Throughput Time Predictions Along the Order Fulfilment Process

authored by: Tobias Hiller, Tabea Marie Demke, Peter Nyhuis
Abstract: Planned times for the throughput of production are key components to production planners for determining delivery dates with customers, capacity planning, scheduling and order coordination. While traditional estimation methods often rely on basic statistics and expert knowledge, data mining respectively machine learning offers the potential to compute more precise predictions for order-specific planned throughput times. Factors that lead to deviations from the plan are diverse and thus challenging to consider in the various production planning tasks along the order fulfilment process. Intelligent throughput time predictions promise a remedy. Yet, predictive models are often not designed to be practically applicable due to a lack of consideration of the various characteristics of each stage of the order fulfilment process. To address this gap, this paper takes a closer look at the prediction of throughput times for the various stages of order fulfilment. Based on the Cross Industry Standard Process for Data Mining, the characteristics of the individual steps to build a prediction model are elaborated with a focus and business and data understanding and then examined in a case study. From that, practical implications are derived and guidance for practitioners is given. A key finding is that predictions are less accurate in the early stages of order fulfillment. Prediction quality naturally enhances over time, since more and more order details are known. In conclusion, an iterative prediction process with an evolving database ensures good prediction quality, especially in the late stages of order fulfillment.
Organisation(s): Institute of Production Systems and Logistics
Type: Article
Journal: IEEE ACCESS
Volume: 12
Pages: 9705-9718
No. of pages: 14
ISSN: 2169-3536
Publication date: 22.01.2024
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: General Computer Science, General Materials Science, General Engineering
Electronic version(s): https://doi.org/10.1109/ACCESS.2024.3353029 (Access: Open)

BibTeX

@article{2ddc304651954f0a989d7321d089b89c,
title = "Throughput Time Predictions Along the Order Fulfilment Process",
abstract = "Planned times for the throughput of production are key components to production planners for determining delivery dates with customers, capacity planning, scheduling and order coordination. While traditional estimation methods often rely on basic statistics and expert knowledge, data mining respectively machine learning offers the potential to compute more precise predictions for order-specific planned throughput times. Factors that lead to deviations from the plan are diverse and thus challenging to consider in the various production planning tasks along the order fulfilment process. Intelligent throughput time predictions promise a remedy. Yet, predictive models are often not designed to be practically applicable due to a lack of consideration of the various characteristics of each stage of the order fulfilment process. To address this gap, this paper takes a closer look at the prediction of throughput times for the various stages of order fulfilment. Based on the Cross Industry Standard Process for Data Mining, the characteristics of the individual steps to build a prediction model are elaborated with a focus and business and data understanding and then examined in a case study. From that, practical implications are derived and guidance for practitioners is given. A key finding is that predictions are less accurate in the early stages of order fulfillment. Prediction quality naturally enhances over time, since more and more order details are known. In conclusion, an iterative prediction process with an evolving database ensures good prediction quality, especially in the late stages of order fulfillment.",
keywords = "Capacity planning, data analysis, Job shop scheduling, machine learning, Predictive models, Procurement, Production, Production planning, Task analysis, Throughput, throughput time prediction",
author = "Tobias Hiller and Demke, {Tabea Marie} and Peter Nyhuis",
year = "2024",
month = jan,
day = "22",
doi = "10.1109/ACCESS.2024.3353029",
language = "English",
volume = "12",
pages = "9705--9718",
journal = "IEEE ACCESS",
issn = "2169-3536",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
}