An algorithm for data-driven shifting bottleneck detection
Artikel i vetenskaplig tidskrift, 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.

shifting bottleneck

data-driven

bottlenecks

decision support

production

active duration

big data

Författare

Mukund Subramaniyan

Chalmers, Produkt- och produktionsutveckling, Produktionssystem

Anders Skoogh

Chalmers, Produkt- och produktionsutveckling, Produktionssystem

Maheshwaran Gopalakrishnan

Chalmers, Produkt- och produktionsutveckling, Produktionssystem

Hans Salomonsson

Chalmers, Energi och miljö, Fysisk resursteori

A. Hanna

Volvo

Dan Lämkull

Volvo

Cogent Engineering

2331-1916 (ISSN)

Vol. 3 1-19 1239516

Ämneskategorier

Produktionsteknik, arbetsvetenskap och ergonomi

Styrkeområden

Produktion

DOI

10.1080/23311916.2016.1239516