All my publications focus on transforming manufacturing operations using data, IIoT, AI and insights.
All my publications focus on transforming manufacturing operations using data, IIoT, AI and insights.
Anders Skoogh, Matthias Thürer, Mukund Subramaniyan, Andrea Matta, Christoph Roser
Production and Manufacturing Research
Publication Year: 2023
Did you know there are at least 14 methods to pinpoint throughput bottlenecks on the shopfloor? My colleagues and I have looked at each one of them. Our study doesn't just list these methods; it simplifies them into easy-to-understand categories. It's an essential read for anyone in the field of production and manufacturing looking to enhance shop floor productivity. This paper is more than a compilation of techniques. It represents a journey of collaboration, shedding light on a critical issue on the shop floor and offering our contribution to making this high-impact problem more manageable.
Jon Bokrantz, Mukund Subramaniyan, Anders Skoogh
Production Planning and Control
Publication Year: 2023
In the factory, your AI model wows everyone and boosts shop floor productivity. But guess what? This success is just the start, not the end. AI needs care beyond launch. The lifecycle of an AI continues long after deployment. The final but continuous phase of AI is model monitoring and maintenance. Just as CNC machines demand consistent maintenance, AI models require perpetual monitoring, fine-tuning, and recalibration to avert performance erosion. So, plan smart. Set up teams skilled in AI, data engineering, and the manufacturing expertise to engineer an infrastructure that nurtures not only routine AI maintenance but a perpetual cycle of advancement. This way, your AI doesn't just shine at the start but keeps impressing and giving good returns on investment
Mukund Subramaniyan, Anders Skoogh, Jon Bokrantz, Muhammad Azam Sheikh, Matthias Thurer, Qing Chang
Journal of Manufacturing Systems, 60, 743-751
Publication Year: 2021
Many engineers spend a significant amount of time searching for bottlenecks on the shop floor. We can now change this situation using digital solutions and artificial intelligence (AI). By analyzing the digital data of the factory, we can quickly identify and predict the bottlenecks. It can also prescribe the necessary elimination actions. That means not only a smoother, faster, and more efficient production but also creating a more predictable, better factory environment without surprises. Curious to know how AI can help eliminate throughput bottlenecks in your factory? Check out the article.
We present the AI architecture, explain with examples how engineers can consume the AI insights, and use them for decision-making in day-to-day operations. We also propose several future research directions and a range of practical recommendations for manufacturers wanting to implement AI in their factories.
Mukund Subramaniyan, Anders Skoogh, Muhammad Azam Sheikh, Jon Bokrantz, Björn Johansson, Christoph Roser
Computers & Industrial Engineering, 106851
Publication Year: 2020
Augmenting maintenance engineers’ judgment with data-driven algorithmic recommendations produces high-quality, impactful decisions to increase shop floor productivity. For this to happen, data scientists' and maintenance engineers’ skills need to be pooled when developing data-driven algorithms. Want to know how this can be realized in practice? Then read the article. The article shows how maintenance engineers and data scientists can co-work to develop data-driven algorithms to diagnose bottlenecks in manufacturing systems. We collaborated with a Swedish auto manufacturer to prove the criticality of domain expertise in developing data-driven algorithms.
Maheshwaran Gopalakrishnan, Mukund Subramaniyan, Anders Skoogh
Production Planning & Control
Publication Year: 2020
The paper presents a data-driven machine criticality tool. The impact is that it will transform the current maintenance practices leading to increased productivity. The tool will help maintenance engineers to quickly identify the very critical machines on the shop-floor.
A generic hierarchical clustering approach for detecting bottlenecks in manufacturing (Outcomes implemented in real-world)
Mukund Subramaniyan, Anders Skoogh, Muhammad Azam Sheikh, Jon Bokrantz, Björn Johansson, Christoph Roser
Journal of Manufacturing Systems, 55, 143-158.
Publication Year: 2020
Can machine learning (ML) algorithms use to analyze the stock prices of companies be used to analyze bottlenecks in manufacturing? Instead of finding companies that show a unique stock price over time compared to other stocks, the target is to identify machines that exhibit unique behavior as compared to other machines on the shop floor. The unique behavior of those machines is a key indicator that they are bottlenecks. Curious to know how? Then read the article! We explain the methodology based on unsupervised ML techniques, demonstrate it on two real production systems and explain how the algorithmic insights can be consumed by engineers to augment their decisions on bottlenecks. Identifying the right bottlenecks help engineers make correct and more confident decisions to improve shop-floor productivity!
