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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.
Page 2 of 27
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
Page 3 of 27
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.
Page 4 of 27
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.
Page 5 of 27
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.
Page 6 of 27
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
Page 7 of 27
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 .
Page 8 of 27
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
Page 9 of 27
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
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 104.0 864.5 0.120 0.94%
Dec-16 Pulverized Coal 122.5 940.0 0.130 -7.29%
Jan-17 Pulverized Coal 106.0 894.9 0.118 2.54%
Feb-17 Pulverized Coal 79.6 657.4 0.121 0.38%
Mar-17 Pulverized Coal 113.5 950.5 0.119 1.69%
Apr-17 Pulverized Coal 116.5 995.6 0.117 3.68%
May-17 Pulverized Coal 117.4 968.0 0.121 0.20%
Jun-17 Pulverized Coal 102.7 829.5 0.124 -1.90%
Jul-17 Pulverized Coal 104.1 847.0 0.123 -1.20%
Aug-17 Pulverized Coal 73.8 571.0 0.129 -6.34%
Sep-17 Pulverized Coal 79.2 688.2 0.115 5.27%
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.
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 95.00 655.500 0.145 -6.72%
Dec-16 Pulverized Coal 103.60 710.600 0.146 -7.36%
Jan-17 Pulverized Coal 90.20 750.200 0.120 11.46%
Feb-17 Pulverized Coal 78.50 465.300 0.169 -24.23%
Mar-17 Pulverized Coal 100.10 766.200 0.131 3.80%
Apr-17 Pulverized Coal 106.50 790.400 0.135 0.78%
May-17 Pulverized Coal 94.50 795.600 0.119 12.53%
Jun-17 Pulverized Coal 79.40 632.400 0.126 7.55%
Jul-17 Pulverized Coal 84.30 605.200 0.139 -2.57%
Aug-17 Pulverized Coal 65.40 406.800 0.161 -18.38%
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
Page 10 of 27
Table 6 Vinubhai Steel Pvt. Ltd.
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 66.30 587.600 0.113 -3.18%
Dec-16 Pulverized Coal 95.30 765.200 0.125 -13.89%
Jan-17 Pulverized Coal 82.30 665.200 0.124 -13.14%
Feb-17 Pulverized Coal 55.30 365.300 0.151 -38.44%
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%
Jun-17 Pulverized Coal 75.46 650.200 0.116 -6.13%
Jul-17 Pulverized Coal 76.89 650.000 0.118 -8.18%
Aug-17 Pulverized Coal 51.56 415.600 0.124 -13.45%
Sep-17 Pulverized Coal 56.50 535.200 0.106 3.46%
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
Page 11 of 27
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
material to TMT bars
950 Rs/ton raw
material to TMT bars
900 -1000/ton raw
material to TMT bars
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.
Page 12 of 27
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
Page 13 of 27
- 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
Page 14 of 27
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
Page 15 of 27
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
= ∑ ∑ ∑
Page 16 of 27
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
Page 17 of 27
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
Page 18 of 27
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
I II I II I II I II I II
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
I II I II I II I II I II
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
Page 19 of 27
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
Page 20 of 27
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.
Page 21 of 27
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
International Journal of Modern
Trends in Engineering and
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
International Journal of Modern
Trends in Engineering and
Research (IJMTER) Volume: 4,
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
International Journal of Modern
Trends in Engineering and
Research (IJMTER) Volume: 4,
UGC
Page 22 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
International Journal of Modern
Trends in Engineering and
Research (IJMTER) Volume: 5,
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.
Turnover growth rate of 7% per year.
Electricity consumption should reduce by 7%
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 750/ton.
Material loss should not exceed 6% of total raw material.
Labour cost should not exceed 450/ton.
Policy Norms at JR Steels.
Turnover growth rate of 7% per year.
Page 26 of 27
Electricity consumption should reduce by 7%
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 750/ton.
Material loss should not exceed 6% of total raw material.
Labour cost should not exceed 450/ton.
Policy Norms at Vinubhai Steels.
Turnover growth rate of 7% per year.
Electricity consumption should reduce by 7%
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 750/ton.
Material loss should not exceed 6% of total raw material.
Labour cost should not exceed 450/ton.
Policy Norms at Laxmi Steels.
Turnover growth rate of 7% 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 09%.
Loss of heat energy should not be less than 40%.
Inventory should not be greater than 40%.
Maintenance cost should not exceed 650/ton.
Material loss should not exceed 7% of total raw material.
Labour cost should not exceed 400/ton.
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