The Performance Analysis of a Fettling Shop Using Simulation
CIM report - final
1. By,
Deepak Chandran A20336465
Kaivalya Chaturvedi A20335367
Praveen S R A20325931
Prasanna Venkatesh Ramkumar A20338828
Rahul Garg A20334391
Saurabh Dhuri A20325517
CONSULTING PROJECT REPORT
MMAE 557: Computer Integrated
Manufacturing- Systems
Course Instructor: Prof. John C. Cesarone
2. I
Table of Contents
1) Client Description............................................................................................................................ 1
1.1) About the factory/operations .................................................................................................. 1
2) Analyze the client............................................................................................................................ 3
3) Optimization of System................................................................................................................... 4
3.1) Optimum number of workstations........................................................................................... 4
3.2) Motion Study Analyses.............................................................................................................. 6
3.3) Analyzing after implementation of new system ....................................................................... 8
3.4) Machine Cluster Analyses ......................................................................................................... 9
3.5) Cost Analyses with Machine Cluster....................................................................................... 10
4) Automation of System................................................................................................................... 11
5) Quality Assurance and Inspection................................................................................................. 13
5.1) Statistical Process and Quality Analyses ................................................................................. 13
5.2) Inspection Analyses................................................................................................................. 18
3. Page 2
CHAPTER 1
CLIENT DESCRIPTION:
Our client is KCP technologies Private Ltd, Goa, India. A firm that makes screw
pumps rotors for South Asian pump manufacturers like ROTO, Flowserve, Netzsch
etc. Their products cater pumping solutions for handling complex fluids in food,
chemical, oil and gas, waste water, renewable energy and further more industries.
In the recent times, they are looking forward to adapt the latest manufacturing
methods or production management tools which would improve their current profit
margin without investing much on technological changes. As a result, we have been
appointed as a CIM consultant to audit, analyze and optimize their present system.
This report will serve as a basis for future expansion and construction of new
optimized plants across the country.
1.1 About the factory/operations:
• The factory consists of 3 CNC workstations, 4 full time workers and 1 utility worker.
There is a loading and unloading bay where raw materials are transported into and
out of the workshop through crates in forklifts. Each workstation has a tool table,
job table where they next part to be loaded is stacked and finished product stock
where the finished part is kept. Upon completion of the manufacturing process, the
utility workers transport the parts to the inspection table where a worker inspects
the finished products with their corresponding part drawings. A basic layout of the
factory is sketched for further understanding.
• They significantly make 2 models of screw pump rotors in a batch production
process. The batch size is normally decided to meet the current demands of their
client. The list of activities within the CNC are facing, turning, taper turning, knurling,
grooving, drilling and whirling.
• According to the classifications of various manufacturing systems, this factory
belongs to ‘Type I M B’. Type I M states that it works on single station manned cell
and B indicates that the cell produces rotors in batch production system.
5. Page 4
CHAPTER 2
ANALYZE YOUR CLIENT:
Our client manufacturers 5050 screw pump rotors (on average) for every financial year in
2 batches. Among this, 3200 units are rotor A and 1850 units are Rotor B. By constant
observations of the machining activities of the workers in the CNC lathe, we have recorded
machining time for 5 samples of Rotor A and 5 samples of Rotor B using stop watches in
order to deduce the average cycle time and thereby calculate the average production rate
of each product.
Machining
Activity
Rotor A Rotor B
1 2 3 4 5 Avg. 1 2 3 4 5 Avg.
