Rochester Institute of Technology
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Presentations and other scholarship
2000
Quantitative decision making models in the
planning and budget process of a hotel
John Wang
Qiyang Chen
Qiang Tu
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Recommended Citation
Wang, John; Chen, Qiyang; and Tu, Qiang, "Quantitative decision making models in the planning and budget process of a hotel"
(2000). Accessed from
http://scholarworks.rit.edu/other/378
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I
QUANTITATIVE DECISION MAKING MODELS IN THE PLANNING AND
BUDGET PROCESS OF A HOTEL
JOM Wang. Depl.lnfo &; Du Sd, Momclair Siale U"I~trsiry,
U. M(Jf1fckJi" Ni 07043. (973) 655-7519. [email protected],.edu
Qiyang CMI1, fkpt. Irt/o &: Dec Sci, MO,lldai, Suite U,,;w.nlry.
U. MOfttdQir. Hi 07043. (973) 6SS·n70. [email protected]
Qiang T... lkpt. I'l!o &: Dec Sc~ Momclair Sftlfe Unil'fl!,siry,
U. MomclClj" Ni 07043. (973) 655-75/1j, [email protected],.edu.
ABSTRACT
Linear and goal progl'lll1Ullin£ models ~ applied to answer questions encountered in the: botel
b"siN'S$ planning prooeu. 8Q(h qLl3lltitative and noo-quantit.aLive flCtOrS ~ laIca> into aa:ount. Th:.
valuc of !he application b discussed in light of the ~ methods used. Sjl\Wion$ described are
llCIuaI probkms faced by \he hoIel manager. The implemenution and use at me hotel are Ikscribed.
u\iRODUcnON
Oficn managers are faced with qllC.'itions of how to belit amlIIgc !he mix of productS or services to
lIWlimiu profits in the face of various constraints. Because of m:tnt innovatiOflS in oomputeriud
inventory rn:uu.gement. the hotcl mmager has the abiliTy ({) w;lju.U product and price mix on a daily
basis. Y".eld managel1leQ1 programs U3I:e ltistorical trends in oocupaDCy md r.I1e lIlld allow the
l1Wlager 10 f()ftllulat.e a sll"alegy for the fut\ll'e whereby the limited. perishable: inventory of rooms is
sold in such a way that profil5 an: maximized. In the busillC5s pllllUling and budget process at the
beginning of every year. the Ilolel manager formulates a very specifIC plan for each month of the
following year. This plan is based llpOIl bistCltieal occupancy uends. mown matnt c:onstrainls $ldl as
the c:onsDUctiOll or rcnoV3lion of a oompetilor. and known demand gener;llOl'$ such as eon~ntiOll$ and
special events. All of these factol1; give the manager a pieturc of what the market opportunities and
cllaUenges will be during the period in question. In light of tbese opportunities and challenges. Ihr;
manager then decide$ whal numbec of rooms can be expected 10 be soM, ...haI ra!c they Q/\ be
apected 10 be sold at. and which segment of the market they will be sold 10. In determining this
stnltegy. the manager must take into consideration cenain constraints presented by known costs.
company policy. past experience of dealing with cenain romet segments. and. most importantly. the
profit goals wh.ich have been SCI: by WipoiilC executhu.
TIlE PROBLEM
Recent iiUlOV3lions in the tecltnology of YICld managemenl ha,"C allowed the botel manager lhe
opportunity 10 quickly adapl invmtory and pricing 10 changes in demand. BUI technologicaJ advances
such as yield management are at best useless and at worst damaging to the goals of the business if
there is nO! a str:Uegic plan in place to deal with the constraints, inlemal and externa1. which the
business unit is subject to. In order for this =gic plan to be dfecti~ those who design it must
b~ a keen awarc:nesl'li of euctly which $ales efforts will produce optimal results given certain
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IUstoricai and environmental factors,
The ck:cisioo m.a.k.inS proo:a.s CUI't'eIltly used in the heMel budseUas atId business planning prIl(:CS6.
....1U1e often adueving good results. lea'"e5 oonsido:rable room for improYelJ""nl. While there i s _
consideralion of quantifiable fllClors, these: at!: very often limited to naive forecasling t.eehlllques such
as averaging of hislOricai performance Of adjusting IUstoricaJ figures by a predt.1.ermined factor. Once
these revenue: goals are arrived al. lhe manager is faced with the diffICult task of determining: \>IlIICh
$ales strategies win achieve the desira:l result It is III this poinl in the process thai. our linear
programming and gOO prognunnung models m<Ih tteu most valuable contribution$.
Most managers presently arrive al decisions about bow many rooms 10 seU to whom and at whal price
by way of I cumbersome process involving the processing of myriad bypolhelical scenario$ OIl a
spreadshoet unlil the ~ result is actueval. This is often done ..... ith only an impm:isc: awareness of
tlx: OOSIS involved in selling certain kin(ls of ~l"IO:S$ or or lhe efreeu wbic:h OC:rWn unf~n
changes may have upon lhe big picture. The sensitivity analysis provided by linear programming is
valuable bert because it makcs the manilger precisely a..... an: of lhe limits or feasibility or" particular
solutiOll. Tlle optimal solutions gencnlted by use of Ihc:se lineaT progmnrnins and gOO prognmmins
models give the l1WIager a keen sense of "'hich mart.et sq;rnat1S should be conc:entnlled vpon in the
saJes effort and whal goals should be: set for volume and price of rooms sold on each period.
