Topic: Math/Physic/Economic/Statistic Problems

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mba_791_unit_4_lectures.xlsx

4.3.1

WEEK 4 Continued Analysis with Simulation Models
Business Issue -- a process with variation -- want to understand the impact of that variation.
Business Problem Definition: BusQues: Can we develop a better understanding of …? (maybe) Which is the preferred alternative for the process?
Decision: Perhaps no decisions. Perhaps alternative settings of some aspect of the process or possible changes to process.
Objective Measure(s): One or more measures, e.g., cost, time, profit,... Risk profiles and/or summary measures like mean, 95 percentile, probability of a loss, etc.
Constraints: Structure of the process/system -- current policies, procedures, process design. Historical data of the random events to be used.
Data: Some data items are single values, some data items are specified probability distributions.
Analysis:
SIMULATION MODELING WITH EXCEL covered in week 3 & homework 3
(1) Build a CORE MODEL of the business process (one time through or one hour or eight customers or eight hours or one week, …)
One or more inputs to the model will be random in addition, model has fixed data inputs
Equally likely any value in range 0 - 1: =RAND()
Discrete distribution: values, probs =VLOOKUP(RAND(),cum prob table, col) Model will have formulas for how the process works
Contin- uous Distrib's Normal distribution: mean, stdev =NORMINV(RAND(),mean,stdev) -- shown in output cells:
Exponential distribution: mean value =-mean*LN(RAND())
Uniform distribution: low and high values =lo + (hi-lo)*RAND() Model may or may not have decisions
Disrete Uniform: low and high =ROUND(lo-.5 + (hi+.5-lo-.5)*RAND(),0) (process settings or choices)
Translated exp: constant value and
mean of added variation =const + (- mean*LN(RAND())) Objective: one or more key output measures, calculated with formulas:
NOTE: can make any continuous distribution discrete using =ROUND.
Will demonstrate in week 4
(2) TEST and VALIDATE this core model.
(3) REPLICATE the Core model many times (e.g., 1,000) in order to observe the "behavior" of the output measures -- A LARGE SAMPLE OF THE PROCESS
In Excel: Data --> What If Analysis --> Data Table -- resulting in 1,000 rows of the objective measure values
(4) ORGANIZE & SUMMARIZE the large number of (sample) values of each objective measure
=AVERAGE These describe the "middle" of all the values
=MEDIAN NOTE: Use the mean or average value of the large sample of that measure as the estimate of that measure for the whole population.
=STDEV
=PERCENTILE All these describe the variability or spread
Frequency Table using =FREQUENCY to show frequencies (#s) and proportions for defined classes. of each objective measure
Histogram using Insert Chart --called a RISK PROFILE -- a picture of the whole distribution
(5) PERHAPS, in the core model, change the decision VALUE or process design.
Repeat steps (2) -- (4)
(6) Produce A TABLE OF RESULTS , e.g., one column for each estimated objective measure and one row for each value of the decision variable (design option).
Include perhaps risk profile graphs for each value of the decision variable (design option).
Conclusions/Insights/Recommendations:
"Answers" to the business questions and other insights and/or recommendations.
ANALYSIS PROCESS:
Bus Sit/Ques with Signif Variation Build a Simulation Model Replicate --> Observe a Large Sample Summarize Sample Results - Answer Qs

