The garment industry has played a pioneering role in the development of industrial sector of Bangladesh. The RMG sector is expected to grow despite the global financial crisis of 2009.As China is finding it challenging to make textile and foot wear items at cheap price, due to rising labor costs, many foreign investors, are coming to Bangladesh to take advantage of the low labor cost. Though Bangladesh produces garment with lowest cost but poor productivity. To survive and prosper in today's economic times, companies can no longer manage using financial measures alone, they have to track non-financial measures also such as customer satisfaction, brand preference, speed of response, employee satisfaction etc.Productivity can improve by applying lean tools like- 5s, JIT, Muda, Root Cause analysis, KPIs, VSM. For improving the ultimate productivity OHS of RMG sector of Bangladesh should be improved. In Ready Made Garments (RMG) sector of Bangladesh, the employees represent an organization's biggest and its most valuable asset. The company's productivity, and ultimately, its profitability depend on making sure all of its workers perform up to their full potential. This paper summarizes that how KPIs analysis improve productivity and OHS of RMG sectors. Appropriate indicators are first selected for KPI scoring then simulate the scores with the help of Adaptive Network-based Fuzzy Inference System (ANFIS) and finally illustrated how KPIs impacts on overall productivity.

1. IPE:4000- Project and Thesis
A Thesis On
IMPROVING OSH & PRODUCTIVITY OF RMG
INDUSTRIES BY IMPLEMENTING LEAN
TOOLS AND VARIOUS KEY PERFORMANCE
INDEX (KPI)
Supervised By: Dr. Mohammad Sarwar Morshed
Submitted By:
Karina Islam

3.  The garment industry played a pioneering role in
the development of industrial sector of
Bangladesh. Bangladesh's exports industry alone
comprised USD 31.2 billion in FY 2014-15,
81.69% of which was made up by ready-made
garments.
 Improving the productivity of RMG industries can
be profitable for our country .Productivity can
improve by applying lean tools like- 5s, JIT,
Muda, Root Cause analysis, KPIs, VSM.

4.  To survive and prosper in today's economic
times, companies have to consider both
-financial measures
-non-financial measures (customer &
employee satisfaction etc
 We have done our study based on a
renowned garments industry and try to
improve its overall production by improving
its schedule, layout , line balancing, OHS
structures & reducing waste .

5. Authors
Name
Title Sources Findings Remarks
Dragana
Velimirovic
Role &
Importance
of KPI
measureme
nt
Serbian
Journal of
Managemen
t
-way of KPI
selection
-some
practical
example
- Focused
only kpi
selection
sector
Koichi
Murata
The
Evaluation
of factory
Performanc
e Utilized
KPI with
data
envelopmen
t analysis
Journal of
the
Operation
research
society of
Japan
Constructin
g structure
of KPI
- Utilized
DEA
-focuses on
utilizing
DEA

6. Authors
Name
Title Sources Findings Remarks
MD.
Jahangir
Hossan
Improving
Working
Environmen
t &
Productivity
of a Sewing
floor in RMG
Inds
Kuet.ac.bd -KIZEN
focused
-Eliminate
non-value
added
activities
- Focused
only sewing
sector
P. Breslin Improving
OHS
standards in
the building
&constructi
on industry
through
safe design
Journal of
Occupation
al Health
and Safety,
Australia
and New
Zealand
Occupation
al health
-Safety
Australia:
Industries
and
resources

7. Authors
Name
Title Sources Findings Remarks
Antonsen,
S. (2009)
Safety
culture:
theory,
method and
improveme
nt
Ashgate Pub
Co, UK
Industrial
safety
culture
Improving
safety
standards
Productivity
required
DeJoy D.M.
(2005)
Behavior
change
versus
culture
change:
divergent
approaches
to managing
workplace
safety,
Safety
Science
Safety
culture
Maturity
model
OHS and
Productivity
model
developed

8. Authors
Name
Title Sources Findings Remarks
Reza
Hemmati &
Hedayat
Saboori
Short-term
bulk energy
storage
system
scheduling
for load
leveling in
unit
commitmen
t: modeling,
optimizatio
n, and
sensitivity
analysis.
Journal of
Advanced
Research
scheduling
for short
term
storage
system
focused
only storage
system

9.  Develop some inputs based on different criteria.
 Input data from qualitative to quantitative
expressions.
 Generate the data using engineering judgment.
 Train, test and check these data using Trial and
Error Method (TEM).
 Determine the least error.
 Develop FIS model using coding.
 Measure the ultimate output key performance
index. (KPI)
 Find the output decision

