International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 11 Issue: 09 | Sep 2024 www.irjet.net p-ISSN: 2395-0072
An Empirical Study on Identifying the Best Qualitative Methods for Sales Forecasting Models of the Top Five FMCG Companies in India Based on Market Capitalization as of March 19, 2024, Using JMP Software.
Shreyas S Vanjare
Student, pursuing MBA at PES University Bengaluru, Banashankari - 560085
Abstract - This paper examines sales forecasting for the five largest FMCG companies as of March 19, 2024: Hindustan Unilever Limited (HUL), ITC Limited, Nestlé India Limited, Varun Beverages Limited, and Godrej Consumer Products Limited. We obtained 12 years of sales data for these companies from the Screener website to predict sales for the next three years, from March 2024 to March 2027. Various forecasting methods were employed, utilizing JMP software to identify the most effective techniques for each company. Our study revealed that each FMCG company has an optimal forecasting model tailored to its specific sales data. For ITC, Nestlé India Limited, and Godrej Consumer Products Limited, the ARIMA model proved most effective. In contrast, Varun Beverages Limited benefited more from the Seasonal Exponential Smoothing model. This highlights the importance of selecting the right forecasting method for accurate predictions and positive outcomes. Additionally, the paper provides an overview of the FMCG sector and its growth trends, driven by urbanization, rising incomes, and changing consumer preferences. Despite economic fluctuations, the FMCG sector remains resilient, offering lucrative investment opportunities. The findings are valuable for industry stakeholders, including companies, investors, and policymakers. Accurate sales predictions enable effective inventory management and strategic marketing. Overall, this study enhances our understanding of sales forecasting in the FMCG industry, underscoring the utility of diverse quantitative models in decision-making.
Key Words: Business forecasting, sales forecasting, ARIMA model, Seasonal Exponential Smoothing, quantitative analysis, economic fluctuations, Screener website, growth potential.
1. INTRODUCTION
Thelong-termsuccessofanyfirmgreatlydependsonitsmanagement'sabilitytorecognizetrendsandmakesounddecisions. Topcompanyexecutivestypicallyknowjustwhentomakechangestostayaheadofthecompetition.Thesecompaniesrarely makemistakesbymisjudgingconsumerdemand,unlikemanyothers.Beingadeptatforecastingcanmakeallthedifference.
Duetointensecompetitionandchallengingtimes,determininghowmuchproducttosellisdifficult.Variousfactorsinfluence consumer demand, but if a company can accurately forecast sales, it can lead to increased customer satisfaction, higher revenue,andmoreefficientproductionplanning.
Accurateandtimelyforecastingiscrucialforinventorymanagement.Ifforecastingmethodsareflawed,acompanymayendup withtoomuchortoolittlestock,whichcannegativelyimpactprofitabilityandcompetitiveness.
Forecastinghelpspredictandexplainpotentialoutcomes,andincorporatingthesepredictionsintoplanningcanleadtobetter decision-making for the company (Makridakis and Wheelwright, 1989). For example, forecasting sales can enhance the effectivenessofthesalesormarketingteam.Thesamepredictionscanaidotherdepartmentsinproductionplanningand scheduling.
Therearevariousmethodsforforecastinginbusiness,rangingfromsimpletosophisticatedapproaches.Themostcommon methodappearstoberelyingonintuition,asmanydecision-makersbasetheirpredictionsonexperienceandpastobservations (Makridakis,1989;DeLurgio,1998;Wright&Goodwin,1998).Whilemostcompaniesfollowthisapproach,somecombineit withdata-driventechniques,andonlyafewrelysolelyonstatisticalmethods(Makridakis,1989).
1) Sales Forecasting:
Salesforecastinginvolvesanalyzingpastandcurrentdatatopredictfuturesales.Everycompanythatsellsproductsmust estimateconsumerdemand.Manufacturersneedtoknowhowmuchtoproduce,andretailersneedtodetermineoptimalstock levels.Failingtounderstandconsumerdemandcanresultinlostsales,dissatisfiedcustomers,andaweakenedmarketposition.
