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Open access Jurnal SINTA 3

SUGAR DEMAND FORECASTING IN PT XYZ WITH WINQSB SOFTWARE

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 17 No. 3 (2023) · pp. 1631–1640

DOI
10.30598/barekengvol17iss3pp1631-1640
ISSN
2615-3017, 1978-7227
Published
2023-09-30
Publisher
PATTIMURA UNIVERSITY
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

PT XYZ is a manufacturing company engaged in the production of sugar and its by-products. Currently, the determination of the amount of production at PT XYZ has not been adjusted to meet customer demand, which may continue to decrease or increase for each period. If there is a condition that the amount of production is greater than demand, it will increase the cost of storage due to accumulation. Meanwhile, if the amount of production is smaller than demand, there will be an out-of-stock condition that can reduce consumer confidence. These problems can be solved by forecasting Tambora Sugar demand at PT XYZ to meet consumer demand using the forecasting method (forecasting) with the help of WinQsb software with input, namely sugar demand data from 2021 PT SMS. The request data will later be analyzed from the request using a scatter diagram. Furthermore, after the pattern is known, the appropriate forecasting method will be determined and inputted into the WinQsb software. Based on the calculation results, it is known that the demand pattern from last year tends to trend down so the chosen method is the Double Exponential Smoothing (DES), Single Exponential Smoothing (SES), and Linear Regression (LR) method, with the best method being Linear Regression which produces the smallest error. The output is in the form of a Master Production Schedule (MPS), namely in the 13th to 18th periods, respectively 2142; 1757; 1373; 989; 604; 220 sacks.

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Sylvert Prian TahaleaAffiliation unknown1
Elvis SalouwAffiliation unknown1

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Metadata source: OAI-PMH (metadata jurnal) https://ojs3.unpatti.ac.id/index.php/barekeng/oai, first harvested 5 Nov 2026, last seen 5 Nov 2026.
Original metadata (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">SUGAR DEMAND FORECASTING IN PT XYZ WITH WINQSB SOFTWARE</dc:title>
	<dc:creator>Wibowo, Astrid Wahyu Adventri</dc:creator>
	<dc:creator>Maimunah, Fitri</dc:creator>
	<dc:description xml:lang="en-US">PT XYZ is a manufacturing company engaged in the production of sugar and its by-products. Currently, the determination of the amount of production at PT XYZ has not been adjusted to meet customer demand, which may continue to decrease or increase for each period. If there is a condition that the amount of production is greater than demand, it will increase the cost of storage due to accumulation. Meanwhile, if the amount of production is smaller than demand, there will be an out-of-stock condition that can reduce consumer confidence. These problems can be solved by forecasting Tambora Sugar demand at PT XYZ to meet consumer demand using the forecasting method (forecasting) with the help of WinQsb software with input, namely sugar demand data from 2021 PT SMS. The request data will later be analyzed from the request using a scatter diagram. Furthermore, after the pattern is known, the appropriate forecasting method will be determined and inputted into the WinQsb software. Based on the calculation results, it is known that the demand pattern from last year tends to trend down so the chosen method is the Double Exponential Smoothing (DES), Single Exponential Smoothing (SES), and Linear Regression (LR) method, with the best method being Linear Regression which produces the smallest error. The output is in the form of a Master Production Schedule (MPS), namely in the 13th to 18th periods, respectively 2142; 1757; 1373; 989; 604; 220 sacks.</dc:description>
	<dc:publisher xml:lang="en-US">PATTIMURA UNIVERSITY</dc:publisher>
	<dc:date>2023-09-30</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/8748</dc:identifier>
	<dc:identifier>10.30598/barekengvol17iss3pp1631-1640</dc:identifier>
	<dc:source xml:lang="en-US">BAREKENG: Jurnal Ilmu Matematika dan Terapan; Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications; 1631-1640</dc:source>
	<dc:source>2615-3017</dc:source>
	<dc:source>1978-7227</dc:source>
	<dc:source>10.30598/barekengvol17iss3year2023</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/8748/6577</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 Astrid Wahyu Adventri Wibowo, Fitri Maimunah</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>