Online lead time prediction supporting situation-aware production control

Gyulai, Dávid; Pfeiffer, András; Bergmann, Júlia; Gallina, V.

doi:10.1016/j.procir.2018.09.071

Cited by 13 publications

(2 citation statements)

References 13 publications

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“…As shown, most of the research relevant to this article addresses the prediction of the completion time; this is logical, since completion time is a key parameter, necessary to provide customers with delivery deadlines and to assess the overall performance of the manufacturing process; however, some researchers have started to shift their focus towards individual process lead-time estimation. Gyulai et al [12] present a data analytics tool for "situation aware" production control. Their tool utilizes a closed-loop control in order to deploy and update online a digital data twin [13]; this tool is based on accurate simulation models of manufacturing systems, which allow performing prospective simulations that forecast deviations in production.…”

Section: Machine Learning and Wind Turbine Tower Manufacturingmentioning

confidence: 99%

Machine Learning-Based Analysis of a Wind Turbine Manufacturing Operation: A Case Study

et al. 2022

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This study analyzes the lead time of the bending operation in the wind turbine tower manufacturing process. Since the operation involves a significant amount of employee interaction and the parts processed are heavy and voluminous, there is considerable variability in the recorded lead times. Therefore, a machine learning regression analysis has been applied to the bending process. Two machine learning algorithms have been used: a multivariate Linear Regression and the M5P method. The goal of the analysis is to gain a better understanding of the effect of several factors (technical, organizational, and experience-related) on the bending process times, and to attempt to predict these operation times as a way to increase the planning and controlling capacity of the plant. The inclusion of the experience-related variables serves as a basis for analyzing the impact of age and experience on the time-wise efficiency of workers. The proposed approach has been applied to the case of a Spanish wind turbine tower manufacturer, using data from the operation of its plant gathered between 2018 and 2021. The results show that the trained models have a moderate predictive power. Additionally, as shown by the output of the regression analysis, there are variables that would presumably have a significant impact on lead times that have been found to be non-factors, as well as some variables that generate an unexpected degree of variability.

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Section: Machine Learning and Wind Turbine Tower Manufacturingmentioning

confidence: 99%

Machine Learning-Based Analysis of a Wind Turbine Manufacturing Operation: A Case Study

et al. 2022

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“…However, there are some studies that investigated engineer-toorder or make-to-order environments. Most of the studies used machine learning and data mining with its different techniques for forecasting [16,17]. However, there are other studies, which used simulation or operations research [18][19][20][21].…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamic Lead-Time Forecasting Using Machine Learning in a Make-to-Order Supply Chain

2021

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This paper investigates the dynamic forecasting of lead-time, which can be performed by a logistics company for optimizing temporal shipment consolidation. Shipment consolidation is usually utilized to reduce outbound shipments costs, but it can increase the lead time. Forecasting in this paper is performed in a make-to-order supply chain using real data, where the logistics company does not know the internal production data of manufacturers. Forecasting was performed in several steps using machine-learning methods such as linear regression and logistic regression. The last step checks if the order will come in the next delivery week or not. Forecasting is evaluated after each shipment delivery to check the possibility of delaying the current arriving orders for a certain customer until the next week or making the delivery to the customer immediately. The results showed reasonable accuracy expressed in different ways, and one of them depends on a type I error with an average value of 0.07. This is the first paper that performs dynamic forecasting for the purpose of shipment temporal consolidation optimization in the consolidation center.

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Predictive Manufacturing: Enabling Technologies, Frameworks and Applications

Pulikottil

Estrada-Jimenez

Nikghadam-Hojjati

et al. 2021

IFIP Advances in Information and Communication Technology

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The impact of globalization and the recent advancements in Information and Communication Technologies has pushed the manufacturing sector towards a new transformation. Current manufacturers with the help of recent advances in Cloud Computing, Artificial Intelligence, and Internet of Things are moving towards a new intelligent system called Predictive Manufacturing Systems (PMS). These systems can be used in a wide array of applications, including proactive maintenance, improved quality control and higher performance. This paper provides an overview of the current trends in Predictive Manufacturing Systems in recent years. The paper discusses the developed frameworks, enabling technologies and various applications of Predictive Manufacturing Systems.

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Online lead time prediction supporting situation-aware production control

Cited by 13 publications

References 13 publications

Machine Learning-Based Analysis of a Wind Turbine Manufacturing Operation: A Case Study

Machine Learning-Based Analysis of a Wind Turbine Manufacturing Operation: A Case Study

Dynamic Lead-Time Forecasting Using Machine Learning in a Make-to-Order Supply Chain

Predictive Manufacturing: Enabling Technologies, Frameworks and Applications

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