Thyssenkrupp, a technology group with traditional strengths in materials, has been on a digital transformation journey over the last few years. With over 1,60,000 employees working across 78 countries, they have been able to develop high-quality products and intelligent industrial processes and services across industries such as automotive, aerospace, elevators, cement, mining, chemicals, materials and marine, among others.
“We started our digital transformation journey a few years ago with initiatives focussed on three broad areas – internal operations, digital product and service offerings, and new business models. Across all these applications the focus has been on starting with a core business problem and using technology as an enabler to solve it.”
Shares Abhinav Singhal with Analytics India Magazine, who is currently the chief strategy officer at thyssenkrupp, Asia Pacific.
They have been reading and analysing data from customers’ plants, machines and equipment and delivering more performance through predictive maintenance or advanced process controls. Some of the other areas are digitising their internal warehouse operations and achieving process improvement and cost savings.
“Ultimately, for us, it’s all about combining our over 200+ years of deep engineering and process expertise with technology to deliver value for our customers,” adds Singhal.
The company has also been expanding its base in India with technology centre that extensively works on analytics and data science solutions. Heading the Technology Center for Analytics & Software India (TCASEI) is Rohit Gupta who has deep expertise in product development in areas such as automotive and railway domain. He has been involved with designing to manufacturing and managing captive R&D centres across companies.
“TCASEI was established as part of our thyssenkrupp’s focus to provide digitally enabled solutions for our industrial customers and also build strong digital skill base within the company. Our goal is to make Tech Center a hub for building solutions in the areas of Artificial Intelligence, Machine learning, Data Analytics and Software Engineering for our internal and external customers globally.”
Shares Gupta.
Below are the interview excerpts with Singhal and Gupta and analytics adoption in the company, plans of furthering their offerings using the emerging tech and more.
AIM: How has thyssenkrupp integrated AI and analytics in its working over the years? What are some of the most beneficial use cases that you have witnessed?
Abhinav Singhal: AI and analytics have become an integral part of our products and services at thyssenkrupp over the years. We have numerous use cases where AI and analytics have helped us improve the performance & availability of our components and equipment or helped in reducing the cost of operations or improved intimacy and responsiveness with our customers. To give you some examples:
- We connected at our hot strip mills the processes of the supplier, the hot strip mill (manufacturer) and the customers in a digital network and then applied analytics to optimise the whole value chain
- We were one of the first to launch predictive maintenance solution in the elevator industry called MAX which is the industry’s first real-time, cloud-based predictive maintenance solution, ensuring that elevators operate smoothly and can slash downtime by as much as 50%
- We provide drone Inspection solutions which help our customers with fast visual inspections and measurements of plants and machines. The collected data in combination with the analytics allows customers to optimise their facilities and to inspect areas more accurately, efficiently and safely than ever before. We also create centimetre-precise models and construction plans of our customers’ plants using our revolutionary 3D Plant Scan technology for more insights, documentation or modernisation projects
- We use “Alfred” our artificial intelligence solution for the materials business to dynamically manage our global logistics network with 271 warehouse sites and more than 150,000 products and services. Using self-learning algorithms “Alfred” analyses all relevant information, generates important findings and supports employees with appropriate recommendations: Which materials have to be assigned to which industry? Where are materials processed? What would be the most intelligent transport route to supply the customers with materials in the best possible way? What are the needs of the individual locations?
AIM: How has this tech centre been shaping up? What are some of the innovations coming up from here?
Rohit Gupta: We have been steadily expanding our talent base and building up strong capabilities in the area of advanced analytics utilising a variety of AI and ML techniques. TCASEI has been working on developing innovative solutions using analytics to solve traditional manufacturing problems. For example, we are using analytics to improve the crystallisation process and provide predictive maintenance for our sugar machines or use condition-based monitoring and machine learning to improve the performance of our mining equipment.
AIM: What kind of datasets do you work on?
Rohit Gupta: Our businesses are quite diverse and we have a wide variety of products in our portfolio. We deal with both structured and unstructured data sets coming either in real-time or edge computed from our machines/integrated processes/plants and equipment.
AIM: How can one be a part of the data science team at thyssenkrupp?
Abinav Singhal: We are always on the lookout for high-quality talent and will be happy to accept any role-specific applications through our recruitment portal. Aspiring candidates can also reach out to our Tech Centre in Pune and we will get back to you if we have any suitable openings.
AIM: What are some of the challenges you face while recruiting data science talent In India?
Rohit Gupta: In general, we find there is a high demand and still comparatively low supply of well-qualified data scientists in the country. As a result, we have had examples where bright candidates tend to have multiple offers and continue to evaluate their options until the end, making it difficult to close the hiring process. Also, in some instances, we have seen that the CVs do not truly reflect the real experience of the candidates and often we need to conduct extensive interview-based screening to get a real understanding of the candidate’s experience and profile. Having said that, as mentioned before we see India as a strong hub to build digital talent base within the company.
AIM: How big is your analytics team?
Rohit Gupta: Our India Tech Centre currently has 7 employees and we are growing. Globally, of course, we have a much bigger team across all our business.
AIM: What other problems can AI/ML solve in your organisation?
Abhinav Singhal: Lot of companies find it difficult to manage to develop disruptive solutions and delivering quarterly financial results at the same time. It is very easy to fall in the trap of focussing only on the day to day business and the simple things that drive the immediate bottom line in lieu of mid- to long-term investments in digital. But, in our experience “Digitalisation is the new core” and it’s important to not lose sight of that.
Having said that, the starting point is always, what is the core business problem to be solved and how can AI/ML efficiently address it. More often than not the greatest potential for AI and analytics is to create value in use cases where already established analytical techniques can be used, but where ML techniques can generate additional insights or broaden the application base.
