Tourism and Travel: Statistical Analysis of Quality Control in Hotel Maintenance

This research report analyzes the quality control of hotel maintenance in the tourism and travel industry using statistical processes such as the Six-sigma process and CTQ method. The report focuses on factors such as guest room cleanliness, man hours per occupied room, direct expense per occupied room, check-in speed, and take rate of multi-night stays. The data analysis is presented in control charts and tables.

Running head: TOURISM AND TRAVEL
Tourism and Travel
Name of the Student:
Name of the University:
Author’s note:

1TOURISM AND TRAVEL
Table of Contents
Introduction:...............................................................................................................................2
Discussion and Data Analysis:...................................................................................................2
Six-sigma Process:.................................................................................................................2
CTQ method:..........................................................................................................................4
Guest Room Cleanliness:.......................................................................................................6
Man Hours per Occupied Room:...........................................................................................8
Direct Expense per Occupied Room:...................................................................................10
Check In Speed:...................................................................................................................12
Take Rate (% of multi-night stays):.....................................................................................14
Conclusion:..............................................................................................................................16
References:...............................................................................................................................17
Appendix:.................................................................................................................................18

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Introduction:
The hotel maintenance of tour and tourism industry considers mainly five factors.
These are Guest Room Cleanliness, Man Hours per Occupied Room, Direct Expense per
Occupied Room, Check in Speed and Take Rate of multi-night stays in percentage.
Cleaning professionals are responsible for having neat and clean guest-rooms. In
addition, the housekeepers should be well-trained to properly remove clogs (CleanLink,
2018). The major man-hours per occupied room is dependent up on labour cost, utilities,
refurbishment due to allocation and necessary utilities. Direct expense per occupied room are
divided in many segments such as laundry expense, food expense, maintenance and utility
expense, reservation fees, royalties in marketing and commissions. In case of check in speed,
operators get to know that a few factors and internet problem hamper the experience. It also
creates problem for online payment and money transaction. When customers are trying to
book a multi-night stay hotel, they possibly book for one reservation. This is a fact that hotels
try to fix loyalty programs. It often ensures promotions and rewards because of number of
days to stay.
Statistical processes help to test trend and nonrandom patterns could be applied to
interpret control-charts.
Discussion and Data Analysis:
Six-sigma Process:
The research report aims to create the control charts in data set of quality. Control
chart illustrates the allowable error limits of quality control. It is a cause-and-effect diagram

3TOURISM AND TRAVEL
placed for generating action items for defining the root cause and method of constant
enhancement. We applied the data mainly in “mean chart” (X-bar chart).
In hotel management purpose, the X-bar chart is a Shewhart control chart used to
handle the arithmetic means of successive samples of fixed size. This type of chart is mainly
used in continuous scale data. The data of defined variables have samples when collected at
regular intervals from a business or management procedures helps to use X-bar chart. We
calculated means (μ) and standard deviations (σ) of each data. The upper control limits
(UCL) and lower control limits (LCL) are calculated as-
UCL = ( μ+3∗σ ) and LCL = ( μ−3∗σ )
If the data points fall outside the control limits both UCL and LCL, process goes out
of control. In this case, we replace the defective items. More than 73% samples falling
outside the control limits are considered an out of control process (Ahmad, Aslam and Jun,
2014). For each set of control limits, there is a dispersion parameter and location parameter.
The control limits are dependent upon the multiple of standard deviation deviated for
averages of the samples. The distance between two control limits is 6σ. Hence, the process is
known as 6σ-sigma process.
“In Control” process refers that all the points are between control limits by
performing a random pattern.

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(Source: ControlCharts, 2016)
CTQ method:
Critical to quality (CTQ) Trees provides a support to translate broad customer needs
into defined, acceptable and measurable performance requirements. The hotel then can use
the need to provide high quality products and services. CTQ Trees originally grows up as part
of the six-sigma methodology.
CTQ in tourism and travelling helps to follow up characteristics of services of the six-
sigma methodology. Hotel authority could focus on target values and evaluate the defect rate.
It helps to measure out and establish the conclusion regarding specification limits.

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(Source: iSix-Sigma, 2016)
CTQ are the interior critical parameters of quality associated with the requirements of
the guests. A quality function deployment (QFD) or CTQ tree helps to link the CTQs to the
CTCs (Asubonteng, McCleary and Swan, 1996). Dimensional tolerance and cushioning
required for generating the conditions that are CTQs for the management (Kan, 2002). It
reflects the expressed requirements of the customer. The data should be analyzed for supreme
service quality, service and performance.