Volume: 11 Issue: 09 | Sep 2024 www.irjet.net
Importance of Sales Forecasting:
1)Improving Problem-Solving Techniques:
a)Managingfinalproductsandinventoryefficiently.
b)Schedulingproductiontooptimizemachineryandplantusage.
c)Maintainingadequaterawmaterialandsupplystockstoensureuninterruptedproduction.
d)Reducingoverstockingwhileminimizingwarehousingandtransportationcosts,andcreatingamanpowerplan.
e)Anticipatingfinancialrequirements.
2)Supporting Management:
a)Providingguidancefortopmanagementinpolicy-making,planning,andoperationalcontrol.
b)Aligningdifferentfunctionaldepartmentsonproducttypesandquantities.
c)Makinginformeddecisionsaboutbusinessexpansion.
d)Planningforbothlong-termandshort-termfinancialneeds.
e)Preparingoperatingandcapitalbudgetsfortheyear.
3)Supporting Sales and Marketing Decisions:
a)Settingsalestargetsforthesalesforce.
b)Developinganeffectivesalesstrategy.
c)Directingsaleseffortsanddeterminingsalesandpromotionalexpenses.
d)Evaluatingtheadvertisingbudget’ssizeandscope.
e)Settingpricesthatensuresustainableprofitability.
f)Identifyingproductswithgrowthpotentialforfurtherdevelopment.
2) Overview of the FMCG Sector:
FMCG,orFast-MovingConsumerGoods,referstoproductswithquickturnoverandlowcost.Consumerstypicallyspendless timedecidingtopurchaseFMCGproductscomparedtomoreexpensiveitems.Althoughindividualearningsfromproductslike soapandsnacksaresmall,theoverallprofitcanbesubstantialwhensoldinlargevolumes.Evenduringeconomicdownturns, consumersmaydelaypurchasingitemslikecars,buttheywillstillbuyessentialgoodslikefoodandcleaningsupplies.
Daily-useitemssuchassoap,shampoo,andtoothpastefallundertheFMCGsector.ThissectorisvitalfortheIndianeconomy andisthefourth-largestcontributortothecountry’sGDP.Itincludesfrequentlypurchaseditemslikepersonalcareproducts, food,householdgoods,andevenelectronics.
Top 5 FMCG companies in India
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 11 Issue: 09 | Sep 2024 www.irjet.net p-ISSN: 2395-0072
Examples of FMCG Products
BelowaresomeexamplesofFMCG(Fast-MovingConsumerGoods)products:
1. Toiletries and Personal Care Products:
o Toothpaste
o Shampoo
o Soap
o Deodorant
o ShowerGel
o HandSanitizers
2. Food and Beverages:
o SoftDrinks
o SnackFoods(chips,cookies,chocolates)
o BreakfastCereals
o PackagedTeaandCoffee
o InstantNoodles
o PackagedFruitsandVegetables
3. Household Products:
o LaundryDetergent
o DishwashingLiquid
o AirFresheners
o CleaningAgents(all-purposecleaners,floorcleaners)
o InsectRepellents
o Batteries
4. Healthcare Products:
o Over-the-CounterMedicines(painrelievers,coughsyrup,antacids)
o VitaminsandSupplements
o BandagesandFirstAidSupplies
o OralCareProducts(mouthwash,dentalfloss)
o DietaryandNutritionalProducts
5. Packaged Foods:
o CannedFoods(soups,vegetables,fruits)
o FrozenFoods(readymeals,pizzas,vegetables)
o PackagedSnacks(nuts,popcorn,pretzels)
o CondimentsandSauces(ketchup,mayonnaise,saladdressings)
o BreakfastBarsandGranolaBars
6. Baby and Infant Care Products:
o Diapers
o BabyWipes
o BabyFood
o InfantFormula
o BabySkincareProducts
ThesearejustafewexamplesofthevastrangeofFMCGproductsavailableinthemarket.Theyarecalled"fast-moving" becausetheyarelow-costitemsthatsellquicklyandhaveahighturnoverrate.
