Analysis of Detergent Inventory Stock at Luch Laundry Using the Linear Regression Method

Inventory stock management is an important aspect in the laundry business to ensure smooth operations and minimize costs. Laundry Detergent shortages or overstocks can cause service disruptions and unnecessary additional costs. Therefore, a method is needed that can help predict stock needs accurately, one of which is the linear regression method. The data used includes historical data on detergent use and other factors that influence demand over several time periods. Through linear regression analysis, a predictive model can be built to estimate detergent needs in the future, so that stocks can be managed more efficiently. Research Method, namely the survey research method, is a research method carried out using surveys or direct data collection from Laundry Luch. The method/algorithm used to analyze the data is the linear regression method. The aim of this research is to apply the linear regression method in detergent inventory stock and to carry out analysis using the linear regression method in detergent inventory stock. The research results from the data that have been collected show that the predicted stock of detergent supplies for Laundry Luch in January 2025, with an estimated total usage of 111 boxes of detergent and a target usage of 95 boxes of detergent, is 129 boxes of detergent. The research conclusion is that the linear regression method provides real benefits in supporting data-based decision making.

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Publication Details

Journal
Journal Of Computer Networks Architecture and High Performance Computing
Published
2025-02-06
DOI
https://doi.org/10.47709/cnahpc.v7i1.5396
Primary Topic
Management and Optimization Techniques
Type
article
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article

Analysis of Detergent Inventory Stock at Luch Laundry Using the Linear Regression Method

Bosker Sinaga, Nera Mayana Br Tarigan, Rahmadina Marpaung, Kristof Rian Zamili
Journal Of Computer Networks Architecture and High Performance Computing
Management and Optimization Techniques
article

Analysis of Detergent Inventory Stock at Luch Laundry Using the Linear Regression Method

Bosker Sinaga, Nera Mayana Br Tarigan, Rahmadina Marpaung, Kristof Rian Zamili
article en

Abstract

Inventory stock management is an important aspect in the laundry business to ensure smooth operations and minimize costs. Laundry Detergent shortages or overstocks can cause service disruptions and unnecessary additional costs. Therefore, a method is needed that can help predict stock needs accurately, one of which is the linear regression method. The data used includes historical data on detergent use and other factors that influence demand over several time periods. Through linear regression analysis, a predictive model can be built to estimate detergent needs in the future, so that stocks can be managed more efficiently. Research Method, namely the survey research method, is a research method carried out using surveys or direct data collection from Laundry Luch. The method/algorithm used to analyze the data is the linear regression method. The aim of this research is to apply the linear regression method in detergent inventory stock and to carry out analysis using the linear regression method in detergent inventory stock. The research results from the data that have been collected show that the predicted stock of detergent supplies for Laundry Luch in January 2025, with an estimated total usage of 111 boxes of detergent and a target usage of 95 boxes of detergent, is 129 boxes of detergent. The research conclusion is that the linear regression method provides real benefits in supporting data-based decision making.

Journal Of Computer Networks Architecture and High Performance ComputingVol. 7(1)
Universitas Dian Nusantara (ID)
Openalex Percentile: Top 97%
Management and Optimization Techniques
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Analysis of Detergent Inventory Stock at Luch Laundry Using the Linear Regression Method — Bosker Sinaga, Nera Mayana Br Tarigan, et al. · Journal Of Computer Networks Architecture and High Performance Computing (2025) | TGRS Research Map | TGRS