4.4.1

Issue: Single Barber Shop EXAMPLE OF A COMPLETE ANALYSIS FOR A MGT REQUEST
Problem: Question: Can we understand better the current operations of this barbershop?
Decisions: Understanding may lead to decisions to improve. Exponential InterArrivals BARBER
Objective Measures: Three: wait time of customers -- in shop waiting (avg & 90 percentile), total time in shop, and idle time percent
Constraints: Only one barber; FCFS, estimates of haircut time and time between arrivals Uniform Service Time
Data: Estimated time to cut hair -- Uniform distribution with min = 15 and max = 35. DEMO:
NOTE: Can choose next service time as: =-avg*LN(RAND()) = min + RAND()*(max-min) 26.6 MIN NOTE: This is a fairly common service facility situation: Variable arrivals and variation in service time.
Note that exponential time (minutes) between arrivals is the same as Poisson # of arrivals per minute.
Estimated arrivals -- random with an average of 2 customers arriving every hour ( 1 every 30 minutes).
Interarrival time can be modeled as an Exponential distribution with mean = 30 minutes. NOTE: can't mix hours and minutes -- choose one.
NOTE: Can choose next inter arr time as: =-avg*LN(RAND()) = -30*LN(RAND()) 37.6 MIN
Analysis: Simulation Model (e.g., time to next arrival)
CORE MODEL: Barber shop open for 10 customers.
Haircut Time: Uniform min: 15 max: 35 minutes
Inter Arrival Time: Exponen'l mean: 30 minutes What will rows be used for? Columns?
Core Model here -- 10 customers ALL TIMES IN MINUTES Possible Objective Measures -- One Day
Inter Arrival Time Time Haircut Begins Time Haircut Ends Time in Barber Shop Barber Idle Avg Wait Time Avg Time in Shop
Customer Time of Arriv Haircut Time Time Waiting 8.0 min 33.0 min
NOTE: Core model here could not be one customer because of inter dependence of customers.* StDev Wait Time StDev of Time in Shop
1 48.2 48.2 48.2 17.6 65.8 0.0 17.6 48.2 14.7 min 18.2 min
2 116.3 164.5 164.5 16.4 180.9 0.0 16.4 98.7 90th Percentile Wait Time 90th Percentile of Time in Shop
3 19.0 183.5 183.5 16.5 200.0 0.0 16.5 2.6 22.6 min 50.5 min
4 0.2 183.6 200.0 27.4 227.4 16.4 43.8 0.0
5 59.9 243.5 243.5 27.2 270.7 0.0 27.2 16.1 Max Wait Time Percent of Time Idle These four requested in this case.
6 54.9 298.4 298.4 32.8 331.2 0.0 32.8 27.7 43.7 min 55%
7 12.6 311.0 331.2 27.7 358.9 20.2 47.9 0.0
8 4.1 315.1 358.9 30.7 389.6 43.7 74.4 0.0 Assumptions:
9 137.3 452.4 452.4 24.3 476.7 0.0 24.3 62.8 FCFS
10 77.8 530.2 530.2 28.5 558.8 0.0 28.5 53.5 No "balking" (leaving before service)
Could perhaps build a core model of 8 hours (480 minutes) using =IF statements to determine if 8 hours is exceeded (if so, no further arrivals). No "speed up" if many waiting
. . .
WHY NOT BUILD CORE MODEL OF ONE CUSTOMER ?? *For this system, performance for one customer (wait, idle, etc) is NOT independent of previous customers.
REPLICATE the core model.
Use Data Table to replicate a large number of successive 10 customer days (1,000) recording one or more of the 6 orange cells in the core model.
OR, model one day (=8*60=480 minutes). More complicated model that stops after 480 min. Then, use Data Table to 10 customer days of Barber Shop - 1000
replicate 1,000 days. Freeze the 1000 values -- so that worksheet is manageable. Avg Wait (min) Ave Time in Shop (min) 90 Percentile Wait (min) Percent of Time Idle
Number 8.0 33.0 22.6 55%
Calculate average time waiting and average time in barber shop (perhaps also stdev, percent tiles, etc) 1 4.8788173927 151.3873007684 164.4869887857 0.0010997497
Show risk profile (graph) of time waiting and time in barber shop 2 20.4356574391 69.074573719 109.9969057192 0.277309742
--estimated average wait time for 10-customer period. 3 31.4617263282 97.4187245934 128.0970022182 0.2505891147
--estimated stdev of wait time for a 10-customer period. 4 20.9102136235 25.1412694435 10.2771711722 0.4606629538
5 67.2650311726 23.3678106505 20.3752539227 0.6019494281
6 4.8370607946 91.5026386475 97.0488095934 0.0189462063
7 52.431141311 121.5165678276 164.0062276432 0.0600835521
8 17.654613798 43.1158571625 38.1810215455 0.409414599
9 7.0954742572 21.1777691915 4.6479296302 0.6612900334
10 34.3932097511 66.8186371722 88.1200997091 0.1008948066
11 23.8406666652 80.559879685 135.5273437853 0.0714488163
12 8.0941156329 13.5160872665 1.2029224459 0.6541940949
13 15.8586568561 92.1602997677 90.6249986341 0.1797881411
14 79.0462999224 47.2896775604 39.0859706616 0.4676034747
15 21.7659571907 29.6997634465 48.1807938713 0.5341841792
16 1.7829372342 42.6952251268 56.0923085953 0.4542872755
17 35.3495758656 16.4759906939 1.7092655823 0.5274458356
18 38.4751049235 81.9445718709 103.3513380092 0.3728851845
19 32.8933805212 12.5402299432 5.0969911488 0.6798208363
20 14.7325227487 181.2883518677 236.4555112988 0.1106526777
21 39.1688372149 43.7842135772 60.3349300333 0.2414501476
22 3.3540432992 176.676054093 192.4496437264 0.007877032
23 67.5104571398 28.8679546114 20.9167493451 0.5717200173
24 38.720871645 63.1684228652 57.6907692075 0.020371323
25 58.7789986162 85.6781826547 124.8582849373 0.2135888817
26 9.2822294483 39.5389192289 26.0628268073 0.3240102644
27 52.3372620684 42.8162862603 65.1911804151 0.3232756333
28 40.5196430751 33.4465220368 22.9630247297 0.1619732375
29 21.6324740747 19.0424762936 9.6444873449 0.5327391072
30 33.902774152 26.8786191375 13.8964293023 0.170598875
31 11.3124865699 143.6006447276 174.8689886319 0.3324993916
32 81.5214467292 108.7879245469 134.0889020895 0.2305072115
33 52.0945860304 70.7995922016 70.3439696629 0.1401124939
34 15.7800658904 18.2950805959 0.629481938 0.4866108938
35 1.1246245468 24.7439455974 23.5413162184 0.4910176845
36 35.9909471683 34.9809461644 30.5925745516 0.401350473
37 49.1760488933 14.120580813 3.0345976975 0.7799394242
38 9.7844595694 64.9433550914 61.6503397066 0.1594425822
39 10.806893887 54.1359940393 41.1761982564 0.3317766859
40 6.5332505238 36.8746560215 27.0378388977 0.4978101256
41 61.2281191251 32.0591258846 22.1515608035 0.1713013105
42 64.3831751113 39.6483969252 30.6430410392 0.3114826506
43 14.1108995295 35.7373733485 36.1400029919 0.255193868
44 20.6791502337 31.5664664998 68.3669663737 0.4784678
45 7.7987152228 33.0029612108 39.8212709608 0.3421450108
46 29.8403712272 72.2992241876 72.8630319452 0.2497966778
47 41.2163651125 117.0929479282 105.1422923283 0.0999810821
48 12.4976403289 47.9295632216 56.3776650323 0.0894372826
49 2.5874124093 44.5364357372 98.6982660492 0.5861012904
50 44.418943677 53.4904934149 67.8002272787 0.4383002657
51 24.1086602025 82.0125069515 90.3792855877 0.0451637181
52 8.0177126123 24.6073248135 17.074513992 0.2072215377
53 49.2847510639 15.1293734804 10.8476113384 0.6506625015
54 50.1695939674 32.7510631999 36.7377521353 0.3864544476
55 15.829179278 113.9086506866 127.8801247922 0.1254850491
56 43.2365304487 49.965060569 52.3011936046 0.1891392291
57 43.9832509233 28.3155173916 5.0260370804 0.3974349946
58 53.0628926535 14.2659117654 2.020733848 0.642027755
59 63.3120078239 23.1016801132 27.4933461897 0.5209857778
60 54.0190240292 104.9420530436 108.5252511213 0.0256468672
61 109.0705234392 30.6970300445 19.9161125125 0.3881701701
62 22.2251074162 25.5921613414 12.9815273632 0.407383098
63 37.5394798421 204.1233687401 292.6288093879 0.1416982817
64 4.1107905941 18.2635646832 15.9965370054 0.7273649531
65 42.5412925882 38.8916180687 53.8118778033 0.4799073442
66 29.7928550473 106.2197383133 169.2138105061 0.0839608681
67 5.6506890669 19.3941798053 0.9910852414 0.5847597206
68 52.565280045 40.5327257736 50.6240305769 0.3801468394
69 14.3039155439 123.3213167148 195.026240408 0.0553652535
70 4.6348066168 39.7989821849 34.2766654631 0.2940599774
71 4.598999624 31.4257191023 20.4315063355 0.4163580299
72 11.5035626411 18.6945715458 8.3417346922 0.5002438094
73 6.7667038879 23.5199926005 10.6368845992 0.4135766314
74 16.7469612303 183.8057469031 249.8548968364 0.1501412096
75 57.2372486918 64.4366006918 73.4729157091 0.1258146861
76 3.6502458421 60.9010887143 67.563139884 0.2755842606
77 5.8353587898 93.9658613452 129.0827072864 0.103415188
78 47.9695917111 42.4291655972 61.0619401286 0.1799741991
79 40.5026802642 35.687288585 43.3212988038 0.4434444386
80 62.3603200508 40.195080455 46.951537131 0.2783053319
81 2.0901929623 56.5782682423 55.8231126284 0.1051094867
82 2.0865328916 93.9867234346 105.8368609522 0.291235488
83 4.0362887717 42.0513087524 48.9614240777 0.2544133374
84 31.2151297849 61.6457642165 95.2490201788 0.2347770646
85 31.613846444 27.1906794907 24.5704241029 0.4887892593
86 7.1945763741 37.4782572408 44.736211618 0.3658689028
87 32.8672073574 91.1478983197 138.860941791 0.1738401086
88 31.5086036671 95.3263992657 136.1966024881 0.1082246329
89 26.2412238751 20.004345828 12.687493983 0.6907548946
90 60.2412868298 25.0924283581 18.3346950126 0.5288109808
91 125.4127310854 70.4387791389 104.4395596978 0.336647259
92 18.3338254359 56.0768075858 71.7905798292 0.2331746209
93 32.6177129708 84.0880000042 145.7285445559 0.1720759763
94 26.4020544192 33.0290790225 36.0662258576 0.5654009661
95 4.8286049214 10.5575313074 0 0.7484681988
96 33.8791410314 42.301924295 42.3886438779 0.0850621466
97 19.5022561658 39.7498331228 40.8870679021 0.6912509089
98 2.8455553391 43.7194658417 53.1184959323 0.1739215264
99 27.8725000477 22.1775557516 19.4259621275 0.5810877767
100 18.1793389936 47.3032339518 48.9974767144 0.3668769977
101 57.3887573432 73.0942627355 66.6843023248 0.1585821716
102 39.6595843401 90.65767571 126.5728300715 0.3246869226
103 12.5334343406 87.375561502 101.9320607143 0.1065373346
104 126.7246228487 89.6689103802 100.433086569 0.004875073
105 10.9254191059 40.8908704866 45.388057956 0.4391264577
106 8.2029184416 23.9502635356 25.6544411762 0.453191298
107 70.5892902753 20.2261819816 0.8069661417 0.5149918851
108 44.5984837162 63.5967389702 75.3382906842 0.3676933527
109 54.6264178652 27.8607650498 17.5291036522 0.4762921014
110 12.585873092 28.9524372333 24.72223424 0.5101362771
111 22.1086418514 28.8103466764 46.1653312064 0.400942451
112 3.1705707597 25.1817420311 7.5916704596 0.5545795662
113 7.9624049765 21.4773467804 25.4548514623 0.5485403175
114 2.3796978392 72.7510580322 96.3770182485 0.1303233929
115 12.4177369406 51.2702401987 69.8052098092 0.534952291
116 11.5519136871 32.3114452688 32.3757866096 0.2126428091
117 9.9233303192 30.9722509617 20.300672464 0.4045427934
118 103.9774607213 46.7953632985 42.542458573 0.1594872489
119 5.1918828488 54.5700752191 65.4927238544 0.2158492292