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CTQ matrix
Guest Room Cleanliness:
The guest room cleanliness in baseline period and in implementation period is within
the control limit. The overall cleanliness in both the periods is also within control limit.
Figure 1: Guest room cleanliness in baseline periods

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1 2 3 4 5 6 7 8 9 10 11 12
70%
72%
74%
76%
78%
80%
82%
84%
86%
88%
90%
Guest room cleanliness in base line period
Guest Room
Cleanliness
mean
mean + sd
mean - sd
mean +3*sd
mean - 3*sd
(Source: ControlCharts, 2016).
All the 12 data of guest room cleanliness in baseline period are within control limits.
Figure 2: Guest room cleanliness in post implementation periods

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13 14 15 16 17 18 19 20
70%
72%
74%
76%
78%
80%
82%
84%
86%
88%
90%
Guest room cleanliness in post implementation period
mean
mean + sd
mean - sd
mean +3*sd
mean - 3*sd
Guest Room
Cleanliness
All the 8 data of guest room cleanliness in post implementation period are in control.
Figure 3: Guest room cleanliness in both types of periods
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
70%
72%
74%
76%
78%
80%
82%
84%
86%
88%
90%
Guest room cleanliness in both types of
periods
mean
mean + sd
mean - sd
mean +3*sd
mean - 3*sd
Guest Room
Cleanliness

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Overall the process guest line cleanliness is out of control.
“Man Hours per Occupied Room”:
The Man Hours per occupied room in baseline period and in implementation period is
within the control limit. The overall Man Hours per occupied room in both the periods is also
within control limit.
Figure 4: Man-hours per occupied room in baseline periods
1 2 3 4 5 6 7 8 9 10 11 12
0.40
0.45
0.50
0.55
0.60
Man hours per occupied room in baseline
period
Man Hours Per
Occupied Room
mean
mean + sd
mean - sd
mean +3*sd
mean - 3*sd
All the 12 samples of man hours per occupied room in baseline period is in control.
Figure 5: Man-hours per occupied room in implementation periods

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13 14 15 16 17 18 19 20
0.4
0.45
0.5
0.55
0.6
Man hours per occupied room in implementation
period
mean
mean + sd
mean - sd
mean +3*sd
mean - 3*sd
Man Hours Per
Occupied Room
In case of implementation period, man hours per occupied room has no values outside the
control limits. Therefore, the dataset is in control.
Figure 6: Man-hours per occupied room in both types of periods

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
0.4
0.42
0.44
0.46
0.48
0.5
0.52
0.54
0.56
Man hours per occupied room in both types of
periods
mean
mean + sd
mean - sd
mean +3*sd
mean - 3*sd
Man Hours Per
Occupied Room
Overall the dataset “Man hours per occupied room” is in control.
Direct Expense per Occupied Room:
The Direct Expense per occupied room in baseline period and in implementation
period is within the control limit. The Direct Expense per occupied room in both the periods
is also within control limit.
Figure 7: Direct expense per occupied room in implementation period

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1 2 3 4 5 6 7 8 9 10 11 12
$4.00
$4.50
$5.00
$5.50
$6.00
$6.50
$7.00
Direct expense per occupied room in implementation
period
Direct Expense
per Occupied
Room
mean
mean + sd
mean - sd
mean +3*sd
mean - 3*sd
No fluctuation for “Direct expense per occupied room” in implementation period is found out
of control limits.
Figure 8: Direct expense per occupied room in implementation periods

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13 14 15 16 17 18 19 20
$4.50
$4.70
$4.90
$5.10
$5.30
$5.50
$5.70
$5.90
Direct expense per occupied room in implementation
period
mean
mean + sd
mean - sd
mean +3*sd
mean - 3*sd
Direct Expense
per Occupied
Room
There is no fluctuated value of “Direct expense per occupied room” in implementation period
outside the upper and lower control limits.
Figure 9: Direct period per occupied room in both types of periods

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
$4.00
$4.50
$5.00
$5.50
$6.00
$6.50
$7.00
Direct expense per occupied room in both
types of periods
mean
mean + sd
mean - sd
mean +3*sd
mean - 3*sd
Direct Expense
per Occupied
Room
For overall outlook of “Direct expense per occupied room” in both types of baseline and
implementation periods, 4th sample is found to be out of control. Otherwise, all the values are
in control.
“Check In Speed”:
The Check in speed in baseline period and in implementation period is within the
control limit. The Check in speed in both the periods is also within control limit.
Figure 10: Check in speed in baseline period

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1 2 3 4 5 6 7 8 9 10 11 12
0.5
0.7
0.9
1.1
1.3
1.5
1.7
1.9
Check in speed in baseline period
Check In
Speed
mean
mean +
sd
mean - sd
mean
+3*sd
mean -
3*sd
The “Check in speed” in baseline period is in control.
Figure 11: Check in speed in implementation period
13 14 15 16 17 18 19 20
0.5
0.7
0.9
1.1
1.3
1.5
1.7
1.9
Check in speed in implementation period
mean
mean
+ sd
mean
- sd
mean
+3*sd
mean
- 3*sd
Check
In
Speed
The “Check in speed” in implementation period has no value outside the control limits.
Therefore, the variable is in control.

