AD605 Boston University Week 2 Stickley Adhesives Group Case Study Questions The introduction of the case is in the attachment.We already completed the sim

AD605 Boston University Week 2 Stickley Adhesives Group Case Study Questions The introduction of the case is in the attachment.We already completed the simulation table. According to the data in the Excel, please complete questions 3,4 and 5. BOSTON
METROPOLITAN COLLEGE
UNIVERSITY
DEPARTMENT OF ADMINISTRATIVE SCIENCES
AD 605 Operations Management
Stickley Adhesives I Case Study (Due Sunday September 22 by 11:59 PM)
Study the case study document and watch the simulation video. Work with your team on the following questions.
Important: For case study assignments, it is NOT a good idea to start by splitting the questions among the team
members. Each team member should read the case and draft a response to every question. Then, the team should get
together to perform the final analyses and then (perhaps) split the writing of the final report (which should be edited
to ensure continuity).
1.
Calculate the number of workers that would be assigned to each process so that the average capacity of each
process (requests per day) is equal to the average demand for the applications service (i.e., 24 requests per
day). Note that this analysis will load the service system with a capacity buffer of zero percent (0%) for each
process. Show how the calculations were done and the results.
2.
Using the number of workers calculated above, run the simulation for forty (40) 30-day periods and
summarize these results statistically using two or more numerical summaries and at least one display.
3.
In 1-2 paragraphs (about 200 words), summary your results from Question 2 for the project team. Include
reference to all interesting findings and the displays and other results from above. Explain the meaning of
each numerical summary and display. Do not simply list or refer to the information presented in Question 2
without explanation.
4.
Analyze the applications service system by running the simulation repeatedly while adjusting the number of
workers (i.e., the capacity buffer) to determine how best to allocate workers to each process. Use only whole
numbers of servers (1, 2, 3, etc.). Show your key results in tabular form or as a display. Remember that you
must run the simulation for multiple iterations so that the effect of random variations does not bias your
results (indicate the number of times you ran the simulation for each analysis). Be prepared to have a team
member present this result orally at the next classroom session.
5.
Make a final recommendation for the number of workers to assign to each process. Justify your
recommendations using 1-2 paragraphs (about 200 words). Incorporate a display that shows the main
options you considered.
6.
List and describe three problems that may occur if the centralized system were implemented. There is no
need to “solve” the problems – just justify why you think the problem may occur. Use at least one reference
citation to support each of your potential problems (use a different reference for each potential problem).
Use the BU library’s on line system to find your reference (www.bu.edu/library). List the reference citation
for your article using the APA format. This link helps you better understand the APA writing style, in
particular, how to cite and reference correctly (http://library.bu.edu/citeys). A link to an excellent guide is
provided here (https://owl.english.purdue.edu/owl/resource/560/01/). Be prepared to have a team
member present one of your potential problems at the next classroom session.
Turn in one Word file (on Blackboard), with a cover page (with course name, case study title, date, and team
members). Put all analysis detail in the Word file (i.e., do not attach an Excel file). The Word file should be
suitable for printing.
BOSTON
METROPOLITAN COLLEGE
UNIVERSITY
DEPARTMENT OF ADMINISTRATIVE SCIENCES
STICKLEY ADHESIVES1
Pasquale “Cocoa” Martinez began reviewing his Operations Management course notes in preparation for
his first big project at Stickley Adhesives. A recent graduate of Boston University’s Metropolitan College,
Cocoa had just been hired to be an operations analyst at a Stickley facility in Puerto Rico. Cocoa holds an
undergraduate degree in biology and a Master’s degree in applied business analytics. This project
requires him to analyze capacity requirements for a newly designed service system. Stickley’s customers
use this service when they are unsure of how to take advantage of Stickley’s extensive product line. This
“applications service” supplements product sales and is an important component of Stickley’s business
strategy. In fact, Cocoa’s work will be the last in a series of analyses on which a project team of scientists
and consultants has been working during the past year. When Cocoa asked why a relatively
inexperienced analyst should take on this role, he was told that the project’s budget did not include
money for a more experienced outside consultant and, in any case, the analysis should not be too
difficult.