Unload & Load
next part
2.70 2.30 2.00 3.00 2.50 2.5 3.00 3.00 3.00 3.50 2.50 3
Unload & Load
next tool
7.50 7.50 7.60 7.70 7.20 7.5 8.00 8.00 7.50 7.80 8.70 8
Part program
checking
1.00 1.00 1.00 1.00 1.00 1 1.00 1.00 1.00 1.00 1.00 1
Facing 2.95 3.05 3.00 2.90 3.10 3 4.95 5.05 5.10 5.15 4.75 5
Turning 6.50 6.50 6.25 6.45 6.80 6.5 8.55 8.50 8.45 8.40 8.60 8.5
Taper turning 3.90 4.00 4.00 3.95 4.15 4 6.00 6.00 6.15 5.90 5.95 6
Grooving 3.50 3.55 3.40 3.40 3.65 3.5 4.50 4.55 4.45 4.40 4.60 4.5
Knurling 4.00 3.90 4.10 3.95 4.05 4 3.50 3.50 3.55 3.55 3.40 3.5
Drilling 5.50 5.35 5.40 5.65 5.60 5.5 6.90 6.85 7.15 7.05 7.05 7
Whirling 10.40 10.70 10.25 10.50 10.65 10.5 16.50 16.45 16.55 16.60 16.40 16.5
Average Cycle
time
(min/part)
48 63
Production rate (Rp) = 60 / Tc
Average Cycle time for rotor A (Tc) = 48 min
Average Cycle time for rotor B (Tc) = 63 min
This implies,
Production rate (A) [parts/hr] = 1.25.
Production rate (B) [parts/hr] = 0.95.
6. Page 5
CHAPTER 3
OPTIMIZATION OF SYSTEM:
After a thorough study and analysis of the present system, we have worked on the
following parameters for optimization of the current process of the manufacturer:
1. Evaluating the optimum number of workstations for the present work scenario.
2. Application of motion-study and thereby reducing the repositioning time of the
active workers.
3. Formulating the possibilities for combining to a machine cluster.
3.1- Optimum number of workstations:
For evaluating optimum number of workstations, we have assumed the following data from
the various sources:
Parameter Unit Quantity Source of data
Units produced Parts/hr
Q(A) - 3200 Average values taken from the financial
report of company for the past 5 years.Q(B) - 1850
Cycle time min
Tc(A) – 48 Average value taken form the series of
sample machining time.Tc(B) – 63
Scrap rate %
µ(A) – 3 Average scrap produced by client for each
rotor. (obtained from client’s end)µ(B) - 7
Worker efficiency
during operation
% α – 92
Values measured/ assumed from client’s
end based on their current manufacturing
scheme.
Reliability during
running
% β – 90
Reliability during
setup
% γ – 95
Setup time min T(sp) - 45
Average Batch size parts N - 20
On reviewing the annual sales reports for
the past 5 years, it said that there is an
average changeover from Rotor A to B for
every 20 rotors produced. Therefore, we
may assume 20 as our batch size as
instructed by our client.
Factory Operating hours:
Hours/day (h/d) = 8.
Days/week (d/wk) = 5.
Weeks/year ( wk/yr) = 50.
7. Page 6
Workload (A) = WL(A) = Q(A) * Tc (A) = 3200 * (48/60)
(1- µ(A) ) (1 - 0.03 )
= 2639.17 hours/yr
Workload (B) = WL (B) = Q(B) * Tc (B) = 1850 * (63/60)
(1- µ(B)) (1 - 0.07 )
= 2088.71 hours/yr
Available time (AT) = 8 * 50 * 5 = 2000 hours/yr
No. of setup changes = Q (A) + Q (B) = 3200 + 1850
N 20
= 252.5
Workload (setup) = WL (sp) = No. of setup changes * T(sp) = 252.5 * (45/60)
= 189.375 hours/ year
Optimum number of workstations = WL (A) + WL (B) + WL (sp)
AT * α * β AT * γ
= 2639.17 + 2088.71 + 189.38
2000 * 0.9 * 0.92 2000 * 0.95
= 2.88 + 0.09
= 2.97
Since there are currently 3 workstations, the system already has the optimum number
of workstations. Also, utilization factor is 99% so there is no possibility of overtime
work. The factory are presumed to meet their sales with the current number of
workstations.
8. Page 7
3.2- Motion Study Analysis:
The factory uses primitive CNC machines which can hold only 4 tools in its tool gallery.
Therefore there will be regular movement of worker between the CNC lathe and tool table
in order to place the right tools for the right object as per the part program. Keeping this
factor in mind, a motion study analysis was conducted. The sole aim of the study was to
provide workers close proximity to the tools they frequently use thereby reducing their
travel time by making a few modifications in the system layout.
Apart from the layout changes, the tools in the tool table are recommended to be
classified according to their application and placed on the adjacent wall nearby the
CNC machine.
