Aftersales and
Lifecycle Services

Faced with limits to growth for new industrial products and systems, managements have begun to appreciate the importance of the installed base and the potential for revenue streams over the product lifetime. It has been recognized that actual product sales constitute only a fraction of the total revenue opportunity – the rest being services such as customer support, repairs and maintenance, spare parts, upgrades, productivity services or, in fact, product disposal. 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, creating a large market opportunity.

After-sales services now constitute a strategic anchor for many manufacturing and industrial companies aiming at a mix of maximizing customer loyalty and retention, generating additional revenues and profits from the installed base, defending against lower cost competitors and protecting the brand. Yet still, strategies are mostly defined in defensive terms, such as improving squeezed margins,  providing buffers in economic downturns, locking out competitors, locking in customers and resisting price pressures. There is less focus on innovation and new offerings creating new opportunities.

As a consequence many companies self-limit service investment and simply rely on too high prices for proprietary parts for profit. Hence service units within many manufacturing companies underperform, both operationally and financially, lack management capacity and coherent strategy, or knowledge of markets, competitors and data on installed base. Others advocate strong services and set high objectives, however strongly subordinate services to products and effectively disallow service growth initiatives. Still others confuse their service opportunities investing too much in skill based businesses which cannot be scaled or approaching businesses which require scale for profit with too little investment in automation and systems.

Furthermore, as technology, Industrial Internet of Things, and Artificial Intelligence become ubiquitous, data is emerging as a new asset class, key for asset performance and customer productivity. Manufacturers and OEMs who lose control over their products’ field operating data run significant risks of commoditization. After-sales service applications and solutions using connectivity, data and analytics to drive performance become crucial in this competitive environment, as do strong customer relationships developed mainly through quality after sales services and good cooperation between product and service units.

Key focus areas:

Programs and Workshops

Aftersales Services

We help our clients develop best-in-class after-sales service businesses. We help them clarify and articulate service intent, optimize service positioning within the organization and enhance product-service relationships. We work with service units to strengthen management capacity, understand their markets and competitive threats, develop compelling strategies, grasp opportunities and drive operational performance. And we help them make the case for technology, improve investment allocations and introduce and sell new, technology enhanced offerings.

Case Studies

Insights

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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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