Major Concerns in the FMCG Sector in India
Currently,allkindsofbusinessesarefacingchallenges,notjustoneparticulartype.It’sbecomingincreasinglydifficultto perform well and maintain growth. The market for everyday products is constantly evolving, with new items launching frequentlyandtrendschangingquickly.Additionally,variouspromotionsandadvertisementsmakesalesforecastingeven morechallenging.Thedistributionofstoresacrossdifferentregionsalsocomplicatesplanning.Therefore,predictingglobal salesfortheseproductsisadifficulttask.
Objectives of the Study
1. Todeterminewhichmodelsaremosteffectiveforforecastingsalesinstoresthatselleverydayproductslikesoapand snacks.
2. Toanalyzethefactorsthatinfluencestheselectionofsalesforecastingmodelsforthesestores.
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 11 Issue: 09 | Sep 2024 www.irjet.net p-ISSN: 2395-0072
3. Tocomparedifferentsalesforecastingmethodsbasedonproductcategories.
4. TosuggestimprovementstoexistingforecastingmodelstoenhancetheiraccuracyforpredictingsalesinFMCGstores.
Expected Contribution from the Study
This study aims to improve our understanding of sales forecasting. The key beneficiaries include researchers, educators, managers,andanyoneinvolvedinsalesforecasting.Thestudywillproposesalesforecastingmodelsfordifferenttypesof FMCGproducts,helpingforecastersmakebetterpredictions.Additionally,itwillidentifythefactorsthatinfluencetheselection offorecastingmodels,especiallyforconsumergoods.Thisresearchisexpectedtoimproveforecastingaccuracy.
a) Reduced inventory:Bettercashflowcanbeutilizedformarketingeffortssuchasadvertisementsandin-storedisplaysto drivesales.
b) Minimizing stockouts:Reducedmissedsalesopportunitiesattheretaillevel.
c) Maximizing resource utilization:Inaproductionsetting,ensuringthetimelyproductionoftherightproductswithin constraints.
FMCGcompaniesmayapplythesefindingstoimprovetheirsalesforecasting,allowingthemtomeetcustomerdemandmore efficientlywhileminimizingexcessinventory.Thisapproachisbeneficialforvarioustypesofbusinesses.
Literature Review:
1. In2008,HanaaE.Sayedandcolleaguesintroducedasimplesystemtoaiddecision-making.Theyusedmathematical techniqueslikeadditionandaveragingtoforecastacompany’sproductionneeds.Byanalyzingrealdatafromvarious products,theyshowedthattheirsystemwasmoreeffectivethanguessing.Bycombiningdifferentmethods,they improvedpredictionsaboutproductiontiming.Theirsystemalsoincorporatedexpertopinionsandmathematical modelstoenhanceaccuracy.
2. DragisaStojanovic(bornin1994)examinedbothdirectandindirectproductsales.Hedevelopedamodelusingasales charttoanalyzesalestrendsandpredictfuturesales.Thismodelhelpsforecastsalesunderdifferentconditions whethertheyremainstableorchange.Thesaleschartormodelallowsforprojectionsbasedonpasttrends,makingit usefulforfuturesalespredictions.
3. MichaelS.MorganandPradeepK.Chintagunta(1997)exploredthechallengesofpredictingrestaurantsales,which are only observable when the restaurant is open. They discussed these difficulties in a paper relevant to many restaurantowners.Theirfindingsdemonstratedthataparticularmathematicalmodelperformedwellinforecasting sales,makingitavaluabletoolforestimatingrestaurantrevenues.
4. Koen Pauwels highlighted the value of time-series data for marketing researchers. This data enables more sophisticatedresearchandinsightsintochangesovertime.Hediscussedthechallengesresearchersfacewhenusing time-seriesdatainmarketingandprovidedexamplesofnewapplications.However,healsonotedthatthereisstill muchtolearninthisfield.
