A prognostic algorithm to prescribe improvement measures on throughput bottlenecks
Journal article, 2019

Throughput bottleneck analysis is important in prioritising production and maintenance measures in a production system. Due to system dynamics, bottlenecks shift between different production resources and across production runs. Therefore, it is important to predict where the bottlenecks will shift to and understand the root causes of predicted bottlenecks. Previous research efforts on bottlenecks are limited to only predicting the shifting location of throughput bottlenecks; they do not give any insights into root causes. Therefore, the aim of this paper is to propose a data-driven prognostic algorithm (using the active-period bottleneck analysis theory) to forecast the durations of individual active states of bottleneck machines from machine event-log data from the manufacturing execution system (MES). Forecasting the duration of active states helps explain the root causes of bottlenecks and enables the prescription of specific measures for them. It thus forms a machine-states-based prescriptive approach to bottleneck management. Data from real-world production systems is used to demonstrate the effectiveness of the proposed algorithm. The practical implications of these results are that shop-floor production and maintenance teams can be forewarned, before a production run, about bottleneck locations, root causes (in terms of machine states) and any prescribed measures, thus forming a prescriptive approach. This approach will enhance the understanding of bottleneck behaviour in production systems and allow data-driven decision making to manage bottlenecks proactively.

Predictive analytics

Throughput bottlenecks

smart maintenance

Prescriptive approach

data-driven

data-driven decision making

smart manufacturing

Manufacturing system

Production system

data-driven maintenance planning

Maintenance

big data

Author

Mukund Subramaniyan

Chalmers, Industrial and Materials Science, Production Systems

Anders Skoogh

Chalmers, Industrial and Materials Science, Production Systems

Muhammad Azam Sheikh

Chalmers, Computer Science and Engineering (Chalmers), CSE Verksamhetsstöd, Data Science Research Engineers

Jon Bokrantz

Chalmers, Industrial and Materials Science, Production Systems

Ebru Turanoglu Bekar

Chalmers, Industrial and Materials Science, Production Systems

Journal of Manufacturing Systems

0278-6125 (ISSN)

Vol. 53 271-281

DAIMP - Data Analytics in Maintenance Planning

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

Subject Categories

Production Engineering, Human Work Science and Ergonomics

Software Engineering

Computer Science

Areas of Advance

Production

DOI

10.1016/j.jmsy.2019.07.004

More information

Latest update

12/21/2019