Travel within workstations Frequency
Distance
w.r.t Old
layout
Distance w.r.t
Proposed
layout
Tool Table to CNC 4 4 1
Job stock to CNC 2 4 3.5
Finished products stock to CNC 2 5.5 3.5
Total Distance covered by worker/ part produced 35 18
Travelling distance will be reduced by (m) = 17
Average human walking speed (m/min) = 33.33
Time saved as per proposed layout in walking = 17/33.33
9. Page 8
= 0.51 min
Time saved in reorganizing tool table and placing tools as per
standard ergonomic ways with proper classification
0.5 min
Total time saved by motion study will be close to 1 min.
Existing cutting tool and job arrangement Proposed classified arrangement
Same is applicable in the case of hardware and spanners. This also
reduces time in fixing and aligning the parts in the CNC.
Existing hardware arrangement Proposed hardware arrangement
10. Page 9
3.3- Analyzing after implementation of new system layout:
Machining
Activity
Rotor A Rotor B
1 2 3 4 5 1 2 3 4 5 Avg
Unload & Load
next part
2.70 2.30 2.00 3.00 2.50 2.5 2.80 2.50 3.20 3.00 3.50 3
Unload & Load
next tool
6.50 6.50 6.60 6.70 6.20 6.5 7.10 7.00 7.20 7.00 6.70 7
Part prog
checking 1.00 1.00 1.00 1.00 1.00
1 1.00 1.00 1.00 1.00 1.00 1
Facing 2.90 3.10 3.00 3.00 3.00 3 5.00 4.70 5.10 4.80 5.40 5
Turning 6.40 6.60 6.20 6.40 6.90 6.5 8.20 8.80 5.50 8.10 8.90 8.5
Taper turning 3.00 4.10 4.00 4.00 4.10 4 6.60 5.90 6.10 5.80 5.60 6
Grooving 3.60 3.40 3.50 3.10 3.90 3.5 4.40 4.60 4.30 4.50 4.70 4.5
Knurling 4.00 4.00 4.00 4.00 4.00 4 3.00 3.20 3.50 4.00 3.80 3.5
Drilling 5.60 5.25 5.30 5.75 5.60 5.5 7.40 6.30 7.10 7.70 6.50 7
Whirling 10.20 10.90 10.20 10.40 10.70 10.5 16.60 16.40 16.00 16.80 16.70 16.5
Average Cycle
Time
(min/part)
47 62
Production rate (Rp) = 60 / Tc
Average Cycle time for rotor A (Tc) = 47 min
Average Cycle time for rotor B (Tc) = 62 min
This implies,
Production rate (A) [parts/hr] = 1.28.
Production rate (B) [parts/hr] = 0.97.
Naturally, the production rates have improved.
11. Page 10
3.4- Machine Cluster Analysis:
We have already optimized the repositioning time of workers by introducing
organizational changes and rearrangement of tables. In this section, we have checked the
possibility for formation of the cluster arrangement since we are using CNC lathes in a
semi-automatic operation.
For Rotor A:
Machining time (Tm) = 38 min
Worker setup time (Ts) = 9 min
Repositioning time (Tr) = 2 min
No. of machines which can be handled by 1 worker = Tm + Ts
Ts + Tr
= 38 + 9 = 4.27
9 + 2
For Rotor B:
Machining time (Tm) = 52 min
Worker setup time (Ts) = 10 min
Repositioning time (Tr) = 2 min
No. of machines which can be handled by 1 worker = Tm + Ts
Ts + Tr
= 52+ 10 = 5.17
10 + 2
This concludes that even while manufacturing rotor A or B, the complete machining
operation can be managed by a single worker.
12. Page 11
3.5- Cost Analysis with Machine Cluster:
Production Cost / Unit as per present situation :
Labor Cost (CL) 7.5 $/hr
Machine Cost (CM) 25 $/hr
No. of labors assigned 3
No. of Machines 3
Cost per unit = Tc * (CL + CM) / 60
Rotor A Rotor B
Cost per unit ($) 26 34.13
Production Cost with respect to our recommendations ( Cluster) :
Labor Cost (CL) 15 $/hr
Machine Cost (CM) 25 $/hr
No. of labors assigned (NL) 1
No. of Machines 3
Cost per unit = Tc * (CL / NL + CM) / 60
Rotor A Rotor B
Cost per unit ($) 23.5 31
For both products, there is a savings in production cost required. Thus, we propose our
cluster arrangement is economical even though the labor wage is doubled.