5. In2004,PeterS.Faderandcolleaguesdevelopedamodeltounderstandhowcustomerspurchasenewproductsover time.Whilesimilartoexistingmodels,theirsismoreflexibleandadaptsovertime.Themodelalsointegratesother commonly used marketing models. Their research showed that this model can predict sales volume and timing, helpingmanagersmakebetterdecisions.
6. In2008,RajivUrspresentedasystematicapproachtoproductionplanningforimprovedresults.Heproposedeight steps: inputting historical data, cleaning it, making predictions using mathematical models, accounting for new products, adjusting predictions with expert insights, aligning forecasts with promotional schedules, tracking inventory,andcollaboratingwithsuppliers.Hismethodensuresthatdecisionsarebasedonconsistentandreliable forecasts.
7. InMay2012,TetyanaKuzhdadevelopedamathematicalequationtoforecastfuturesales.Theequationfactorsin variablessuchasconsumerincomeandadvertisingexpenditure.Kuzhda'sstudyfoundthatthisequationaccurately predictssalesandcanbeappliedtootherbusinessforecastingscenarios.
8. TomWallace(2006)identifiedfourkeyelementsinSales&OperationsPlanning:demand,supply,volume,andmix. Thisprocessensuresthatsupplymatchesdemandandthatproductvarietyalignswithconsumerneeds.Wallace emphasizedthatplanningiseasierwhenconditionsarestablebutbecomeschallenginginvolatilesituations.Henoted thatbusinessesincreasinglyfacesuchunpredictableconditions,makingforecastingmoredifficult.
9. LarryLapide(2009)pointedoutthatsomeproductsarehardertoforecast,particularlythosewithirregulardemand ornewproductlaunches.Thisstudyhighlightsthedifficultiesforecastersfacewhenpredictingdemandinfluencedby
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 11 Issue: 09 | Sep 2024 www.irjet.net p-ISSN: 2395-0072
promotions or economic shifts. The unpredictability of customer reactions and limited historical data for new productsmakeforecastingevenmorechallenginginrapidlychangingeconomies.
10. Manisha Gahirwal and Vijayalakshmi M. (2011) found that combining multiple forecasting techniques improves accuracy.Theydivideddataintothreecomponents trend,seasonalpatterns,andrandomfluctuations andapplied different mathematical models, such as ARIMAand Holt-Winter, to each. Their study showed that using the best forecastingmethodsfromonedatasetcanimprovepredictionsinsimilardatasets.TheirmethodoutperformedHoltWinteralone.
11. McKinsey’sBudgetingControl,introducedin1922,revolutionizedbusinessplanningbyimprovingsalesforecasting,a keyfactorineffectivebudgeting.
12. In1988,ChatfielddiscussedtheeffectivenessofHolt-Winter'sexponentialsmoothingforforecasting,notingthat despiteitssimplicity,itperformsaswell asmorecomplexmethods.Hearguedthat combining thismethod with human judgment can further enhance forecasts. Chatfield emphasized that simplicity often rivals complexity in forecasting,andsometimesyieldsbetterresults.
13. MichaelKelleher(2012)describedhowHollister,Incorporatedimproveditsglobalplanningbycleaningolddata, focusingonunpredictableproducts,developinga systemtodetectunusual forecastpatterns early,andfostering regularcommunicationbetweentheSales,Marketing,andOperationsteams.
Research Gap:
Afterreviewingvariousforecastingmethods,itisclearthatnosinglemethodisuniversallyperfect.Thechoiceofmethod dependsonfactorslikethetypeofdataavailable,thepurposeoftheforecast,thepeopleinvolved,andthesuitabilityofthe model. Additionally, the complexity of forecasting increases with the number of products involved. The central question remains:whichforecastingmethodshouldbeused?
a) TimeSeriesMethods
b) CausalMethods
c) JudgmentalMethods
Using the same method for all popular products is often not effective. Factors such as the type of data available and the product’slifecyclealsoinfluencepredictionaccuracy.Therefore,selectingtheappropriateforecastingmethodischallenging, andthetypeofproductmatters.Amethodthatworkswellforshampoomightnotbeaseffectiveforcookies.
