How AI transformed ABB customer support in just 90 days

By | 3 minute read | October 9, 2018

When most manufacturers think of using IT solutions to support their business, they generally focus on augmenting their physical assets, or upgrading their inventory or supply chain management systems. ABB is different. With our unified, cross-industry ABB Ability™ digital offering, ABB is transforming into a data-driven manufacturer. We want to use data to drive all our business outcomes.

Getting a handle on customer satisfaction data

ABB, valued at USD40 billion, is a global pioneering technology leader. We produce power generation, distribution, automation and consumption equipment. We also generate an immense volume of data. For example, we had an enormous number of customer comments, in multiple languages, stored across numerous customer care systems. We needed a solution that could not only collect, analyze and translate the comments into English to identify trends at scale, but also to understand how our customers were feeling. If we could somehow capture their sentiment and emotions, we could use the information to improve our manufacturing process and avoid future problems.

Using Artificial Intelligence to drive better customer service

We realized we had a great opportunity to implement an AI customer service solution and have IBM analyze our data. In just 90 days, Watson Explorer has helped us change the way we support our customers by saving us time and improving our manufacturing process. For example, we may see a pattern in comments about one of our products, but they are written in natural language sentences and in many different languages. With Watson’s Natural Language Processing (NLP) we can analyze the comments and translate them into English. At the same time, we apply Natural Language Understanding (NLU) to analyze the text to extract metadata from the content such as concepts, entities, keywords, categories, relations and semantic roles to learn and understand sentiment, emotion and tone.

We can now link this information to our structured data to map an issue to the root cause. For example, we might discover that a specific manufacturing process can cause a potential issue for customers. We can now build analytical models around the cause-and-effect analysis for better decision making and improved workplace productivity. We consider it collaborative analytics – or a convergence of analytics – when we bring the structured and unstructured data together to help us make better decisions.

This analytics tool is very helpful to our quality engineers. In the past, they were required to pull the unstructured customer comments, translate them into understandable language and manually review and correlate them. With Watson Explorer they become more efficient while better understanding the root cause of problems. In addition, if the solution helps engineers in one country identify a problem, the same solution can be applied in other countries; we can create a global knowledge base for the whole company. As we progress, we want to teach our systems to avoid problems in the future. Already, we know enough from our data to start predicting specific patterns. Now, we can teach our systems to address the behavior or patterns immediately when they arise.

Smarter manufacturing creates happier customers

If you’re considering AI solutions for your business, I would say: understand the business problem first. Don’t start with the solution. AI is just another tool to solve the problem. Be flexible, open-minded and start on a small project. We started with a small use case that showed the viability of AI technology for ABB. Next came the proof of value. Yes, this technology would fit, but how could we get value from it? We found it was viable and could deliver a significant outcome. Only after that could we scale up for mass production.

Watson Explorer can make our business smarter by helping employees make smarter decisions. And employees making smarter decisions results in smarter processes and improved productivity. Only then will you have smarter factories. This is not about one plus one equals two; it’s not transactional. It’s about having a tool that unlocks insights from your data and assists you in your decision-making processes. I’m excited when I think about extending these capabilities to other areas of the business.

Watson Explorer is helping to elevate our thinking process, our decision-making process, our collaboration process and helping us do things that we never tried before. It’s making us a smarter ABB.

Listen to Babu Kuttala talk about ABB using IBM Watson Explorer for smarter manufacturing:

 

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