If you’re an industrial equipment, telecommunications, or healthcare organization with hardware on a customer site, you’re going to want to understand how artificial intelligence (AI), business analytics, and machine learning fit into the picture. Through conversations with our membership community and TSW conference attendees, it’s become clear that these are are all hot topics that technology suppliers are interested in learning more about but are still having a hard time understanding. In this post, I’ll share what each of these terms mean, how they are fitting into the business strategies of hardware suppliers across the industry, and how you can learn to apply these emerging and highly beneficial capabilities and apply them to your business.
Defining Business Analytics, Artificial Intelligence, and Machine Learning
Before we can dive into the benefits, it’s important to first understand what each of these terms mean.
Business Analytics is the organizational process of improving outcomes by leveraging data. There are three different types of business analytics, each with a distinct business purpose:
- Descriptive: A data-driven model that explains why something happened.
- Predictive: Leveraging current and historical data, with statistical models and computational algorithms, to identify relationships and patterns that predict future events and/or behavior.
- Prescriptive: Given the insights from the estimated model and current score of a situation, identify the next best steps that your organization should take to optimize the outcome. Prescriptive = Predictive + Recommendations.
Machine learning essentially works on a system of probability, where statements, decisions, or predictions can be made with a degree of certainty based on the available data.
Artificial intelligence (AI) is the promise of automating mundane tasks, as well as offering creative insight to improve business outcomes. It’s the overarching concept of machines being able to carry out tasks in a way that we would consider “smart”, using algorithms that mimic the human brain for self-learning and pattern detection to make predictions and detect the user’s intent.
Machine learning is a subset of artificial intelligence, and refers to a portfolio of algorithms and techniques that you can employ to uncover patterns, relationships, or correlations in data to create valuable information and predictions.
Machine learning essentially works on a system of probability, where statements, decisions, or predictions can be made with a degree of certainty based on the available data. The addition of a feedback loop enables “learning”, and by sensing or being told whether the decisions are right or wrong, to modify the approach for future use.
An example of machine learning that you have likely encountered before is when e-tailers like Amazon see you’re buying a product and make “frequently bought with” recommendations that are relevant to your purchase. For instance, if you are about to purchase golf balls, Amazon will suggest golf tees and golf gloves. Over time, the algorithms are improved and refined based on your purchase history.
How Artificial Intelligence, Business Analytics, and Machine Learning Fit into the World of Hardware
By capturing a digital representation of the product’s physical properties, a “digital twin” can be stored in the Cloud, which can then be leveraged to improve business outcomes. In many cases, the data being collected across an entire install base can be used to deliver more business value than the physical component itself.
For instance, capturing historical performance data can predict an impending failure. This can become “prescriptive” by identifying the required spare part with a repair notification. Or, data can be used to identify upsell and cross-sell opportunities by recommending upgrades that can result in better business outcomes for your customers.
TSIA has developed a framework that we call the Remote Services Continuum to help members navigate this digital transformation.
The Remote Services Continuum
The Remote Services Continuum is a three-step roadmap for hardware manufacturers to better understand how IoT capabilities can provide more value for their customers, as well as how to monetize services based on those capabilities.
I’ll use the Service Efficiency step to illustrate how to monetize business analytics and machine learning capabilities.
The primary goal of this step along the Remote Services Continuum is to reduce service costs and improve responsiveness to service incidents. Through smart, connected products and descriptive business analytics, you’re able to capture and analyze data from your equipment on a customer site, remotely monitor their performance, and spot issues as they arise. The data collected can include:
- Performance Data: Monitor the real-time function of the equipment itself, including temperature, vibration, fan/motor RPM, voltage, etc.
- Component Data: Understand the function of critical components so you can keep an eye on and better understand failure rates.
- Usage Patterns: See how your equipment is actually being used by the customer.
The collection and storage of performance data across your entire on-premise install base can be used by engineers for predictive and prescriptive business analytics. For example:
- Self-Diagnostics: Prior to the field service engineer’s arrival on-site, algorithms are used to accurately diagnose a problem and provide repair instructions and replacement parts needed for the repair.
Self-diagnostics is an illustration of machine learning capabilities. In order to successfully self-diagnose, equipment must have awareness of its function, learn what is “normal”, and understand failures, while also making the correct recommendations for parts, services, and support. This automated approach to break/fix and spare parts is what will create value for your customers, both by providing proactive support, but also reducing their operational down-time. But, how do you monetize this functionality?
In order to successfully self-diagnose, equipment must have awareness of its function, learn what is “normal”, and understand failures, while also making the correct recommendations for parts, services, and support.
Service efficiency capabilities are typically part of your basic support tier, which are the traditional product-focused services that have always come with the purchase of your products. Cost savings are realized from dispatch avoidance, reduced first-visit repair times, and increased first-visit repair rates. Here are some examples of some ways you can monetize service efficiency, as referenced in our free ebook, “Why Services is the Big IoT Opportunity for Hardware Manufacturers.”
Learn About Business Analytics, Artificial Intelligence, and Machine Learning at TSW
The concepts of business analytics, artificial intelligence, and machine learning aren’t without risks and difficulties, such as data security, receiving customer permission to collect data, and speeding up and monetizing remote hardware. This is why we’re going to be focusing so much time talking about this topic at our upcoming TSW conference in Las Vegas this October. Here are a few of the many great sessions we have lined up that will cover various aspect of these concepts from leading companies such as GE Digital, HP, ServiceSource, and more.
- “Improving Service Delivery Performance with Machine Learning and Artificial Intelligence,” presented by TSIA. Learn how to enable smart, connected products, build the data model, and operationalize data analytics.
- “Power Panel: The future of Machine Learning in Industrial Equipment Industries,” co-presented by TSIA, Emerson Automation Solutions, Rockwell Automation, and GE Digital. Hear a panel of experienced service executives talk about how their companies leverage data collected from their install base to create business value for customers.
- “How Intelligent Algorithms and Spare Parts Combine to Deliver Outcome-Based Offers,” co-presented by Becton Dickinson & Company and TSIA. With 60-70% of service incidents involving spare parts and the need for an on-site visit from an engineer/technician, the use of intelligent algorithms for install base demographics, failure rates, and stocking locations have enabled Becton Dickinson & Company to improve their operations.
- “Delivering Digital Industrial Outcomes with Customer Success Plans,” presented by GE Digital. Learn how GE Digital customers have achieved a return on their software investment within eight months by leveraging newly-created Outcome Service plans.
To view the full conference schedule, be sure to visit the Technology Services World site. We hope you’ll join us this fall to learn from the best and brightest about these hot topics that are impacting hardware suppliers worldwide.
In the meantime, TSIA is actively researching the latest developments in these areas, so please reach out to learn more about how we can help you navigate these concepts, avoid pitfalls, and overcome the challenges that come with adapting to the new digital era.
About the Author
Vele Galovski is vice president of research, Field Services, for TSIA. Using his nearly 30 years of industry experience, he has consistently helped companies both large and small drive double-digit top-line growth with a proven retain, gain, and grow strategy. Vele has also written a book, The Perpetual Innovation Machine, which describes a holistic approach to management based on ambitious goal setting, data driven analysis, skillful prioritization, inspiring leadership, and the lost art of employee engagement. He may be contacted at [email protected]