Thisstudyaimstoaddressthesegapsandidentifyaforecastingmodelthatdeliversthemostaccuratepredictionsforawide rangeoffast-movingconsumergoods(FMCG).
FORECASTING TECHNIQUES AND THEIR ATTRIBUTES
Forecasting
Forecastinginvolvespredictingfutureeventsbyanalyzingpastdata.Itistheprocessofusinghistoricalinformationtomakean educatedguessaboutwhatmighthappennext.Predictionalsoreferstoanticipatingfutureevents,butunlikeforecasting,itcan involvepersonalintuitionandfeelings,whichmaynotfollowaspecificpatternormethod.
Aforecastisessentiallyapredictionaboutthefuture.Sometimes,asinglepredictionissufficient,whileatothertimes,multiple predictionsarenecessary.Thenumberofpredictionsandthedepthofanalysisdependonhowtheforecastwillbeused.
Therearetwoprimaryreasonsfortheneedforforecastinginanyfield:
1. Purpose –Forecastinghelpsinplanningforfutureevents.Bypredictingwhatmighthappen,wecantakestepsto improveoutcomesormitigatepotentialproblems.
2. Time –Timeplaysacrucialroleinplanning,preparation,execution,andcompletion.Differentsituationsrequire varyingtimeframes somemayneedshort-termsolutions,whileothersmayspanseveralyears.Knowingwhatthe futureholdsenablesustoplanandactattherighttime.
Types of forecasts
Forecastingcanbeclassifiedinto:
a) Long-term Forecast:Executivesneedlong-termforecaststosetthecompanyontherightpathforthefuture.Therefore, theypaycloseattentiontosuchforecasts.
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 11 Issue: 09 | Sep 2024 www.irjet.net p-ISSN: 2395-0072
b) Short-term Forecast:Short-termforecastsassistmanagersinplanningfortheimmediatefuture.Theseforecastshelpguide decision-makinginday-to-dayoperations.
c) Quantitative Techniques:Theseinvolvemathematicalapproachesthatyieldnumericalresults.Theyrelyonestablished rulesandformulasratherthanintuition.
FORECASTING STEPS
Understandingthatforecastingmethodsrelyonhistoricaldatahelpsoutlinethefivebasicstepsintheforecastingprocess:
a) Data Collection
b) Data Reduction or Condensation
c) Model Building and Evaluation
d) Model Extrapolation (the actual forecast)
e) Forecast Evaluation
Belowisabriefexplanationofeachstep:
a) Data Collection:Gatheringaccurateandrelevantdataiscrucial.Ensuringthequalityandrelevanceofthedataisessential becauseincorrectorirrelevantdatacanleadtoflawedforecasts.
b) Data Reduction or Condensation:Insomecases,dataneedstobesimplified,eitherbecausethereistoomuchortoolittle information.Irrelevantdatacanreducetheaccuracyofpredictions,whilesomedatamayonlyprovideinsightintopastevents ratherthanfuturetrends.
c) Model Building and Evaluation:Thisstepinvolvesconstructingamodelbasedonthecollecteddatatomakeaccurate predictions.Thesimplerthemodel,themorelikelymanagerswillunderstandanduseitfordecision-making.
d) Model Extrapolation (the actual forecast):Oncethemodelisbuiltandtherightdataisselected,itisusedtopredictfuture outcomes.
e) Forecast Evaluation:Thisinvolvescheckinghowwelltheforecastmatchesactualoutcomes.Sometimes,themostrecent dataissetasidetoverifytheaccuracyofthepredictions.
METHODS OF SALES FORECASTING
a) Qualitative Method / Survey Method:Thisresearchpaperdoesnotcoverthisforecastingmethod.
b) Quantitative Method / Statistical Method:Statisticalmethodsinclude:
a) Time Series Analysis
b) Moving Average
c) Exponential Smoothing Method
d) Regression Analysis
e) Econometric Models
RESEARCH METHODOLOGY
The research method outlines the approved procedures used for a specific study, defining the scope of the proposed investigation.Italsoincludesresearchobjectives,explainingthescope,timeframe,anddatasources.