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Figure 12: Check in speed in both types of periods
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
0.5
0.7
0.9
1.1
1.3
1.5
1.7
1.9
Check in speed in both types of periods
mean
mean +
sd
mean -
sd
mean
+3*sd
mean -
3*sd
Check In
Speed
Overall the check in speed is in control due to absence of any fluctuated value.
Take Rate (% of multi-night stays):
The Take rate of multi-night is inherent in baseline period and in implementation
period is within the control limit. The Take rate of multi-night stays in both the periods is also
within control limit.
Figure 13: Take rate of multi-night stays in baseline periods

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1 2 3 4 5 6 7 8 9 10 11 12
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
3.0%
3.5%
4.0%
Take Rate (% of multi-night stays) in baseline
period
Take Rate (%
of multi-night
stays)
mean
mean + sd
mean - sd
mean +3*sd
mean - 3*sd
“Take Rate” in baseline period is in control.
Figure 14: Take rate of multi-night stays in implementation periods
13 14 15 16 17 18 19 20
0.00%
0.50%
1.00%
1.50%
2.00%
2.50%
3.00%
3.50%
4.00%
4.50%
Take Rate (% of multi-night stays) in implementation
period
mean
mean + sd
mean - sd
mean +3*sd
mean - 3*sd
Take Rate (%
of multi-night
stays)

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“Take Rate” in implementation period is also a in control process.
Figure 15: Take rate of multiple-night stays in both types of periods
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
0.000%
0.500%
1.000%
1.500%
2.000%
2.500%
3.000%
3.500%
4.000%
Take Rate (% of multiple-night stays) in both
types of periods
mean
mean + sd
mean - sd
mean +3*sd
mean - 3*sd
Take Rate (% of
multi-night stays)
Overall, the “Take Rate” is in control as all the data points are within control limits.
Conclusion:
All the limits are within control limits. Direct expense per occupied room is overall
out of control as fourth period is out of control. The method is not totally in control. The
reasons may be inflation, presence of mediator and preventive functions. Sometimes, out of
routine expenses, emergency and corrective expenses for guest maintenance increases. The
cost of labor and asset impairment could be nightmare sometimes.
Based on the provided observed data, we found no significant changes throughout
different periods for almost all the variables. Therefore, the exact root cause of the problem
was not found.

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References:
Ahmad, L., Aslam, M., & Jun, C. H. (2014). Designing of X-bar control charts based on
process capability index using repetitive sampling. Transactions of the Institute of
Measurement and Control, 36(3), 367-374.
Asubonteng, P., McCleary, K. J., & Swan, J. E. (1996). SERVQUAL revisited: a critical
review of service quality. Journal of Services marketing, 10(6), 62-81.
Defining CTQ Outputs: A Key Step in the Design Process. (2018). Isixsigma.com. Retrieved
10 February 2018, from https://www.isixsigma.com/methodology/voc-customer-
focus/defining-ctq-outputs-key-step-design-process/
Delivering The Cleanliness Hotel Guests Expect. (2018). CleanLink. Retrieved 10 February
2018, from http://www.cleanlink.com/news/article/Delivering-The-Cleanliness-Hotel-
Guests-Expect--20903
Kan, S. H. (2002). Metrics and models in software quality engineering. Addison-Wesley
Longman Publishing Co., Inc.
What are ControlCharts?. (2018). Itl.nist.gov. Retrieved 10 February 2018, from
http://www.itl.nist.gov/div898/handbook/pmc/section3/pmc31.htm

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Appendix:
CTQ Baseline and post implementation data set
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Guest Room Cleanliness 82% 83% 80% 85% 84% 79% 83% 85% 80% 81% 84% 80% 84% 79% 83% 85% 80% 81% 84% 80%
Man Hours Per Occupied Room 0.50 0.49 0.47 0.48 0.49 0.51 0.52 0.48 0.51 0.48 0.47 0.50 0.49 0.51 0.52 0.48 0.51 0.48 0.47 0.50
Direct Expense per Occupied Room $5.35 $5.50 $5.85 $6.25 $5.50 $5.35 $5.25 $5.50 $5.10 $5.25 $5.40 $5.50 $5.50 $5.35 $5.25 $5.50 $5.10 $5.25 $5.40 $5.50
Check I n Speed 1.1 1.2 1.3 1.5 1.1 1.0 1.2 1.3 1.4 1.5 1.0 1.3 1.1 1.0 1.2 1.3 1.4 1.5 1.0 1.3
Take Rate (% of multi-night stays) 1.8% 1.4% 2.5% 2.3% 1.8% 1.4% 2.4% 1.6% 1.1% 2.5% 3.0% 1.9% 1.8% 1.4% 2.4% 1.6% 1.1% 2.5% 3.0% 1.9%
Baseline Periods Post I mplementation Periods