Goa! Programming is especially useful in dealing with the many conflicting goals that prucnt
themscl,"CS in the busifICM planning process. Many times in !he budgd process. w:.w Of revised gOOs
~ inuoduc::cd IWfway through the proaess by fadOn such as unforeseen nwtet conditions Of ~'1SCd
profit upc:cutions from corporme StnllCgislS. In such situations. reVIsions of = n t ways of doing
busillCS5 must be considered. The ~lutions offered by goal progr.unming models allow the manager
to see which policies mUSl be changed in order to satisfy the new sel of goals. For example. the
m.an.ager m.ay be: forced to abandon a policy of selling rooms to a CCfUin martr.et segment because there
,s no way in which selling to thai segmenl coukl oonuibulto to the achie~IDelll of the revised goals of
the organiwion. These models provtde the manager with all of the neltibility ner.ded to adapt policies
10 changing conditiOlls.
TIle IUe of a linear programming model llS$Ul1lC$ thaI maximiutiOll of profit in the face of the givcn
quantifiable conwainlS is the only cons>deratioo for the heMel IT\31A&Crnent. In ruJity. while profit
muimiwion IS an imponanl considerat,OIl, there at!: usually many other eonsideraliQns wh.ich are
broUghlto bear upon lhe m!lDagelllenl decision making process.
The manager is often faced With gOOs ",·lIidt may connlC1 with each ocber. Often. for example. simply
maxinuung profit is DOl enough. TIle manager is Idually given a profit goal II the beginning of the
planning process and required to do wh:tl is necessa:y to moet thaI goal, The dcc;SIODS made in order
10 meet this goal mUSl be considen:d in lighl of lheir potential confliclS with Other more ephemeral or
long range goals such as lhe Sallsfxtion of certain special customers. nounshing relationships WLth
sP"'C'aJ groups. and the CSUlblishment of" good im1ge in the COI1UTbIity.
The goal programming model is used to deal with decision problems thaI
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ha~
more than one
•
objecti= We have SCI-Up the GP formulations in lhrcc diffclalI sa:nario$: acbievement of a profit
~t IS primary. acc:ommodating the Ir.I.veling business penon is prinwy. and wisfaetioo of existing
eontnClS 10 m.ailItain COllJOlalC image is primary. The: solutions offcml by p i pmgramm.mg models
allow the manager to $ee which policies must be changed in orda (0 wisfy the _
5d of goaJs.
The application of both linear pogl&mming and goal prognmming in the botel ioduwy would
inc::ruse the amourl\ of infOl'T'lWion available .II the prescnt time. This would enhance !be abiliry of
hOld manager! to plan and aCCUle their busineS$ $lI'I.lC:gles with I keener knowledge of the possible
effects of their docisions.
BENEFTTSAND LThfITATIONS OF TIlE USE OF THE MODElS
In our experience, there lICe a number of way$ to apply the mathematical models in a se .....ice industly
such a.~ the hold indusuy. Unlike the !t.r.ltegic planning which lakes place on the multi-unit level in
which planning horizOlI$ can be five or Icn yean long. the typical planning horizon for the operation of
a hotel is onc ~ar. In the annual hoIel business plan and budgeting process, the following fiscal year
is considered mooth by month. All appOcablc history for each month is considered and all known
fu= CQrIstr.QnlS wluch aCfed supply. demand. company =gic goals and. customer saLisfaetion ~
.surfaced. Judgements lICe !lEn made aboIJl how to maximize yield on the Iimital. perishable ll$Sd$ in
light of historical and fORlC&Sled coosuainl.$ using output from various rnatIJcmaticaI modds as a
source of information. TIle =uIt of !his procas is a decision aboul bow many rooms will be sold 10
ea:h marl<cr. scgn""l for each moruh of the cornin& year and al wNi price !bose l"I)Om'l; will be sold. A
$&Ies action plan ii then devised 10 a1kx:ale expentlil1JftS necessary 10 accomplish lbe$e goals for ea:h
~"'-
In adcbtion 10 the annual business planning process. il is -=t$5Iry for the manager 10 revu:w the iW.US
of available in~iitOf)' on a daily basis Iii order 10 adjust for changes in lhe level of demaJ>d so that
sales opportunities may be llWimized iii lighl of the goals set (0I11l in the annual p1l1ll. These
deeisions involve closing off sales to cenain market segments Md opening up sales 10 others
depending on the CUrTenl level of demand. This is done in order 10 avoid selling ar rates which are too
low when demand is high and offering rooms 111 too high a price when demand is low. To make
decisions such as lhcse. marhematical models which tell the manager at what point a certain strategy
becomes unfeasible or countetprOOoctive are helpful.
There are bas.ically lWO problems llSSOCi;ued wilb the application of matllemar.icaJ models iii the
planning procc$S described above. First. the input of data is eXlJ"Cmely time consuming Md
cumbe~. especially in an indusuy in which unexpected changes ~uire repositioning on an llmosl
ibily basis_ Socoodly. the accurate intef]lfCUlrion of outpul is difflcull for those wbo are not
profc:ssi<xWJy lr.lined in malhematiclll~. These two limitatioM will need 10 be overcome by
the dc:s.ign of roore SImple software and by the integration of input and output wilb bolel programs
...·hlch are already commonly used if the application of lhcse rna1he:mallcaI models is 10 be prolifCl11cd.
Note: Rderenoes available upon R':Cjuesl.
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