The data used for this research paper is secondary data. Sales data of the top 5 FMCG companies was obtained from the Screenerwebsite,whereinformationonpubliclylistedcompaniesisavailable.Additionalreliablesources,suchasonline articlesrelatedtotheFMCGsector,businessforecasting,andtextbooksonbusinessforecasting,werealsoutilized.
Limitations of this research paper
ThisresearchpaperdoesnotcovertheQualitativeMethod/SurveyMethodforecastingmethod.
However,toovercomethelimitationsandmaintaintheeffectivenessofthisresearchpaper,sincereeffortshavebeenmadeby theauthor.
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 11 Issue: 09 | Sep 2024 www.irjet.net p-ISSN: 2395-0072
Data presentation of companies
HUL company sales data
Year Amount(inrscrores)
March2012-March2013
March2013-March2014
March2014-March2015
March2015-March2016
March2016-March2017
March2017-March2018
March2018-March2019
March2019-March2020 39,783.00
March2020-March2021 47,028.00
March2021-March2022 52,446.00
March2022-March2023 60,580.00
March2023-March2024 61,896.00
Source:https://www.screener.in/company/HINDUNILVR/consolidated/#profit-loss
Nestle India LTD company sales data
Year
Amount(inrscrores)
March2012-March2013 8335
March2013-March2014 9101
March2014-March2015 9855
March2015-March2016 8175
March2016-March2017 9141
March2017-March2018 10010
March2018-March2019 11292
March2019-March2020 12369
March2020-March2021 13350
March2021-March2022 14741
March2022-March2023 16897
March2023-March2024 19126
Source:https://www.screener.in/company/NESTLEIND/#profit-loss
ITC LTD company sales data
Year
Amount(inrscrores)
March2012-March2013 26516
March2013-March2014 31618
March2014-March2015 35306
March2015-March2016 38817
March2016-March2017 39192
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
March2017-March2018 42768
March2018-March2019 43449
March2019-March2020 48340
March2020-March2021 49388
March2021-March2022 49257
March2022-March2023 60645
March2023-March2024 70919
Source:https://www.screener.in/company/ITC/consolidated/#profit-loss
Varun Beverages LTD company sales data
Year
Amount(inrscrores)
March2012-March2013 1800
March2013-March2014 2115
March2014-March2015 2502
March2015-March2016 3394
March2016-March2017 3861
March2017-March2018 4004
March2018-March2019 5105
March2019-March2020 7130
March2020-March2021 6450
March2021-March2022 8823
March2022-March2023 13173
March2023-March2024 16043
Source:https://www.screener.in/company/VBL/consolidated/#profit-loss
Godrej Consumer products LTD company sales dat
Year
Amount(inrscrores)
March2012-March2013 4853
March2013-March2014 6412
March2014-March2015 7599
March2015-March2016 8273
March2016-March2017 8424
March2017-March2018 9268
March2018-March2019 9847
March2019-March2020 10314
March2020-March2021 9911
March2021-March2022 11029
March2022-March2023 12276
March2023-March2024 13316
Source:https://www.screener.in/company/GODREJCP/consolidated/#profit-loss
Volume: 11 Issue: 09 | Sep 2024 www.irjet.net p-ISSN: 2395-0072 © 2024, IRJET | Impact Factor value: 8.315 | ISO 9001:2008 Certified Journal | Page898
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 11 Issue: 09 | Sep 2024 www.irjet.net p-ISSN: 2395-0072
Data Analysis with Qualitative Methods
Basedontheabovedata,wewillforecastforthenextthreeyears,fromMarch2024toMarch2027.Wehavechosenathreeyearperiodbecauseeconomicfluctuationsmayoccur,makingalongerforecastlessreliable.
