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

data analytics

shop floor

decision support

real-time

active duration

performance analysis

big data

bottlenecks

increase productivity

MES

industry4.0

decision making

data driven method

Production Systems

throughput analysis

active period

Constraint theory

manufacturing systems

shifting bottleneck

manufacturing execution systems

Resource management

smart manufacturing

production

Industry 4.0

Industrial Engineering

shifting

data-driven maintenance planning

data-driven

shifting 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

Chalmers, Energy and Environment, Physical Resource Theory

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, 2013-12-02 -- 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

11/10/2019