13. Page 12
CHAPTER 4
AUTOMATION OF SYSTEM
Considering the manufacturing system has an optimized process, analysis is further going
to be conducted on the feasibility on automating the process. The different areas that can
be automated are:
1. Machining operation.
2. Material handling and transport
3. Inspection.
Since, the factory is already operating with CNC machines where the worker attention is
required only for some time within the machining cycle, we are not further investigating
into this.
On a detailed study of the plant layout, we feel that the there is more scope in automation
of material handling and transport with the help of a conveyor belt which loops through
all the workstations, inspection desk and loading & unloading bay. This conveyor belt
intermittently operates and is manually overridden by a push button station near the
conveyor belt. The part moves around the conveyor only during their cycle time.
This will reduce time synthesized for material handling, part loading and unloading
operations and transport of finished products to the inspection desk. Additionally, the
space required for storage of job and finished products can be reduced as same will be
taken care by the conveyor.
14. Page 13
4.1- Analyzing after implementation of conveyor:
Machining Activity
Rotor A Rotor B
1 2 3 4 5 Avg 1 2 3 4 5 Avg
Unload & Load next
part
1.60 1.80 1.30 1.20 1.60 1.5 1.60 1.80 1.30 1.20 1.60 1.5
Unload & Load next
tool
6.30 5.90 6.70 6.60 7.00 6.5 6.80 6.40 7.20 7.10 7.50 7
Part prog checking 1.00 1.00 1.00 1.00 1.00 1 1.00 1.00 1.00 1.00 1.00 1
Facing 2.80 2.70 3.50 3.20 2.80 3 4.80 4.70 5.20 5.30 5.00 5
Turning 6.20 6.00 5.90 6.80 7.60 6.5 8.50 8.20 8.80 8.10 8.90 8.5
Taper turning 4.20 3.70 3.90 4.30 3.90 4 6.60 5.90 6.10 5.80 5.60 6
Grooving 3.40 3.80 3.10 3.50 3.70 3.5 4.40 4.60 4.30 4.50 4.70 4.5
Knurling 4.20 3.70 3.90 4.30 3.90 4 3.40 3.80 3.10 3.50 3.70 3.5
Drilling 6.00 5.10 5.40 6.10 4.90 5.5 6.50 6.30 7.40 7.10 7.70 7
Whirling 10.20 10.10 10.80 10.30 11.10 10.5 16.60 16.40 16.00 16.80 16.70 16.5
Average Cycle Time
(min/part)
46 60.5
Production rate (Rp) = 60 / Tc
Average Cycle time for rotor A (Tc) = 46 min
Average Cycle time for rotor B (Tc) = 60.5min
This implies,
Production rate (A) [parts/hr] = 1.31.
Production rate (B) [parts/hr] = 0.99.
This proves that there this an increase in production rates for both the models of
rotors produced by the factory through installing a conveyor system. This may also
remove the need of utility workers as transport of rotors are controlled by the workers.
15. Page 14
CHAPTER 5
QUALITY ASSURANCE
Quality inspections are the essential part of any manufacturing industry. Routine
inspections are typically conducted as a daily, weekly, monthly or yearly quality inspection
audit that may be focused on worker productivity as well as the quality of a finished
product such as a manufactured toy or piece of clothing. Quality inspection objectives vary
depending on the company's primary objective, but most quality inspections are designed
to promote business profitability. The main intention is to check whether the part or
assembly is made exactly or close to what it’s designed for.
The quality assurance technique presently followed by the company are very traditional.
They inspect all the rotors produced everyday manually. Vernier Calipers are used to
inspect the two outer diameters of the rotor at various locations. Holes, grooves, and tapers
are also measured manually with basic instruments. These were compared with the
measurements in the part drawings visually. If the measurements are within tolerance they
were shipped to their client else it was sent for rework or scrap. The profile of the mid-
section profile and helical angle was not inspected.
Before, checking importance of inspection for the client we have analyzed whether the
process is in statistical control.
