An algorithm for data-driven shifting bottleneck detection

Mukund Subramaniyan,Hans Salomonsson,Anders Skoogh,Maheshwaran Gopalakrishnan,Atieh Hanna,Dan Lämkull,Jun Guo

doi:10.1080/23311916.2016.1239516

Abstract

AbstractManufacturing 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 ave...

Highlights

Digital solutions are helping manufacturing companies to improve productivity and remain globally competitive
Incorporating real-time data into the simulation model of the production system can greatly improve the accuracy of the bottleneck detection (Chang, Ni, Bandyopadhyay, Biller, & Xiao, 2007)
The algorithm is applied at the time instant 14:30:00, which is the end time instant of the production run during that day, and the shifting and the sole patterns across the machines are observed from the start time instant of production and 14:30:00

Summary

Introduction

Digital solutions are helping manufacturing companies to improve productivity and remain globally competitive. A preliminary study at an automotive manufacturing company in Sweden reveals that, on an average, 100 data rows are collected per hour per machine by the MES. This means that 500,000 data rows are collected per year per machine (Subramaniyan, 2015). Many manufacturing companies have started to explore better ways to utilise big data, using advanced analytics to make fact based decisions (O’Donovan, Leahy, Bruton, & O’Sullivan, 2015). Big data has the potential to enable data-driven decision-making, which helps to make better decisions (Bean & Kiron, 2013; Davenport, Barth, & Bean, 2012)

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