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Forecasting and Inventory Management...

Shafik, Engy Osama Abdelkader.

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Forecasting and Inventory Management of Service Parts: Implications for Electronic Textiles (ETextiles) Serviceable Components.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Forecasting and Inventory Management of Service Parts: Implications for Electronic Textiles (ETextiles) Serviceable Components./
Author:	Shafik, Engy Osama Abdelkader.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	321 p.
Notes:	Source: Dissertations Abstracts International, Volume: 81-03, Section: A.
Contained By:	Dissertations Abstracts International81-03A.
Subject:	Management. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27528201
ISBN:	9781085644730

Forecasting and Inventory Management of Service Parts: Implications for Electronic Textiles (ETextiles) Serviceable Components.
Shafik, Engy Osama Abdelkader.

Forecasting and Inventory Management of Service Parts: Implications for Electronic Textiles (ETextiles) Serviceable Components. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 321 p.

Source: Dissertations Abstracts International, Volume: 81-03, Section: A.

Thesis (Ph.D.)--North Carolina State University, 2019.

This item must not be sold to any third party vendors.

Over the past decade, industrial and academic interest in e-textiles has increased dramatically. However, few researchers have considered the future of e-textiles' mass production. Upon reviewing e-textiles' literature, it can be concluded that most current products and prototypes are composed of fabric (usually embedded with sensors) and a removable electronic component. However, no research has been conducted to address the serviceability of the electronic components of e-textiles from a forecasting/inventory standpoint. As soon as the market for etextiles matures, managing the flow of products and components required for maintenance is going to be quite complex. Furthermore, in-depth research and cost analyses need to be conducted to develop models and strategies that minimize costs. Subsequently, this highlights the need for new forecasting techniques and inventory models to service the reverse flow of e-textiles products under warranty.To contribute to both the fields of e-textiles and service parts management, the existing knowledge and research needs of service parts management were integrated to propose new forecasting techniques. The newly proposed forecasting techniques demonstrated superior levels of accuracy compared to the benchmarked forecasting techniques in the field of spare parts management. Each of the new forecasting techniques incorporated different levels of information including demand, probability of demand occurrence, installed base and life cycle phase. The methods were designed with the goal of better handling the complexity of spare parts management, which exists due to the interplay between multiple factors affecting the decision making process (e.g., life cycle stage, product failure rates, installed base information, warranty period, technology and innovation, and component demand patterns). Furthermore, the forecasting approaches were analyzed with respect to their inventory management performance within this study. The forecasting-inventory models were tested on both generated data sets and industrial data sets. The data sets represented different levels of demand intermittency and included parts in different life cycle phases. The results showed that the newly proposed methods achieved higher levels of accuracy compared to the benchmarked forecasting techniques and managed to carry lower levels of on-hand inventory for most of the considered scenarios.

ISBN: 9781085644730Subjects--Topical Terms:

516664
Management.

Forecasting and Inventory Management of Service Parts: Implications for Electronic Textiles (ETextiles) Serviceable Components.
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500 $a Advisor: Hodge, George;Uzsoy, Reha;Barletta, Kristin;Joines, Jeffrey.
502 $a Thesis (Ph.D.)--North Carolina State University, 2019.
506 $a This item must not be sold to any third party vendors.
520 $a Over the past decade, industrial and academic interest in e-textiles has increased dramatically. However, few researchers have considered the future of e-textiles' mass production. Upon reviewing e-textiles' literature, it can be concluded that most current products and prototypes are composed of fabric (usually embedded with sensors) and a removable electronic component. However, no research has been conducted to address the serviceability of the electronic components of e-textiles from a forecasting/inventory standpoint. As soon as the market for etextiles matures, managing the flow of products and components required for maintenance is going to be quite complex. Furthermore, in-depth research and cost analyses need to be conducted to develop models and strategies that minimize costs. Subsequently, this highlights the need for new forecasting techniques and inventory models to service the reverse flow of e-textiles products under warranty.To contribute to both the fields of e-textiles and service parts management, the existing knowledge and research needs of service parts management were integrated to propose new forecasting techniques. The newly proposed forecasting techniques demonstrated superior levels of accuracy compared to the benchmarked forecasting techniques in the field of spare parts management. Each of the new forecasting techniques incorporated different levels of information including demand, probability of demand occurrence, installed base and life cycle phase. The methods were designed with the goal of better handling the complexity of spare parts management, which exists due to the interplay between multiple factors affecting the decision making process (e.g., life cycle stage, product failure rates, installed base information, warranty period, technology and innovation, and component demand patterns). Furthermore, the forecasting approaches were analyzed with respect to their inventory management performance within this study. The forecasting-inventory models were tested on both generated data sets and industrial data sets. The data sets represented different levels of demand intermittency and included parts in different life cycle phases. The results showed that the newly proposed methods achieved higher levels of accuracy compared to the benchmarked forecasting techniques and managed to carry lower levels of on-hand inventory for most of the considered scenarios.
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