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Medical Devices and Technology

We work with component and product manufacturers of diverse industries and geographies to grow and optimize their service business.  

A Sector in Flux

As competition in manufacturing and engineering industries intensified, companies responded by developing increasingly global operations and supply chains and offshoring production. They implemented lean processes in the pursuit of lower costs and higher quality and enhanced their products and brand.

Faced with limits to growth for new industrial products and systems, managements began also to appreciate the importance of the installed base and the potential for revenue streams over the product lifetime. They recognized that actual product sales constitute only a fraction of the total revenue opportunity – the rest being services, from product support and spare parts to upgrades and productivity improvements. Estimates have shown that total service revenues associated with an industrial product over its lifetime amount to between 5 and 20 times the sales value of the product. After-sales services now constitute a strategic anchor for many industrial companies, aiming at a mix of maximizing customer loyalty and retention, generating additional revenues and profits from the installed base and defending against lower cost competitors.

Now new forces are driving ever more powerful shifts: Demand is fragmenting, both geographically and in terms of customer requirements — more options and customization, faster product cycles and more frequent upgrades, better and more after-sale service. At the same time, rapid digitization is radically transforming how manufacturers operate and interact with customers, while enabling entirely different kinds of products and services. Products, in fact, are no longer at the center of all things. They are increasingly defined by the outcomes they achieve and the data they generate. Data collected from products in the field become raw material for new services and customized solutions. To deliver outcomes, manufacturers must integrate more into their customers’ processes, inducing customers to place greater emphasis on the total customer experience. Therefore, making things is starting to matter less while knowing things more. In many cases successful companies will no longer be the ones that make the best products, but the ones that gather the best data and combine them to offer the best services, whether on their own or together with others.

Service Characteristics

The product service market is usually characterized by large installed bases, large numbers of customers and multiple channels to market.

Competition is intense with large numbers of market players. Customers usually have high in-house service competence and competition in aftermarket is also intense, including from customers, third party service providers (often low cost), and, increasingly, product competitors.

Product downtime is expensive for customers and its avoidance provides the competitive framework while operational superiority drives competitiveness. Component and product services are largely a matter of scale and logistics to drive high efficiencies and productivity while maximizing recurring revenues from the installed base is a critical success factor.

Given the flux  what is the best way forward for manufacturers? Should they try to get into the digitization game as early as possible in spite of uncertainties, or should they wait until things become clearer? One significant risk of waiting is the claim to the data: Digitization of assets is vendor neutral. If third parties (customers, competitors, independent providers) seize the initiative and digitize product data without the original manufacturer, this might not only endanger the manufacturer’s influence on product utility and performance (potentially jeopardizing service revenue streams), but also increases the risk of product commoditization and limits the manufacturer’s differentiation and margin potential. So access to, even “ownership”, of data is something manufacturers can ill afford to lose. And often the best way of keeping that access is by building on data to design and sell services and solutions.

For manufacturers to take full advantage of new technology to strengthen market positions, often requires transformational strategic, cultural and organizational shifts and upgrading of capabilities: Sales & Marketing must focus on value co-creation and customer experience; R&D must incorporate service thinking; Offerings, delivery systems and supply chains must integrate through the Industrial Internet of Things (IIoT) and turn data into actions.

Nevertheless, surveys indicate that only a minority of manufacturers are planning radical changes to harness the potential of digitization and services or have introduced training in digital skills. But it is wrong to underestimate the speed of technology driven disruption. Companies risk being left behind as competitors from within and from outside their industry draw ever closer to their customers.

Insights and Success Stories

Service Innovation for value-driven opportunities:

Facilitated by Professor Mairi McIntyre from the University of Warwick, the workshop explored service innovation processes that help us understand what makes our customers successful.

In particular, the Customer Value Iceberg principle goes beyond the typical Total Cost of Ownership view of the equipment world and explores how that equipment impacts the success of the business. It forces us to consider not only direct costs associated with usage of the equipment such but also indirect costs such as working capital and risks.

