All my publications focus on transforming manufacturing operations using data, IIoT, AI and insights.

1. Throughput bottleneck detection in manufacturing: a systematic review of the literature on methods and operationalization modes

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.

2. Realizing the promises of Artificial Intelligence in manufacturing by enhancing CRISP-DM 

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

3. Artificial intelligence for throughput bottleneck analysis – State-of-the-art and future directions

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. 

4. A data-driven approach to diagnosing throughput bottlenecks from a maintenance perspective

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. 

5.  Data-driven machine criticality assessment–maintenance decision support for increased productivity 

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. 

6. 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 

7.  A prognostic algorithm to prescribe improvement measures on throughput bottlenecks  

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.  

8. 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 

9. Data-driven algorithms for throughput bottleneck analysis of production systems   

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. 

10. 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.

11. Applications of Big Data analytics and Related Technologies in Maintenance — Literature-Based Research

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. 

1. An Enhanced Data-Driven Algorithm for Shifting Bottleneck Detection

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.

2. Adaptive Stream-based Shifting Bottleneck Detection in IoT-based computing architecture

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.

3. Stream-IT: Continuous and dynamic processing of production systems data-throughput bottlenecks as a case-study 

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.

4. 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.

1. How do you select the shop floor problems for the AI solution? 

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.

2. AI on the shop floor: A game-changer to achieve economies of speed

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!

3. Balancing the role of domain experts and data scientists in an AI-powered factory 

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?”.

1. Introduction to innovation analytics                         

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.

2. Data-driven throughput bottleneck analysis in production systems

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.

3. Production data analytics - To identify productivity potentials                                 

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. 

1. Improving data quality of outbound transport event log data aiming to heighten its impact on downstream consumption

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

2.  An automotive company used AI to improve the accuracy of logistics lead time forecasting 

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