Exploring Machine Learning for Supplier Selection : A case study at Bufab Sweden AB

Detta är en Magister-uppsats från Linnéuniversitetet/Institutionen för ekonomistyrning och logistik (ELO); Linnéuniversitetet/Institutionen för ekonomistyrning och logistik (ELO)

Författare: Adam Allgurin; Filip Karlsson; [2018]

Nyckelord: ;

Sammanfattning: ABSTRACT Course: Degree project in Logistics, the Business Administration and Economics Programme Authors: Adam Allgurin and Filip Karlsson Supervisor: Hana Hulthén Examiner: Helena Forslund Title: Exploring Machine Learning for Supplier Selection – A case study at Bufab Sweden AB   Background: One of the most important parts of purchasing management is the selection of suppliers due to suppliers’ ability to greatly affect the performance of the supply chain. Selecting the right supplier(s) can be a complex process where there can be many number of variables, both quantitative and qualitative, to consider. One of the methods for assisting companies’ supplier selection process is artificial intelligence (AI) where machines can be trained by decision-makers or historical data to make predictions and recommendations. One technology within AI that might change procurement is Machine Learning.   Purpose: The purpose is that this study is going to be a first step for Bufab towards an implementation of Machine Learning (ML). The study aims to provide a framework for the variables needed to create a ML algorithm for supplier selection and how the identified variables can be ranked. The study also aims to provide a list of benefits and challenges with ML, in general and for supplier selection.   Methodology: This is a qualitative case study of the supplier selection process in Bufab Sweden AB. The theoretical chapter is based mainly on current literature from both articles and books. The empirical data collected is done by unstructured and semi-structured interviews and data received from Bufab. There have been six respondents in this study, both internal and external from Bufab.   Findings: The study identified 26 variables that are important for supplier selection and that can be used for a ML algorithm. These variables have been ranked based on theory and empirical data, in order to determine their importance. There are several benefits and challenges with ML, one benefit is that ML can handle standard and repetitive work while a challenge is that employees tend to get nervous about losing their job. A full table can be found in the conclusion. A framework for the first step in implementing ML for Bufab have been created, this includes three steps. Step one: Identify relevant data (variables), step two: prepare the data and step three: consider ML algorithms.   Key words: supplier selection, machine learning, supplier selection variables, supplier selection with machine learning

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