An algorithm for data-driven shifting bottleneck detection
J. Guo (Eds.) Cogent Engineering, 3 (1239516):
1-19(September 2016)
Abstract
Manufacturing companies continuously capture shop floor information using
sensors technologies, Manufacturing Execution Systems (MES), Enterprise Resource Planning systems. The volumes of data collected by these technologies are growing and the pace of that growth is accelerating. Manufacturing data is constantly changing but immediately relevant. Collecting and analysing them on a real-time basis can lead to increased productivity. Particularly, prioritising improvement activities such as cycle time improvement, setup time reduction and maintenance activities on bottleneck machines is an important part of the operations management process on the shop floor to improve productivity. The first step in that process is the identification of bottlenecks. This paper introduces a purely data-driven shifting bottleneck detection algorithm to identify the bottlenecks from the real-time data of the machines as captured by MES. The developed algorithm detects the current bottleneck at any given time, the average and the non-bottlenecks over a time interval. The algorithm has been tested over real-world MES data sets of two manufacturing companies, identifying the potentials and the prerequisites of
the data-driven method. The main perquisite of the proposed data-driven method is that all the states of the machine should be monitored by MES during the production run.
Description
A novel method to detect the current, average and shifting bottlenecks using the online data available in manufacturing.
%0 Journal Article
%1 noauthororeditor
%D 2016
%E Guo, Jun
%J Cogent Engineering
%K Data-driven Shifting active algorithms and big bottleneck bottlenecks complexity data decision duration operations production research support systems
%N 1239516
%P 1-19
%T An algorithm for data-driven shifting bottleneck detection
%U http://www.tandfonline.com/doi/abs/10.1080/23311916.2016.1239516
%V 3
%X Manufacturing companies continuously capture shop floor information using
sensors technologies, Manufacturing Execution Systems (MES), Enterprise Resource Planning systems. The volumes of data collected by these technologies are growing and the pace of that growth is accelerating. Manufacturing data is constantly changing but immediately relevant. Collecting and analysing them on a real-time basis can lead to increased productivity. Particularly, prioritising improvement activities such as cycle time improvement, setup time reduction and maintenance activities on bottleneck machines is an important part of the operations management process on the shop floor to improve productivity. The first step in that process is the identification of bottlenecks. This paper introduces a purely data-driven shifting bottleneck detection algorithm to identify the bottlenecks from the real-time data of the machines as captured by MES. The developed algorithm detects the current bottleneck at any given time, the average and the non-bottlenecks over a time interval. The algorithm has been tested over real-world MES data sets of two manufacturing companies, identifying the potentials and the prerequisites of
the data-driven method. The main perquisite of the proposed data-driven method is that all the states of the machine should be monitored by MES during the production run.
@article{noauthororeditor,
abstract = {Manufacturing companies continuously capture shop floor information using
sensors technologies, Manufacturing Execution Systems (MES), Enterprise Resource Planning systems. The volumes of data collected by these technologies are growing and the pace of that growth is accelerating. Manufacturing data is constantly changing but immediately relevant. Collecting and analysing them on a real-time basis can lead to increased productivity. Particularly, prioritising improvement activities such as cycle time improvement, setup time reduction and maintenance activities on bottleneck machines is an important part of the operations management process on the shop floor to improve productivity. The first step in that process is the identification of bottlenecks. This paper introduces a purely data-driven shifting bottleneck detection algorithm to identify the bottlenecks from the real-time data of the machines as captured by MES. The developed algorithm detects the current bottleneck at any given time, the average and the non-bottlenecks over a time interval. The algorithm has been tested over real-world MES data sets of two manufacturing companies, identifying the potentials and the prerequisites of
the data-driven method. The main perquisite of the proposed data-driven method is that all the states of the machine should be monitored by MES during the production run.},
added-at = {2016-11-25T16:01:47.000+0100},
biburl = {https://www.bibsonomy.org/bibtex/234366f6e46268f3396d0d78187a28bc2/mukunds},
description = {A novel method to detect the current, average and shifting bottlenecks using the online data available in manufacturing.},
editor = {Guo, Jun},
interhash = {e65544e032b49795ddb4d986337b4b31},
intrahash = {34366f6e46268f3396d0d78187a28bc2},
journal = {Cogent Engineering},
keywords = {Data-driven Shifting active algorithms and big bottleneck bottlenecks complexity data decision duration operations production research support systems},
month = {September},
number = 1239516,
pages = {1-19},
timestamp = {2016-11-25T16:01:47.000+0100},
title = {An algorithm for data-driven shifting bottleneck detection},
url = {http://www.tandfonline.com/doi/abs/10.1080/23311916.2016.1239516},
volume = 3,
year = 2016
}