Forecasting Monthly Product Sales for Tech Tierra: A Comprehensive Analysis Using Time Series Methods Nova Southeastern University

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Forecasting Monthly Product Sales for Tech Tierra: A ComprehensiveAnalysis Using Time Series MethodsNova Southeastern UniversityNova Southeastern UniversityH. Wayne Huizenga Schoolof Business & EntrepreneurshipAssignment for Course:Business Modeling QNT 5040Submitted to:Phillip RokickiSubmitted by:Ralph PetitDate of Submission:12/1/13Title of Assignment:Case AnalysisAs part of the case study analysis for Tech Tierra, a Texas-based company, variousforecasting techniques were applied to predict the company's monthly product sales for theperiod from September 2013 to August 2014. In your response, evaluate the effectiveness ofthe four forecasting methods used in the analysis: Moving Average, Simple ExponentialSmoothing, Holt’s Linear Method, and Winter’s Method for Trend and Seasonality. Based onthe Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) values,explain why Winter’s Method is considered the best forecasting technique for Tech Tierra.Additionally, discuss the implications of accurate forecasting for Tech Tierra’s businessoperations, especially considering its expansion into new markets.Word Count Requirement:600-800 words4o mini

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CERTIFICATION OFAUTHORSHIP: I certify that I am the author of this paper and thatany assistance I received in its preparation is fully acknowledged and disclosed in the paper. Ihave also cited any sources from which I used data, ideas or words, either quoted directly orparaphrased. I also certify that this paper was prepared by me specifically for this course.Student's Signature:_Ralph Petit_____________________________*****************************************************************Instructor's Grade on Assignment:Instructor's Comments:TITLE OF RUBRIC: Case Analysis (Page 1 of 2)Course: QNT 5040LEARNING OUTCOME/S: CC4, 6, 7 & 8; (seesyllabus)Date:PURPOSE: To facilitate effective decision makingunder uncertain conditions by quantifying risk.Name ofStudent:VALIDITY: Best practices in Monte Carlosimulation.Name of Faculty:COMPANION DOCUMENTS: Assignment and format instructions, CaseEarning maximum points in each box in ‘PROFICIENT’ column and / orpoints in columns to the right of ‘PROFICIENT’ meets standard.<<<<<<<<<< less quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . more quality>>>>>>>>>>Performance CriteriaBasicDevelopingProficientAccomplishedExemplaryScoreIdentify theproblem(CC4)Does notidentify theproblem, ordoes notidentify therightproblem.(0 pts)Identifiessymptoms(5 pts)Identifiessomeelements oftheproblem.(10 pts)Substantiallyidentifies theproblem.(12 pt)Effectivelyandsuccinctlyidentifiesthe problem.(15 pts)Describesassumptionsand methods(CC4)Does notdescribeassumptions andmethodsusedDoes notpreciselydescribe theassumptionsand methodsusedSomewhatdescribesassumptions andmethodsusedSubstantiallydescribesassumptionsand methodsusedEffectivelydescribesassumptionsand methodsused

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(0 pts)(3 pts)(7 pts)(8 pts)(10 pts)Calculatestatisticsusing aspreadsheet(CC6)Does notcalculateappropriatestatisticsusing aspreadsheetand/ordoes notprovideevidence ofcalculations(0 pt)Calculatesappropriatestatisticsusing aspreadsheet(mostanswers arenotcorrect)(13 pts)Calculatesappropriatestatisticsusing aspreadsheet(not allanswers arecorrect)(21 pts)Calculatesappropriatestatistics usinga spreadsheet(most answersare correct)(25 pts)Effectivelycalculatesstatisticsusing aspreadsheet(almost allanswers arecorrect)(30 pts)Explainimplicationsofoutput ofstatisticalanalysis(CC7)Does notexplainimplications ofoutput ofstatisticalanalysis(0 pt)Partiallyexplainsimplicationsofoutput ofstatisticalanalysis(3pts)Somewhatexplainsimplications ofoutput ofstatisticalanalysis(7 pts)Substantiallyexplainsimplications ofoutput ofstatisticalanalysis(8 pts)Effectivelyexplainsimplicationsofoutput ofstatisticalanalysis(10 pts). . . continued . . .TITLE OF RUBRIC: Case Analysis, cont. (Page 2 of2)Course: QNT 5040LEARNING OUTCOME/S: CC4, 6, 7 & 8; (seesyllabus)Date:PURPOSE: To facilitate effective decision makingunder uncertainconditions by quantifying risk.Name of Student:VALIDITY: Best practices in Monte Carlosimulation.Name of Faculty:COMPANION DOCUMENTS: Assignment and format instructions, CaseEarning maximum points in each box in ‘PROFICIENT’ column and / orpoints in columns to the right of ‘PROFICIENT’ meets standard.<<<<<<<<<< less quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . more quality>>>>>>>>>>Performance CriteriaBasicDevelopingProficientAccomplishedExemplaryScoreGeneratessolutionsbased onanalysis andcontextDoes notgenerateappropriate solutionsbased onanalysisGeneratessolutions(does notjustifyconclusions).Partially:*generatesand justifiessolutionsbased onanalysis andSubstantially:*generates andjustifiessolutions basedon analysis andcontext; andEffectively:*generatesand justifiessolutionsbased onanalysis and

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(CC4)andcontext.(0 pt)(7 pts)context; and*justifiesconclusions.(15 pts)*justifiesconclusions.(17 pts)context; and*justifiesconclusions.(20 pts)Usesprescribedformat(includingcover sheetand gradingrubric)andwriting style(language,grammar,punctuation,and spelling)(CC8)Does notuseprescribedformat andwritingstyle(0 pt)May useprescribedformat ORwriting style(only one)(3 pts)Generallyusesprescribedformat andwritingstyle(7 pts)Substantiallyuses prescribedformat andwriting style(8 pts)Effectivelyusesprescribedformat andwriting style(10 pts)Uses APAformat(APA StyleManual 6.0)(CC8)Does notprovidereferences.(0 pt)Does notapply APAstyle toreferences.(1pts)PartiallyappliesAPA styletoreferences.(3 pts)Substantiallyapplies APAstyle toreferences.(4 pts)Effectivelyapplies APAstyle to allreferences.Optimalquality andquantity ofcitations.(5 pts)OVERALL GRADE (100 total possible points):%Comments:__________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________Executive SummaryTech Tierra is a Texas based company, founded by Millie Granger and Jose Mendes, whosefunction is to involveolder Hispanics of the area in the technology decision-making process.The company was founded in mid-2004 witha single store in Lubbock and has grownsignificantly over the years to currently havingeight stores in Lubbock, Austin, San Antonio,Houston, and is consideringfurtherexpanding into the Dallas area.The aim of this case studywasto forecast the monthly and annual growth of Tech Tierra over a twelve month period fromSeptember 2013 to August 2014.Wehave used the various forecast analysis techniques, namelymoving average forecast, simple exponential smoothing forecast, Holt’slinear method for trendsand Winter’s method for trend and seasonality to forecast the growth of the company over this

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