Expert Answer:Simulation Project: Improving the X-Ray Process at

  

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Simulation Project: Improving the X-Ray Process at County Hospital
County Hospital wishes to improve the service level of its regular Xray operation, which runs from 8 A.M.
to 5 P.M. Patients have identified the total required time as their main concern with this process.
Management, on the other hand, is concerned with utilization of available resources. Management has
created a process-improvement team to study this problem. The process might be redesigned as a result
of the team’s recommendations. The team has defined the entry point to the X-ray process to be the
instant a patient leaves the physician’s office en route to the X-ray lab. The exit point has been defined
as the instant at which the patient exits the X-ray facility and the completed X-ray is delivered to the
physician’s office. Patients are registered as they enter by a desk assistant, and a sample of the arrival
time data is provided in Table-1. Until now, no attempt has been made to further analyze this data, so
there is no insight into what the arrival process looks like. The team has identified 9 activities in the
current X-ray process (see Table-2), which is the same irrespective of the patient. The activity times and
their distributions are specified in Table-3. The resource data for the X-ray process is specified in Table3. There are one desk clerk, three X-ray technicians, one dark room technician, two X-ray labs, one dark
room and one X-ray inspection room. The two X-ray labs and the inspection room are staffed by Xray
technicians while the dark room is staffed by a dark room technician.
PART I: ANALYZING THE CURRENT PROCESS DESIGN
1. Step #1–Draw a flowchart of the current X-ray process. (Lexie)
2. Step #2–Develop a simulation model of this process. (Jill)
The model requires analysis of input data regarding the arrival process of patients. Look at the
lecture PowerPoint materials on “analyzing input data” as a guide for your modeling. A combination of
descriptive statistics and a histogram can help you “eyeball” the data to help determine the most
appropriate probability distribution function to help model interarrival times.
Modeling Hint: Build the model incrementally based on your flowchart. Do not try to put
everything together at once and then test whether it works. The key is to use individual, small steps.
Be sure to specify your priorities and resources correctly with SimQuick. Remember, a higher
priority has a lower number workstation. For instance, if you have two workstations that compete for a
resource, the workstation with the higher priority is always entered into the model first.
Table-1 presents patient arrival times. You need to come up with some estimate of interarrival
times, meaning the time between arrivals, to come up with a pattern for how patients enter into the Xray facility. I highly encourage you to create a histogram, run the descriptive statistics from the analysis
toolpack in Excel and estimate an appropriate, average interarrival time.
You may need to include more buffers than noted in Table-2. Assume that there’s plenty of
space available for patients to wait while they’re in the service system.
3. Step #3—First cut analysis based on one simulation run: As a first-cut analysis, run one simulation,
using the correct activity time distributions. Look at the average cycle time, the throughput rate, the
queue lengths, and the descriptive statistics, such as the mean and standard deviations. What are the
problems in this process? (Jill)
4. Step #4—Multiple simulation runs: Run 100 simulations and compute the cycle time and daily
throughput (average and standard deviation). Also compute the queue and resource utilization statistics
with 95 percent confidence intervals. Assume that any patients remaining in the system at the end of
the day will be taken care of by the night shift. Every morning, the system is assumed to be empty. Are
there any surprises when you compare these results with the ones in question 3? (Lexie)
5. Step #5—Analysis based on multiple simulation runs: Assess the performance of the process using the
values calculated in question 4. Where is the bottleneck? What is the problem area in terms of reducing
the cycle time and increasing the throughput rate? (Lexie)
PART II: SUGGEST AND EVALUATE NEW PROCESS DESIGNS
6. Step #6—Creative process design based on acquired process understanding: Based on your insight
about the current operations, identify and model at least two plausible ways of reducing the average
cycle time by redesigning the process. For example, what if more personnel are hired? What type of
personnel would be most useful? What, if any, improvement would adding a dark room and/or an X-ray
lab have on cycle time and throughput? What if the X-ray technicians receive training designed to
reduce the probability of rejecting X-rays from 25% to 10%? Test at least two alternative, future-state
models against your baseline model. (Brandon)
7. Step #7—Compare the as-is process with two alternative state processes and two alternative state
processes against each other: Investigate the performance of the redesigned process in terms of the
cycle time, daily throughput and resource utilization of key resources, such as the x-ray rooms and the
dark room(s). Also look at the resource and activity utilization statistics and queue statistics with 95
percent confidence intervals as before. What are your conclusions? Is the new design significantly better
than the old process with regards to the cycle time and throughput? Are any drawbacks obvious?