120 14.251517913 168.3543795515 195.9105236299 0.2477035811
121 22.1444498807 57.7444140603 47.481453953 0.3587641567
122 37.3109767298 157.0746780591 185.9962123344 0.1280212086
123 3.7413263389 18.683726079 0.3157912882 0.6375822795
124 21.7614984778 27.1439553611 18.2311015128 0.6735316475
125 40.362812603 15.3735902748 18.5052244027 0.7134700955
126 21.7622168205 47.1456729197 60.3012252638 0.245991052
127 74.5089972237 37.8063775573 31.7249962842 0.4028707015
128 13.4190543059 11.7435024494 0.4500395127 0.6840123725
129 51.9209108088 71.2293424202 100.9655107779 0.3284924517
130 85.482445394 21.8506483209 3.562442333 0.5115519954
131 38.5546318786 60.6149166768 61.7463494215 0.3188480223
132 10.5160338413 26.0268416705 7.3335172166 0.5258140186
133 52.3461804192 141.9280088853 174.610859055 0.0462439141
134 157.7272257884 50.0366549184 88.5087402089 0.4334180818
135 44.7593205172 20.3647332663 22.8569584222 0.5741834035
136 117.9159002841 100.5105335284 121.3551810722 0.1034308489
137 61.0426649039 61.8909158265 70.9068408931 0.2447551196
138 43.4269284679 31.2033106848 32.9786932761 0.4867277072
139 44.5684472801 25.5250211836 14.2790489811 0.3110184384
140 10.5518945995 57.6470884992 85.8308120713 0.3047834535
141 19.4378555579 28.4299406533 15.2525306983 0.4461014021
142 97.1241588331 46.3468008239 34.341759491 0.462198828
143 14.5569929285 37.6479278587 41.3202101144 0.1285708533
144 9.8499907145 90.0233281835 90.873866599 0.1120873258
145 8.9851169795 36.8287683088 56.2082110672 0.2492677346
146 64.9483312775 41.8555847166 56.9473341327 0.1567114482
147 44.7952922294 14.1598662605 12.3898693501 0.6496295097
148 73.935426985 44.4760624013 90.4756602726 0.5145009709
149 14.195975617 40.2912745109 32.8875155433 0.3951272916
150 17.3694883272 23.0522435802 17.8900233584 0.5695385369
151 18.369827187 57.3229810973 63.3599490247 0.2720391491
152 76.6547381333 47.8284060131 63.7397690859 0.3328081665
153 19.8911680583 25.4529673376 15.7276571107 0.3377730015
154 18.9412598567 79.9391877659 118.8747809708 0.0764366916
155 13.3064281064 34.6507654564 59.7831985312 0.5720431484
156 19.5377058021 27.5101167752 34.874706738 0.3300407608
157 21.3137625879 68.1686630821 81.7012850437 0.3439241704
158 57.9311342948 26.1005936579 22.0753061787 0.2657009893
159 121.69237511 61.1025989433 71.6642510345 0.128002688
160 18.3112131443 103.4836594893 146.4061853944 0.0890725727
161 13.6231630549 154.7366057949 167.5043425284 0.0140782732
162 23.2830354976 49.8927358827 77.0663613788 0.4444355335
163 28.7852937592 34.6084475237 35.6828632293 0.4954672249
164 9.2692999766 27.1459523633 10.7446883071 0.3103661756
165 46.564798368 25.3505899948 26.4882892423 0.448760964
166 0.9561480025 62.5751699971 59.2234745892 0.0151440328
167 30.665469966 15.9536008691 9.9694027938 0.6677159908
168 53.4048249256 34.0417058411 35.0388476939 0.1741943373
169 6.5390849093 30.9576000739 34.6973914197 0.5149343353
170 28.2111461278 39.1516927731 59.3949951421 0.4643079485
171 89.03588015 26.3995215266 20.0503928904 0.4698718019
172 21.6731011696 29.2272252903 28.9748003018 0.4474931293
173 24.7067824777 41.9090977551 47.0337008819 0.282638903
174 24.5792643151 14.9161601532 1.6794449009 0.67353666
175 16.6374303604 15.9609965378 12.2535464767 0.6398308677
176 5.8255770897 25.1968150693 10.5806307469 0.4260570182
177 57.093214717 45.1922504658 61.8959403398 0.3206745248
178 18.2369303348 10.6754848567 3.9769814983 0.6286549455
179 20.2822633013 78.400516714 92.961958165 0.1022020824
180 9.0122996939 30.3524820694 26.6145473104 0.5054912338
181 98.5684558269 40.0924550197 29.0564734446 0.36831317
182 46.3103109897 18.9856891773 11.2504864152 0.5082717354
183 11.5105331503 152.1339009341 173.7000797043 0.03641494
184 11.0715810645 40.137914402 48.4361256812 0.324422644
185 16.1958724244 64.5075471903 59.8659776822 0.1296797639
186 70.0561378532 18.4303090487 5.9079040305 0.560136082
187 15.0094317727 97.9732354232 95.2926294832 0.0429232655
188 16.2068054973 25.9126844148 5.3280933897 0.533301006
189 26.0949528213 76.5042712207 108.3536168545 0.1122150137
190 1.007361333 40.2894300631 58.6115976939 0.543841947
191 8.2444817117 40.5835786313 28.563735024 0.3971349445
192 32.9099071337 40.7245422576 19.5714566199 0.3268313805
193 4.1682600332 124.2288074526 145.2887031345 0.0554018641
194 8.5078956871 32.9097721511 36.8006009682 0.3980558422
195 61.3975951765 39.0661441942 38.8011319769 0.4038750009
196 100.2738374226 41.1509181039 31.2342984247 0.3184867026
197 35.5454029221 50.3631198372 64.7298263605 0.3886390887
198 5.8301231914 33.0232512813 51.1079018259 0.3924709162
199 24.2758711135 41.5985419955 37.8874180765 0.2946842315
200 10.5997773583 20.5718746107 8.5792405792 0.548368854
201 4.3982017219 88.813722432 99.0663772997 0.2080811587
202 15.7854347122 15.8266613906 7.0879146516 0.6819443595
203 52.7321147516 94.695155511 143.2805214393 0.0637267168
204 44.89510027 51.4158827553 61.7811763119 0.0905728333
205 40.9765515079 61.2736565432 104.712546457 0.2131836329
206 97.2528515479 41.1765511744 57.1267197238 0.1422523349
207 46.0658733938 30.4764871369 35.674007088 0.2034752529
208 18.560542195 52.2003390823 85.220935513 0.2807717562
209 43.3798830874 76.9593836113 102.0986530786 0.1470041667
210 16.2578897857 54.314038547 98.8743638266 0.0218498834
211 5.8247044804 99.7307701522 114.9697043222 0.0583690598
212 100.2302270009 26.959063354 7.2162185972 0.3662831021
213 18.4144591067 53.6181422551 94.1624738201 0.3371339455
214 31.7277050696 53.3136881835 62.1161182991 0.3260950332
215 8.3703385241 64.4683220392 70.7249313579 0.1489587317
216 2.2740803145 35.1306295064 23.0415070556 0.4729456897
217 89.2146680872 29.0147266661 17.9308902078 0.3394978904
218 1.4063003283 21.7580325971 7.9274349489 0.5494669745
219 10.7565473625 44.8034518471 46.9573235637 0.3198628815
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730 25.8531301582 66.748295985 82.3624870764 0.1918033593
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752 6.0447228386 52.02374486 48.627232394 0.0796156759
753 60.6157124066 34.0944255495 25.7055448874 0.3184292934
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755 33.4147934382 57.2737096692 67.1269905543 0.138359165
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758 84.2232759433 39.3107216182 49.0580669835 0.3250194342
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768 37.2818137052 17.8724464234 3.3525332696 0.5192621227
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773 33.3813914835 94.2351012748 183.1964512824 0.2299926017
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775 23.5063207045 40.6246065889 48.9580513669 0.4555250921
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777 19.1795954466 52.1209348617 42.7296515624 0.2459822408
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779 9.4090569056 52.8923229082 73.6410409996 0.3575085851
780 66.4232380581 67.5956608679 92.2800117557 0.2507533566
781 9.5116561202 60.245213685 87.7633113524 0.4619544519
782 3.4739443919 23.9258173433 7.9569309691 0.4568175221
783 48.9390853249 43.4868315439 42.3983991366 0.4207861797
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785 48.4292613089 52.3252861696 64.0112844127 0.2456012108
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799 56.1209886784 75.6632425763 87.1553722127 0.130148836
800 21.1318908378 19.9068045329 1.9749683386 0.4074339033
801 30.5922037966 45.9354246831 40.5659925073 0.0842417665
802 19.0551559995 35.8783502131 23.1641791199 0.4422246004
803 26.4520402833 29.9292571962 26.8186574752 0.1555879376
804 15.9890340701 140.8396303424 251.8873585255 0.0768938308
805 27.61657187 75.4571057478 80.0321814453 0.0599017581
806 39.335476234 24.4817556015 25.2775818915 0.4015594517
807 90.9024286647 105.0823346504 167.2273120303 0.3662485324
808 37.954417209 60.1514176392 103.455312506 0.2720052235
809 21.6889286506 53.1424452741 70.3844396017 0.1318137768
810 24.0661272158 41.0659210669 38.5578837446 0.0154531786
811 17.8814325059 34.9990140098 34.920028115 0.2854665061
812 18.8135836668 62.3644962596 80.2460378401 0.2968383882
813 7.7336978729 85.3167783326 135.9476083741 0.3932134009
814 97.6943569968 42.8830517566 37.4935029981 0.2545820842
815 6.0054885913 31.0721587917 38.1835129763 0.5119154342
816 9.0143823717 34.5787067148 31.4379600984 0.3050130872
817 23.8003222086 43.9234768157 49.9030011499 0.3905254254
818 39.7894658507 27.3709462789 26.8348351725 0.3920899572
819 1.5090405714 49.3454827218 53.3592416904 0.0728705808
820 78.9045097959 58.7776350829 69.5902308609 0.3965788739
821 32.9965308406 69.8175637415 74.209380728 0.1025861045
822 92.9679458747 47.8407686537 57.0556184799 0.2720993815
823 9.5356286818 10.029428663 5.5059536177 0.6909033388
824 22.8846265163 41.482975189 41.1364106828 0.4221130232
825 2.8452823279 122.7909519931 120.572960946 0.2484774764
826 8.4129597943 164.8983988438 213.8749764241 0.0883370775
827 85.5401774171 26.0504595188 35.165093221 0.6232016996
828 47.8544273249 46.5018385144 67.1037145497 0.3600714346
829 74.0111791099 101.6267243532 121.0645372684 0.1102062392
830 92.2739356531 79.7520455073 73.5786767338 0.1628771692
831 5.3132643821 20.9155118102 15.251047184 0.4799006989
832 6.4817621354 24.5350450732 20.5162807121 0.4954235803
833 29.6784197771 109.9598806298 136.9079794028 0.1169884482
834 60.3283042774 41.5939934674 44.5460856789 0.3194290614
835 10.4054423087 25.948872433 23.6775024894 0.4929563475
836 52.7824668988 15.791351092 12.2090768742 0.6782623273
837 1.8422491817 230.2189430491 355.3905005536 0.0504260216
838 27.3326764149 56.5482235206 54.8250944299 0.2517351871
839 45.6774155272 19.235669486 30.2651770163 0.6886412456
840 16.4490440952 25.3369521453 25.5215294419 0.4830441726
841 94.6192012068 42.1989192763 23.2755961056 0.3591212687
842 48.3591136138 170.5964797603 211.9254087292 0.0360993395
843 47.1689598333 53.0325526119 77.0562168566 0.1999922512
844 0.9758571023 81.8800002307 105.9773595926 0.0414762133
845 11.4294010652 24.8077895983 5.4369742265 0.4886952529
846 30.8878045855 64.7938957119 74.4006518513 0.2664077832
847 20.907727563 75.0555533422 116.353616744 0.2185296177
848 5.8270250508 76.0905083049 85.926249251 0.1926825392
849 11.1440455567 82.2715666047 92.6427301116 0.0154630048
850 76.6744698755 103.0989655385 128.9988832209 0.1908946847
851 49.8847487298 16.9131622452 4.6129605075 0.6281721195
852 74.7137837278 26.6062295783 8.7642097807 0.5927785543