10.  What Is Scheduling?
Assigning an appropriate number of workers to
the jobs during each day of work is called
scheduling. Scheduling is that phase of
production control which rates the work in the
order of its priority and then provides for its
release to the plant at the proper time and in
the correct sequence

11.  Step 1: Identify Available Time
 Step 2: Schedule Essential Actions
 Step 3: Schedule High-Priority Activities
 Step 4: Schedule Contingency Time
 Step 5: Schedule Discretionary Time
 Step 6: Analyze Your Activities

12.  Scheduling is the way we actually manage a project.
Without scheduling, nothing or nobody is managing
the project and hence amounts to failure of a
project. Scheduling describes guidance and pathway
for a project to run.
 It defines certain milestones and deliverables which
need to be achieved on a timely basis for successful
completion of a project. Monitoring the schedule
provides an idea of the impact the current problems
are having on the project, and provides opportunities
to enhance or reduce the scope of a milestone/phase
in the project.

13. Category Quantity/Month
(May)
Quantity/Month
(May, June, July)
Order
Percentage
Allocated Number
of
Machi
nes
A 198518 577100 23% 1
5
B 9282 301076 12% 8
C 48536 125456 5% 3
D 131674 351354 14% 9
E 7316 100320 4% 3
F 31547 97312 3.8% 3
G 91440 378256 15% 9
H 4209 4209 0.17% Safety Machines of 15
for
these rest of irregular
categories against
23.2% order
percentages
I 4209 4209 0.17%
J 0 30359 1.2%
Others 0 539488 21.5%
Total 802407 2509130 99.84% 6
5

15.  We use lekin software for our desired
scheduling.We use it in sewing
section,washing section and iron section.
1. Accuracy
2. Security
3. People Impact

20. The layout of an operation is concerned with
the physical location of its transforming
resources, that is deciding where to put the
facilities, machines, equipments and other
staffs in the operation

21.  Facilitate attainment of product or service
quality
 Use workers & space efficiently
 Avoid bottlenecks
 Minimize unnecessary material handling cost
 Eliminate unnecessary movement of workers
or materials
 Minimize production time or customer
service time
 Design for safety

22.  Requires sustainable investment of money &
effort
 Involves long term commitments
 Has significant impact on cost & efficiency of
short term operations

23.  Process Layout
 Product Layout
 Fixed position Layout
 Cellular / Combination Layout
In our thesis work , we worked on cellular
layout

27. Line balancing means allocation of machine or work to
individual operation.
 In garments industry , line balancing is the process of
-Selecting an effective production line
-With competent machines regarding to worker
-To produce a specific order
- A certain time deadline
 We used line balancing technique to achieve :
1.The minimization of the number of
workstations
2.The minimization of cycle time
3.The maximization of workload smoothness
4. The maximization of work relatedness
- To improve per day production
- To minimize the production cost

28.  To maximize the production
 To improve per day production
 To minimize the production cost
 To find out the wasting time during a product making and
to minimize the wasting time
 Find out the problem restricting the production,
generating proper solutions and implement them in a
proper way
 To analyze what affects productivity in respect to a
garment factory
 To develop the best method
 To balance the line for best utilization of men , machine
and material.

30. Process Washing Ironing Sewing PQC Packing
Available
work
time(min/d
ay)
480 480 480 480 480
Demand
rate
(units/day)
2500 2500 2500 2500 2500
Takt time
(min/50unit
)
10 10 10 10 10
Current
loading
(min/50unit
)
15 15 10 7 5

31. Target

32. Operation Capacity/hour Target/hour
Washing 200 313
Ironing 200 313
Sewing 300 313
PQC 428 313
Packing 600 313

33. Balancing case no.1:In Washing section the required capacity
will be gained when we add extra 7 machines. Then the no of
machines will =(12+7)=19.
Balancing case no.2:In Ironing section the required capacity
will be gained when we add additional 6 machines. Then the
no of machines will be=(10+6)=16.
Balancing case no.3: In PQC section we remove 5 machines
.Then the no of machines will be =(20-5)=15.
Balancing case no.4 :In packing section, there are 17
workstations available. If we remove 8 workstations, then
the required capacity will be gained.
When we balance these all sections, all of these sections will
follow the Takt time(10 min).

