productivity index of the steel rolling mill which lead to

identify low productive area of the manufacturing process.

Application of developed model in the re-rolling mill for more productive process and attain

sustainability in manufacturing.

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RESEARCH METHODOLOGY

The flow chart as shown figure-1 is the whole

methodology of research carried out during

this process.

A. Derivation of Key Performance

Indicators.

The key performance indicators are the

parameters which directly or indirectly affect

the manufacturing process. Key performance

indicators were derived by consulting the

mill personnel. The key performance

indicators derived are shown in table-1

below:

B. Computing the weightages of key

performance indicators

The prioritizing of key performance

indicators was done by consulting the re-

rolling mill personnel and energy

auditors of re-rolling mills. The

hierarchical according to the importance

was checked for consistency by

analytical hierarchical process (AHP) and weightages of each KPI where found which were playing

role in manufacturing.

C. Mapping and allocating KPI into subcategories.

The key performance indicators were divided under the roof of sub category Cost and Energy. Cost

and energy were considered as the most important part of the whole manufacturing system by the

owners and energy auditors of re-rolling mills.

The KPI’s which directly affect the cost of the finished product are:

Raw material cost

Raw material availability

Inventory cost

Material cost

Conclusion

Validation of Model

Probing of Low Productive area in SRRM

Performance Objective Productivity model [PO-P Model]

Calculation of Energy Productivity and Energy Productivity Ratio

Developing relation between fuel consumption and raw material used

Data Collection

Splitting into subcategory and mapping of Key Performance Indicators

Calculating the weightages and prioritizing of Key Performance Indicators

Deriving Key Performance Indicators of process in SRRM

Sustainability in Manufacturing Process in Steel Re-rolling Mills

Figure-1 Flow Chart of research methodology

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Labour cost

Fuel cost

Table 1 Key performance indicators of steel re-rolling mills

Sustainability Factors Key Performance Indicators

Economic

• Inventory Cost

• Labour Cost

• Material Cost

• Maintenance Cost

• Raw Material Availability

• Product Delivery

Environmental

• Air Emission

• Energy Consumption

• Fuel Consumption

• Material Consumption

• Noise Pollution

• Water Utilization

• Land Utilization

Social

• Accident Rate

• Occupational Health and Safety

• Training and Education

The KPI’s which indirectly affect the cost of the finished product depending on the Energy usage

are:

Energy Consumption

Fuel consumption

Energy generation

Fuel waste

Water utilization

Idle energy consumption

The mapping and division of KPI’s into sub-category are as shown in figure - 2 .

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D. Data Collection

A questionnaire was developed to acquire the annual data related fuel consumption and raw

material consumption and other data supporting the key performance indicators. The data related to

parameter shown in table – 2 was collected.

Table- 2 Type of data collected

SR.

NO. DATA REQUIRED

SR.

NO. DATA REQUIRED

1 Name of Industry 11 Fuel cost

2 Working hours/day 12 Fuel consumption

3 Furnace working time 13 Electricity Energy Charges

4 Furnace working temperature 14 Idle time

5 Preheating time of furnace 15 Energy charges idle w/o production

6 Post cooling time of furnace 16 Raw material consumed

7 Preheating temperature of furnace 17 Finished material

8 Post cooling temperature of furnace 18 Labour cost

9 Total furnace working time 19 Transport cost

10 Fuel type 20 Fix cost

The annual data related to fuel and raw material consumption is shown in table -3,4,5,6:

Table -3 Sachdeva Steel Products, Sihor, Bhavnagar

Month/Year Fuel Type

Fuel

consumption

tons

Raw

material

tons

Coal

Consumption

per ton of

raw material

Variation

with the

average

value

Nov-16 Pulverized Coal 86.70 785.877 0.110 -0.89%

Dec-16 Pulverized Coal 102.10 955.056 0.107 2.24%

Jan-17 Pulverized Coal 88.30 813.559 0.109 0.75%

Feb-17 Pulverized Coal 66.30 597.701 0.111 -1.44%

Raw Material

Availability

Inventory Cost

Material Loss

Maintenance Cost

Labour Cost

Fuel Cost

Fuel Consumption

Energy Consumption

Energy Generation

Fuel Waste

Energy Waste

Raw Material Cost

Water Utilization

Product Cost Energy cost

Idle Energy Consumption

Figure -2 Mapping of Key Performance Indicators

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Mar-17 Pulverized Coal 94.60 898.876 0.105 3.76%