853 6.815078045 49.4029577231 66.545958633 0.3198088161
854 15.1262835393 63.7544057408 75.621938663 0.0955123952
855 58.1339920185 83.2333779581 99.7967083554 0.0624364179
856 45.1593204923 197.1700182163 200.5073236483 0.0709879642
857 7.1232161191 39.2327373355 44.3947469822 0.2374970541
858 7.1135634534 85.9956891985 103.5837896459 0.020158558
859 30.8920411058 98.5085165069 117.4506819927 0.0805711141
860 9.022768909 21.0531066637 21.1996562379 0.55588355
861 14.7213396419 49.3359204263 88.2977717399 0.2938885899
862 6.5298541669 23.0677703867 9.5148421423 0.5619827246
863 38.861473009 21.737687755 3.4741249626 0.5554330246
864 16.3681762997 49.0991551254 37.7007792306 0.1977407881
865 24.9879263527 46.9952344271 71.3443960881 0.1980849573
866 83.6436204562 46.3137535254 39.1550082677 0.1471721977
867 42.3263871688 14.9569406059 1.7117973677 0.6126128962
868 55.9990122398 54.4030594132 71.5519297719 0.2612867154
869 25.5349490729 58.0372037888 72.2655212844 0.00953443
870 27.0816333457 74.4412492337 87.527708409 0.1562411762
871 15.2702578758 28.3159553526 26.2047750356 0.3169568701
872 18.6834322183 16.765241006 18.1403371151 0.6166152411
873 7.2595635861 14.4553544208 6.638134985 0.5580291395
874 2.4502014366 56.8463907261 88.8727524341 0.1571358711
875 44.7125971368 63.6447192341 58.269542571 0.134895588
876 12.0150888999 136.9110991095 143.7691455682 0.0659924214
877 35.7727622097 61.6673713064 81.6688550932 0.1751360745
878 8.7897227957 50.3048729082 38.0612082691 0.0048349747
879 53.5855265627 46.5788430677 42.4340408098 0.2562281732
880 7.3908146092 48.9184977314 45.8063653439 0.1699783378
881 61.2176755367 53.5590465325 85.1848398015 0.3801719847
882 1.7782636737 18.0360137393 5.350861875 0.6224286398
883 86.9770794208 71.5462814615 93.1618861331 0.3057235807
884 50.2610723387 73.538105413 91.0331588713 0.2814277422
885 1.6886942562 27.7120503784 29.2212610402 0.5415466659
886 36.3639410011 36.5725044734 26.6375583928 0.2902832596
887 21.3675901409 30.8701607956 36.5595224977 0.4602937555
888 5.416244671 139.966490831 166.4827925316 0.2237653097
889 16.1820087733 46.9529233307 58.5896083964 0.2529776516
890 36.209524813 84.2803925846 129.9073675517 0.3390780829
891 18.2342555239 51.2175949939 50.6245104835 0.3090670003
892 3.6497657054 26.9247539321 24.433906479 0.4788585664
893 18.3251936797 43.4512821362 44.7705197356 0.2776800865
894 116.0019122872 57.7465287132 64.6431032904 0.0069451994
895 11.3125729032 92.3799054289 134.2412114001 0.0912822801
896 41.5006942257 45.5865391799 30.8365510959 0.1417598504
897 85.0111047132 125.7803826842 174.9680636087 0.0887833611
898 8.94798633 34.1615593151 33.8739945111 0.2109364829
899 6.9137562512 37.7946937286 34.0532388458 0.2732572322
900 115.7296289723 131.1358654826 160.1963136876 0.0316020297
901 42.3186239258 134.7567448873 170.0999002247 0.1713728046
902 36.5106604854 31.7048462202 25.0247069798 0.2963775521
903 3.4033407336 40.0771668168 45.5946599425 0.2052112109
904 23.1430295582 52.5254249919 59.0789031733 0.1677568956
905 5.0167167351 107.8298128531 149.4671379416 0.2075063883
906 74.0823932171 30.846386146 38.6328970296 0.4600193342
907 0.5618492705 78.8277287229 82.3946876666 0.0872858144
908 5.0928718508 119.0179563158 105.3541073133 0.2255208688
909 43.6690751955 47.3783083923 38.2570059674 0.1227864973
910 9.1446758366 33.8211502234 29.59061022 0.4300248453
911 25.6830153545 126.4330458909 241.9646377724 0.2023624278
912 16.306493248 33.3026854378 14.5349205301 0.5211279905
913 44.5865548385 123.4911788597 183.2423196535 0.1706792024
914 57.1473869711 67.0339567475 77.0426533195 0.2780361253
915 50.3722311753 110.9666153192 143.9336355401 0.1827863334
916 40.1070875392 31.1104045593 44.7403789275 0.3738991626
917 28.4553227644 35.3989039083 45.1521056087 0.3749985467
918 7.2637557706 61.4719210561 68.4749718984 0.4068836426
919 11.8571531242 69.4750082967 92.7362282464 0.113401985
920 17.4329032196 162.049499438 190.8558525091 0.0675978619
921 7.6469773211 85.2008878079 70.7142899679 0.1709078484
922 45.622692184 41.5154747907 52.8023230036 0.3928229159
923 22.9149608895 36.58699602 30.9538338067 0.1048610481
924 67.9012985922 52.3706507097 74.012126306 0.255235375
925 8.5188684024 142.3257072192 149.0377978249 0.0330643391
926 5.8136143672 38.5243712049 61.8100495163 0.3729836192
927 10.0725952335 16.9256660455 20.4180079143 0.4391703349
928 24.8689126334 28.5886710704 23.0289928748 0.4478762795
929 9.3390940132 78.223328634 93.7821430834 0.1687473333
930 4.6984493505 118.4387671513 165.675642678 0.085673677
931 7.1379792975 22.6947868006 23.6074505879 0.5710858033
932 12.7490153202 80.8375457276 113.6709909366 0.3885511427
933 4.2543560364 48.5107802812 71.3339318726 0.5893890792
934 45.1062888949 86.5729168628 134.5135248256 0.1833507447
935 53.42270493 11.5454982023 8.3574895827 0.6731431681
936 11.8855780412 26.5510030525 33.9894746516 0.4685108806
937 129.6775678074 33.9792116244 37.7121398199 0.6544808961
938 38.1359427782 35.6585220296 34.9534870342 0.117197621
939 31.2380288161 22.8870348552 18.2421620656 0.5680076758
940 6.4386916261 37.6732596651 58.2415745097 0.4464641732
941 0.6748193899 36.1656458259 20.7799157962 0.3318087593
942 69.735896779 24.5885223597 3.3734835423 0.4353739897
943 25.4258217257 68.3963359015 109.8800603417 0.1511558116
944 27.0747984252 40.4102277691 51.891818852 0.5345886671
945 5.0397271416 66.2059103906 78.4549116645 0.0128333111
946 13.0641941799 43.3345367413 66.8960913622 0.5499877939
947 97.6266206263 22.7845726775 11.7817618251 0.533790353
948 28.2076507449 16.4340485107 6.1303773645 0.588657916
949 64.3843568054 119.3015497149 141.3958643757 0.1332247424
950 18.7812873435 82.1701428735 69.2063610512 0.0233963209
951 24.9998821756 66.5931628565 83.8205885811 0.1052357583
952 6.1579316447 25.1761234873 19.8982372968 0.4546994507
953 3.0847681978 73.8806002935 137.807906641 0.1723744633
954 6.2276976263 96.3031108789 130.2358108419 0.1105683718
955 1.2527507106 56.9941520995 51.3925272376 0.0184018442
956 0.288012533 80.8993631702 102.3789809223 0.243611058
957 2.4677095078 72.8331012335 103.5907860785 0.123689896
958 13.986785467 20.5363969493 17.595303547 0.2515428685
959 54.4844583248 57.4747330916 90.0150804973 0.2988981571
960 140.2835410107 44.7996368597 29.6957030285 0.3606450204
961 2.0153935298 12.8849394344 3.2446218214 0.6617911042
962 12.4444697333 41.1587469841 48.1845124768 0.2067737195
963 31.9854409335 46.5405264577 42.5975694787 0.1172122916
964 13.4524358364 27.1395524219 22.4064784861 0.4212485789
965 26.4783314112 36.8983068402 53.0908278695 0.3568296598
966 9.878575519 53.4747526643 72.0292248854 0.3838101032
967 11.5301339619 27.7009974899 20.1524035169 0.4890836991
968 26.4770370528 26.2335974412 12.2848058866 0.5316755236
969 86.0718631824 42.772934456 50.4530698851 0.1785031467
970 28.0803378401 21.1118296511 22.8517196826 0.5450397507
971 34.244305244 27.1926788948 31.3006217795 0.5646742961
972 17.536350142 61.5616639631 69.5092020006 0.3789098529
973 13.2855573256 44.8432867651 46.7029841076 0.2495616897
974 38.022725203 54.7344875182 94.1076298859 0.3696590565
975 14.878550715 69.4328824052 90.4414430006 0.0584621923
976 52.5538940858 73.1160115787 101.69247865 0.1620337776
977 72.5718299114 65.8641886013 94.1002952779 0.2044534147
978 6.8820220835 25.3950312694 18.0725174548 0.5437926931
979 83.8205837665 116.5840023026 108.7996671435 0.0531657465
980 6.263504446 22.915801589 10.2337467481 0.451777729
981 42.3312949477 32.7745081648 32.246448841 0.5315348352
982 3.5307359126 119.6218545149 150.0336274151 0.1757770926
983 2.6720650904 51.020995632 41.0382788442 0.1217075138
984 12.3061629153 48.2868212688 61.0958530194 0.4617018876
985 35.0566305974 29.1200114978 58.1502164836 0.4966260126
986 36.5145048251 133.7123214875 183.6946502432 0.1516889766
987 33.5101831141 28.1403998984 36.8852571045 0.5954597863
988 59.1247901845 49.0289390962 78.7944072466 0.3342875576
989 20.7945560395 57.2819587158 109.5285916734 0.379606993
990 21.1281316256 29.1790069417 38.5638873204 0.3586795556
991 27.8644848357 74.8752826367 73.1172152616 0.4518999187
992 21.2075662932 21.3653715163 13.9081814329 0.6386686143
993 19.1007381011 43.5985356235 41.8690700437 0.3392099337
994 21.2307097609 126.9311550338 152.1255794728 0.0339219376
Avg Wait (min) -- 10 cust 995 65.6112446461 53.6539260916 69.6711240929 0.0797165479
Freq Table -- Sample of 1000 996 6.8609019719 39.1035244393 30.1437737805 0.4291527583
Class Width Upper Val Classes Freq Proportion 997 8.851303264 85.6014595492 118.9388582638 0.1378331039
5 5 <=5 117 0.12 ORGANIZE/SUMMARIZE 998 2.2052310653 39.2034899974 50.3290183608 0.1304394514
10 15 >5 to <=15 263 0.26 999 6.7946454266 33.0466682269 37.6774642433 0.2853360981
10 25 15 - 25 194 0.19 1000 7.8104363866 70.5906186449 104.806405172 0.3653045705
10 35 25 - 35 106 0.11 Summary Table (10 customers): 10 Customers in Barber Shop Summary Table
10 45 35 - 45 86 0.09 (sample of 1000) Key Measure: Avg Wait (min) Ave Time in Shop (min) 90 Percentile Wait (min) Percent of Time Idle
10 55 45 - 55 72 0.07
10 65 55 - 65 42 0.04 use for best estimate of these objective measures--> Average: 30.3 55.4 64.3 31%
20 85 65 - 85 56 0.06 Est 90 Percentile of Avg Wait for 10 customers --> 90 Percentile: 72.6 101.6 134.6 58% Possible Add'l Measures
20 105 85-105 39 0.04 Est StDev of Avg Wait for 10 customers --> StDev: 28.3 35.3 51.3 19%
135 240 >105 25 0.03 Est Max of Avg Wait for 10 customers --> max: 182.7 314.2 421.1 79%
Totals: 1000 1.00 Est Median of Avg Wait for 10 customers --> Median: 20.7 46.1 51.3 30%
Conclusions and Insights:
For this barbershop, given the estimates of haircut time
and customer arrivals, the average customer wait is
just over 30 minutes. Customers spend about 55 min
in the shop. There is, however, much variation. For
example, About 10 percent of days, the average customer wait
is more than one hour. For a typical day, the barber
is idle (not cutting hair) about 30 percent of the time.
The owner/barber's reaction might be:
Average wait is too long.
90 percentile of wait is too long
"I can speed up some."
What if I make my average haircut 20 minutes?
What if I make my average haircut 15 minutes?
What if I change my pattern -- normal?
Uniform between 20 and 30?
The analyst can easily make modifications to the core model, replicate, organize results, and report on how the performance measures would change.