Company Background
In the early 1910’s, Gerald Stickley and his cousin (Dean Happ) began experimenting with making glue
for various bonding applications. Gerald, an enthusiastic science student finishing the 11th grade, had
just learned that the protein collagen was used to make glue. Living on a farm in Kansas, Gerald’s
interest was piqued because collagen, contained in connective animal tissues, was abundant on his
family’s farm. After Dean went off to college, Gerald continued to work on his experiments while
assisting with his farming chores. After graduation from high school, he continued to work full time on
the farm, while taking on an increasing number of responsibilities for both the operation of the farm and
the business of farming.
The severe drought of the 1930’s and the subsequent economic depression forced Gerald and his family
to abandon farming and move to the city of Wichita, Kansas. There, Gerald took a series of low paid jobs.
He supplemented his meager income with sales of glue products. Once the economic situation improved,
sales of “Stickley Glue” increased, and eventually Gerald opened a small factory to make glue products.
Over the years, glue production transitioned from the use of animal products to synthetics and, in 1975,
the name of the company was changed to Stickley Adhesives. Gerald Stickley died in 1978 and the
company was sold to an international corporation, Congisto A/S. Congisto has maintained the brand
name Stickley Adhesives, due to its excellent reputation for high quality products that can be used for a
multitude of bonding applications.
The Stickley Adhesives division of Congisto A/S employs 6,000 people worldwide. They include about
900 scientists, engineers, and technicians who are devoted to the development of new adhesive products
and the support of customers using existing products. Because the majority of Stickley’s customers are
not familiar with the science of adhesives, Stickley offers numerous support services. These support
services include troubleshooting, equipment rental, and applications consultation, which are considered
by Stickley management to be a key source of competitive advantage. It is the reason why Stickley can
charge a premium for its products. Although its headquarters remains in Wichita, Stickley’s design,
manufacturing, and support functions are located at various U.S. and international locations. It has
customers located in the America’s, Europe, and Asia.
This case was developed by John Maleyeff based on his work in service process capacity planning. All references to
people and organizations are fictional. © 2018 All rights reserved.
1
Stickley Adhesives Case Study
Page 1
Applications Service Operations
Cocoa’s project concerns Stickley’s applications service. This service responds to requests from
customers who wish to know the best adhesive product to use for a specific application, and the
recommended application characteristics. These characteristics could include the amount, the
recommended cure time, the holding (or clamping) pressure, and other factors that affect the quality of
the resulting bond. Examples include bonding requirements for products such as medical (e.g., syringes),
personal (e.g., cosmetics), acoustics (e.g., speakers), and automotive (e.g., engine hoses). Often, the
materials used or the conditions under which the bond must hold are unusual or extreme. For example, a
manufacturer may wish to bond a rubber hose to a specialized metal alloy in an automobile engine with
extreme temperature variations, excessive vibrations, and oily or dirty conditions.
The service also includes recommendations regarding the equipment needed to achieve a high quality
result. Both standardized equipment (such as dispensing setups and ultraviolet curing systems) and
customized equipment may be needed by customers. Customers who do not own their own equipment
may rent or purchase the equipment from Stickley (they usually start by renting the equipment with an
option to purchase later). Currently, the applications service is decentralized, with each region
performing its own analyses for regional customers. Each region charges customers for the service in
different ways, in some cases offering the service free of charge for some important customers. In every
region, the system makes extensive use of scientists, engineers, and technicians to study each application,
run experiments, analyze experimental results, and report results to customers.