Apr-17 Pulverized Coal 97.08 933.485 0.104 4.89%

May-17 Pulverized Coal 97.80 880.000 0.111 -1.63%

Jun-17 Pulverized Coal 85.58 754.045 0.113 -3.78%

Jul-17 Pulverized Coal 86.78 770.125 0.113 -3.05%

Aug-17 Pulverized Coal 61.47 519.610 0.118 -8.18%

Sep-17 Pulverized Coal 66.00 625.630 0.105 3.53%

Oct-17 Pulverized Coal 80.85 734.850 0.110 -0.61%

Year Total 1013.56 9268.815 0.109

Average 84.46 772.41 0.109

Table 4 Triveni Rolling Mill Ltd., Bhavnagar


Fuel

consumption

tons

Raw

material

tons

Coal

Consumption

per ton of

raw material

Variation

with the

average

value

Nov-16 Pulverized Coal 104.0 864.5 0.120 0.94%

Dec-16 Pulverized Coal 122.5 940.0 0.130 -7.29%


Feb-17 Pulverized Coal 79.6 657.4 0.121 0.38%



May-17 Pulverized Coal 117.4 968.0 0.121 0.20%





Oct-17 Pulverized Coal 97.0 805.0 0.121 0.79%

Year Total 1216.28 10011.600 0.121

Average 101.357 834.3 0.121

Table 5 J R Steel Pvt Ltd.


Fuel

consumption

tons

Raw

material

tons

Coal

Consumption

per ton of

raw material

Variation

with the

average

value







May-17 Pulverized Coal 94.50 795.600 0.119 12.53%

Jun-17 Pulverized Coal 79.40 632.400 0.126 7.55%



Sep-17 Pulverized Coal 70.20 510.600 0.137 -1.24%

Oct-17 Pulverized Coal 88.30 687.300 0.128 5.40%

Year Total 1056.00 7776.100 0.136

Average 88 648.01 0.136

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Table 6 Vinubhai Steel Pvt. Ltd.


Fuel

consumption

tons

Raw

material

tons

Coal

Consumption

per ton of

raw material

Variation

with the

average

value



Jan-17 Pulverized Coal 82.30 665.200 0.124 -13.14%


Mar-17 Pulverized Coal 88.40 780.000 0.113 -3.64%

Apr-17 Pulverized Coal 90.30 765.300 0.118 -7.90%

May-17 Pulverized Coal 86.12 780.000 0.110 -0.97%





Oct-17 Pulverized Coal 75.60 665.300 0.114 -3.91%

Year Total 900.03 7624.900 0.118

Average 75.01 635.41 0.118

The data related to KPI’s of all the four mills is shown in table - 7 :

Table 7 data related to KPI’s

SR.

NO.

DATA

REQUIRED semi-automatic manual

1 Name of

Industry

Sachdeva

Steel Products.

Triveni

Rolling mills.

J.R.Steels

Pvt. Ltd.

Vinubhai

Steels Ltd.

2 Working

hours/day 12 hours 12 hours 12 hours 12 hours

3 Furnace

working time 08 hours 08 hours 08 hours 08 hours

4

Furnace

working

temperature

1100 °C 1100 °C 1100 °C 1100 °C

5 Preheating time

of furnace 03 hours 3.5 hours/day 04 hours/day 04 hours/day

6 Post cooling

time of furnace 13 hours 12.5 hours /day 12 hours /day 12 hours /day

7

Preheating

temperature of

furnace

700 °C 760 °C 700 °C - 800 °C upto 800 °C

8

Post cooling

temperature of

furnace

1100 °C to 700 °C 1100 °C to 760 °C 1100 °C to 700 °C 1100 °C to 800 °C

9 Total furnace

working time 11 hours 11 hours/day 12 hours/day 12 hours/day

10 Fuel type Coal [Indonesian] Coal [Indonesian] Coal [Indonesian]

5400-6200 GCV

Coal [Indonesian]

less than 6200 GCV

11 Fuel cost 10 lakhs/month 9-10lakhs/month 5-08 lakhs/month 04-07 lakhs/month

12 Fuel

consumption 150 ton/month 140-70 tons/month 60-120 tons/month 50-100 tons/month

13

Electricity

Energy

Charges

18 lakhs/months 16 lakhs/months 17 lakhs/months 14 lakhs/months

14 Idle time 16 hours 16 hours 16 hours 16 hours

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15

Energy charges

idle w/o

production

4000unit/month 3500 units/month 3000 units/month 3000 units/month

16 Raw material

consumed 1800 ton/month 1200 ton/month 800 ton/month 900 ton/month

17 Finished

material

1750 ton/month

[2%-5%]

1135 ton/month

[3% - 6%]

760 ton/month

[3%-5%]

850-870 ton/month

[3%-6%]

18 Labour cost 400 Rs/ton 400-450 Rs/ton 500-600 Rs/ton 550-650 Rs/ton

19 Transport cost 850 Rs/ton raw

material to TMT bars

900 Rs/ton raw


950 Rs/ton raw


900 -1000/ton raw


20 Operation Fix

cost 1100 Rs/ton 1200 Rs/ton 1500 - 1700 Rs/ton 1600 - 1850 Rs/ton

E. Relation between fuel consumption and raw material processed

The annual data of fuel consumed and raw material processed is used to develop empirical relation

which is very important for knowing the relation of this two important parameters.