Risk Profile - Avg Wait for 10 customer day (sample of 1000)

< =5 > 5 to < =15 15 - 25 25 - 35 35 - 45 45 - 55 55 - 65 65 - 85 85-105 > 105 0.11700000000000001 0.26300000000000001 0.194000 00000000001 0.106 8.5999999999999993E-2 7.1999999999999995E-2 4.2000000000000003E-2 5.6000000000000001E-2 3.9E-2 2.5000000000000001E-2

Avg Wait (minutes)

Proportion

4.5.1

Modeling Approach for Repeated Stocking Decision for One Period of Demand
Issue Freddie the News "Boy"
Sells Financial Times Journal each day at a newstand.
Orders, receives copies early, sells some number, if any leftover -- returns next day for small refund
Bus Prob Bus Ques: How many copies should be ordered per day from the distributor ? Can we understand the impacts of different order levels?
Decision: # copies to order
Objective measures: average daily profit (revenues - costs), chance that profit/day is at least $50, average unsatisfied customers
Constraints: system rules, demand random with historical data
Bus Data Costs: Revenues: Daily Demand:
$1.50 per copy delivered $2.50 per copy sold Discrete Uniform between 40 and 70 copies
$0.50 per copy refunded We'll use Uniform Distribution (as before) but will round to integer
value so that only possible values are 40, 41, 42, …,69, and 70.
Analysis:
Simulation Core Model -- One Day or Consecutive Days? Assumptions:
Cost/cy $1.50 Daily DISCRETE UNIFORM Demand distrib same each day
Rev/cy $2.50 Demand: MIN 39.5 No quantity discounts -- cost or rev Q: What will we record for each day?
Refund/cy $0.50 MAX 70.5 No late day discounts A: The details of the decision and its impact
Order Qty 65 No impact of unsatisfied demand focused on the objective measures.
One Day of Operation

