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I72 PART i
Problems*
Introduction to Operations Management
.^ .4.1 The lollowing Sil_"1 the number of pints of type Ar- - * -.,r-. rr \\b6flln1y1 Hospital in the past 6 w".i., a) Forecast the mileage fbr next year using a 2_year moving aver-ltgc.
b) Find the MAD basecl on the 2_vear moving average fbr.ecast inpart (a). (Hinr: you will have only 3 years Jf matched data.)c) Use a weighted ?-year moving ,,".ug. ,r ith u,eigfrts of .,1 and .6to forecast next year.s mileage. (The"wei-eht otl.E i. fi, the mostrecent year.) What MAD results ftom Lrsing this approach toforecasring? (Hinr: yot r,,,ill have "rly 3 y;,;;;matched data.)d) Compute the tbrecast fo1 flar 6 usingexponenrial srxoothing, aninitial forecast lor year 1 of 3,000 ,rit"r,'un,Lo
=' f _,._: ' 4.6 The monthly sales fbr Telco Batteries, lnc.. were asfollou,s:
Week Of Pints UsedAugust 31September 7September 1zlSeptember 21September 28October 5
36038941038i368374
ill
b)
Forecast the demand tbr the week of October 12 using a 3_weekinor,ing average.Use a, 3-week weightecl movins averuge, r,r,ith weights of .1, .3,and .6, using .6 fbr the l:rtost recent week. Forecast demancl fbrlhe r,r eck ol October jl.Compute the forecast for the week of October 12 using exponentiaJsmoothing with a forecast forAugr-rst 3I of 360 onJo'= 2,,.,
4.2
Month Sales
Year 2
JanuaryFebruaryMarchAprilMuyJuneJulyAugustSeptemberOctoberNovemberDecember
202115141316t7l820202123
11l0Demand 7 13 11l312at Plot the above data on a graph. Do you observe any trencl. cycles,ot rundom r arilrl.ions..,b) starting in year 4 and going to year. r2. fbrecast demancr using a3-yeal movin-e average. piot your forecast un ifr" .r_" graph asthe original data.c) Starting in year,l and going to year 12, ibrecast demancl using a3-year moving averase.with w.igfrt, of . f , .:, anA .0, u.irg .O fo,the most recent year plot this f,xecast on tlte same grapn.d ) As 1,.u corrpare fbrecasrs with the originai iuiu, *rri"f, seems ro
,sive the better results? -l{:, 4.9 Refertoproblem4.2.Developaforecastfbryears2through1 I using erponentiar srnoothing with o ='.4 anJ u rorJ..* ro, year 1 of6. Plot your new fbrecast on a graph with the actual data and the naiveI,rrecast. Based on a visu:Ll inrp"ctio,r, *1".h f;r;;is better? ,?+, 4.4 A check_processrng center uses er.ponential smoothing.,r iorecast the number of incoming checks each month. The number,i ;hecks received in June was 40 million. while the fbrecast was ,X2:.rllron. A smoothing constant of .2 is used.. \\'hat is the forecast fbr July?: ii the center receivecl 45 rnillion checks in July. what would beihe tbrecast for August? - --'J'
-"' i i ntisht this be an i.
_..,rj,rn.., :,:i Lnappropriate lbrecasting method fbr this4.5 The Carbonclale Hospital is considering the purchase
. , : .:t :ll;nce. The clecision will rest partly on ihe anticipated- :-_: : :riien next year. The miles dr.ive.n during the past 5: _
-: .. .r,:
a) Plot the ntonthly sales data.b) Forecast January sales using each ofthe follou,ing:i) Naive method.
ii) A 3-month rnoving average.iii) A.6-monrh weighted uu"*g. using.l,
.1. .1. .2. .2, and.3,with the heaviest weights applied tJ the most recent months.iv) Exponential smoothing uring un
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: J o16 a Septemberlbrecast of lg.v) A trend pr.ojecrion.
c) With the dara given. which method would allow you to fbrecastnex t Murch's :ale:
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4.2 The actllal derland ibr. the patients at (Jmaha Emer_gency Medical Clinic tbr the first six we&s of this year fbllows:Week Actual No. of patients
1
2J456
656270486352
l ear MileageClinic administrator Mare Schniederjans wants you to forecastpatient demand at the clinic fbr ueek Z Uy,,.irSj,fris data. ylludecide_to use a weighted mori,g a'erage methocr t fincl this fore_cast. Your methocl uses four. actual clemand levels, w_ith rveights of0.333 on the present period, 0.25 n,r. p",.iJrgir.'br-, ,*u per.ioclsago, and 0. 1 67 three periods ago.a) Whal i: the r aluc ol') our. for.ectrrt?b) [f insreltl the u.eights were 20. 1-5, 15, and 10, r.espectrr.elr.. howwoulJ rhe lbrecust uhurrge,.,Erplrrin wh1.c) What if the weights u,ere 0.40. b.:0, O.:0. ancl 0. I0. respectivelv?N(ru \ hct is thc Ii,r.er.u.l ltrr riccL -..,
3.00021.000
3,4003.8003.700
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:l q ith PON{ for Windorvs