Utilizing Cluster Analysis for Decision Support in Material Planning
Publicerad
Författare
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Currently, in the manufacturing industry, planning parameters are
infrequently updated. This entails that they over time will become outdated
and inaccurate, which in turn results in inefficient operations. To increase
the frequency of parameter updates, reducing the amount of work required
would be useful. A way of doing this is to group items that have similar
demand patterns, or what this study calls item behavior. Grouping the
items opens the possibility of handling them in bulk. Therefore, the purpose
of this study was to identify the characteristics that make up the item
behavior and use these in a cluster analysis to find suitable groups of
items. These groups should then be used as decision-support in material
planning. This study was carried out in collaboration with Meridion AB
which is a consultancy company that specializes in improving
organizations' supply chains.
The methodology used to conduct the study included quantitative and
qualitative data collection. The main methodology used was cluster
analysis, along with supporting methods like Z-score, min-max scaling, and
the DeD method. In addition, several other methods were used to conduct
the analysis, including an interview, discussions, and a workshop.
The study resulted in five item behavior characteristics that were identified
and then combined with each other as well as other parameters to group
items based on their behavior. Three cluster analyses were then conducted
and validated. The first was regarding safety stock levels, the second was
used for ABC-XYZ classification, and the third was used to identify items
where production quantities deviated from EOQ. These serve as examples
of how cluster analysis can be used to increase the efficiency regarding the
process of performing parameter updates, and how it can be applied to
material planning issues. All three of these examples successfully provided
decision support in material planning, although in different ways. In
addition, there is also some more general knowledge regarding the use of
cluster analysis for this purpose included that was gathered during the
course of the study.
Beskrivning
Ämne/nyckelord
Cluster analysis, Item behavior, Material planning, Planning parameters, Safety stock, ABC-XYZ analysis, EOQ