As an example, we looked at how MAN Truck UK used this method to develop services that went beyond the prevailing repairs, parts and maintenance to methods (through telematics and clever analytics) to monitor and improve the performance and  fuel consumption of their trucks. This approach helped grow their business by an order of magnitude over a number of years.

Mining Service Management Data to improve performance

We then took a deep dive into how Endress + Hauser have developed applications that can mine Service Management data to improve service performance:  

Thomas Fricke (Service Manager) and Enrico De Stasio (Head of Corporate Quality & Lean) facilitated a 3 hour discussion on their journey from idea to a real working application integrated into their Service processes. These were the key learning points that emerged:

Leadership

In 2018 the Senior leadership concluded that to stay competitive they needed to do far more to consolidate their global service data into a “data lake’ that could be used to improve their own service processes and bring more value to customers. As a company they had already seen the value of organising data as over the past 20 years for every new system they already had a “digital twin” which held electronically all the data for that system in an organised fashion. Initially, it was basic Bill of Material data, but has since grown in sophistication. So a good start but they needed to go further, and the leadership team committed resources to do this.

  • The first try: The project initially focused on collecting and organising data from its global service operations into a data lake.  This first phase required the development of infrastructure, processes and applications that could analyse service report data and turn it into actionable intelligence. The initial goal was to make internal processes more efficient, and so improve the customer experience. E+H looked for patterns in the reports of service engineers that could:
    • Be used to improve the performance of Service through processes and individuals
    • Be used by other groups such as engineering to improve and enhance product quality.
  • Outcome: Eventhough progress was made in many areas, nevertheless, even using advanced statistical methods, they could not extract or deliver the value they had hoped   for from the data. They needed to look at something different.
  • Leveraging AI technologies: The Endress+Hauser team knew they needed to look for patterns in large data sets. They had the knowledge that self-learning technologies that are frequently termed as AI, could potentially help solve this problem. They teamed up with a local university and created a project to develop a ‘Proof of Concept’. This helped the project gain traction as the potential of the application they had created started to emerge. It was not an easy journey and required “courage to trust the outcomes, see them fail and then learn from the process”. However after about 18 months they were able to integrate the application into their normal working processes where every day they scan the service reports from around the world in different languages to identify common patterns in product problems, or anomalies in the local service team activities. This information is fed back to the appropriate service teams for action. The application also acts as a central hub where anyone in the organisation can access and interrogate service report data to improve performance and develop new value propositions.
  • Improvement:  The project does not stop there. It is now embedded in the service operations and used as a basic tool for continuous improvement. In effect, this has shifted the whole organization to be more aware of the value of their data.

Utilizing AI in B2B services

Regarding AI, our task was to uncover some of the myths and benefits for service businesses and the first task was to agree on what we really mean by AI among the participants. It took time, but we discovered that there are really two interpretations which makes the term rather confusing. The first is a generic term used by visionaries and AI professionals to describe a world of intelligent machines and applications. Important at a social & macroeconomic level, but perhaps not so useful for business operations -at least at a practical level. The second is an umbrella term for a group of technologies that are good at finding patterns in large data sets (machine learning, neural networks, big data, computer vision), that can interface with human beings (Natural Language Processing) and that mimic human intelligence through being based on self-learning algorithms. Understanding this second definition and how these technologies can be used to overcome real business challenges is where the immediate value of AI sits for today’s businesses. It was also clear that the implication of integrating these technologies into business processes will require leaders to look at the change management challenges for their teams and customers.

To understand options for moving ahead at a practical level we first looked briefly at Husky through an interview with CIO Jean-Christophe Wiltz to CIOnet where we learned that i) real business needs should tailored drive technology implementation, and ii) that before getting to AI technologies, there is a need to build the appropriate infrastructure in terms of database and data collection, and, most importantly, the need to be prepared to continually adapt this infrastructure as the business needs change.

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