The correlation tested between fuel consumed and raw material processed generated from the data was

0.89 (better when correlation which is near to 1).

Regression analysis was carried for generating optimal mathematical. Raw material is considered as

independent variable and fuel used is considered as dependent variable. Further carrying ANOVA test

the mathematical model (Eq. – 01) was developed having R2 value 0.80.

Fuel [Coke Coal –tons] = 17.33 + 0.097 x (Raw Material Used [tons]) Eq – 01

F. Calculation of Energy productivity and energy productivity ratio

Energy efficiency is simply using less power by installing equipment that uses less energy to

perform the processes.

Energy productivity is performing the process without enhancing the instrumentation and making

energy work harder and reducing the energy waste.

Energy productivity is the ratio of total revenue generated [INR] in mills to energy input [Kwh]

from raw material to finish product (Eq. - 02)

Energy Productivity =

Eq – 02

Energy productivity focuses on maximizing the economic benefit for every unit of energy which is

consumed in the process.

Energy productivity of all the mills is shown in figure-3 and table –8 along with the data which is

used for calculation of energy productivity.

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Table 8 Data for Calculating Energy Productivity

Name of Industry

semi-automatic manual

Sachdeva

Steel

Products

Triveni

Rolling

mills

J.R.Steels

Pvt. Ltd.

Vinubhai

Steels Ltd.

OUTPUT

Finished Product Ton/Month 1750 1135 760 860

42000/ton INR 73500000 47670000 31920000 36120000

INPUT

Raw Material Ton/Month 1800 1200 800 900

electricity Usage INR 1800000 1600000 1700000 1400000

electricity Usage kwh/Month 240000 213333 226667 186667

Idle Electricity Usage kwh/Month 4000 3500 3000 3000

Coal Consumption ton 150 105 90 75

Coal Consumption Kwh/Month 1221150 854805 732690 610575

Energy Productivity INR/Kwh 50.17 44.48 33.17 45.14

Energy Productivity Ratio

Energy productivity ratio [EPR] is defined

as the ratio to the total energy of raw

materials, electricity, and other energy

used (Eq.-03).

The energy productivity so obtained provides a way to make the decision, rather to continue the

production process or the process is having energy losses which are to be rectified as it calculates the yield

of energy. If the EPR is greater than 1 than the production process can be continued and if less than 1 than

there energy waste in the system.

For the calculation of EPR of the re-rolling mills the parameters considered are as follows:

Input Parameter

- Finished Product [Ton]

Input Parameters

𝐄𝐧𝐞𝐫𝐠𝐲 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐯𝐢𝐭𝐲 𝐫𝐚𝐭𝐢𝐨 𝐄𝐏𝐑 = 𝑶𝒖𝒕𝒑𝒖𝒕 𝑬𝒏𝒆𝒓𝒈𝒚

𝑰𝒏𝒑𝒖𝒕𝑬𝒏𝒆𝒓𝒈𝒚 Eq – 03

50.17 44.48

33.17

45.14

0.00

10.00

20.00

30.00

40.00

50.00

60.00

Sachdeva

Steel

Products

Triveni

Rolling

mills

J.R.Steels

Pvt. Ltd.

Vinubhai

Steels Ltd.

I

N

R

/

K

w

h

Energy Productivity of SRRM

Energy Productivity

Figure 3 Energy productivity of SRRM

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- Raw material [INR]

- Electricity usage [Kwh]

- Idle electricity usage [Kwh]

- Coal consumption [INR]

- Labour cost, transport cost, fix cost [INR]

Each parameter is to be converted in form of energy i.e equivalent energy.

INR is converted to Kwh by ratio of INR to the price of diesel/litre with calorific value of diesel

[Kwh] (Eq.-04).

Eq – 04

Equal energy for coal is taken as 1 ton of coal = 8141 Kwh.

The converted data and values of parameters are shown in table – 9 and Energy Productivity Ratio-EPR is

shown in figure-4:

Table 9 Data for calculating Energy Productivity Ratio

Name of Industry

semi-automatic manual

Sachdeva Steel

Products

Triveni

Rolling

mills

J.R.Steels

Pvt. Ltd.

Vinubhai

Steels Ltd.

