Data-driven algorithm for throughput bottleneck analysis of production systems
Artikel i vetenskaplig tidskrift, 2018

The digital transformation of manufacturing industries is expected to yield increased productivity. Companies collect large volumes of real-time machine data and are seeking new ways to use it in furthering data-driven decision making. A challenge for these companies is identifying throughput bottlenecks using the real-time machine data they collect. This paper proposes a data-driven algorithm to better identify bottleneck groups and provide diagnostic insights. The algorithm is based on the active period theory of throughput bottleneck analysis. It integrates available manufacturing execution systems (MES) data from the machines and tests the statistical significance of any bottlenecks detected. The algorithm can be automated to allow data-driven decision making on the shop floor, thus improving throughput. Real-world MES datasets were used to develop and test the algorithm, producing research outcomes useful to manufacturing industries. This research pushes standards in throughput bottleneck analysis, using an interdisciplinary approach based on production and data sciences.

productivity

statistical approach

Bottleneck

Smart Maintenance

Data-driven

Smart manufacturing

Maintenance

data science

Manufacturing Execution System

production

bottlenecks

big data

Production system

Analytics

machine learning

active period

manufacturing

maintenance

Throughput

MES

industry 4.0

Throughput bottleneck detection

Författare

Mukund Subramaniyan

Chalmers, Industri- och materialvetenskap, Produktionssystem

Anders Skoogh

Chalmers, Industri- och materialvetenskap, Produktionssystem

Hans Salomonsson

Chalmers, Data- och informationsteknik

Pramod Bangalore

Chalmers, Data- och informationsteknik

Maheshwaran Gopalakrishnan

Chalmers, Industri- och materialvetenskap, Produktionssystem

Muhammad Azam Sheikh

Chalmers, Data- och informationsteknik

Production and Manufacturing Research

2169-3277 (eISSN)

Vol. 6 1 225-246

DAIMP - Dataanalys inom underhållsplanering

VINNOVA, 2016-03-01 -- 2019-02-28.

Ämneskategorier

Produktionsteknik, arbetsvetenskap och ergonomi

Annan data- och informationsvetenskap

Styrkeområden

Produktion

DOI

10.1080/21693277.2018.1496491

Mer information

Senast uppdaterat

2019-11-10