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Manufacturing Production Demand Fore...

Chitwadgi, Basavaraj Sanjiv,

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Manufacturing Production Demand Forecasting Using the Prophet Algorithm /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Manufacturing Production Demand Forecasting Using the Prophet Algorithm // Basavaraj Sanjiv Chitwadgi.
Author:	Chitwadgi, Basavaraj Sanjiv,
Description:	1 electronic resource (144 pages)
Notes:	Source: Masters Abstracts International, Volume: 85-12.
Contained By:	Masters Abstracts International85-12.
Subject:	Industrial engineering. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31296537
ISBN:	9798383098721

Manufacturing Production Demand Forecasting Using the Prophet Algorithm /
Chitwadgi, Basavaraj Sanjiv,

Manufacturing Production Demand Forecasting Using the Prophet Algorithm /Basavaraj Sanjiv Chitwadgi. - 1 electronic resource (144 pages)

Source: Masters Abstracts International, Volume: 85-12.

This thesis explores the application of using Prophet forecasting model to production planning within a manufacturing environment, a context where this approach has not previously been applied. The purpose of the study is to compare the Prophet model's performance to the well-known XGBoost and ARIMA models in order to determine which forecasting technique is most accurate for the production needs expected in the upcoming year. This comparison is critical as accurate production forecasting is essential for operational efficiency in manufacturing. The Prophet model, pioneered by Facebook for time series data forecasting, is recognized for its adeptness in managing the typical seasonal patterns and trends inherent in production datasets. This makes it potentially well-suited for the complexities of manufacturing environments where demand patterns can vary significantly. In this study we would use XGBoost, a widely recognized machine learning algorithm renowned for its competitive performance in predictive modeling and the traditional ARIMA model, renowned for its ability to capture linear seasonality and patterns in data from time series to perform a comparative analysis to offer a comprehensive comprehension of forecasting performance. The research will utilize historical production data from a manufacturing firm to train the Prophet, XGBoost and ARIMA models. The key metrics for comparison will include forecast accuracy, the ability to capture seasonal demand fluctuations, and the computational efficiency of each model. Additionally, the thesis will highlight the innovative application of the Prophet model in a manufacturing setting, discussing its strengths and limitations. The comparative analysis aims not only to reveal which model performs better but also to provide insights into how these models can be integrated into the existing production planning processes of manufacturing firms. This will include a discussion on the potential for these models to improve decision-making in production planning, inventory management, and resource allocation. This research intends to expand the subject of operations management by bringing sophisticated forecasting techniques to production planning, offering a thorough comparison of the top forecasting methodologies, and investigating their real-world applications in manufacturing. The outcomes of this study will be particularly valuable for manufacturing managers and industry professionals seeking to enhance their forecasting capabilities, thereby improving overall operational effectiveness and efficiency.

English

ISBN: 9798383098721Subjects--Topical Terms:

526216
Industrial engineering.
Subjects--Index Terms:

Additive regressive model

Manufacturing Production Demand Forecasting Using the Prophet Algorithm /
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520 $a This thesis explores the application of using Prophet forecasting model to production planning within a manufacturing environment, a context where this approach has not previously been applied. The purpose of the study is to compare the Prophet model's performance to the well-known XGBoost and ARIMA models in order to determine which forecasting technique is most accurate for the production needs expected in the upcoming year. This comparison is critical as accurate production forecasting is essential for operational efficiency in manufacturing. The Prophet model, pioneered by Facebook for time series data forecasting, is recognized for its adeptness in managing the typical seasonal patterns and trends inherent in production datasets. This makes it potentially well-suited for the complexities of manufacturing environments where demand patterns can vary significantly. In this study we would use XGBoost, a widely recognized machine learning algorithm renowned for its competitive performance in predictive modeling and the traditional ARIMA model, renowned for its ability to capture linear seasonality and patterns in data from time series to perform a comparative analysis to offer a comprehensive comprehension of forecasting performance. The research will utilize historical production data from a manufacturing firm to train the Prophet, XGBoost and ARIMA models. The key metrics for comparison will include forecast accuracy, the ability to capture seasonal demand fluctuations, and the computational efficiency of each model. Additionally, the thesis will highlight the innovative application of the Prophet model in a manufacturing setting, discussing its strengths and limitations. The comparative analysis aims not only to reveal which model performs better but also to provide insights into how these models can be integrated into the existing production planning processes of manufacturing firms. This will include a discussion on the potential for these models to improve decision-making in production planning, inventory management, and resource allocation. This research intends to expand the subject of operations management by bringing sophisticated forecasting techniques to production planning, offering a thorough comparison of the top forecasting methodologies, and investigating their real-world applications in manufacturing. The outcomes of this study will be particularly valuable for manufacturing managers and industry professionals seeking to enhance their forecasting capabilities, thereby improving overall operational effectiveness and efficiency.
546 $a English
590 $a School code: 0792
650 4 $a Industrial engineering. $3 526216
650 4 $a Systems science. $3 3168411
653 $a Additive regressive model
653 $a Comparative analysis
653 $a Manufacturing environment
653 $a Production forecast
653 $a Prophet algorithm
653 $a Time series
690 $a 0546
690 $a 0790
710 2 $a State University of New York at Binghamton. $b Systems Science Industrial Engineering. $e degree granting institution. $3 3765714
720 1 $a Wang, Yong $e degree supervisor.
720 1 $a Yoon, Sangwon $e degree supervisor.
773 0 $t Masters Abstracts International $g 85-12.
790 $a 0792
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792 $a 2024
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