OUTPUT

Finished Product TON 1750 1135 760 860

[42000/TON] INR 73500000 47670000 31920000 36120000

Finished Product KWH 11707352 7593054 5084336 5753327

INPUT

Raw Material TON 1800 1200 800 900

Raw Material INR 57600000 38400000 25600000 28800000

Raw Material[32000/Ton] Kwh 9174741 6116494 4077663 4587370

electricity Usage Kwh 240000 213333 226667 186667

Idle electricity Usage Kwh 4000 3500 3000 3000

Coal Consumption Ton 150 105 90 75

Coal Consumption kwh 1221150 854805 732690 610575

Labour Cost INR 720000 150000 440000 540000

Labour Cost kwh 114684.3 23892.55 70084.83 86013.2

Transport cost INR 1530000 1080000 760000 855000

Transport cost kwh 243704.1 172026.4 121055.6 136187.6

FIX Cost INR 1980000 1440000 1280000 1530000

FIX Cost kwh 315381.7 229368.5 203883.1 243704.1

ENERGY PRODUCTIVITY RATIO

[EPR]

EPR

1.035 0.997 0.935 0.983

Note: The data highlighted are used for calculation.

INR to Kwh = 𝑰𝑵𝑹 ×𝟏𝟏.𝟖𝟑 𝒌𝒘𝒉

𝟕𝟒.𝟐𝟕

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G. Performance Objectives – Productivity Model

[PO-P Model] :

The PO-P model emphasis on the achievement of

goals related to the system within the constraints of

the resource available in manufacturing process.

Energy productivity is only related to current data

while PO-P model takes into consideration the

objectives of the process which is to be achieved

considering the unaltered present condition.

PO-P Methodology

The methodology to calculate the productivity index [PI] of the whole system comprises of the

following steps (figure-5):

i. Identification of Sub-systems

ii. Identification of KPA’S [Key Performance Area] in each of the Sub-systems.[4,5,6]

iii. The setting of Performance Objectives.

iv. Ranking and Weighing of Sub-systems, KPA’s

and Performance Objectives.

v. Determination of objectivized output.

vi. Calculation of productivity index

vii. Identification of Sub-system, KPA’s with low

performance.

The process of PO-P model and the measurement of

productivity of the whole system is structured in stages as

shown in figure – 6. To arrive to this stage the whole

calculation in divided into stages:

i. Calculation of productivity index of KPI’s of

subsystem.

ii. Calculation of productivity index of subsystem.

iii. Calculation of productivity index of system.

The Productivity index of a sub-system is generated from the productivity indices of the key

performance areas [KPA’S] of the sub-system.

1.035

0.997

0.935

0.983

0.850

0.900

0.950

1.000

1.050

Sachdeva

Steel

Products

Triveni

Rolling

mills

J.R.Steels

Pvt. Ltd.

Vinubhai

Steels

Ltd.

EPR

EPR

Figure 4 Energy Productivity Ratios

Figure 4 Flow Chart of PO-P model

for productivity measurement

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The productivity indices of the system is

= ∑

Where,

∑ =

, the Productivity Index of a Sub-system u, is determined as

= ∑

Where, for all u’s

∑ =

, the Productivity Index of a Key Performance Indicator, v of Sub-system u, is determined as

= ∑

Where, for all u’s and v’s

∑ =

Substituting the values of Eq-07 in Eq.-06 the productivity index of the Sub-system u, is

= ∑ ∑

Substituting the values of Eq.-08 in Eq.-01 the productivity index of the system S, is

= ∑ ∑ ∑

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Productivity model of steel re-rolling mills

To develop a productivity model of manufacturing system, two sub-systems and their respective KPA’s

were taken into account after having a conversation with the directors of the industries. The prioritization of

KPA’S was analytically sequenced by Analytical Hierarchical Process (AHP). The Subsystems and their

importance KPA’s are listed in table - 10.

Table 10 Sub-system and their KPI’s

Sub-System KPA’S

Energy

Energy Consumption

Fuel Consumption

Energy Generation

Fuel Waste

Energy Waste

Water Utilization

Idle Energy Consumption

Cost

Raw Material Cost

Raw Material

Availability

Inventory Cost

Maintenance Cost

Material Loss

Labour Cost

Fuel Cost

Steel Re-rolling Mill

Energy (u1)

Energy Consumption (u1v1)

Fuel Consumption (u1v2)

Energy Generation (u1v3)

Fuel Waste (u1v4)

Energy Waste (u1v5)

Water Utilization (u1v6)

Idle Energy Consumption (u1v7)

Cost (u2)

aw Material Cost (u2v1)

Raw Material Availability (u2v2)