Recently, Congisto’s management became aware of complaints from both customers and Stickley’s staff
regarding the operation of the applications service. A Lean Six Sigma project found that the number of
“reapplications” (i.e., requests concerning applications that had already been evaluated but did not
succeed) had increased substantially. The root causes were found to include many inconsistences in the
operation of the applications service across the global landscape. Many of these inconsistencies did not
appear to be especially new. However, requests have become more complex and expectations of
Stickley’s customers have increased as the service has become more popular. Finally, Stickley’s technical
staff complained of having multiple job responsibilities with conflicting incentives. As a result, the
decision was made to centralize the operation of the applications service.
Centralized Applications Service Offering
The new applications service system will centralize applications services operations while taking
advantage of scale economies and low labor costs when sensible. It will use new information technology
(IT) systems that will allow for the transmittal of information over great distances. At the time of Cocoa’s
involvement, the system design is complete and the anticipated operational challenges have been
addressed. The only remaining questions concern its capacity requirements and the revenue model
(whose determination will be made by corporate finance after Cocoa’s analysis).
The service will be available 7 days per week; its process flow is shown in the Appendix. It is anticipated
that an average of 24 customer requests will be received on a daily basis, and that the number of requests
per day will vary. Analysis of worldwide demand data indicated that there would be no differences in
the average demand across days of the week. Daily demand is expected to vary from 14 to 34 requests,
uniformly distributed across this range.
Every customer request will be received on a standard form originating from the company’s web site.
The first process, prioritizing, consists of an expert evaluation. It determines if the request represents a
project that should be handled by a scientist or a technician. Prioritizing will take place in Puerto Rico
(down the hallway from Cocoa’s office). On average, each worker can prioritize an average of 12 requests
per day, but the actual number of requests each day varies depending on their complexities.
Once prioritizing is completed, the request is sent to the scoping process. Like all other transitions, the IT
system will process transactions at the end of each day. Hence, the output from each process will be
Stickley Adhesives Case Study
Page 2
ready at the next process on the following day. Based on past scoping requests, about 1 in 8 (12.5%) of
requests need to be handled by scientists, with the remainder handled by a technician. On average, a
scientist can scope an average of 3 requests per day and a technician can scope an average of 7 requests
per day. Again, the actual number of requests per day will vary. Scoping will be performed at the U.S.
headquarters in Wichita.
After scoping, each request is sent to the experimenting process. This process will be done by engineers
located on a Stickley subsidiary located in Bangladesh (at a facility known to have the best quality results
in the decentralized system). Here, each worker can complete an average of 6 requests per day. After the
experiments are complete, requests are sent for analysis. The analyzing process is performed at the R&D
facility in Wichita. About half of these requests require equipment rental (which requires design of the
equipment system and price determination), and workers performing these analyses can complete an
average of 2 requests per day. The remaining half do not require equipment rental, and workers
performing these analyses can complete an average of 6 requests per day.
Finally, the last process in the applications service system is reporting. This process will take place at a
Stickley subsidiary located in the Philippines. Workers at this facility can complete an average of 8
customer requests per day. As with the other processes, the actual number of requests completed will
vary by day.
The characteristics of each process including location, average completions per day and “fully loaded”
labor costs per day are shown in Table 1 (fully loaded labor costs include allocations for benefits and
other overhead expenses).
Table 1: Process Characteristics
Location
Average Capacity per
Worker (customers/day)
Labor Cost per
Worker ($/day)
Puerto Rico
12
125
Scoping: Scientist
Wichita (U.S.)
3
400
Scoping: Technician
Wichita (U.S.)
7
250
Process
Prioritizing
Experimenting
Bangladesh
6
75
Analyzing w/o Rental
Wichita (U.S.)
6
300
Analyzing w/Rental
Wichita (U.S.)
2
350
Philippines
8
50
Reporting
Service System Simulation
During earlier phases of the project, a consultant developed a Monte Carlo simulation of the proposed
system. Cocoa is told that he should make use of this simulation. He is informed that the simulation is
accurate, and no changes will be necessary to its internal logic. It simulates 30 days of operation based on
the structure and parameters described above. Users must enter the number of workers assigned to each
process. Each “iteration” of the simulation will mimic 30 days of operations. By running the simulation
repeatedly, users can observe the variation in throughput (completed requests at the end of 30 days) and
other process characteristics.
