umu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
An Optimization of a Retailer's Allocation Algorithm: MRP and Demand Forecasting Cosmetic Products
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
2019 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

To satisfy a continuously changing customer demand, retailers needs an efficient allocation process. It is of great importance to get the process right as it is vital for companies in the retail business to have sufficient stock levels at all times.

The employer of our master thesis project is a Swedish retail company. In this report it will be named with the pseudonym "The Company". Further, the articles included in our scope will be referred to as "Article A", "Article B", "Article C", and "Article D". The purpose of our project at The Company was to analyze the flow network of cosmetics. The aim was to investigate the possibility of optimizing the prediction of sales in the material requirement planning process, as The Company wants a demand forecasting as close to actual customer demand as possible.

After analyzing and quantifying the current situation, we optimized the model that creates the prediction of sales by minimizing the forecast error. The result showed that our optimized model performed better as it produced a forecasted demand closer to actual customer demand, namely 20 pieces closer for every prediction. Implementing our recommendation and results will lead to increased flexibility, more accurate stock prognosis, and an increased sales of 1% of total revenue, only looking at the articles included in our scope. Furthermore, this project will contribute to the possibility of an expansion to other markets.

Place, publisher, year, edition, pages
2019. , p. 51
National Category
Mathematics
Identifiers
URN: urn:nbn:se:umu:diva-160661OAI: oai:DiVA.org:umu-160661DiVA, id: diva2:1328150
External cooperation
The Company
Educational program
Master of Science in Engineering and Management
Supervisors
Examiners
Available from: 2019-08-12 Created: 2019-06-20 Last updated: 2019-08-12Bibliographically approved

Open Access in DiVA

No full text in DiVA

By organisation
Department of Mathematics and Mathematical Statistics
Mathematics

Search outside of DiVA

GoogleGoogle Scholar

urn-nbn

Altmetric score

urn-nbn
Total: 4 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf