Effects Of Different Order Processing Strategies On Operating Curves Of Logistic Models: A Comparison Of Make-to-Order And Make-to-Stock

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Effects Of Different Order Processing Strategies On Operating Curves Of Logistic Models: A Comparison Of Make-to-Order And Make-to-Stock. / Wolff, Katharina ; Maier, Janine Tatjana; Heuer, Tammo et al.
in: Journal of Production Systems and Logistics, Jahrgang 2021, Nr. 1, 18, 2021.

Publikation: Beiträge in ZeitschriftenZeitschriftenaufsätzeForschungbegutachtet

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@article{76c7625463684595956662015a3a9d1c,
title = "Effects Of Different Order Processing Strategies On Operating Curves Of Logistic Models: A Comparison Of Make-to-Order And Make-to-Stock",
abstract = "Complexity in the decision-making process results from the interaction of various parameters. Exemplary influencing factors are the durability of products, the desired delivery times, and the required product variants. In practice, uncertainties of customer behavior and existing time pressure complicate decision-making processes even more. Given this scenario, continuously determining the most suitable order processing strategy for each product presents a great challenge for companies. Logistic models reflect logistic interdependencies and are influenced by the order processing strategy. It affects the shape of operating curves as well as the position of operating points in logistic models. Therefore, logistic models can serve as a starting point for a holistic model providing assistance selecting the order processing strategy. This paper considers the impacts of various strategies on the operating curves of logistic models. This includes a description of the procedure for analyzing the effects of switching the order processing strategy. After determining a suitable company{\textquoteright}s internal supply chain, an analysis of the operating curves of different logistic models for the order processing strategies Make-to-Order and Make-to-Stock on a single product base follows. A comparison of the operating curves of logistic models for these two strategies and a detailed analysis of processes help to evaluate whether differences result from the strategies or the processes. An exemplary application on a data set from industry demonstrates the general practicability of the approach.",
keywords = "Engineering, Order processing strategies, Logistic models, operating curves, Make-to-order, Make-to-stock",
author = "Katharina Wolff and Maier, {Janine Tatjana} and Tammo Heuer and Peter Nyhuis and Matthias Schmidt",
year = "2021",
doi = "10.15488/11532",
language = "English",
volume = "2021",
journal = "Journal of Production Systems and Logistics",
issn = "2702-2587",
publisher = "publish-Ing.",
number = "1",

}

RIS

TY - JOUR

T1 - Effects Of Different Order Processing Strategies On Operating Curves Of Logistic Models

T2 - A Comparison Of Make-to-Order And Make-to-Stock

AU - Wolff, Katharina

AU - Maier, Janine Tatjana

AU - Heuer, Tammo

AU - Nyhuis, Peter

AU - Schmidt, Matthias

PY - 2021

Y1 - 2021

N2 - Complexity in the decision-making process results from the interaction of various parameters. Exemplary influencing factors are the durability of products, the desired delivery times, and the required product variants. In practice, uncertainties of customer behavior and existing time pressure complicate decision-making processes even more. Given this scenario, continuously determining the most suitable order processing strategy for each product presents a great challenge for companies. Logistic models reflect logistic interdependencies and are influenced by the order processing strategy. It affects the shape of operating curves as well as the position of operating points in logistic models. Therefore, logistic models can serve as a starting point for a holistic model providing assistance selecting the order processing strategy. This paper considers the impacts of various strategies on the operating curves of logistic models. This includes a description of the procedure for analyzing the effects of switching the order processing strategy. After determining a suitable company’s internal supply chain, an analysis of the operating curves of different logistic models for the order processing strategies Make-to-Order and Make-to-Stock on a single product base follows. A comparison of the operating curves of logistic models for these two strategies and a detailed analysis of processes help to evaluate whether differences result from the strategies or the processes. An exemplary application on a data set from industry demonstrates the general practicability of the approach.

AB - Complexity in the decision-making process results from the interaction of various parameters. Exemplary influencing factors are the durability of products, the desired delivery times, and the required product variants. In practice, uncertainties of customer behavior and existing time pressure complicate decision-making processes even more. Given this scenario, continuously determining the most suitable order processing strategy for each product presents a great challenge for companies. Logistic models reflect logistic interdependencies and are influenced by the order processing strategy. It affects the shape of operating curves as well as the position of operating points in logistic models. Therefore, logistic models can serve as a starting point for a holistic model providing assistance selecting the order processing strategy. This paper considers the impacts of various strategies on the operating curves of logistic models. This includes a description of the procedure for analyzing the effects of switching the order processing strategy. After determining a suitable company’s internal supply chain, an analysis of the operating curves of different logistic models for the order processing strategies Make-to-Order and Make-to-Stock on a single product base follows. A comparison of the operating curves of logistic models for these two strategies and a detailed analysis of processes help to evaluate whether differences result from the strategies or the processes. An exemplary application on a data set from industry demonstrates the general practicability of the approach.

KW - Engineering

KW - Order processing strategies

KW - Logistic models

KW - operating curves

KW - Make-to-order

KW - Make-to-stock

UR - https://www.repo.uni-hannover.de/handle/123456789/11621

U2 - 10.15488/11532

DO - 10.15488/11532

M3 - Journal articles

VL - 2021

JO - Journal of Production Systems and Logistics

JF - Journal of Production Systems and Logistics

SN - 2702-2587

IS - 1

M1 - 18

ER -

DOI

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