34. Process Washing Ironing Sewing PQC Packing
Available
Work time
(min/day)
480 480 480 480 480
Demand rate
(units/day)
2500 2500 2500 2500 2500
Takt time
(min/50
units)
10 10 10 10 10
Current load –
Ing(min/50
units)
9.58 9.58 10 9.58 9.58

35. 0
2
4
6
8
10
12
14
16
Washing Ironing Sewing PQC Packing
Loading(min/50 units)
Target

37. Lean manufacturing:
Eliminating any kind of waste within a manufacturing system that
does not add value to the product is called ‘lean manufacturing’
or ‘lean production’, often simply lean.
Muda:
Anything in the manufacturing process that does not add value
from the customer’s perspective.
VSM( Value Stream Map):
Value stream mapping (VSM) is a lean manufacturing tool that
seeks to map a process from supplier to customer, highlighting
the flows of product and information and identifying delays
and non-value adding processes.

41. Waste Amount
Overproduction -
Waiting 15 sec (for m/c in iron section)
12sec (for WIP product in sewing section)
Transport -
Motion 9sec (in sewing section)
Over processing -
Inventory -
Defects Avg 2 defects/batch

42. Process Name Washing Iron Sewing
Cycle time (secs) 76 75 69
Average Changeover/day 1 1 1
Changeover time (secs) 0 0 18
Batch Size 300 300 300
Effective Cycle Time (secs) 76 75 87
WIP Inventory 0 0 43
Available time (secs) 43200 43200 43200
Uptime 100% 100% 99.95%

45. Iron Section Sewing section
Before lean tools
implementation
After lean tools
implementation
Before lean tools
implementation
After lean tools
implementation
Effective cycle time
(secs)
75 62 87 77
WIP inventory 0 0 43 19
Changeover time (secs) 0 0 18 15

46. 0
50
100
150
200
250
value added time non value added time
before lean
after lean
21
195
180
43

48. What is KPI?
Key performance indicators (KPI) are a set of
quantifiable measures that a company uses
to gauge its performance in terms of meeting
their strategic and operational goals over
time.
Also referred as Key Success Indicator(KSI)

49. Why Should KPI Analyzing is needed?
 Determine present performance level
 Assess whether its manufacturing system
need to be improved
 Analyze where need improvements
 Work on that critical points and improve it
 By improving keeps a company on track

50. In this study for assessment of a RMG industry's
maturity mainly focuses on two sectors –
 OHS maturity assessment
 Lean maturity assessment.

51.  OHS structure
 Accident Investigation
 Targets & Performance Measurement(OHS)
 Welfare & Job satisfaction
 Workforce Involvement for OHS policy

52.  Employee Involvement in improvement
 Training for improvement
 Value Stream Mapping
 5S/ Housekeeping
 Preventive Maintenance

53. For Analyzing KPIs ANFIS tool has been used
What is ANFIS?
 Adaptive Neuro-Fuzzy Inference System
 Similarly to neural networks
 it integrates both neural networks and Fuzzy logic
principles, it has potential to capture the benefits of
both in a single framework

54. How it works?
 Using a given input/output data set, the toolbox
function ANFIS constructs a fuzzy inference system
(FIS)
 Whose membership function parameters are tuned
(adjusted) using either a back propagation algorithm
alone or in combination with a least squares type of
method. This adjustment allows your fuzzy systems
to learn from the data they are modeling.

56. Load DATA
Training Error
Design Fuzzy Logic
Insert Membership
Function
Insert Rules
Run ANFIS Model
Structure

57.  Step1: Load data in ANFIS  Step 2: Fuzzy Logic design

58. Step 3: Insert MFs for Input & Output

59.  Step 4: Insert Rules  Step 5: View Rules

60.  Step 6: Final ANFIS Structure

61.  For OHS Maturity Assessment : 2.7 (poor but acceptable)
 For Lean Maturity Assessment : 3.3 (Acceptable)
 Output of OHS M.A  Output of Lean M.A

62.  Expanded into more data sets.
 Implemented in different industries.
 Other methods can also be used or combined.
 Other methods can be used to determine the criteria.
 Increased sufficient data sets, more predictable data
and more precise results.
 More experts’ opinion can give the result more
accuracy.
 Increased the number of membership function or
rule, and selection input
parameters.

63.  Important to perform KPIs analysis.
 Necessity of performance measurement tool.
 ANFIS provide more accuracy on prediction.
 Applicable to other companies.
 Decision Support System (DSS).