Helpful Hint: Although regression models or ANOVA models would be most appropriate, the SimQuick
book (pages 98-106) walks you through statistical modeling with descriptive statistics and the T-test for
unequal variances. Develop the appropriate T-test comparisons and report. Remember, to fully assess
the likely effectiveness of alternative, future-state models vis-à-vis a baseline model or two or more
alternative models, we need to compare the results using statistical inference methods to provide us
assessments of process averages as well as likely variation in our processes. You need to report on both
process averages and process variation. (Brandon)
PART III: REPORT TO MANAGEMENT (Jill)
You are the leader of the process improvement team, and you were chosen to write-up the results from
the process simulation and report your findings and recommendations to the management team of the
hospital. In particular, the CEO, the CFO and the CMO are members of the executive team and have
their concerns you must address. The CFO is concerned with resource utilization and throughput rate
and is skeptical about the need to hire additional staff while the CMO is more concerned with patient
satisfaction with the X-ray process. You need to address both types of concerns as well as support your
findings with analyses drawn from your process simulations.
Your grade is assessed based on your report to management and whether you followed the steps in the
simulation process. Remember, the CEO, CFO, and CMO do not want the technical details about how
you ran the analyses; they’re not the technicians and really don’t have any interest in the technical
aspects of process simulation models. They primarily want to know about the current process and
whether your recommendations save time, save money, save resources or create potential new
revenue. Don’t bore them with the details. Save the details for a technical appendix, footnotes, or end
notes.
X-Ray Process at County Hospital
Patient exits physican’s
office
(Entrance)
Lobby – LIne 1
(Buffer)
Patient Registration
(Workstation)
Line 2 – X-Ray Lab
(Buffer)
X-Ray Lab 1
(Workstation)
X-Ray Lab 2
(Workstation)
Line 3 – Darkroom
(Buffer)
No
Darkroom
Development
(Workstation)
Line 4 – Tech Check
(Buffer)
Technician Check
(Workstation)
Is x-ray acceptable?
Yes
Line 5 – Out Process
(Buffer)
Out Process
(Workstation)
Completed x-rays
delivered
(Buffer)
Flow Chart
SimQuick
SimQuick is a freely-distributed Excel-based software package for building
simulation models of processes such as: waiting lines, supply chains,
manufacturing facilities, and project scheduling. SimQuick is designed to
be easy to learn and use, with clearly-defined functionality. The package
has been used both to model real-world processes as well as in
educational settings to introduce fundamental concepts of modeling,
process simulation, and operations management.
For an example of a SimQuick model and more details on SimQuick visit
the website: SimQuick.net.
SimQuick models consist of linked combinations of five basic elements.
The element types are Entrances, Exits, Work Stations, Buffers, and
Decision Points. You have a lot of discretion in how these elements are
combined to build a model of a process. Characteristics of the elements
are entered into SimQuick by filling in tables. Statistical distributions are a
key characteristic; they capture the uncertainty inherent in almost all
processes: arrival times of people at a service, times to process a
document or machined part, demand for a product in a store, and so on.
SimQuick is accompanied by an inexpensive 125-page booklet that covers
the basics of process simulation and how to build models using SimQuick.
Information on the booklet and how to order it can be found at
SimQuick.net.
David Hartvigsen
Mendoza College of Business
University of Notre Dame
Hartvigsen.1@nd.edu
Results
Simulation Results
Element
types
Element
names
Statistics
Overall Simulation Numbers
Means
1
Entrance(s)
Door
Objects entering process
Objects unable to enter
Service level
86.00
0.00
1.00
Work Station(s) Registration
Final status
Final inventory (int. buff.)
Mean inventory (int. buff.)
Mean cycle time (int. buff.)
Work cycles started
Fraction time working
Fraction time blocked
NA Not Working
0.00
0
0.00
0.00
0.00
0.00
86.00
86
0.54
0.54
0.00
0.00
X-Ray Lab 1
Final status
Final inventory (int. buff.)
Mean inventory (int. buff.)
Mean cycle time (int. buff.)
Work cycles started
Fraction time working
Fraction time blocked
NA Working
0.00
0
0.00
0.00
0.00
0.00
38.00
38
0.98
0.98
0.00
0.00
X-Ray Lab 2
Final status
Final inventory (int. buff.)
Mean inventory (int. buff.)
Mean cycle time (int. buff.)
Work cycles started
Fraction time working
Fraction time blocked
NA Working
0.00
0
0.00
0.00
0.00
0.00
39.00
39
0.96
0.96
0.00
0.00
Darkroom
Final status
Final inventory (int. buff.)
Mean inventory (int. buff.)
Mean cycle time (int. buff.)
Work cycles started
Fraction time working
Fraction time blocked
NA Working
0.00
0
0.00
0.00
0.00
0.00
60.00
60
0.95
0.95
0.00
0.00
Technician Check
Final status
Final inventory (int. buff.)
Mean inventory (int. buff.)
Mean cycle time (int. buff.)
Work cycles started
Fraction time working
Fraction time blocked
NA Working
0.00
0
0.00
0.00
0.00
0.00
59.00
59
0.29
0.29
0.00
0.00
Out Process
Final status
86
0
1.00
NA Working
Page 2
Results
Buffer(s)
Final inventory (int. buff.)