Video explanation: https://www.youtube.com/watch?v=v0h9A4qnGpw&t=89s
Mukund Subramaniyan, Anders Skoogh, Muhammad Azam Sheikh, Jon Bokrantz, Ebru Turanoğlu Bekar
Journal of Manufacturing Systems, 53, 271-281
Publication Year: 2019
The production and maintenance teams have many difficult decisions to make regarding bottleneck management on the shop floor. Having advance notice of a bottleneck’s location and possible root causes can help them make better-informed and more confident decisions. Want to know how? The read our article. The article exploring how can shop floor data be used for prescribing actions towards bottleneck management using machine learning techniques.
A data-driven algorithm to predict throughput bottlenecks in a production system based on the active period of the machines (Frequently Cited)
Mukund Subramaniyan, Anders Skoogh, Hans Salomonsson, Pramod Bangalore, Jon Bokrantz
Computers & Industrial Engineering, 125, 533-544
Publication Year: 2018
In this paper, we propose a prognostic algorithm based on machine learning techniques that can predict future dynamics of the production system based on the historical data and also predict the future throughput bottleneck locations. The insights from the algorithm can be used to forewarn manufacturing practitioners about the upcoming dynamics of the production system and possible throughput bottleneck locations.
Video explanation: https://www.youtube.com/watch?v=UM5OWI2tMaM&t=78s
Mukund Subramaniyan, Anders Skoogh, Hans Salomonsson, Pramod Bangalore, Maheshwaran Gopalakrishnan, Muhammad Azam Shiekh
Production and Manufacturing Research, 6(1), 225-246
Publication Year: 2017
In this paper, we propose a long-term throughput bottleneck identification algorithm based on statistical methods. Manufacturing practitioners can use this algorithm to identify a set of long term throughput bottlenecks and plan for long term improvement activities on them to get sustainable throughput over time.
An algorithm for data-driven shifting bottleneck detection ( Outcomes implemented in real-world)
Mukund subramaniyan, Anders Skoogh, Maheshwaran Gopalakrishnan, Hans Salomonsson, Atieh Hanna, Dan Lämkull
Cogent Engineering, 3(1), 1239516
Publication Year: 2016
In this paper, we propose an algorithm that can identify the throughput bottlenecks in real-time. Practitioners can use this algorithm to continuously track throughput bottlenecks during a production shift and take actions in real-time to achieve the shift throughput target.
Jens Baum, Christoph Laroque , Benjamin Oeser, Anders Skoogh, Mukund Subramaniyan
Machines, 6(4), 54
Publication Year: 2018
In this paper, we conduct a systematic literature review on the available data-analytics methods and technologies for effective maintenance decision making in shop floor.
Sabino Francesco Roselli, Martin Dahl, Mukund Subramaniyan, Ebru Turanoglu Bekar, Anders Skoogh
IFIP International Conference on Advances in Production Management Systems, Vol. 732
Publication Year: 2024
Can you use quality data to predict machine failures? Our research suggests it's a possibility! We put this hypothesis to the test on real production system data and uncovered compelling insights. Our findings indicate that integrating quality data with machine failure data can significantly enhance the accuracy of failure predictions. This is a step forward in the quest for smart maintenance!
Christoph Roser, Mukund Subramaniyan, Anders Skoogh, Björn Johansson
IFIP International Conference on Advances in Production Management Systems, Vol. 630,
Publication Year: 2021
Bottleneck detection is vital for improving production capacity or reducing production time. Many different methods exist, although only a few of them can detect shifting bottlenecks. The active period method is based on the longest uninterrupted active time of a process, but the analytical algorithm is difficult to program requiring different self-iterating loops. Hence a simpler matrix-based algorithm was developed. This paper presents an improvement over the original algorithm with respect to accuracy.
Hannaneh Najdataei, Mukund Subramaniyan, Vincenzo Gulisano, Anders Skoogh, Marina Papatriantafilou
24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), pp. 993-1000. IEEE
Publication Year: 2019
We propose a processing framework, STRATUM, and an algorithm, AMBLE, for continuous, data stream processing for identifying throughput bottlenecks in production systems. STRATUM seamlessly distributes and parallelizes the processing across the tiers and AMBLE guarantees consistent analysis in spite of timing fluctuations, which are commonly introduced due to e.g. the communication system; it also achieves efficiency through appropriate data structures for in-memory processing.
