Forecasting Sales in the Supply Chain Based on the LSTM Network: The Case of Furniture Industry
Ładowanie...
Data
2021
Inny tytuł
Typ
Artykuł recenzyjny
Redaktor
dc.contributor.advisor
Dyscyplina PBN
Informatyka techniczna i telekomunikacja
Czasopismo lub seria
EUROPEAN RESEARCH STUDIES JOURNAL
ISSN
1108-2976
ISBN
DOI
10.35808/ersj/2291
Strona internetowa
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Tytuł monografii
item.page.defence
Tytuł tomu
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Purpose: The aim of the article is to develop an algorithm for forecasting sales in the supply
chain based on the LSTM network using historical sales data of a furniture industry
company.
Design/Methodology/Approach: Machine learning was used to analyze the data. The
method of predicting the behavior of sales value in a specific time horizon in terms of a time
series was presented. The LSTM network was used to predict sales. The network used is a
special case of recursive neural networks with an important difference in the repeating
module. Due to the fact that the activities are carried out on time series, the data was
analyzed in terms of the stationarity of such series or trends and seasonal effects. The data
used in the analysis includes the daily sales values of a group of certain furniture collections
over a specified time horizon. The stationarity of the time series can have a significant
impact on its properties and behavior prediction, where failure to bring the time series to the
correct form of stationarity can lead to false results.
Findings: The result of the research was the analysis of sales forecasting in the supply chain
based on machine learning. As a result of the data transformations, the algorithm was able
to recognize and learn long-term relationships.
Practical Implications: The presented method of predicting the behavior of sales value in a
specific time horizon allows for a look at the forecasting of demand in terms of the supply
chain. The sales data of a company from the furniture industry were used for the analysis.
Originality/Value: A novelty is the use of the LSTM network trained on real transaction data
of a furniture company that has based its business on the supply chain and cooperates with
its suppliers and recipients in Central and Eastern Europe.