5.1- Statistical Process & Quality Analysis:
Statistical process control involves the use of various methods to measure and analyze a
process. Control charts have been used to check whether the process of the company is
under statistical control. The purpose of the control chart is to identify when the process
has gone out of statistical control.
For evaluating the above analysis,
• The mean and the range charts are plotted with sample number(S) in the x-
direction and the mean and range of the dimension of the rotor diameters measured
at 5 different points in the y- direction.
• Sample size of 5 is taken to plot the charts (n=5) and (m=8).
• The charts are plotted for both Rotor A (2 inch) and Rotor B (2.5 inch).
19. Page 18
4. Range chart for Rotor B:
Sample Measure Mean(CL) UCL LCL
0.01 0.009375 0.02371214 -0.00496214
0.012 0.009375 0.02371214 -0.00496214
0.006 0.009375 0.02371214 -0.00496214
0.014 0.009375 0.02371214 -0.00496214
0.003 0.009375 0.02371214 -0.00496214
0.005 0.009375 0.02371214 -0.00496214
0.017 0.009375 0.02371214 -0.00496214
0.008 0.009375 0.02371214 -0.00496214
With reference to the samples we have analyzed, the process seems to be in statistical
control and is found to be stable. However, defect rates may be found due to the
variability in machining operations.
-0.01
-0.005
0
0.005
0.01
0.015
0.02
0.025
0.03
1 2 3 4 5 6 7 8
Sample Measure
Mean(CL)
UCL
LCL
3σ
3σ
Sample number
Range
20. Page 19
5.2- Inspection Analysis:
As mentioned earlier, there are many variables associated with the manufacturing
processes. In order to keep up with client’s requirement, products are manufactured in
excess. We have considered the defect rates in the manufacturing operations to find out
whether the present production rate can meet up the client’s annual requirement i.e.,
3000 units of Rotor A & 1750 units of Rotor B.
Effect of defect rate in a series production
Since the diameters are always in statistical control and the helical profile of the rotor have
not been inspected. The probable defects that the parts experience could be from drilling or
whirling operation. Taking this into consideration, we have analyzed whether the current
production scheme would be able to meet the client requirements.
Let Qo be the quantity of parts produced and q be the defect rates of the machining
operation.
Good products (Q) = Qo x (1-q)
Defected products (D)= Qo x q
Q = Qo (1-q1) (1-q2)........................ (1-qn) [When there is a series of ‘n’ operations in the
products & q is their corresponding defect rates]
For Rotor A:
Qo = 3200
n = 2
q= 0.03 (Manufacturer’s standard)
Qo= 3200 (1-0.03)2
So, Qo = 3010 which is greater than 3000.
For Rotor B:
Qo = 1850
n = 2
q = 0.0275
Q0 = 1850 (1 - 0.0275)2
So, Qo = 1758 which is greater than 1750.
Hence, with the assumed defect rates they should meet their client demands.
21. Page 20
Despite the above conclusions, we have received and reviewed several reports which have
been provided from client’s inspection. These commonly state that there pump rotors do
not rub the stators of the screw pump with optimum rubbing velocity and this had
decreased the performance of the pumps.
Thus we recommended the company to send random samples of part A and part B to a
third party inspection contractor who uses a multi touch probe cantilever coordinate
measuring machine to inspect the profile of the rotor and it was found out that the
number of defects had increased as follows:
Part A had 5 defects out of 23 parts.
Part B had 3 defects out of 17 parts.
This action helped the company improve on their quality check. They decided to
send random samples of rotors to a third party Inspection Company who regularly
checks for any deviations in the helical profile and provide necessary suggestions for
preventing the same in machining on a weekly basis. M/s KCP felt this was an
effective way in understanding their variability based on the feedback received.
Additionally, the company considered in purchasing a CMM in future expansion of
the plant.
Further recommendations for improved quality:
We suggest to buy a test rig prototype of the pumps that use these rotors and
conduct dye penetration tests to analyze the contact region between stator and
rotor.
Once the factory has well expanded, several clusters can be grouped. Pick and place
robots facilitated with tongs, could be considered to handle parts efficiently and
same could be supported with an automatic pallet changer to reducing the
repositioning time spent on part handling. For a large volume of production this
would certainly help in reducing the cycle time despite the investment.