Mean inventory (int. buff.)
Mean cycle time (int. buff.)
Work cycles started
Fraction time working
Fraction time blocked
0.00
0.00
0.00
44.00
0.29
0.00
0
0.00
0.00
44
0.29
0.00
Line 1 – Lobby
Objects leaving
Final inventory
Minimum inventory
Maximum inventory
Mean inventory
Mean cycle time
86.00
0.00
0.00
5.00
0.71
4.44
86
0
0
5
0.71
4.44 0.074 hrs.
Line 2 – Xray Lab
Objects leaving
Final inventory
Minimum inventory
Maximum inventory
Mean inventory
Mean cycle time
77.00
23.00
0.00
25.00
13.29
93.19
77
23
0
25
13.29
93.19 1.553 hrs.
Line 3 – Darkroom
Objects leaving
Final inventory
Minimum inventory
Maximum inventory
Mean inventory
Mean cycle time
60.00
15.00
0.00
20.00
9.00
81.00
60
15
0
20
9.00
81.00
Line 4 – Tech Check Objects leaving
Final inventory
Minimum inventory
Maximum inventory
Mean inventory
Mean cycle time
59.00
0.00
0.00
2.00
0.04
0.37
59
0
0
2
0.04
0.37 0.006 hrs.
Line 5 – Out Process Objects leaving
Final inventory
Minimum inventory
Maximum inventory
Mean inventory
Mean cycle time
44.00
0.00
0.00
7.00
1.33
16.36
44
0
0
7
1.33
16.36 0.273 hrs.
Completed Xrays
Decision Point(s) Decision Point
Objects leaving
Final inventory
Minimum inventory
Maximum inventory
Mean inventory
Mean cycle time
Objects leaving
Final inventory (int. buff.)
Mean inventory (int. buff.)
Page 3
0.00
43.00
0.00
43.00
16.91
Infinite
0
43
0
43
16.91
Infinite
58.00
0.00
0.00
58
0
0.00
1.35 hrs.
Results
Mean cycle time (int. buff.)
0.00
0.00
2.23
0.95
0.83
2.23
0.95
0.83
Resource(s)
X-ray technician
Mean number in use
Darkroom Tech
Mean number in use
Front-desk assistant Mean number in use
Page 4
Results
Problems with process:
86 patients entered the process; however, at the end of the 9
hour work day, only 43 x-rays were completed and delivered to
the physician’s office (leaving 43 patient x-rays incomplete).
Resources do not seem to be an issue as none of the personnel
are over-burdened. The longest cycle time in the process is the
wait time associated with entering one of the two x-ray labs.
Patients wait an average of 1.5 hours before one of the two
labs is available. There are 5 steps in the process where the
patient is required to wait, and wait times in these buffers
averages over 3 hours per patient. Wait time is non-value
added and opportunities to decrease these times should be
addressed.
Page 5
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
Interarrival
time
6.30
3.83
6.94
0.02
6.85
2.12
1.59
1.56
12.44
3.04
15.38
10.27
0.39
3.59
10.27
7.18
4.01
2.42
19.04
5.61
7.73
6.88
2.30
2.76
8.18
1.25
2.79
2.74
35.77
2.21
2.43
7.61
9.92
4.53
3.55
9.83
1.56
4.31
5.09
3.00
2.61
Time of Arrival (in min.
from time 0)
6.3
10.13
17.07
17.09
23.94
26.06
27.65
29.21
41.65
44.69
60.07
70.34
70.73
74.32
84.59
91.77
95.78
98.2
117.24
122.85
130.58
137.46
139.76
142.52
150.7
151.95
154.74
157.48
193.25
195.46
197.89
205.5
215.42
219.95
223.5
233.33
234.89
239.2
244.29
247.29
249.9
0.00-1.00
1.00-5.00
5.00-10.00
10.00-15.00
15.00-20.00
20.00-25.00
25.00-30.00
30.00-35.00
35.00-40.00
35
29
30
25
Frequency
Patient #
20
18
15
10
5
3
0
1.00
5.00
10.00
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
0.35
6.09
1.56
11.07
7.85
3.61
1.51
11.29
6.56
3.96
2.83
1.55
5.93
8.76
3.69
11.70
1.70
13.74
6.14
5.9965
5.6204
35.77
0.02
250.25
256.34
257.9
268.97
276.82
280.43
281.94
293.23
299.79
303.75
306.58
308.13
314.06
322.82
326.51
338.21
339.91
353.65
359.79
Mean
Standard Deviation
Max
Min
Bin
Frequency
1.00
5.00
10.00
15.00
20.00
25.00
30.00
35.00
40.00
Exponential distribution
6.00 Mean
5.62 Standard Deviation
3
29
18
7
2
0
0
0
1
Histogram
18
7
2
10.00
15.00
20.00
Bin
0
0
0
25.00
30.00
35.00
1
40.00
Frequency

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