Inventory Cost (u2v3)

Maintenance Cost (u2v4)

Material Loss (u2v5)

Labour Cost (u2v6)

Fuel Cost (u2v7)

PI of System

PI of Sub-system (u)

PI of Key Performance

Indicators (uv)

𝑷𝑰 = ∑𝑾𝒖 𝑷𝑰 𝒖𝒖 𝟏

𝑷𝑰 𝒖 = ∑𝑾𝒗𝒖 𝑷𝑰 𝒗𝒖𝒗 𝟏

𝑷𝑰𝒗𝒖 = ∑𝑾𝒚𝒖𝒗

𝑶𝒚𝒖𝒗

𝑶𝒚𝒖𝒗

𝒚 𝟏

Figure 5 Productivity measurement structure for Steel Re-Rolling Mill

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Comprehensive conversation was carried out with the senior personnel of the industries to determine the

relative ranking of the sub-systems out of 10 and their weightages were calculated. The result is shown in

table 11.

Table 11 Sub-system’s relative priorities and weightages

Sub-

Systems

Sachdeva

Steels

Triveni

Steels

Vinubhai

Steels

JRsteels Laxmi

Steels

I II I II I II I II I II

Energy 7 0.44 8 0.47 8 0.47 7 0.44 9 0.56

Cost 9 0.56 9 0.53 9 0.53 9 0.56 7 0.44

I – Relative Grades; II – Weightage

The key performance areas, relative ranks, and weightage related to sub-systems energy and cost are

shown in table 12 and 13 respectively. Data related to the performance of the industries was extracted in the

form of actual and objectivated values as listed in table 14. The objectivated are taken as per the company

norms listed in Appendix – I. The weightage factors of the KPA’s of the subsystems with respect to their

objectivated values and actual values are shown in table 15.

Productivity Index of the system

The productivity index PI of the subsystem Energy and Cost is achieved by summation of product of

weightage of KPA, weightage of observed value and weightage of actual value are shown in figure 7 and

the detailed calculation are shown in Appendix – II table 1.

The productivity index of whole system is achieved by summation of product of productivity index of sub-

systems and their respective weightage as shown table 16 and detailed calculations to achieve the

performance index of the system is shown in Appendix – II table 2.

Validation of Model

Validation of developed was done in Laxmi

Steel Re-rollers Pvt. Ltd. and the output of the

developed model is shown along with the data

of other rolling mills.

0.5906 0.5679 0.5342 0.5459 0.5374

0.7037 0.7247 0.7311 0.7712 0.7516

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

SACHDEVA

STEELS

TRIVENI

STEELS

JRSTEELS VINUBHAI

STEELS

LAXMI

Steels

Perfo

rm

an

ce I

nd

ex

ENERGY COST

Figure 6 Productivity Index of the subsystem Energy and

Cost

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Table 12 Key performance areas, relative ranks and weightage related to sub-system energy.

KPA’s Sachdeva

Steels

Triveni

Steels

Vinubhai

Steels

JR

Steels

Laxmi

Steels


Energy Consumption 7 0.15 8 0.16 8 0.17 8 0.17 9 0.17

Fuel Consumption 8 0.17 8 0.16 8 0.17 8 0.17 8 0.15

Energy Generation 7 0.15 7 0.14 8 0.17 7 0.15 9 0.17

Fuel Waste 6 0.13 7 0.14 7 0.15 7 0.15 8 0.15

Energy Waste 9 0.19 9 0.18 8 0.17 8 0.17 9 0.17

Water Utilization 5 0.10 5 0.10 4 0.08 4 0.08 4 0.08

Idle Energy

Consumption 6 0.13 6 0.12 5 0.10 6 0.13 5 0.10

I – Relative Grades; II – Weightage

Table 13 Key performance areas, relative ranks and weightage related to sub-system cost.

KPA’s Sachdeva

Steels

Triveni

Steels

Vinubhai

Steels

JR

Steels

Laxmi

Steels


Raw Material Cost 7 0.16 7 0.16 8 0.17 7 0.15 7 0.15

Raw Material

Availability 6 0.14 6 0.14 5 0.11 6 0.13 5 0.11

Inventory Cost 5 0.12 6 0.14 6 0.13 6 0.13 6 0.13

Maintenance Cost 5 0.12 6 0.14 6 0.13 6 0.13 6 0.13

Material Loss 7 0.16 7 0.16 8 0.17 8 0.17 9 0.20

Labour Cost 5 0.12 4 0.09 5 0.11 5 0.11 6 0.13

Fuel Cost 8 0.19 8 0.18 8 0.17 8 0.17 7 0.15

I – Relative Grades; II - Weightage

Table 14 Objectivated and actual values of system

Sachdeva Steels Triveni Steels Vinubhai Steels JRsteels Laxmi Steels

Sub

systems

KPA of

subsystems

Performance

Objectives O A O A O A O A O A

EN

ER

GY

Electric

Energy

Consumpti

on

kwh/month 216000 240000 198400 213334 168000 186667 210800 226667 138000 153334

Fuel

Consumpti

on

Coal Ton/month 200 210 144 151 146 153 130 153 138 144

Fuel Waste Coal Ton/month 3 6 2 5 2 6 3 7 1 5

Energy

Generation kwh/month 1465380 1465380 1053678 1053678 1067634 1067634 960871 960871 1004832 1004832

