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Prognostisering av försäljning på andrahandsmarkaden för bilar: En tidsserieanalys
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
2016 (Swedish)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
Forecasting of sales on the second hand market för cars : A time series analysis (English)
Abstract [sv]

Prognostisering är ett essentiellt verktyg för företag vid beslutsfattning inom flera områden. Kapacitetsplanering  är ett av dessa och vi har i det h är arbetet analyserat hur företaget KVD Kvarndammen AB b ör använda prognoser för att tillämpa en optimal strategi inom kapacitetsplanering. Vi har tillämpat olika modeller inom tidsserieanalys såsom AR, MA och ARMA för att skapa en prognos ett år framåt för total försäljning av begagnade bilar i Sverige.

Resultatet av tillämpningen visar att en modellering med en ARMA-baserad tidsseriemodell är genomförbar och indikerar att en prognos kan förbättras med en sådan metod jämfört med en icke-tidsseriebaserad modell. De största skillnaderna mellan de applicerade modellerna var inte mellan AR-, MA-, eller ARMA-modellerna, utan snarare vilken typ av förbehandling av data som användes, där differentiering gav bäst resultat. Vi kom även fram till att KVD bör använda sig utav en chase demand-attityd och mer specifikt en match demand-strategi för att optimalt planera sin kapacitet.  

Abstract [en]

Forecasting is an essential tool for business desicion making in several areas. Capacity planning is one of these, and in this project we have analyzed how the company KVD Kvarndammen AB should use their forecasts in order to optimize their strategy regarding capacity planning. We have also applied several models in the field of time series analysis, specifically AR, MA and ARMA to create a forecast of the total amount of sales on the used-car market in Sweden.

The results of the application show that modelling with an ARMA-based time series model is feasible and suggests that a forecast can be improved upon with the use of such a model compared to one not based on time series analysis. The largest differences between the quality of the applied models were not found between the AR, MA or ARMA models themselves, but rather depended on how the data was preprocessed, where differencing achieved the best results. We also concluded that KVD should use a chase demand-attitude and more specifically a match demand-strategy to optimize their capacity planning.

Place, publisher, year, edition, pages
2016.
Series
TRITA-MAT-K, 2016:04
National Category
Mathematics
Identifiers
URN: urn:nbn:se:kth:diva-189000OAI: oai:DiVA.org:kth-189000DiVA: diva2:942652
External cooperation
Kvd Kvarndammen AB
Subject / course
Applied Mathematical Analysis
Educational program
Master of Science in Engineering - Industrial Engineering and Management
Supervisors
Examiners
Available from: 2016-06-26 Created: 2016-06-26 Last updated: 2016-06-26Bibliographically approved

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CiteExportLink to record
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