Spreadsheet Modeling & Decision Analysis A Practical Introduction to Management Science 4th edition
Cliff T. Ragsdale
C hapter hapter
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Introduction to Modeling & Problem Solving
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Introduction We fac e nu merou s d eci sion s in li fe & bu sin ess. We c an u se co mput er s to an alyze t he pot enti al outco mes o f d eci sion alt ern ati ves. Spr ead sheet s ar e t he too l o f c hoic e f or tod ays man ager s.
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Wha t is Ma na g ement Sci enc e? A f ie ld of st udy t ha t us es c omput ers, ma tics t o s olve st at istics, a n d ma t he busin ess problems . Als o kn own a s: Opera ti ons res ea rc h Decisi on sci enc e
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H om e
g em ent Sci enc e Runs in Ma na
M erri l Lync h 5 mi llion custom ers ci a l a dviso rs 16,000 f in an pro duct Develo ped a mo del to desi gn fea tu res a n d pricin g o ptions to bett er reflect custom er va lu e Ben ef its: $80 mi llion inc rea se in a nnu al revenu e $22 bi llion inc rea se in n et a ssi ets 1-5
H om e
Runs in M a na g em ent Sci enc e
Ja n de Wit Co . Bra zi l s la rg est li ly f a rm er Annu a lly pla nts 3.5 mi llion bu lbs a nd pro duc es 420,000 pots & 220,000 bun dles o f li li es in 50 va ri eti es . Develo ped mo del to det ermin e wha t to ho w to s ell it . pla nt, when to pla nt it, a nd Ben ef its: 26% inc rea s e in revenu e 32% inc rea s e in cont ri bution m ar g in 1-6
H om e
Runs in M a na g em ent Sci enc e
NBC Must det ermin e pro gr a m sc hedu les Sc hedu les must m eet a dvertis ers demo gr a phic a nd cost requi rem ents Develo ped o ptimi za tion mo del to pricin g o f det ermin e o ptim a l timin g a nd comm erci a ls Ben ef its: $50 mi llion inc rea se in a nnu al revenu e 1-7
H om e
Runs in M a na g em ent Sci enc e
sun g Elect ronics S am Lea din g DR AM m a nu fa ctu rer S emicon ducto r f a ci liti es cost $2-$3 bi llion equi pm ent uti li za tion is key H ig h Develo ped com prehensi ve pla nnin g a nd sc hedu lin g s yst em to cont ro l WIP Ben ef its: Cut c yc le tim es in ha lf $1 bi llion inc rea se in a nnu al revenu e 1-8
Wha t is a Comput er Mod el? m at i cal r ela tio ns h ips a nd A s et o f m at he l o gi cal a ssumptio ns imp l em ent ed i n a comput er a s a n a bstr ac t r epr es ent at io n o f a r eal -wor l d o bject o f p h enom eno n. ets pro vid e t h e most Spr ea ds he to co nveni ent wa y f or busi ness p eop le bui l d comput er mod el s. 1-9
T h e
Mod el in g Appro ac h to Decision M ak in g
Ever yon e us es mod el s to mak e d ecisions . T yp es o f mod el s: M ent al (a rr a n gi n g furnit ur e) Vis ual (bl ueprints, ro a d ma ps ) Ph ysic al/Sc al e (a erod yn a mics, bui l din gs ) ma tic al (wha t we ll be st ud yin g) M a th e
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t eristics of Mod el s C ha r ac Mod el s a r e us uall y si mpl ifi ed versio ns of t h e t h i ngs t h ey r epr es ent A val id mod el a cc ur at el y r epr es ents t he r el eva nt c ha ra c t eristics of t h e o bject or d ecisio n bei ng st udi ed
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B en efits of Mod el in g
Econo my - it is oft en l ess cost l y to an al yze d ecision pro bl ems usin g mod el s. Ti mel in ess - mod el s oft en d el i ver n eed ed info rmation mo re qui ckl y t han t hei r real -wo rl d co unt erparts . Feasi bi l it y - mod el s can be us ed to do t hin g s t hat wo ul d be i mpossi bl e. t & und erst andin g Mod el s g i ve us insi gh t hat i mpro ves d ecision makin g. 1-12
Ex am p le
m a tic al Mod el of a M a th e
Profit = Revenue -
nses Ex pe
o r Profit = f (Revenue, Ex penses) or Y = f (X 1, X 2)
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A G en eric M a th em at ic al Mod el Y = f (X1, X2, «, Xn) Where:
Y = dependent variable (ak a bottom-line performance measure) X i = independent variables (inputs having an impact on Y) f (.) = function defining the relationship between the X i & Y 1-14
ets M a th em at ic al Mod el s & Spr ea ds he Most spr ea ds h eet mod el s a re ver y simi la r m at ic al mod el : to o ur generic m at he Y = f (X1, X2, «, Xn)
Most spreadsheets have input cells (representing X i) to which mathematical functions ( f (.)) are applied to compute a bottom-line performance measure (or Y). 1-15
m at ic al Mod el s C a t eg ori es of M at he Model
Independent
OR/MS Techniques
Category
Form of f (.)
