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# Hrm1

FIVE MANAGEMENT TECHNIQUES ACROSS THE WORLD

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### Hrm1

1. 1. BASIL JOHN I ST YEAR MBA SECTION B | DMS PONDICHERRY UNIVERSITY FIVE MANAGEMENT TECHNIQUES ACROSS THE WORLD SUBMITTED TO, DR. R PRABHAKARA RAYA DEAN/ASSOCIATE PROFESSOR SCHOOL OF MANAGEMENT PONDICHERRY UNIVERSITY HRM ASSIGNMENT
2. 2. Statistical techniques 1.Time trends and forecasting Time-series methods make forecasts based solely on historical patterns in the data. Time-series methods use time as independent variable to produce demand. In a time series,measurements are taken at successive points or over successive periods. The measurements may be taken every hour, day, week, month, or year, or at any other regular (or irregular) interval. A first step in using time- series approach is to gather historical data. The historical data is representative of the conditions expected in the future. Time-series models are adequate forecasting tools if demand has shown a consistentpatterninthe pastthat isexpectedtorecurin the future.Forexample,new homebuilders in US may see variation in sales from month to month. But analysis of past years of data may reveal that salesof newhomesare increasedgraduallyoverperiodof time.In thiscase trend isincrease in newhome sales.Timeseriesmodelsare characterizedof fourcomponents:trendcomponent,cyclical component,seasonal component,andirregularcomponent.Trendisimportantcharacteristicsof time series models. Although times series may display trend, there might be data points lying above or belowtrendline.Anyrecurringsequence of pointsabove andbelow the trendline thatlastformore than a year is considered to constitute the cyclical component of the time series—that is, these observationsinthe time seriesdeviate fromthe trenddue to fluctuations.The real Gross Domestics Product(GDP) providesgoodexamplesof atime seriesthat displayscyclicalbehavior.The component of the time series that captures the variability in the data due to seasonal fluctuations is called the seasonal component.The seasonal component issimilartothe cyclical componentinthat theyboth referto some regularfluctuationsina time series.Seasonal componentscapture the regularpattern of variabilityinthe time serieswithinone-yearperiods.Seasonal commoditiesare bestexamplesfor seasonal components.Randomvariationsintimesseriesisrepresentedbythe irregularcomponent. The irregularcomponentof the time seriescannotbe predictedinadvance.The randomvariationsin the time seriesare causedbyshort-term, unanticipated andnonrecurringfactorsthataffectthe time series. Smoothingmethods(stable series) are appropriate whena time seriesdisplaysnosignificant effectsof trend,cyclical,orseasonalcomponents.Insuchacase,the goalistosmoothoutthe irregular componentof the time seriesbyusinganaveragingprocess.The movingaveragesmethodisthe most widelyusedsmoothingtechnique.Inthismethod,the forecastis the average of the last“x” number of observations, where “x” is some suitable number. Suppose a forecaster wants to generate three- period moving averages. In the three-period example, the moving averages method would use the average of the most recentthree observationsof data in the time seriesas the forecastfor the next period.Thisforecastedvalueforthe nextperiod,inconjunctionwiththe lasttwoobservationsof the historical time series, would yield an average that can be used as the forecast for the secondperiod in the future. The calculationof a three-periodmovingaverage isillustratedinfollowingtable.Basedonthe three- period moving averages,the forecastmay predict that 2.55 million new homes are most likely to be sold in the US in year 2008.