4.5.4

One Day of Operation
Day Deliver Demand Sold Unsold Revenues Refund Costs Profit Unsat Cust
1 65 ERROR:#VALUE! ERROR:#VALUE! ERROR:#VALUE! ERROR:#REF! ERROR:#REF! $0.00 ERROR:#REF! ERROR:#VALUE!
Use Data Table to REPLICATE the Core Model: Examine other possible order quantities: Profit Unsat Cust
Order 65 ERROR:#REF! ERROR:#VALUE! NOTE: If you do not "freeze" the Order 50 ERROR:#REF! ERROR:#VALUE! These results have
CORE 1 15 0 Data Table results and repeat 1 50 10 been "frozen"
MODEL 2 39 0 one can "see" the variation 2 50 20
CAN BE 3 63 0 3 38 0
JUST ONE 4 17 0 RESULTS: (Order 65) From your Bus Statistics: 4 50 17 RESULTS: (Order 50) Sampling
DAY 5 31 0 sample 1000 days 95 percent MOE: $1.00 5 50 17 sample 1,000 days 95 percent MOE: $0.35
Why? 6 41 0 Avg Pr $44.77 "possible error in using samp mean 6 38 0 Avg Pr $46.99 "poss error in using samp mean
Answer: Days are 7 43 0 Stdev Pr $15.90 as estimate of true mean" 7 50 4 Stdev Pr $5.50 as estimate of true mean"
independent 8 53 0 95 Percentile Pr $65.00 8 50 11 95 Percentile Pr $50.00
Can't do this with 9 65 3 5 Percentile Pr $19.00 9 50 10 5 Percentile Pr $34.00
a service facilty or 10 51 0 P(Pr>=$50) 0.44 10 50 15 P(Pr>=$50) 0.70
with weather where 11 65 2 Risk Profile Freq Table ----> Histogram (not shown here) 11 50 13 Risk Profile Freq Table ----> Histogram
one day's weather 12 65 3 Avg Unsat Cust 0.39 12 50 3 Avg Unsat Cust 6.85
influences the next 13 23 0 13 48 0
day's weather. 14 25 0 14 50 4
15 49 0 15 46 0
16 15 0 THEN, investigate other possible order quantities. 16 50 14
17 29 0 17 50 7
18 57 0 ORGANIZE/SUMMARIZE 18 34 0
19 65 4 60 19 50 16
20 23 0 65 $44.77 $15.90 $65.00 $19.00 20 50 19
21 65 5 70 21 50 6
22 37 0 22 50 20
23 55 0 23 50 8
24 59 0 24 38 0
25 65 4 25 50 18
26 53 0 26 50 10
27 45 0 27 50 14
28 35 0 28 50 14
29 61 0 29 50 17
30 25 0 30 50 16
31 41 0 31 50 11
32 51 0 32 46 0
33 65 0 33 50 14
34 27 0 34 50 13
35 65 5 35 42 0
36 37 0 36 50 8
37 45 0 37 38 0
38 39 0 38 36 0
39 53 0 39 32 0
40 39 0 40 50 11
41 53 0 41 50 10
42 65 3 42 50 15
43 49 0 43 34 0
44 49 0 44 50 17
45 49 0 45 50 1
46 65 1 46 50 19
47 29 0 47 48 0
48 65 3 48 50 16
49 65 3 49 40 0
50 57 0 50 32 0
51 55 0 51 30 0
52 33 0 52 50 9
53 29 0 53 50 13
54 31 0 54 50 20
55 65 4 55 50 19
56 53 0 56 50 0
57 65 0 57 50 1
58 33 0 58 50 3
59 65 4 59 50 17
60 47 0 60 46 0
61 65 3 61 50 2
62 33 0 62 50 2
63 21 0 63 50 17
64 17 0 64 38 0
65 19 0 65 50 13
66 57 0 66 50 17
67 65 3 67 50 8
68 65 1 68 50 1
69 25 0 69 40 0
70 47 0 70 50 16
71 57 0 71 50 1
72 45 0 72 42 0
73 65 2 73 50 15
74 39 0 74 50 4
75 23 0 75 50 18
76 29 0 76 50 0
77 43 0 77 50 3
78 65 2 78 50 13
79 43 0 79 50 2
80 47 0 80 50 16
81 25 0 81 34 0
82 17 0 82 48 0
83 33 0 83 50 11
84 65 3 84 50 10
85 49 0 85 50 8
86 63 0 86 50 10
87 65 3 87 50 3
88 35 0 88 36 0
89 57 0 89 50 11
90 65 3 90 50 9
91 59 0 91 50 3
92 31 0 92 50 3
93 19 0 93 50 11
94 39 0 94 50 9
95 39 0 95 48 0
96 51 0 96 48 0
97 35 0 97 36 0
98 39 0 98 50 4
99 65 0 99 50 5
100 21 0 100 32 0
101 49 0 101 50 13
102 51 0 102 50 18
103 39 0 103 50 0
104 29 0 104 50 15
105 59 0 105 50 13
106 19 0 106 50 17
107 27 0 107 50 9
108 25 0 108 50 14
109 65 4 109 50 9
110 41 0 110 50 20
111 61 0 111 50 8
112 49 0 112 50 18
113 37 0 113 44 0
114 57 0 114 50 2
115 45 0 115 50 0
116 27 0 116 50 9
117 23 0 117 30 0
118 31 0 118 30 0
119 23 0 119 50 3
120 21 0 120 42 0
121 63 0 121 50 11
122 35 0 122 40 0
123 31 0 123 42 0
124 37 0 124 50 11
125 35 0 125 40 0
126 59 0 126 50 16
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833 61 0 833 50 16
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840 27 0 840 36 0
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846 65 1 846 50 16
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853 25 0 853 50 0
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893 17 0 893 50 6
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910 55 0 910 50 12
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920 53 0 920 50 2
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925 39 0 925 50 18
926 63 0 926 50 17
927 61 0 927 50 7
928 59 0 928 50 4
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930 51 0 930 50 11
931 17 0 931 50 17
932 49 0 932 38 0
933 51 0 933 50 16
934 17 0 934 50 16
935 47 0 935 50 2
936 35 0 936 50 1
937 23 0 937 50 9
938 65 4 938 50 18
939 23 0 939 50 12
940 65 0 940 50 14
941 39 0 941 32 0
942 59 0 942 50 5
943 51 0 943 50 0
944 17 0 944 38 0
945 53 0 945 50 16
946 29 0 946 50 5
947 35 0 947 50 13
948 31 0 948 50 16
949 17 0 949 34 0
950 53 0 950 36 0
951 39 0 951 50 18
952 57 0 952 50 13
953 51 0 953 50 12
954 65 4 954 46 0
955 21 0 955 50 12
956 25 0 956 46 0
957 45 0 957 50 10
958 21 0 958 50 10
959 53 0 959 50 1
960 47 0 960 50 7
961 35 0 961 50 13
962 17 0 962 50 16
963 15 0 963 50 8
964 65 1 964 50 15
965 57 0 965 50 20
966 41 0 966 50 12
967 37 0 967 50 1
968 43 0 968 44 0
969 23 0 969 46 0
970 27 0 970 50 4
971 35 0 971 50 8
972 45 0 972 48 0
973 59 0 973 50 7
974 21 0 974 50 15
975 53 0 975 34 0
976 33 0 976 42 0
977 65 0 977 50 2
978 53 0 978 50 8
979 25 0 979 50 16
980 33 0 980 50 5
981 65 5 981 50 6
982 19 0 982 32 0
983 65 2 983 50 4
984 25 0 984 50 6
985 53 0 985 50 17
986 57 0 986 50 17
987 41 0 987 50 14
988 43 0 988 50 15
989 65 0 989 44 0
990 31 0 ANALYSIS RESULTS: 990 50 5
991 37 0 Possible Order Qty Estimated Profit/day Est Avg Unsat Cust 991 50 7
992 59 0 Average Stdev 95 Percentile P(Pr>=$50) 992 38 0
993 23 0 40 993 44 0
994 15 0 45 994 50 12
995 41 0 50 $46.99 $5.50 $50.00 0.70 6.8 995 50 12
996 15 0 55 996 50 14
997 61 0 60 997 50 19
998 27 0 65 $44.77 $15.90 $65.00 0.44 0.4 998 50 16
999 65 2 70 999 48 0
1000 59 0 1000 50 3

4.6.2

A Financial Investment Analysis:
An investor wants to evaluate her current investment plan. The investor may change the portfolio if the evaluation shows some problems.
Currently (at beginning of this year) there is $10,000 invested in this portfolio. Here is what is predicted for return (return is variable) over the next three years.
(These predictions come based on conversations with investment advisors.)
Year 1 Return 1 Uniform between 5 and 15 percent return
Year 2 Return 2 Normal with mean = 5 percent and stdev = 10 percent
Year 3 Return 3 either -15 percent, 10 percent, or 20 percent -- with equal likelihoods
In this evaluation, the investor would like to know (estimate) the following about portfolio value after three yrs:
average and stdev of all possible portolio values (at end of third year)
10 percentile of value "How bad might it be?"
90 percentile of value "How really good might it be?"
likelihood of value after three years of at least a value of $12,500 "A target she'd like to achieve."
The investor plans to reinvest each year's returns back into the portfolio. Can you help this investor?
BFDA
Issue: Investment portfolio over three years
Problem (to model) Definition:
BusQues:
Decision:
Obj Meas(s):
Constraints:
Data: (we'll put in the model)
Analysis with a Spreadsheet Model: Insert blue input data cells first. Then, what will rows represent? What will columns represent?
Should have blue (fixed input values), green (variable input values), gray (formulas to calculate results), and orange (obj measure formula) cells