Variables
Pr escri pti ve
kno wn, well -d efi ned
kno wn or und er er s d ecisio n m ak co ntro l
LP, Net wor ks, IP, C PM, EOQ, NLP, GP, MOLP
Pr edicti ve
unkno wn, i ll -d efi ned
kno wn or und er er s d ecisio n m ak co ntro l
Reg r essio n Anal ysis, Tim e Seri es Anal ysis, Discrimi na nt Anal ysis
Descri pti ve
kno wn, well -d efi ned
unkno wn or unc ert ai n
Sim ula tio n, PERT, Queuei ng , Inventor y Mod el s 1-16
T h e
Identify Problem
Pro bl em So lv in g Proc ess
Formulate & Implement Model
Analyze Model
Test Results
Implement Solution
unsatisfactory results
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T h e
l og of Decision M ak in g y Psyc ho
be us ed fo r s truc tura bl e Mo del s c an a sp ec t s of decision pro bl ems . r a sp no t be s truc tured Ot he ec t s c an ea si l y a nd requi re in tui t ion a nd judg men t. C a ut ion: Huma n judg men t a n d in tui t ion is no t al wa ys ra t ion al!
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rin g E ff ects A nc ho
A ris e
wh en tri vi al f actors inf luenc e initi al t h in kin g abo ut a pro blem. Decision-makers us ually un der-adjust ir initi al anc ho r. fro m t he Ex ample: Wh at is 1x 2x 3x 4x 5x 6x7x 8 ? Wh at is 8x7x 6x 5x 4x 3x 2x 1 ?
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F r a min g E ff ects
ers vi ew a Ref ers t o how decisi on-m ak pr oblem fr om a win-loss pers pecti ve. ed oft en The wa y a pr oblem is fr am inf luenc es c hoic es in irr at i on al wa ys Suppos e youve been g i ven $1000 a n d m ust c hoos e bet ween: A. Rec ei ve $500 m or e imm edi at ely B. F li p a c oin a nd r ec ei ve $1000 m or e if hea ds occ urs or $0 m or e if t ai ls occ urs 1-20
F r a min g E ff ects (Ex a mp le )
Now s upp os e youve been g i ven $2000 a nd m ust c hoos e bet ween: A. Gi ve ba ck $500 imm edi at el y B. F l ip a c oin a nd g iv e ba ck $0 if hea ds occ urs or g iv e ba ck $1000 if t ai l s occ urs
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A Decision T r ee
for B ot h Ex a mp l es Payoffs $1,500
Alternative A Initial state Heads (50%) Alternative B (Flip coin)
Tails (50%)
$2,000 $1,000
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G ood Decisions v s. G ood O utcom es
G ood d ecisions do not always lead to good
outcom es ...
A st ructu red, mod elin g appro ac h to d ecision m akin g helps us m ake good d ecisions, but c an t gu arant ee good outcom es . 1-23
End of Chapter 1
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