3. 3. Year Actual sale(inmillion) Forecast(inmillion) Calculation 2003 4 2004 3 2005 2 2006 1.5 3 (4+3+2)/3 2007 1 2.67 (3+2+3)/3 2008 2.55 (2+3+2.67)/3 Example:Three-periodmovingaverages In calculatingmovingaveragestogenerateforecasts,the forecastermayexperimentwithdifferent- lengthmovingaverages.The forecasterwillchoose the lengththatyieldsthe highestaccuracyfor the forecastsgenerated.Weightedmovingaveragesmethodisavariantof movingaverage approach.In the movingaveragesmethod,eachobservationof datareceivesthe same weight.In the weightedmovingaverages method,differentweightsare assignedtothe observationsondata that are usedincalculatingthe movingaverages.Suppose,once again,thata forecasterwantsto generate three-periodmovingaverages.Underthe weightedmovingaveragesmethod,the three data pointswouldreceivedifferentweightsbefore the average iscalculated.Generally,the most recentobservationreceivesthe maximumweight,withthe weightassigneddecreasingforolder data values. Year Actual sale(inmillion) Forecast(inmillion) Calculation 2005 2 (.2) 2006 1.5 (.3) 2007 1 (.4) 2008 .42 (2*.2+1.5*.3+1*.4)/3 Example:Weightedthree-periodmovingaveragesmethod A more complex formof weightedmovingaverage isexponential smoothing.Ithismethodthe weightfall off exponentiallyasthe data ages.Exponentialsmoothingtakesthe previousperiod’s forecastand adjustsitby a predeterminedsmoothingconstant,ά (calledalpha;the value foralphais lessthanone) multipliedbythe difference inthe previousforecastandthe demandthatactually occurredduringthe previouslyforecastedperiod(calledforecasterror).Exponentialsmoothingis mathematicallyrepresentedasfollows:New forecast=previousforecast+alpha(actual demand− previousforecast) Orcanbe formulatedas F = F + ά(D− F) Othertime-seriesforecastingmethodsare,forecastingusingtrendprojection,forecastingusing trendand seasonal componentsandcausal methodof forecasting.Trendprojectionmethodused the underlyinglong-termtrendof time seriesof datatoforecastitsfuture values.Trendand seasonal componentsmethodusesseasonal componentof atime seriesinadditiontothe trend
4. 4. component.Causal methodsuse the cause-and-effectrelationshipbetweenthe variablewhose future valuesare beingforecastedandotherrelatedvariablesorfactors.The widelyknowncausal methodiscalledregressionanalysis,astatistical technique usedtodevelopamathematical model showinghowa setof variablesisrelated.Thismathematical relationshipcanbe usedto generate forecasts.There are more complex time-seriestechniquesaswell,suchasARIMA and Box-Jenkins models.These are heavierdutystatistical routinesthatcancope withdata withtrendsandthe seasonalityinthem. Time seriesmodelsare usedinFinance toforecaststock’sperformance orinterestrate forecast,usedinforecastingweather.Time-seriesmethodsare probablythe simplestmethodsto deployandcan be quite accurate,particularlyoverthe shortterm. Variouscomputersoftware programsare available tofindsolutionusingtime-seriesmethods. For ex., Epidemiologistcanconstruct endemiccurvesbasedonincidence of disease andalso establishthe likelylimitsof variations. If the incidence of adisease exceedsthe expectationbycertainlimits,the occurrence of an increasedincidence orepidemiccanbe anticipated. Activity analysis 2. Work sampling and activity analysis Work samplingisthe statistical technique fordeterminingthe proportionof time spentbyworkers invariousdefinedcategoriesof activity(e.g.settingupamachine,assemblingtwoparts,idle…etc.). It isas importantas all otherstatistical techniquesbecauseitpermitsquickanalysis,recognition,and enhancementof jobresponsibilities,tasks,performance competencies,andorganizational work flows.Othernamesusedforitare 'activitysampling','occurrence sampling',and'ratiodelaystudy'. In a worksamplingstudy,alarge numberof observations are made of the workersoveran extended periodof time.Forstatistical accuracy,the observationsmustbe takenatrandom timesduringthe periodof study,andthe periodmustbe representativeof the typesof activitiesperformedbythe subjects. One importantusage of the work samplingtechnique isthe determinationof the standardtime fora manual manufacturingtask.Similartechniquesforcalculatingthe standardtime are time study, standarddata, and predeterminedmotiontimesystems. Characteristics ofwork samplingstudy The study of worksamplinghassome general characteristicsrelatedtothe workcondition:  One of themisthe sufficienttimeavailable toperformthe study.A worksamplingstudy usuallyrequiresasubstantialperiodof time tocomplete.There mustbe enoughtime available (severalweeksormore) toconduct the study.  Anothercharacteristicismultiple workers. Worksamplingiscommonlyusedtostudythe activitiesof multipleworkersratherthanone worker.