4.6.4

Core Model:
to use
in VLOOKUP
Core Model: 3 years of portfolio performance
$ at start $10,000 Return 1 Uniform low: 5% high: 15%
Return 2 Normal mean: 5% stdev: 10% Return Prob CumProb Return
Return 3 Discrete -15% 0.33 0.00 -15%
Portfolio Performance 10% 0.33 0.33 10%
YEAR Start $ RAND Return % Return $ End $ 20% 0.33 0.67 20%
Year 1 $10,000 0.5930 10.9% $1,093 $11,093
Year 2 $11,093 0.2321 -2.3% -$257.4 $10,836 Assumptions:
Year 3 $10,836 0.6798 20.0% $2,167.1 $13,003 Value after 3 years Years' returns are independent
Repl: Three-Yr Value Portfolio Value -- End of three years (1000 simulations)
$13,003
1 $10,628 "Results Table"
2 $14,090 Average $12,097
3 $15,463 Stdev $2,043
4 $16,150 10%tile $9,348
5 $11,146 90%tile $14,776
6 $12,384 Pr(>=12500) 0.45
7 $12,161
8 $10,294 Can you produce a picture -- Risk Profile -- of possible portfolio value? A "Results Figure"?
9 $11,741
10 $12,908
11 $13,197
12 $10,741
13 $13,970
14 $10,449
15 $13,114
16 $12,513
17 $9,789
18 $10,643
19 $13,115
20 $13,651
21 $13,611
22 $15,936
23 $17,433
24 $9,867
25 $15,564
26 $8,962
27 $14,887
28 $10,293
29 $9,898
30 $8,698
31 $10,531
32 $8,741
33 $12,580
34 $11,076
35 $12,904
36 $9,331
37 $13,776
38 $14,372
39 $15,609
40 $15,809
41 $11,429
42 $9,873
43 $9,214
44 $11,840
45 $9,405
46 $13,816
47 $11,119
48 $13,618
49 $14,325
50 $10,297
51 $12,431
52 $12,605
53 $14,960
54 $13,173
55 $13,941
56 $14,924
57 $13,287
58 $13,521
59 $15,905
60 $12,400
61 $10,398
62 $11,224
63 $15,196
64 $8,427
65 $8,536
66 $11,060
67 $15,577
68 $14,717
69 $14,028
70 $11,165
71 $11,973
72 $10,454
73 $12,152
74 $16,488
75 $10,611
76 $12,083
77 $9,926
78 $14,711
79 $11,225
80 $13,630
81 $8,506
82 $12,256
83 $10,452
84 $12,140
85 $13,700
86 $12,413
87 $12,811
88 $10,313
89 $14,561
90 $13,827
91 $7,435
92 $13,104
93 $10,731
94 $12,224
95 $15,583
96 $12,186
97 $9,730
98 $12,319
99 $13,714
100 $10,003
101 $13,680
102 $18,254
103 $12,119
104 $11,107
105 $10,610
106 $12,448
107 $16,465
108 $8,966
109 $15,409
110 $9,616
111 $14,069
112 $11,090
113 $9,505
114 $12,746
115 $11,523
116 $14,138
117 $10,411
118 $13,642
119 $13,436
120 $12,747
121 $15,369
122 $9,815
123 $12,370
124 $13,276
125 $15,572
126 $13,518
127 $12,443
128 $11,109
129 $10,152
130 $9,994
131 $12,860
132 $14,247
133 $10,914
134 $11,764
135 $9,657
136 $10,193
137 $15,218
138 $13,302
139 $13,338
140 $9,867
141 $12,432
142 $11,349
143 $14,486
144 $13,285
145 $14,541
146 $11,842
147 $12,677
148 $13,790
149 $15,976
150 $9,653
151 $12,992
152 $12,152
153 $12,667
154 $10,818
155 $11,579
156 $13,143
157 $12,830
158 $10,814
159 $9,717
160 $13,879
161 $10,010
162 $13,790
163 $11,602
164 $10,809
165 $10,226
166 $12,399
167 $15,810
168 $14,043
169 $11,962
170 $12,912
171 $15,235
172 $10,926
173 $10,634
174 $13,521
175 $11,779
176 $9,262
177 $10,505
178 $10,665
179 $9,681
180 $9,879
181 $15,655
182 $15,182
183 $15,185
184 $9,147
185 $10,067
186 $15,434
187 $12,535
188 $11,852
189 $14,038
190 $12,381
191 $14,456
192 $13,404
193 $8,106
194 $11,427
195 $12,937
196 $13,406
197 $13,935
198 $12,573
199 $11,407
200 $12,912
201 $11,022
202 $16,070
203 $13,688
204 $13,297
205 $11,094
206 $13,267
207 $14,896
208 $9,413
209 $14,174
210 $12,942
211 $13,021
212 $10,661
213 $13,050
214 $11,827
215 $14,132
216 $10,171
217 $12,423
218 $10,406
219 $15,066
220 $13,778
221 $11,786
222 $12,936
223 $13,154
224 $13,686
225 $14,201
226 $13,664
227 $15,104
228 $13,969
229 $13,244
230 $12,911
231 $10,577
232 $10,895
233 $13,099
234 $16,442
235 $11,314
236 $10,383
237 $8,071
238 $8,925
239 $12,826
240 $14,070
241 $9,486
242 $14,856
243 $8,870
244 $9,909
245 $12,938
246 $13,815
247 $12,470
248 $13,648
249 $11,392
250 $13,582
251 $9,118
252 $12,570
253 $11,497
254 $15,461
255 $9,528
256 $8,239
257 $8,994
258 $15,594
259 $13,888
260 $9,095
261 $8,038
262 $13,933
263 $10,420
264 $7,925
265 $8,493
266 $10,787
267 $13,483
268 $16,197
269 $16,879
270 $12,317
271 $11,043
272 $9,491
273 $13,730
274 $12,245
275 $12,998
276 $11,179
277 $13,539
278 $11,215
279 $12,226
280 $11,077
281 $12,547
282 $11,082
283 $16,105
284 $13,013
285 $11,252
286 $13,117
287 $9,540
288 $12,177
289 $13,308
290 $10,479
291 $10,463
292 $13,598
293 $13,625
294 $8,228
295 $12,275
296 $13,276
297 $8,285
298 $13,429
299 $13,554
300 $13,552
301 $10,321
302 $14,816
303 $14,421
304 $14,029
305 $12,979
306 $11,233
307 $14,249
308 $11,423
309 $7,904
310 $13,958
311 $12,163
312 $15,233
313 $13,587
314 $13,618
315 $11,236
316 $11,757
317 $9,478
318 $13,774
319 $9,470
320 $9,376
321 $12,849
322 $12,313
323 $9,583
324 $14,945
325 $9,890
326 $11,726
327 $10,377
328 $9,938
329 $13,294
330 $12,000
331 $13,802
332 $8,903
333 $7,911
334 $11,238
335 $16,741
336 $9,447
337 $14,736
338 $13,818
339 $12,978
340 $9,625
341 $8,802
342 $11,625
343 $14,555
344 $16,268
345 $10,530
346 $10,714
347 $14,600
348 $13,872
349 $12,598
350 $12,635
351 $12,146
352 $8,481
353 $15,250
354 $10,256
355 $11,324
356 $13,934
357 $11,630
358 $12,974
359 $14,765
360 $14,411
361 $10,286
362 $8,192
363 $12,806
364 $11,911
365 $10,595
366 $9,912
367 $13,598
368 $9,691
369 $13,172
370 $11,638
371 $11,112
372 $10,298
373 $15,210
374 $10,478
375 $11,960
376 $15,779
377 $12,702
378 $13,064
379 $11,787
380 $11,068
381 $15,246
382 $12,456
383 $14,231
384 $10,021
385 $11,407
386 $13,071
387 $12,671
388 $12,179
389 $14,140
390 $9,180
391 $11,576
392 $9,355
393 $12,939
394 $14,299
395 $9,410
396 $14,594
397 $15,282
398 $11,262
399 $8,616
400 $14,023
401 $16,236
402 $15,977
403 $11,002
404 $12,984
405 $14,265
406 $14,303
407 $13,142
408 $11,601
409 $9,221
410 $13,857
411 $11,607
412 $13,761
413 $13,162
414 $6,771
415 $14,200
416 $13,309
417 $11,216
418 $13,783
419 $9,774
420 $11,531
421 $14,388
422 $12,927
423 $9,242
424 $12,323
425 $9,831
426 $12,183
427 $11,249
428 $11,772
429 $11,857
430 $12,384
431 $14,835
432 $14,348
433 $13,989
434 $10,179
435 $9,326
436 $14,137
437 $15,594
438 $10,061
439 $10,595
440 $13,704
441 $10,888
442 $11,156
443 $11,507
444 $15,354
445 $8,980
446 $12,007
447 $8,521
448 $12,397
449 $12,019
450 $11,777
451 $12,750
452 $10,297
453 $9,740
454 $13,738
455 $12,260
456 $8,803
457 $13,010
458 $13,239
459 $9,267
460 $10,404
461 $14,828
462 $15,268
463 $11,966
464 $11,793
465 $10,692
466 $12,211
467 $12,218
468 $9,065
469 $15,654
470 $12,019
471 $13,705
472 $11,884
473 $10,938
474 $9,837
475 $14,062
476 $10,304
477 $13,495
478 $9,014
479 $12,538
480 $9,036
481 $11,936
482 $15,108
483 $14,389
484 $13,432
485 $9,409
486 $10,577
487 $15,418
488 $15,089
489 $16,863
490 $14,497
491 $11,722
492 $11,540
493 $12,896
494 $10,417
495 $11,559
496 $9,455
497 $13,136
498 $14,907
499 $11,623
500 $9,993
501 $10,131
502 $9,272
503 $8,981
504 $13,051
505 $10,215
506 $10,420
507 $14,258
508 $12,399
509 $12,103
510 $13,151
511 $12,971
512 $10,790
513 $14,095
514 $14,803
515 $12,945
516 $10,979
517 $10,240
518 $10,204
519 $15,669
520 $10,537
521 $9,667
522 $14,577
523 $11,413
524 $13,214
525 $10,147
526 $12,987
527 $12,974
528 $14,927
529 $11,869
530 $9,078
531 $9,489
532 $9,928
533 $13,333
534 $12,805
535 $13,520
536 $10,714
537 $13,492
538 $14,040
539 $13,470
540 $15,903
541 $10,033
542 $13,507
543 $12,767
544 $10,614
545 $11,896
546 $13,908
547 $11,703
548 $14,701
549 $14,214
550 $12,475
551 $11,697
552 $10,911
553 $9,312
554 $10,384
555 $14,894
556 $11,701
557 $14,074
558 $8,850
559 $11,507
560 $11,350
561 $14,620
562 $12,394
563 $8,469
564 $13,856
565 $10,207
566 $9,649
567 $11,505
568 $14,772
569 $15,553
570 $9,983
571 $11,495
572 $14,561
573 $13,688
574 $12,208
575 $12,840
576 $10,881
577 $7,982
578 $15,850
579 $12,861
580 $13,375
581 $9,789
582 $9,446
583 $10,107
584 $11,852
585 $9,421
586 $15,307
587 $11,552
588 $11,805
589 $12,162
590 $9,103
591 $12,941
592 $13,531
593 $13,863
594 $13,617
595 $12,844
596 $14,970
597 $9,288
598 $12,417
599 $11,337
600 $9,875
601 $9,532
602 $15,126
603 $12,229
604 $15,247
605 $10,545
606 $13,706
607 $10,585
608 $16,301
609 $9,936
610 $14,387
611 $10,405
612 $14,893
613 $9,412
614 $12,660
615 $13,519
616 $12,109
617 $10,823
618 $14,658
619 $8,988
620 $9,833
621 $10,875
622 $10,768
623 $12,656
624 $13,341
625 $11,084
626 $13,574
627 $9,873
628 $9,472
629 $9,966
630 $13,351
631 $13,115
632 $8,317
633 $12,441
634 $12,062
635 $8,388
636 $12,285
637 $16,125
638 $14,144
639 $9,067
640 $8,791
641 $17,427
642 $13,715
643 $10,015
644 $9,847
645 $10,374
646 $10,924
647 $12,883
648 $14,109
649 $11,059
650 $13,229
651 $12,940
652 $11,848
653 $12,876
654 $14,014
655 $10,693
656 $8,347
657 $12,826
658 $13,205
659 $14,279
660 $9,524
661 $14,038
662 $9,597
663 $8,331
664 $8,230
665 $10,135
666 $9,107
667 $11,728
668 $10,403
669 $13,045
670 $14,189
671 $9,773
672 $13,321
673 $9,246
674 $9,433
675 $12,344
676 $12,982
677 $11,209
678 $13,720
679 $13,410
680 $12,202
681 $10,681
682 $10,008
683 $10,476
684 $11,185
685 $11,595
686 $12,293
687 $11,928
688 $8,319
689 $12,097
690 $12,157
691 $10,783
692 $10,260
693 $10,217
694 $12,927
695 $12,869
696 $8,725
697 $12,930
698 $13,648
699 $11,347
700 $9,821
701 $10,534
702 $10,493
703 $9,595
704 $13,223
705 $12,653
706 $14,151
707 $9,326
708 $9,958
709 $12,414
710 $8,827
711 $10,510
712 $8,968
713 $12,231
714 $13,278
715 $10,075
716 $13,028
717 $9,159
718 $13,905
719 $12,957
720 $13,224
721 $9,469
722 $8,710
723 $11,639
724 $11,698
725 $11,808
726 $13,731
727 $13,680
728 $12,411
729 $14,533
730 $10,526
731 $15,008
732 $13,745
733 $10,821
734 $9,295
735 $11,889
736 $10,319
737 $15,637
738 $11,692
739 $12,055
740 $8,534
741 $14,540
742 $9,209
743 $11,769
744 $9,392
745 $14,502
746 $13,297
747 $8,587
748 $14,226
749 $11,963
750 $14,048
751 $12,421
752 $13,344
753 $8,300
754 $10,751
755 $13,083
756 $15,144
757 $13,764
758 $10,156
759 $10,840
760 $8,840
761 $9,588
762 $14,038
763 $9,348
764 $10,372
765 $9,256
766 $9,341
767 $15,277
768 $13,027
769 $12,302
770 $14,800
771 $12,005
772 $9,023
773 $14,077
774 $10,681
775 $13,729
776 $9,938
777 $14,613
778 $12,522
779 $9,691
780 $10,918
781 $11,865
782 $12,509
783 $10,451
784 $11,541
785 $9,265
786 $11,765
787 $13,410
788 $9,226
789 $9,987
790 $12,879
791 $9,299
792 $11,622
793 $10,987
794 $10,480
795 $14,248
796 $9,257
797 $12,842
798 $13,359
799 $8,338
800 $12,316
801 $12,061
802 $10,178
803 $11,232
804 $9,765
805 $9,872
806 $12,798
807 $12,056
808 $12,913
809 $9,951
810 $13,508
811 $10,998
812 $9,880
813 $11,338
814 $15,382
815 $11,763
816 $12,306
817 $9,946
818 $13,733
819 $11,274
820 $15,236
821 $12,722
822 $10,340
823 $13,197
824 $14,726
825 $9,528
826 $8,539
827 $13,616
828 $14,339
829 $13,353
830 $12,690
831 $12,854
832 $10,884
833 $10,128
834 $14,365
835 $12,836
836 $10,555
837 $11,970
838 $14,968
839 $14,950
840 $14,908
841 $11,006
842 $13,149
843 $14,202
844 $14,224
845 $10,452
846 $10,546
847 $11,552
848 $14,383
849 $13,821
850 $9,246
851 $13,937
852 $15,154
853 $12,532
854 $11,391
855 $13,072
856 $14,871
857 $9,262
858 $8,745
859 $15,133
860 $12,017
861 $13,536
862 $14,618
863 $14,728
864 $14,258
865 $14,140
866 $12,796
867 $10,510
868 $10,919
869 $14,030
870 $15,436
871 $13,526
872 $9,897
873 $9,728
874 $12,135
875 $9,602
876 $8,899
877 $11,804
878 $13,004
879 $8,612
880 $12,757
881 $11,037
882 $13,741
883 $11,707
884 $10,007
885 $13,727
886 $14,557
887 $13,901
888 $12,969
889 $14,317
890 $9,459
891 $12,168
892 $9,975
893 $12,973
894 $9,556
895 $13,823
896 $12,000
897 $12,277
898 $10,946
899 $11,999
900 $9,953
901 $10,288
902 $13,956
903 $11,255
904 $12,578
905 $10,221
906 $15,605
907 $11,677
908 $10,207
909 $8,410
910 $15,974
911 $8,256
912 $14,128
913 $10,335
914 $10,091
915 $15,167
916 $15,011
917 $8,859
918 $10,004
919 $10,807
920 $11,812
921 $14,183
922 $12,972
923 $12,594
924 $13,298
925 $12,856
926 $10,418
927 $8,965
928 $13,149
929 $13,172
930 $11,038
931 $10,258
932 $9,430
933 $11,823
934 $12,761
935 $10,647
936 $13,566
937 $13,602
938 $15,774
939 $11,849
940 $10,558
941 $11,284
942 $10,876
943 $11,884
944 $13,732
945 $15,415
946 $9,712
947 $9,465
948 $13,925
949 $14,355
950 $13,056
951 $10,718
952 $10,537
953 $13,163
954 $13,873
955 $13,257
956 $13,502
957 $12,057
958 $13,531
959 $9,342
960 $10,831
961 $9,707
962 $12,802
963 $9,009
964 $13,611
965 $9,191
966 $14,517
967 $12,361
968 $11,426
969 $13,377
970 $13,673
971 $9,941
972 $12,495
973 $11,270
974 $12,007
975 $14,783
976 $13,789
977 $9,308
978 $11,420
979 $12,098
980 $16,624
981 $13,603
982 $13,428
983 $9,100
984 $10,313
985 $14,775
986 $10,670
987 $15,321
988 $9,494
989 $11,914
990 $14,686
991 $12,004
992 $11,871
993 $11,545
994 $10,672
995 $10,697
996 $14,494
997 $13,593
998 $13,655
999 $9,030
1000 $12,603
Business Concl, Insights, Recommendations

