The application of machine learning in solving inventory management problem

Setyawan, Emmanuela Maria Jessica (2021) The application of machine learning in solving inventory management problem. Undergraduate thesis, Widya Mandala Surabaya Catholic University.

[thumbnail of ABSTRAK] Text (ABSTRAK)
ABSTRAK - Emmanuela Maria Jessica Setyawan.pdf
Download (771kB)
[thumbnail of BAB 1] Text (BAB 1)
BAB 1 - Emmanuela Maria Jessica Setyawan.pdf
Download (210kB)
[thumbnail of BAB 2] Text (BAB 2)
BAB 2 - Emmanuela Maria Jessica Setyawan.pdf
Restricted to Registered users only
Download (494kB)
[thumbnail of BAB 3] Text (BAB 3)
BAB 3 - Emmanuela Maria Jessica Setyawan.pdf
Restricted to Registered users only
Download (315kB)
[thumbnail of BAB 4] Text (BAB 4)
BAB 4 - Emmanuela Maria Jessica Setyawan.pdf
Restricted to Registered users only
Download (469kB)
[thumbnail of BAB 5] Text (BAB 5)
BAB 5 - Emmanuela Maria Jessica Setyawan.pdf
Download (321kB)
[thumbnail of LAMPIRAN] Text (LAMPIRAN)
LAMPIRAN - Emmanuela Maria Jessica Setyawan.pdf
Restricted to Registered users only
Download (367kB)

Abstract

Inventory is an investment and a potential source of waste that needs to be carefully controlled. Frequent changes and new events which occur in the market create another challenge for forecasting. This makes the Small and Medium Industry (SMI) who plan production not totally sure that the prepared prognoses will come true 100%. This is where machine learning comes into play. Artificial intelligence needs to be teamed with human oversight and should be treated as part of the system but not as a replacement for the system or the system itself. This research aims to develop a machine learning model for inventory management of Small and Medium Industry (IKM) based on demand prediction. To achieve the purpose of the study, the study uses descriptive methods. The result is an application called Managerku. The demand prediction shows that it captures the seasonality of the data. In this research, we will discuss the challenges of building an application using machine learning for inventory management problems.

Item Type: Thesis (Undergraduate)
Department: S1 - Manajemen
Contributors:
Contribution
Contributors
NIDN / NIDK
Email
Thesis advisor
Wibowo, Wahyudi
NIDN0715047402
UNSPECIFIED
Uncontrolled Keywords: Machine learning, inventory management, LSTM, demand forecasting.
Subjects: Business
Business > International Business Management
Divisions: Faculty of Business > International Business Management Undergraduate Study Program
Depositing User: Sri Kusuma Dewi
Date Deposited: 31 Aug 2021 01:47
Last Modified: 31 Aug 2021 01:47
URI: https://repository.ukwms.ac.id/id/eprint/26112

Actions (login required)

View Item View Item