Mukund portrait

Data-driven throughput bottleneck analysis in production systems

Mukund Subramaniyan, Production Systems​ IMS, new doctor IMS.




Summary figure: "Data-driven throughput bottleneck analysis in production systems"








Public defence - Mukund Subramaniyan​
2021-02-12 10:00
Opponent: Amos Ng, University of Skövde, Sweden
Examiner: Johan Stahre, IMS

Popular science text
My thesis offers a series of data-driven solutions that can provide a multifold increase to a company's bottom line by eliminating throughput bottlenecks in the factory. Today manufacturing companies' factory floor productivity is alarmingly low at 50%. Practitioners are exploring new ways to increase factory floor productivity. One way to increase productivity is to get higher factory throughput. Some machines constraints the throughput on the factory floor. These machines are called throughput bottlenecks. When practitioners eliminate throughput bottlenecks, they can get higher throughput. But how can practitioners find, analyze, and eliminate throughput bottlenecks? Currently, practitioners spend a lot of time (sometimes hours or days) on the shop floor to search for bottlenecks and make ambiguous experience-based decisions. But this can be changed using digital solutions. How? My research answers this question. In my thesis, I build data-driven approaches to analyze throughput bottlenecks in less than seconds. The input to a data-driven approach is digital machine data. Then, the data-driven approach quickly analyses the digital data using artificial intelligence techniques. The outputs of a data-driven approach are the insights on throughput bottlenecks.

Within the 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 data-driven approaches to eliminate throughput bottlenecks and create a more predictable and better factory environment without surprises.


Page manager Published: Mon 22 Feb 2021.