SLN event Success at Zeiss

Winning with Total Cost of Ownership
A Smarter Path to Growth

2 days of Structured peer Discussion

16 participants

14 organisations from optics to energy

3 deep-dive topics chosen by the group

The  Service Leaders Network Summits never seem to fail to deliver, and the latest held at Carl Zeiss SMT in October was no exception.  Here is a brief overview:

The questions attendees wanted answers to

  • How to use TCO to sell more services and solutions
  • How to use TCO to transform the aftermarket, especially data transparency
  • How to use TCO to shape service operations, design,clogistics and processes
  • How to learn from others
 

Key learnings based on lived experiences

  1. TCO is about performance, not price:
    This was illustrated with discussions around the Customer Value Iceberg which using TCO techniques helps take companies deep into their customers value chain. going beyond direct and indirect costs to waste and to where the hidden value is often found: the opportunity costs, risk and emotional factors, which often drive the purchasing decision. We heard real stories from service directors in Semi-Conductor, equipment manufacturers, HVAC and fasteners that illustrate that the art of making money is to understand and focus on these value drivers.
  2. Make the value visible to the customer
    We shared experiences of how to make this hidden value real to customers in terms of the services we offer. For example  a service manager for injection molding equipment who combined his service cost data (inside-out) with machine downtime (outside-in). Lost production far outweighed service costs. A preventive maintenance contract cut losses sharply, but the customer cancelled it because the value was invisible to them, and losses returned.   An equipment manufacturer in the fish processing sector who collected data to develop, a “service cost per fish” metric and using this together with services to improve  yield to get a customer to accept a week of downtime: payback was under a month.
  3. Service makes lifecycle value tangible
    We heard how the  Points of Selling (PoS) model maps more than 20 moments across the product lifecycle where a service can be offered, from feasibility and handover to inspections, upgrades, end of warranty and end of life.

    Customer events such as a
    change of management or regulation, relocation or obsolete parts are triggers we can anticipate rather than react to.

  4. Collaboration is the working model
    The supplier’s customer has a customer and a customers customer.  One participant producing equipment for the farming sector even went as far as to talk about there end customer being the “cow”.  Value flows from the OEM through dealers and contractors to a farmer whose economics depend on the milk price and the health of that cow. Value and data pass through every link, and a company that sees only its own link misses most of the value it creates. Collaboration therefore works on two levels: externally with customers, dealers and partners, and internally between R&D, Sales and Service. Sales should know the customer’s value drivers so that performance, not price, leads the conversation.

  5. Selling TCO to senior leadership

    The group’s advice for getting management to invest: frame TCO as a strategic framework linked to vision and strategy, not a service-department tool; make a small first ask, a green light to analyse TCO in our sector and among our customers (direct and indirect costs); define tasks and governance, including partner integration, up front; and present both the internal case (profitability) and the external case, a sales argument that is often about winning the sale at all rather than commanding a higher price.

Three key takeaways

  1. Use TCO as a framework for analysing customers
  2. Bring stakeholders in: sales, dealers, contractors and the customer, to collaborate and gain synergies
  3. Reusable tools: Use Points of Selling to enhance services through the lifecycle, the Customer Value Iceberg to identify hidden drivers of value, and Ecosystem mapping to understand the opportunities in the wider value chain.

In the words of attendees

“Two inspiring days focused on Total Cost of Ownership. The sessions provided valuable insights and new ideas on how to demonstrate the value of a machine beyond its purchase price. Looking at acquisition costs, service costs, and process costs throughout the entire lifecycle helps customers make better decisions and supports them in achieving long-term success. My key takeaway: understand your customer segments and clearly communicate the value you create for each of them. This is where TCO becomes a powerful tool for both sales and customer support.”

“I took away plenty  of fresh ideas and a better understanding of how different perspectives connect. I see great potential for applying these concepts globally, even though the German machinery industry still has some catching up to do in terms of adoption and mindset. That’s exactly why these meetings are so valuable: bringing decision-makers together, sharing experiences, and developing new ideas.”

‘This was time well spent with working case studies from Zeiss SMT, Baader and Trumpf, as well as shared insights between service leaders from 14 different organisations”

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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.