Energy

Waste kwh/month 732690 879228 474155.1 632206.8 533817 640580.4 432391.95 576522.6 502416 602899.2

Water

Utilization liter/day 12000 15000 10000 12000 6500 6500 6500 6500 6000 6000

Idle

electric

Energy

Consumpti

on

kwh/month 3800 4000 3200 3500 2700 3000 2700 3000 2500 3500

CO

ST

Raw

Material

Cost

Rs/month 57600000 64800000 39600000 43200000 27000000 30600000 24000000 27200000 24000000 27200000

Inventory

Cost Rs./ton 1710000 1980000 1260000 1440000 1305000 1665000 960000 1360000 1120000 1440000

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Maintenance Cost

Rs/ton 900000 1260000 720000 900000 540000 675000 480000 600000 520000 640000

Material

Loss

ton/month

[Rs.] 864000 1600000 1188000 2592000 810000 1836000 720000 1632000 810000 1836000

Labour

Cost Rs./ton 693000 720000 456000 540000 369000 585000 328000 400000 320000 488000

Fuel Cost Rs./month 1360100 1404000 978200 1014000 991600 1033500 891100 928000 931300 968500

O – Objectivated Value; A – Actual Value

Table 15 Weight factors of the KPA are of the subsystems - objectivated values and actual values.

Sachdeva Steels Triveni Steels Vinubhai Steels JRsteels Laxmi Steels

Sub-

syst

ems

KPA’ Ratio WF OW AW WF OW AW WF OW AW WF OW AW WF OW AW

EN

ER

GY

Electric

Energy

Consumpti

on

Useful

Energy

Consumpti

on /Total

Electric

Units

consumed

0.15 0.9827 0.9836 0.16 0.9841 0.9839 0.17 0.9842 0.9842 0.17 0.9874 0.9869 0.17 0.9822 0.9777

Coal

Consumpti

on

Coal

Used/Total

Coal

0.17 0.9852 0.9722 0.16 0.9863 0.9679 0.17 0.9865 0.9623 0.17 0.9774 0.9563 0.15 0.9928 0.9664

Useful Heat

Energy

Heat

Energy

Used/Total

Heat

Generated

0.19 0.5000 0.4000 0.18 0.5500 0.4000 0.17 0.5000 0.4000 0.17 0.5500 0.4000 0.17 0.5000 0.4000

Water

Utilization

Used Water

/Total

Water

0.10 0.8000 1.0000 0.10 0.8333 1.0000 0.08 1.0000 1.0000 0.08 1.0000 1.0000 0.08 1.0000 1.0000

CO

ST

Material

Yield Cost

Useful

Conversion

Of

Material/Co

st Of Raw

Material

0.16 0.9850 0.9753 0.16 0.9700 0.9400 0.17 0.9700 0.9400 0.15 0.9700 0.9400 0.15 0.9663 0.9325

Inventory

Turnover

COST

Inventory

Cost/Cost

Of Raw

Material

0.12 0.0297 0.0306 0.14 0.0318 0.0333 0.13 0.0483 0.0544 0.13 0.0400 0.0500 0.13 0.0467 0.0529

Maintenanc

e Cost

Maintenanc

e Cost/Cost

Of Raw

Material

0.12 0.0156 0.0194 0.14 0.0182 0.0208 0.13 0.0200 0.0221 0.13 0.0200 0.0221 0.13 0.0217 0.0235

Labour

Cost

Labour

Cost/Cost

Of Raw

Material

0.12 0.0120 0.0111 0.09 0.0115 0.0125 0.11 0.0137 0.0191 0.11 0.0137 0.0147 0.13 0.0133 0.0179

Fuel Cost

Index

Fuel Cost/

Cost Of

Raw

Material

0.19 0.0236 0.0217 0.18 0.0247 0.0235 0.17 0.0367 0.0338 0.17 0.0371 0.0341 0.15 0.0388 0.0356

WF – Weigth Factors; OW – Objectivated Values Weight factors; AW – Actual Values Weight factors

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Table 16 Productivity Index of Steel Re-rolling mills.