A video has been prepared by the consultant describing the use of the simulation in detail. It is
recommended that Cocoa watch the video before answering the analysis questions posed to him.
Stickley Adhesives Case Study
Page 3
APPENDIX
Applications Service Operations: Process Flow
CUSTOMER REQUEST
Prioritizing
12.5%
87.5%
Scoping: Technician
Scoping: Scientist
Experimenting
50%
50%
Analysis w/ Rental
Analysis w/o Rental
Reporting
REQUEST COMPLETED
Stickley Adhesives Case Study
Page 4
Inputs only in yellow cells (Workers) – hit F9 (or Fn F9) to generate new sets of random numbers
Stickley Adhesives: Applications Services Simulation
Prioritizing
No. Workers:
Day No.
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
Totals
Averages
Demand
21
25
19
21
23
17
23
28
17
32
24
16
21
20
25
27
33
18
18
22
30
20
31
19
25
25
34
30
32
23
719
24.0
InBox
21
25
19
21
23
17
23
28
20
32
24
16
21
20
25
27
33
18
18
22
30
26
31
24
25
25
34
30
32
23
733
24.4
Potential
36
29
44
36
32
35
37
25
38
34
32
48
42
38
48
45
37
39
38
26
24
32
26
44
47
31
39
37
47
48
1114
37.1
Scoping: Scientist
3
No. Workers:
Move InBox
21
3
25
2
19
2
21
3
23
2
17
3
23
6
25
6
20
4
32
6
24
7
16
2
21
4
20
3
25
3
27
8
33
5
18
10
18
9
22
9
24
7
26
7
26
5
24
9
25
12
25
11
34
9
30
20
32
22
23
22
719
221
24.0
7.4
Potential
2
3
6
2
1
1
4
4
1
2
6
0
5
3
0
6
0
3
1
3
3
4
1
0
1
6
0
1
2
5
76
2.5
Scoping: Technician
1
No. Workers:
Move InBox
2
21
2
20
2
23
2
16
1
20
1
21
4
13
4
19
1
23
2
17
6
29
0
23
4
14
3
18
0
17
6
20
0
24
3
28
1
16
3
17
3
21
4
21
1
24
0
21
1
21
6
25
0
21
1
23
2
27
5
30
70
633
2.3 21.1
Potential
47
26
27
31
34
31
39
39
23
39
46
46
47
35
41
33
45
40
36
35
39
24
47
37
40
25
46
27
38
30
1093
36.4
5
Experimenting
No. Workers:
Move InBox
21
24
20
23
23
22
16
25
20
18
21
21
13
22
19
17
23
23
17
24
29
19
23
35
14
23
18
18
17
21
20
17
24
26
28
26
16
31
17
17
21
20
21
24
24
25
21
25
21
21
25
22
21
31
23
21
27
24
30
29
633
694
21.1 23.1
Potential
45
29
42
35
42
30
38
29
41
36
46
43
24
46
42
45
24
47
39
45
44
26
25
41
48
29
46
30
34
25
1116
37.2
Analyzing w/o Rental
6
No. Workers:
Move InBox
24
12
23
14
22
8
25
12
18
14
21
5
22
11
17
11
23
11
24
14
19
14
35
9
23
16
18
10
21
7
17
9
24
7
26
14
31
10
17
14
20
8
24
16
25
13
25
12
21
11
22
10
31
12
21
13
24
10
25
8
688
335
22.9 11.2
Potential
37
30
46
44
31
27
46
34
47
24
35
29
45
46
30
46
25
42
24
24
30
27
32
34
30
48
38
36
27
27
1041
34.7
6
Analyzing w/Rental
No. Workers:
Move InBox
12
12
14
14
8
16
12
12
14
11
5
13
11
16
11
20
11
13
14
9
14
