Data-Driven Throughput Bottleneck Analysis in Production Systems
Other conference contribution, 2021

Manufacturing companies are increasingly adopting digital solutions to monitor and manage production systems. By
adopting digital solutions, it has become possible for manufacturing companies to collect huge volumes of widely
varying production system data. Alongside this, significant advances in recent years in machine learning and artificial
intelligence fields have opened up opportunities to develop data-driven approaches for analyzing the huge emerging
volumes of data, deriving insights, and using those insights to improve shop floor productivity. 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 throughput bottlenecks. Throughput bottlenecks are a set of machines that constrain the system
throughput. 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 (based on building explicit recursive equations) or discrete-event simulation-model-based ones.
These approaches are better suited for static analysis and more useful for early configurations of the production
systems. On the other hand, 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
the 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. The research outcomes of this thesis were implemented in the real world. Two automotive manufacturing
companies (one in Sweden and one in Germany) have implemented a datadriven approach to detect historical
throughput bottlenecks. A US-based manufacturing analytics software provider has implemented the data-driven
approach to detect historical bottlenecks into their package. They provide it directly to their different customers who
are using their software.

Manufacturing system

Production system

Throughput Bottlenecks



Mukund Subramaniyan

Chalmers, Industrial and Materials Science, Production Systems

Proceedings of the International Conference on Industrial Engineering and Operations Management

2169-8767 (ISSN) 2169-8767 (eISSN)

978-1-7923-6127-2 (ISBN)

International Conference on Industrial Engineering and Operations Management
Rome, Italy,

DAIMP - Data Analytics in Maintenance Planning

VINNOVA (2015-06887), 2016-03-01 -- 2019-02-28.

Subject Categories

Production Engineering, Human Work Science and Ergonomics

Other Computer and Information Science

Software Engineering

Areas of Advance


More information


2/9/2022 9