Sr. No. Steel Re-Rolling mills Performance Index

1. Sachdeva Steels 0.6542

2. Triveni Steels 0.6509

3. J.R.Steels 0.6450

4. Vinubhai Steels 0.6652

5. Laxmi Steels 0.6311

The output of developed PO-P model is the performance index of whole re-rolling

mill and further analyzing the model the subsystem energy is the weak performer of the

whole system i.e energy losses are more or in other words energy productivity of the

manufacturing system is less. Comparing the performance index of each subsystem as shown

in table – 17 the performance indices of the energy subsystem are ranges from 53% to 60%

while that of the cost are nearly 70%-75%. Thus this result reveals that energy sub-system is

the weak performer in the re-rolling mills. The energy subsystem is the area where more

emphasis is to be given for increasing the productivity of the whole system.

Table 17 Performance indices of Sub-system

Sub-

System

Sachdeva

Steels

Triveni

Steels

JR

Steels

Vinubhai

Steels

Laxmi

Steels

Energy 0.5906 0.5679 0.5342 0.5459 0.5374

Cost 0.7037 0.7247 0.7311 0.7712 0.7516

ACHIEVEMENTS WITH RESPECT TO OBJECTIVES

Objectives Achievements

Deriving and prioritizing Key Performance

Indicators of Steel Re-Rolling mill.

Key Performance Indicators of steel re-rolling

mills were derived and prioritizing of KPI’s was

by Analytical Hierarchical Process.

Developing a model to compute

productivity of the steel re-rolling mill,

this will lead to a sustainable

manufacturing process.

Performance Objectives – Productivity model

is developed for steel re-rolling mill.

Probing low productive area and

rectification to increase productivity in

SRRM.

Energy sector is having the lowest productivity

and to support this conclusion, energy

productivity and energy productivity ratio was

also calculated.

To validate the developed productivity

model.

The POP model was applied to other re-rolling

mill and the outcome was validated.

Implementation of developed productivity

model in steel re-rolling mills.

The model was applied in working re-rolling

mills giving encouraging results.

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CONCLUSION

• The derived key performance indicators of steel re-rolling mills are the major support

for developing a sustainable manufacturing process.

• The analytical model developed will be an aid for approximating the quantity of fuel

quantity for processing the raw material.

• Energy Productivity -EP and Energy Productivity Ratio -EPR will be an aid for the

managers of steel re-rolling mills for calculating the productivity of the process.

• Performance Objective- Productivity (PO-P model) developed will provide assistance

for knowing the weak key performance indicator of the whole system leading to

rectify it for a sustainable manufacturing process.

• Energy subsystem is the major concern for the low productivity of the whole system.

• The inefficient technologies, procedures and neglecting the periodic maintenance

resulted in major loss of energy in the industry.

• Energy savings is the most vital area in steel re-rolling mills.

FUTURE SCOPE

• Only process element is considered in this research which makes a gateway to further

analyze the other two elements of sustainability i.e. system & product.

• More in-depth study can be done of individual Key Performance Indicators.

• This model can also be designed and developed for other manufacturing process.

DETAILS OF RESEARCH PAPER PUBLISHED

Sr. No. Title of Research Paper Main Author Journal Name Approved

1 A Review: Sustainability In

Manufacturing Industries - Case

Study Of Steel Re-Rolling

Mills.

Rita K. Jani

Dr. J.A. Vadher

A.D.Kalani

International Journal of Modern

Trends in Engineering and

Research (IJMTER) Volume 04,

Issue 8, August– 2017.

UGC

2 Prospects for Energy Saving

and Increasing the Efficiency in

Steel Re-Rolling Mills.

Rita K. Jani

Dr. J.A. Vadher

A.D.Kalani



Research (IJMTER) Volume: 4,

Issue: 10, October– 2017.

UGC

3 Energy Performance

Assessment By Means Of

Process Heating Assessment

And Survey Tool (PHAST) And

Feasibility Evaluation Of Waste

Heat Recuperation In The

Reheat Furnace At Steel Re-

Rolling Mill.

Rita K. Jani

Dr. J.A. Vadher

A.D.Kalani




Issue: 11, November– 2017.

UGC

4 Key Performance Indicators of

Steel Re-Rolling Mills for

Sustainable Manufacturing.

Rita K. Jani

Dr. J.A. Vadher

A.D.Kalani




UGC

of 27

Issue: 12, December– 2017.

5 Empirical Relation of Raw

Material and Fuel Consumption

in Steel Re-Rolling Mills.

Rita K. Jani

Dr. J.A. Vadher

A.D.Kalani




Issue: 2, February– 2018.