Note: FortheSeasonalExponentialSmoothingandWinters'methodmodels,wehaveselectedthreeobservationsperperiod forallcompaniesinthesetwoforecastingmodels.
HUL Company
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 11 Issue: 09 | Sep 2024 www.irjet.net
March 2021March 2022
March 2022March 2023
March 2023March 2024
March 2024March 2025
March 2026March 2027
Interpretation:
Company
ITC
Volume: 11 Issue: 09 | Sep 2024 www.irjet.net
Interpretation: TheMAPEvalueoftheARIMAmodelis5.641563,whichisthelowestamongthemodels.Therefore,weselect theARIMAmodelasthebestoptionforforecastingthenext3years,fromMarch2024toMarch2027.
NESTLE India LTD Company
Volume: 11 Issue: 09 | Sep 2024 www.irjet.net
March 2018
March 2018March 2019
March 2021March 2022
March 2022March 2023
2023March
International Research Journal of Engineering and Technology (IRJET)
Volume: 11 Issue: 09 | Sep 2024 www.irjet.net
March 2025March 2026
March 2027
Interpretation: TheMAPEvalueoftheARIMAmodelis5.61067,whichisthelowest,sowechoosetheARIMAmodelasthe bestmodelforforecastingthenextthreeyears,fromMarch2024toMarch2027.
Varun Beverages LTD
International Research Journal of Engineering and Technology (IRJET)
Volume: 11 Issue: 09 | Sep 2024 www.irjet.net
Interpretation: TheMAPEvalueoftheSeasonalExponentialSmoothingmodelis5.61067,whichisthelowestamongthe models.Therefore,wechoosetheSeasonalExponentialSmoothingmodelasthebestoptionforforecastingthenextthree years,fromMarch2024toMarch2027.
Godrej Consumer Products LTD
Volume: 11 Issue: 09 | Sep 2024 www.irjet.net
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 11 Issue: 09 | Sep 2024 www.irjet.net p-ISSN: 2395-0072
Interpretation: TheMAPEvalueoftheARIMAmodelis4.53325,whichisthelowestamongthemodels.Therefore,weselect theARIMAmodelasthebestoptionforforecastingthenextthreeyears,fromMarch2024toMarch2027.
Findings:
Basedontheinterpretationsprovidedforeachcompany'ssalesforecastingmodel,thefindingsareasfollows:
1. ITC Company:
o The ARIMA model produced the lowest MAPE value of 5.641563, indicating its superior performance comparedtoothermodels.
o Therefore,theARIMAmodelisrecommendedforforecastingsalesforITCCompanyforthenextthreeyears, fromMarch2024toMarch2027.
2. NESTLE India LTD:
o Similar to ITC Company, the ARIMA model achieved the lowest MAPE value of 5.61067, confirming its effectivenessasaforecastingtool.
o Hence,theARIMAmodelisthepreferredchoiceforforecastingsalesforNESTLEIndiaLTDforthenextthree years,fromMarch2024toMarch2027.
3. Varun Beverages LTD:
o Inthiscase,theSeasonalExponentialSmoothingmodeldeliveredthelowestMAPEvalueof5.61067.
o As a result, the Seasonal Exponential Smoothing model is recommended for forecasting sales for Varun BeveragesLTDforthenextthreeyears,fromMarch2024toMarch2027.
4. Godrej Consumer Products LTD:
o TheARIMAmodelresultedinthelowestMAPEvalueof4.53325forGodrejConsumerProductsLTD.
o Therefore,theARIMAmodelistheoptimalchoiceforforecastingsalesforthiscompanyforthenextthree years,fromMarch2024toMarch2027.
Summary: The ARIMA model is recommended as the preferred choice for ITC Company, NESTLE India LTD, and Godrej Consumer Products LTD, while the Seasonal Exponential Smoothing model is suggested for Varun Beverages LTD. These findingshighlightthesuitabilityofspecificforecastingtechniquesbasedoneachcompany'ssalesdatacharacteristics.