10
9
10
16
19
10
23
7
24
9
20
7
17
14
19
10
24
14
32
8
34
16
17
13
14
12
19
11
21
10
25
12
26
13
33
10
31
8
29
335
583
11.2 19.4
Potential
8
13
14
21
12
7
7
13
14
11
19
18
9
10
16
13
8
11
9
7
21
14
8
12
7
9
11
13
18
16
369
12.3
7
Reporting
No. Workers:
Move InBox
8
24
13
20
14
27
12
22
11
24
7
28
7
12
13
18
13
24
9
24
10
23
10
24
9
19
10
25
16
20
13
23
8
22
11
15
9
25
7
19
21
21
14
29
8
34
12
23
7
24
9
18
11
19
13
23
18
26
16
28
339
683
11.3 22.8
Potential
43
26
29
30
21
29
35
29
36
36
38
29
32
36
40
33
26
29
39
25
43
25
32
26
33
36
42
26
34
31
969
32.3
4
Done
24
20
27
22
21
28
12
18
24
24
23
24
19
25
20
23
22
15
25
19
21
25
32
23
24
18
19
23
26
28
674
22.5
Average
Demand
646
747
723
717
667
671
738
771
688
668
672
758
735
686
724
777
744
729
725
719
779
736
702
713
701
816
735
703
725
728
698
724
726
763
746
718
759
717
727
755
21.5
24.9
24.1
23.9
22.2
22.4
24.6
25.7
22.9
22.3
22.4
25.3
24.5
22.9
24.1
25.9
24.8
24.3
24.2
24.0
26.0
24.5
23.4
23.8
23.4
27.2
24.5
23.4
24.2
24.3
23.3
24.1
24.2
25.4
24.9
23.9
25.3
23.9
24.2
25.2
582
655
627
618
628
609
596
619
611
593
637
644
628
624
615
624
625
597
610
618
612
618
624
627
606
562
607
616
647
611
643
618
612
648
611
653
642
589
659
645
Average
Reporting
19.4
21.8
20.9
20.6
20.9
20.3
19.9
20.6
20.4
19.8
21.2
21.5
21.9
20.8
20.5
20.8
20.8
19.9
20.3
20.6
20.4
20.6
20.8
20.9
20.2
18.7
20.2
20.5
21.6
20.4
21.4
20.6
20.4
21.6
20.4
21.8
21.4
19.6
22
21.5
724.4
24.15
620.25
20.6975
Demand
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
Average
Reporting
Demand
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
1
2,1,4,5,6,5,3
Average
731.85
Demand
2
2,3,4,5,6,5,4
664
726
752
729
717
705
771
696
757
742
786
747
769
772
729
702
714
770
647
742
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
748
736
688
715
714
753
641
757
710
695
746
660
772
737
759
684
716
642
Average Demand
Average Reporting
Reporting
Average Reporting
22.1
21.1
634
21.1
24.2
21.6
649
21.6
25.1
20.7
620
20.7
24.3
19.7
590
19.7
23.9
21.1
632
21.1
23.5
21.3
638
21.3
25.7
20.8
625
20.8
23.2
19.9
597
19.9
25.2
20.1
603
20.1
24.7
19.9
596
19.9
26.2
20.8
624
20.8
24.9
21.7
650
21.7
25.6
21.1
634
21.1
25.7
20.7
620
20.7
24.3
20.6
619
20.6
23.4
20.9
627
20.9
23.8
20.9
628
20.9
25.7
21.5
644
21.5
21.6
20.1
603
20.1
24.7
22.5
675
22.5
24.39
20.85
625.4
20.85
Average Demand
Average Reporting
Reporting
Average Reporting
24.9
22
659
22
24.5
22
660
22
22.9
20.7
622
20.7
23.8
21….
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