Analyzing the Suitability of Time Series and Associative Forecasting Methods for Cotton Bud Product

Siti Cahya Santini (), Devilia Sari and Lidya Nur Assifa
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Siti Cahya Santini: Telkom University, International Business Administration Department
Devilia Sari: Telkom University, International Business Administration Department
Lidya Nur Assifa: Bandung Institute of Technology, Faculty of Earth Science and Technology

A chapter in Proceedings of the 4th Asia Pacific Management Research Conference (APMRC 2022), 2023, pp 259-268 from Springer

Abstract: Abstract At PT DBAS, the main problem is the problem of BABY-GRADE-A supply/demand. If a production shortage occurs, the company loses sales leading to overwork and costs. Likewise, suppose there is overproduction, even though the product is durable with a more extended expiration date. In that case, warehousing problems may arise, such as limited warehouses, higher inventory costs, damaged products, and environmental issues. Thus, this study is conducted to discover a suitable forecasting method for BABY-GRADE-A to forecast its future demand and sales figures of BABY-GRADE-A in 2021. This research uses a quantitative descriptive method by comparing the measurement of sales data errors in the time series and polynomial regression methods. To conclude, the most suitable forecasting method for BABY-GRADE-A is Polynomial Regression. MSE and MAPE resulting from the technique are 6,103.18 and 9.07%, respectively. Then, the forecast demand in 2021 is predicted to be 11,426 cartons. Significantly, several aspects should be considered by the company and future researchers, such as market change, marketing strategy, inventory management, and other operations activities.

Keywords: Forecasting; Forecasting Error; Moving Range Chart; Polynomial Regression (search for similar items in EconPapers)
Date: 2023
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DOI: 10.2991/978-94-6463-076-3_19

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