Automating Operational Business Decisions Using Artificial Intelligence: an Industrial Case Study

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dc.contributor.authorJanssen, Pier
dc.contributor.authorWichrowski, Maciej
dc.contributor.departmentChalmers tekniska högskola / Institutionen för data- och informationsteknik (Chalmers)sv
dc.contributor.departmentChalmers University of Technology / Department of Computer Science and Engineering (Chalmers)en
dc.date.accessioned2019-07-03T13:17:33Z
dc.date.available2019-07-03T13:17:33Z
dc.date.issued2012
dc.description.abstractThe process of making business decisions is increasingly reliant upon analyzing very large data-sets. Due to the amount of decisions having to be made on a daily basis, this becomes time-consuming and expensive to carry out manually. The purpose of this thesis was to determine whether using Artificial Intelligence to automate business decisions is feasible. This was done by carrying out a proof of concept project at IFS World, a software company developing Enterprise Resource Planning systems. Procurement decision making was chosen as a case for this study. Automating these decisions can not only result in speeding up the decision making process, but also in making more accurate decisions. To achieve this, three machine learning algorithms were proposed. Their goal was to learn preferences from historical procurement data and apply this knowledge to new situations. Prototyped versions of the algorithms were developed, tested and compared using both real-world and artificial datasets. The results showed that after a short period of supervised learning, two algorithms were able to make decisions automatically, with a low error-rate. Furthermore, sensitivity analysis showed that the algorithms are robust enough to recover from errors in the training data. The study also revealed several constraints and prerequisites related to feature selection, data freshness, and completeness. It was concluded that automating operational business decisions using Artificial Intelligence is achievable if certain preconditions are met. It can provide several advantages over manual decision making: it will speed up the decision making process, and can, in certain scenarios, improve the quality of the decisions.
dc.identifier.urihttps://hdl.handle.net/20.500.12380/184462
dc.language.isoeng
dc.setspec.uppsokTechnology
dc.subjectData- och informationsvetenskap
dc.subjectComputer and Information Science
dc.titleAutomating Operational Business Decisions Using Artificial Intelligence: an Industrial Case Study
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster Thesisen
dc.type.uppsokH
local.programmeSoftware engineering and technology (MPSOF), MSc
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