UGC

6 Probing Low Productive area in

Steel Re-Rolling Mills

Rita K. Jani

Dr. J.A. Vadher

A.D.Kalani

International Journal of

Innovative Technology and

Exploring Engineering (IJITEE),

Volume-8 Issue-11, September

2019.

SCOPUS

INDEXED

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APPENDIX – I

POLICY NORMS

Policy Norms at Sachdeva Steels.

Turnover growth rate of 10% per year.

Electricity consumption should reduce by 10%

Always use the best quality of coal available in market.

Fuel waste should be as low as possible

Loss of water should not be greater than 10%.

Loss of heat energy should not be less than 40%.

Inventory should not be greater than 40%.

Maintenance cost should not exceed 700/ton.

Material loss should not exceed 3% of total raw material.

Labour cost should not exceed 400/ton.

Working days 06/week and Friday holiday.

08 hours /day working time 10:00pm to 06:00 am.

Policy Norms at Triveni Steels.











Policy Norms at JR Steels.


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Policy Norms at Vinubhai Steels.











Policy Norms at Laxmi Steels.


Electricity consumption should reduce by 10%.









APPENDIX – II

Table 1 Productivity Index of the subsystem Energy and Cost

Table 2 Productivity Index of the System

Industry Subsystems

Energy PI Cost PI

Sachdeva

Steels

(0.15 x 0.9827 x 0.9836) +

(0.17 x 0.9852 x 0.9722) +

(0.19 x 0.50 x 0.40) +

(0.10 x 0.80 x 1)

0.5906

(0.16 x 0.9850 x 0.9753) + (0.12 x

0.0297 x 0.0306) + (0.12 x 0.0156x

0.0194) +

(0.12 x 0.0120 x 0.0111) + (0.19 x

0.0236 x 0.0217)

0.7037

Triveni

Steels

(0.16 x 0.9841 x 0.9839) +

(0.16 x 0.9863 x 0.9679) +

(0.18 x 0.5 x 0.4) +

(0.10 x 0.8333 x 1.00)

0.5679

(0.16 x 0.97 x 0.94) +

(0.14 x 0.0318 x 0.0333) +

(0.14 x 0.0182 x 0.0208) +

(0.09 x 0.0115 x 0.0125) +

(0.18 x 0.0247 x 0.0235)

0.7247

Vinubhai

Steels

(0.17 x 0.9874 x 0.9869) +

(0.17 x 0.9774 x 0.9563) +

(0.17 x 0.55 x 0.40)+

(0.08 x 1.00 x 1.00)

0.5342

(0.15 x 0.97 x 0.94) +

(0.13 x 0.04 x 0.05) +

(0.13 x 0.02 x 0.0221) +

(0.11 x 0.0137 x 0.0147)+

(0.17 x 0.0371 x 0.0341)

0.7311

JRSteels

(0.17x 0.9842 x 0.9842) +

(0.17 x 0.9865 x 0.9623) +

(0.17 x 0.50 x 0.40) +

(0.08 x 1.00 x 1.00)

0.5459

(0.17 x 0.97 x 0.94) +

(0.13 x 0.0483 x 0.0544) +

(0.13 x 0.02 x 0.0221) +

(0.11 x 0.137 x 0.0191) +

(0.17 x 0.0367 x 0.0338)

0.7712

Laxmi

Steels

(0.17 x 0.9822 x 0.9777) +

(0.15 x 0.9928 x 0.9664) +

(0.17 x 0.50 x 0.40) +

(0.08 x 1.00 x 1.00)

0.5374

(0.15 x 0.9663 x 0.9325) +

(0.13 x 0.0467 x 0.0529) +

(0.13 x 0.0217 x 0.0235) +

(0.13 x x0.0133 x 0.0179) +

(0.15 x 0.0388 0.0356)

0.7516

Industry

System

Energy Cost Overall Performance Index

PI Weightage PI Weightage

Sachdeva

Steels 0.5906 0.44 0.7037 0.56

(0.5906 x 0.44) +

(0.7037 x 0.56) 0.6542

Triveni

Steels 0.5679 0.47 0.7247 0.53

(0.5679 x 0.47) +

(0.7247 x 0.53) + 0.6509

Vinubhai

Steels 0.5342 0.47 0.7311 0.53

(0.5342 x 0.47) +

(0.7311 x 0.53) 0.6450

JRSteels 0.5459 0.44 0.7712 0.53 (0.5459 x 0.44) +

(0.7712 x 0.53) 0.6652

Laxmi

Steels 0.5374 0.56 0.7516 0.44

(0.5374 x 0.56) +

(0.7516 x 0.44) 0.6311

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