Hannaneh Najdataei, Mukund Subramaniyan, Vincenzo Gulisano, Anders Skoogh, Marina Papatriantafilou
28th International Symposium on Industrial Electronics (ISIE),pp. 1328-1333. IEEE
Publication Year: 2019
In this paper, we propose an automated configurable streaming-based method for data validation to sanitize and organize manufacturing execution system (MES) data for throughput bottleneck detection. We also show insights on continuous, low-latency data processing as a methodology for throughput bottleneck analysis in production systems.
Analysis of Critical Factors for Automatic Measurement of OEE (Frequently cited)
Richard Hedman, Mukund Subramaniyan, Peter Almström
Procedia CIRP, 57, 128-133
Publication Year: 2016
In this study, we identify critical factors and potential pitfalls when operating an automatic measurement of OEE. It is accomplished by analyzing raw data used for OEE calculation acquired from a large data set; 23 different companies and 884 machines.
Mukund Subramaniyan
Capgemini
Publication Year: 2022
With shop floor AI transformations becoming complex and challenging to scale and achieve a quicker return on investments, production and maintenance engineers may need to a step back and re-think their strategy of selecting the shop floor problems for AI-based solutions.
Check out my article in which I give recommendations on how to think about selecting impactful shop floor problems for AI-based solutions.
Mukund Subramaniyan
Capgemini
Publication Year: 2021
Shop floor engineers have many questions about AI. Will AI make a difference in shop floor management? What are the use cases? How to prioritize the use cases for AI implementation? Is the impact significant? How should the AI journey start in a factory? In my recent article, I have answered these questions using a combination of my academic research and real-world manufacturing experience. Take a look at the white paper!
Mukund Subramaniyan
World Manufacturing Foundation (WMF) Report (Page 86)
Publication Year: 2020
Manufacturing companies have concerning questions on the role of shop floor and AI engineers in AI-powered factories. E.g., Will the manufacturing expertise of the shop floor engineers gained through years of education and experience be relevant in the future? Will AI experts replace the shop floor engineers? I have shared my perspectives in the white paper.
This essay was has been selected by the WMF editorial board as the best one on the topic “The ethical questions surrounding AI in manufacturing: What needs to be done?”.
Nachiappan Subramanian , S. G. Ponnambalam , Mukund Janardhanan , Mukund Subramaniyan
Innovation Analytics:Tools for Competitive Advantange
Publication Year: 2023
Innovation analytics (IA) is an emerging paradigm that integrates advances in the data engineering field, innovation field, and artificial intelligence field to support and manage the entire life cycle of a product and pro-cesses. In this chapter, we have identified several possibilities where ana-lytics can help in innovation. First, we aim to explain using a few cases how analytics can help in innovating new products to the market specifi-cally through collaborative engagement of designers and data. Second, we will explain the use of artificial intelligence (AI) techniques in the manu-facturing context, which progresses at different levels, i.e., from process, function to function interaction, and factory-level innovations.
Mukund Subramaniyan
Ph.D. (Doctoral) Thesis, Chalmers University of Technology
Publication Year: 2021
This 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 this 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 insights on throughput bottlenecks.
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 data-driven approaches to eliminate throughput bottlenecks and create a more predictable, safe, and better factory environment without surprises.
Mukund Subramaniyan
Master Thesis, Chalmers University of Technology
Publication Year: 2015
In this thesis, manufacturing system data from different real-world Swedish manufacturing companies were used to identify the productivity potentials. The analysis results show that the average OEE of Swedish manufacturing industries in 2015 was 50% approx. This indicates there is an enormous potential to increase the OEE of Swedish manufacturing industries. Also, this thesis shows concrete examples of how Swedish manufacturing companies can increase the OEE.
Improving data quality is crucial for successful AI implementation. However, fixing issues at the source may require expensive investments in re-engineering the entire ecosystem and re-designing the process, leading to delays in realizing downstream AI use cases. Statistical and machine learning algorithms can help solve this problem by refining and enriching the data. My work used these algorithms to address quality issues in logistics data for an automotive company, driving impactful outcomes.
Mukund Subramaniyan
Capgemini
Publication Year: 2023
Reducing costs is critical for a margin-driven automotive business. Data analytics can help reduce operations costs significantly. Read my article on data analytics can help reduce the logistics operations cost of an automotive company.
Mukund Subramaniyan
Capgemini
Publication Year: 2022