Wibowo, Anang Wahyu and Baskoro, Gembong and Sudarto, Sumarsono (2021) Improving the Prediction and Accuracy of Parts Marketing Promotion Program for Heavy Equipment Spare Parts Business Through Digitalization Approach. Masters thesis, Swiss German University.

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Abstract

In the face of changes in customer policy that are very fast due to changing market conditions, a promotional approach and a customer approach are also needed that are in accordance with existing conditions. This also occurs in the field of heavy equipment spare parts trading, where the spare parts offered and promoted to the customer must be in accordance with what the customer needs. It often happens now that what is trying to promote to customers is not in accordance with the needs due to the condition of the unit that is not operational or has even been standardized. In this research, the author tries to propose a prediction method for customer spare parts needs with a digital technology approach, where the purpose of this research is so that parts salesmen can easily find out customer needs quickly and continue with promotions to increase sales. In this research, the Author used a Focus Group Discussion (FGD) with the team and DMAIC analysis in finding the necessary improvements. The findings of this research are that by using a historical demand and sales approach and analyzed with a machine learning algorithm, it will obtain a prediction of the opportunity needed, besides that the Author also uses the End of Life ( EOL) Spare Parts approach to predict when there will be a change. With these two approaches, it can be used effectively in predicting the needs of heavy equipment spare parts.

Item Type:	Thesis (Masters)
Additional Information:	United Tractors
Uncontrolled Keywords:	Machine Learning, Parts Prediction, Heavy Equipment Parts, Opportunity Prediction. Parts Sales Opportunity
Subjects:	H Social Sciences > HF Commerce > HF5410 Marketing Q Science > Q Science (General) > Q325.5 Machine learning T Technology > TJ Mechanical engineering and machinery T Technology > TJ Mechanical engineering and machinery > TJ163.12 Mechatronics
Divisions:	Faculty of Engineering and Information Technology > Department of Mechatronics Engineering
Depositing User:	Faisal Ifzaldi
Date Deposited:	19 Aug 2021 12:26
Last Modified:	06 Oct 2023 07:10
URI:	http://repository.sgu.ac.id/id/eprint/2153

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