An algorithm for data-driven shifting bottleneck detection
Journal article, 2016

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 prerequisite of the proposed data-driven method is that all the states of the machine should be monitored by MES during the production run.

bottleneck

shifting bottleneck

manufacturing execution systems

real-time

industry4.0

MES

data-driven

decision support

performance analysis

Constraint theory

data-driven maintenance planning

smart manufacturing

Resource management

Industrial Engineering

active duration

shifting production

manufacturing systems

shop floor

big data

shifting

throughput analysis

data analytics

Production Systems

active period

Industry 4.0

bottlenecks

decision making

increase productivity

data driven method

production

Author

Mukund Subramaniyan

Chalmers, Product and Production Development, Production Systems

Anders Skoogh

Chalmers, Product and Production Development, Production Systems

Maheshwaran Gopalakrishnan

Chalmers, Product and Production Development, Production Systems

Hans Salomonsson

CSE Verksamhetsstöd

A. Hanna

Volvo Group

Dan Lämkull

Volvo Cars

Cogent Engineering

2331-1916 (ISSN)

Vol. 3 1 1-19 1239516

Streamlined Modeling and Decision Support for Fact-based Production Development (StreaMod)

VINNOVA (2013-04726), 2013-12-02 -- 2016-12-01.

Subject Categories

Production Engineering, Human Work Science and Ergonomics

Areas of Advance

Production

DOI

10.1080/23311916.2016.1239516

More information

Latest update

12/10/2021