4.7.1

REVIEW OF MBA 791: BUSINESS ANALYTICS -- SO FAR
ANALYSIS -- an important function supporting organizational decision making
"Business Analytics" is for some companies part of their business strategy
Get the most out of analysis of all aspects of the business using Big Data Bases
Analytic personnel, technology, methods, continuing development of capabilities
Top management understanding and strategies for competitive advantage.
For any situation:
Business Frame- work for Decision Analysis (BFDA) Issue Name of business area we are concerned about
Problem Definition business question(s) (include all specific business questions to answer)
(to be modeled) decisions (including how many values)
objective measure
constraints (and how many are there)
Data gather/organize
Analysis build model -- LP Models -- one column for ea decision variable; one rows for the constraints (except Transportation models);
one row for decision cells; one row for calculating the objective measure; list of key assumptions. Use color scheme.
Simulation Models -- description of business process with data and formulas using rows and columns
test/validate model
manipulate model -- using Solver & SolverTable (LP models) or replication & change decision cells (Sim models)
what if? or sensitivity analysis with model -- resolving with new data values, sensitivity report (if linear model), SolverTable
summarize analysis results -- into Results Tables and Results Figures
Conclusions, Insights,& Recommendations
Business language "answers" to all the business questions.
TWO MAJOR CATEGORIES OF MODELING & ANALYSIS:
1. DETERMINISTIC OPTIMIZATION MODELS types names
Linear Programming Models 1 Resource Allocation (<=) All can be solved with Solver using Simplex LP
2 Cost-Benefit Tradeoff (>=) 1, 2, & 3 -- one column for ea decision value and one row for ea constr
3 Mixed Constraints (<=, >=, =) 4 -- uses a matrix of decision cells. Integer solns guarranteed.
4 Transportation
What If/ Sensitivity Analysis
Make changes to the model, resolve, record results, repeat
Use Sensitivity Report provided by Solver after solving a model with Simplex LP -- changes to 1 data input value
Top -- changes to per unit objective -- soln doesn't change
Bottom -- change to availability/target -- shadow price doesn't change
Use Solver Table (excel add-in) that resolves the model for a range of values for a data input and provides table of results. <-- most useful.
Special types of Models
Integer Programming models -- restricting values of decisions to be whole numbers. Specify in Solver.
Binary Integer Programming models -- Use of 0-1 variables to represent No (0) or Yes (1) decisions.
All binary integer variable models -- e.g., choosing some combination of possible investments
Mixed binary and non binary variables models
2. PROBABALISTIC SIMULATION MODELS See first worksheet of this file.
ANALYSIS PROCESS:
Bus Sit/Ques with Signif Variation Build a Simulation Model Replicate --> Observe a Large Sample Summarize Sample Results - Answer Qs
REMAINING WORK OF THE COURSE
Problem Set 2
-- to give you more experience in simulation modeling and analysis
-- to prepare you for the exam and for the case
-- MUST BE DONE INDIVIDUALLY -- but can discuss problems and simulation modeling with teammates
-- a solution guide will be provided after turn-in
Project -- Decision Analysis Case Deliverables due at end of week 5.
-- student teams of two
-- take role of professionals working in an organization, boss requests your work to help the organization
-- you receive a written request
-- boss requests a business memo (short report) of the results of your analysis -- company format specified
-- deliverables (each team):
(1) a Word file containing your business memo;
(2) an Excel file containing your BFDA that includes the modeling and analysis work
-- provides backup support your business memo.
The two files constitute the documentation of your work -- so be complete and use plenty of labels.
Final Exam Weekend following week 5
-- three hours in length
-- several cases requiring modeling and analysis (both types) to answer business questions
-- MUST BE DONE INDIVIDUALLY
-- can use any WRITTEN notes you develop or print to bring to the exam -- no other files open
-- NO COMMUNICATING WITH OTHER STUDENTS DURING EXAM
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