Conclusion:
TheFMCG(Fast-MovingConsumerGoods)industryhasexperiencedsignificantgrowthovertime,drivenbyfactorssuchas increasedurbanization,risingincomelevels,andevolvingconsumerpreferences.Inthisstudy,weanalysedmajorcompanies likeITC,NestleIndiaLtd,VarunBeveragesLtd,andGodrejConsumerProductsLtd,allofwhichoperateinahighlycompetitive marketwhereaccuratesalesforecastingiscriticalformakinginformedstrategicdecisions.
The forecasting methods we selected, such as ARIMA and Seasonal Exponential Smoothing, have demonstrated their effectivenessinpredictingfuturesalesforeachcompany.Thisinformationishighlyvaluabletoindustryprofessionalsasit enablesthemtoanticipatefluctuationsinconsumerdemand,optimizeinventorymanagement,anddevelopmoreeffective salesstrategies.
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 11 Issue: 09 | Sep 2024 www.irjet.net p-ISSN: 2395-0072
References:
Heavy.ai.(n.d.). Business forecasting.Heavy.ai.RetrievedfromURL-https://www.heavy.ai/technical-glossary/businessforecasting#:~:text=Business%20forecasting%20refers%20to%20the,based%20on%20these%20informed%20predictio ns
Agicap.(n.d.). Business forecasting.Agicap.RetrievedfromURL-https://agicap.com/en/article/businessforecasting/#:~:text=4%20Basic%20forecasting%20techniques,The%20four%20basic&text=Trend%20analysis%3A%20This%20method%20identifies,where%20multiple%20variables %20are%20involved
Investopedia.(n.d.). Basics of business forecasting.Investopedia.RetrievedfromURLhttps://www.investopedia.com/articles/financial-theory/11/basics-business-forcasting.asp
ForbesIndia.(2023,September21). Top 10 FMCG companies in India by market cap.ForbesIndia.RetrievedfromURLhttps://www.forbesindia.com/article/explainers/top-10-fmcg-companies-in-india-by-market-cap/89367/1
Screener.(n.d.). ITC profit & loss statement.Screener.RetrievedfromURLhttps://www.screener.in/company/ITC/consolidated/#profit-loss
Screener.(n.d.). Hindustan Unilever profit & loss statement.Screener.RetrievedfromURLhttps://www.screener.in/company/HINDUNILVR/consolidated/#profit-loss Screener.(n.d.). Godrej Consumer Products profit & loss statement.Screener.RetrievedfromURLhttps://www.screener.in/company/GODREJCP/consolidated/#profit-loss
Screener.(n.d.). Varun Beverages profit & loss statement.Screener.RetrievedfromURLhttps://www.screener.in/company/VBL/consolidated/#profit-loss
Screener.(n.d.). Nestle India profit & loss statement.Screener.RetrievedfromURLhttps://www.screener.in/company/NESTLEIND/#profit-loss
Deliverect.(n.d.). What is FMCG? Understanding the fast-moving consumer goods industry.Deliverect.RetrievedfromURLhttps://www.deliverect.com/en/blog/fmcg-and-grocery/what-is-fmcg-understanding-the-fast-moving-consumer-goodsindustry#:~:text=The%20FMCG%20industry%20is%20characterized,other%20low%2Dcost%20household%20items
Hanke,J.E.,&Reitsch,A.G.(2017). Business forecasting (9thed.).PearsonEducation.
Smith,L.(2020). Business forecasting: A practical approach.Wiley.
Biography:
Shreyas S. Vanjare, hailing from Bagalkot City, is a dedicated and ambitious student currently pursuing an MBA at PES University,Bengaluru,Banashankari.Knownforhispositiveattitudeandresilienceinallsituations,Shreyasdemonstratesa strongcommitmenttobothpersonalandacademicgrowth.Hisfocusondevelopingstrategicbusinessacumenreflectshis drive to excel in the corporate world. As an Indian national, Shreyas is passionate about leveraging his skills to make meaningfulcontributionsinthefieldsofbusinessandmanagement.