Manikandeswaran Thirumal Mod 4.3 .xlsx
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School
Miami Dade College, Miami *
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Course
109
Subject
Industrial Engineering
Date
Dec 6, 2023
Type
xlsx
Pages
12
Uploaded by manickxtheboss
Errors Reported in a Seven Day Period
Event
Sun
Mon
Tues
Wed
Thur
Jet bridge delayed
1
1
1
2
1
Baggage belt broken
0
0
0
1
0
Refueling delayed
0
0
0
2
0
Galley servicing delayed
3
6
5
11
5
Cabin cleaning delayed
0
0
0
2
0
Cabin not properly cleaned
3
5
3
10
3
Baggage loading delayed
1
0
1
2
0
Total
8
12
10
30
9
Sun
Mon
Tues
Wed
Thur
Fri
Sat
0
5
10
15
20
25
30
35
Histogram Chart
Fri
Sat
Total
1
2
9
1
1
3
0
1
3
4
10
44
0
0
2
2
7
33
1
1
6
9
22
100
The Histogram chart shows that all
other days of the week have almost
the same amount of errors, with
Wednesday and Saturday having the
highest number of errors. I would
advise determining the cause of the
increasing number of faults these days
and fixing them.
Errors Reported in a Seven Day Period
Event
Sun
Mon
Tues
Wed
Thur
Jet bridge delayed
1
1
1
2
1
Baggage belt broken
0
0
0
1
0
Refueling delayed
0
0
0
2
0
Galley servicing delayed
3
6
5
11
5
Cabin cleaning delayed
0
0
0
2
0
Cabin not properly cleaned
3
5
3
10
3
Baggage loading delayed
1
0
1
2
0
Total
8
12
10
30
9
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Fri
Sat
Total
1
2
9
1
1
3
0
1
3
4
10
44
0
0
2
2
7
33
1
1
6
9
22
100
.
The events and the total errors by each event are
shown in descending order in this pareto chart. T
data indicates that a considerable portion of the
events are errors related to improperly cleaned
cabins and galley servicing.
Thus, by analyzing the data, we can determine th
Tuesday and Saturday have the highest number
errors because of improperly cleaned cabins and
galley servicing. We therefore need to investigat
these two problems and resolve them.
re
The
e rest
hat
of
d
te
Servicing Delay vs. Support Staf
Event
Sun
Mon
Tues
Wed
Thur
Galley servicing delayed
3
6
5
11
5
Missing galley support staf
2
3
4
6
3
2
3
4
5
6
7
8
9
10
11
12
0
1
2
3
4
5
6
7
8
Servicing Delay vs. Support Staf
Galley servicing delayed
Missing galley support staf
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Fri
Sat
4
10
3
7
The Scatter Diagram indicates a positive correlation between the paucity
of galley support staff and the delay in galley servicing. Put another way,
the galley's servicing is delayed in proportion to the staffing shortfall.
I would suggest that in order to minimize servicing delays, there should be
a minimum staffing shortfall. This can be accomplished in one of two
ways: by recruiting temporary employees to work on days when demand is
high, or by hiring permanent employees who will work every day, or by
combining the two, either hiring permanent workers who will work every
day, recruiting temporary workers to fill in on busy days, or a combination
of the two. A cost-benefit analysis that balances the benefits of hiring more
staff against the expenses of reducing service delays is necessary to decide
the optimal course of action.
Galley Servicing Delayed Data
Quality control/c-chart
Number of samples
7
Data
Results
# Defects
Total units sampled
Sample 1
3
Total Defects
Sample 2
6
Sample 3
5
Standard deviation
Sample 4
11
z value
Sample 5
5
Sample 6
4
Upper Control Limit
Sample 7
10
Center Line
Lower Control Limit
Graph information
Sample 1
3 1.27144892 6.28571429 11.2999796
Sample 2
6 1.27144892 6.28571429 11.2999796
Sample 3
5 1.27144892 6.28571429 11.2999796
Sample 4
11 1.27144892 6.28571429 11.2999796
Sample 5
5 1.27144892 6.28571429 11.2999796
Sample 6
4 1.27144892 6.28571429 11.2999796
Sample 7
10 1.27144892 6.28571429 11.2999796
Defect rate,
l
1
0
2
4
6
8
10
12
Mean
Enter the number of defects for each of the
samples/items.
Enter the number of defects for each of the
samples/items.
7
44
6.28571429
2.50713268
2
11.2999796
6.28571429
1.27144892
2
3
4
5
6
7
c-chart
Column B
Column C
Column D
Column E
Sample
Because fewer errors will always be allowed, this
circumstance did not necessarily require the LCL. Despite
this, the LCL can still show the viewer that there were far
less errors on the day than usual.
The two issues might be
related because the trend in the statistical process control
chart is evident. Additionally, the procedure is under
control with the little sample data provided because every
sample defect falls between the LCL and UCL range. The
company could look into why errors were so low on that
particular day if the sample flaws were outside of the LCL
and UCL ranges. Then, they could apply the lessons learned
from the day when errors were below the LCL to the other
days.
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Human Cause
Procedure/
Material Cause
Shortage of employees
Negligence
Untrained cleaners
Equipment Failure
Cleaning supplies
unavailable
Operational Cause
Cabin not prop
cleaned
Efe
/methods Cause
Delayed aircraf
Short time between
takeofs
Too many aircraf to
clean in short period
Poor scheduling of work
Poor commmuncation
Bad weather causing disruption
perly
ect
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