Data-driven throughput bottleneck analysis in production systems
Doctoral thesis, 2021
One way to increase shop floor productivity is to strive to achieve an even flow within production systems. However, even system flows may be disturbed by various factors such as random breakdowns, minor stops, setups, variations in cycle time, waiting for an operator, and so on. Such disturbances may constrain production system throughput. However, previous research has shown that not all disturbances in different machines in the production system
constrain the production system throughput. The disturbances are significant in a set of machines in the production system that constrains the system throughput. This set of machines is called throughput bottlenecks. Quick and correct identification of throughput bottlenecks will help practitioners plan appropriate eliminating actions.
Existing academic research efforts to investigate throughput bottlenecks have largely adopted analytical approaches or discrete-event simulation-model-based ones. However, now that companies are collecting huge volumes of digital data, this data can be analyzed directly by developing data-driven approaches. This allows the derivation of insights into throughput bottlenecks in production systems.
This doctoral thesis constructs a series of data-driven approaches to analyze throughput bottlenecks. Firstly, a data-driven approach is proposed to identify historical throughput bottlenecks in a production system. But merely identifying bottlenecks is not enough if informed actions are to be taken. Secondly, a data-driven approach is proposed for diagnosing historical throughput bottlenecks. Specifically, a diagnosis is made based on a maintenance perspective. Combined with identification and diagnosis, practitioners may then plan and execute different corrective actions. However, when such corrective actions are applied, the dynamics of the production system change. That means that bottlenecks will not act as bottlenecks in the future. Thirdly, to thus predict how the system dynamics will change (and thereby to predict future throughput bottlenecks), a data-driven approach is proposed to predict future throughput bottlenecks in a production system. Fourthly, to help practitioners plan for proactive actions on the predicted throughput bottlenecks, a further data-driven approach is proposed; one which prescribes actions to combat the bottlenecks.
The different data-driven approaches proposed in this thesis have been tested using production system data sets extracted from different real-world production systems. The insights obtained from applying these approaches may help practitioners to better understand the dynamics of throughput bottlenecks and plan for specific actions to eliminate them. Such elimination helps achieve an even flow in production systems, thus increasing productivity.
Chalmers, Industrial and Materials Science, Production Systems
A data-driven approach to diagnosing throughput bottlenecks from a maintenance perspective
Computers and Industrial Engineering,; Vol. 150(2020)
A generic hierarchical clustering approach for detecting bottlenecks in manufacturing
Journal of Manufacturing Systems,; Vol. 55(2020)p. 143-158
A prognostic algorithm to prescribe improvement measures on throughput bottlenecks
Journal of Manufacturing Systems,; Vol. 53(2019)p. 271-281
A data-driven algorithm to predict throughput bottlenecks in a production system based on active periods of the machines
Computers and Industrial Engineering,; Vol. 125(2018)p. 533-544
Data-driven algorithm for throughput bottleneck analysis of production systems
Production and Manufacturing Research,; Vol. 6(2018)p. 225-246
An algorithm for data-driven shifting bottleneck detection
Cogent Engineering,; Vol. 3(2016)p. 1-19
Within this thesis, I propose four data-driven approaches for different types of throughput bottleneck analysis. First, I present different data-driven approaches to identify historical throughput bottlenecks. With these, practitioners can quickly identify the bottleneck location in a production system. Second, I propose a data-driven approach to diagnosing historical throughput bottlenecks. It will help to understand the possible root-causes of the throughput bottlenecks. Third, I offer a data-driven approach to predict throughput bottlenecks for the next production day. It will help to take proactive actions on throughput bottlenecks. Fourth, I propose a data-driven approach to prescribe actions on predicted throughput bottlenecks. It will give information on specific measures one can proactively perform on predicted throughput bottlenecks. In sum, these data-driven approaches will help practitioners to make faster, confident, and informed decisions on throughput bottlenecks, which will help to maximize the throughput from production systems.
Overall, data-driven approaches are similar to GPS. People use GPS to find the best way. The GPS eliminates blind alleys. Similarly, practitioners can use the data-driven approaches to eliminate throughput bottlenecks and create a more predictable, safe, and better factory environment without surprises.
DAIMP - Data Analytics in Maintenance Planning
VINNOVA, 2016-03-01 -- 2019-02-28.
Production Engineering, Human Work Science and Ergonomics
Areas of Advance
Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 4887
Chalmers University of Technology
VDL and Zoom (Password: 534528)
Opponent: Amos Ng, University of Skövde, Sweden