Purpose, People and Process is the focus of most service professionals when implementing data-driven solutions

Focusing on Pupose, People and Process is key to implementing data-driven solutions in service organizations.

When it comes to implementing data and knowledge solutions, it is Purpose, People and Process that most service professionals are focused on. That was the feedback from the  Service Leaders Network experience exchange in April where 15 professionals shared their experiences of implementing these types of solutions into their operational processes.

Hosted by Si2Partners, these virtual experience exchanges are limited in attendees in order to promote intensive discussion and then coming together to review lessons learned.

To get the attendees thinking, we first reviewed a framework developed during a SLN collaboration project. For more on this see our article https://serviceinindustry.com/2021/03/08/a-knowledge-and-data-implementation-framework/

The results were interesting for all professional who may have these projects on their desks.

Two common themes ran through all groups:

  1. Purpose is key: It is so important to be able to define a clear value driven objective for the project to not only give it a clear direction, but also to be able to communicate to and get commitment from stakeholders. Although this may appear to be a very basic project management skill, we heard numerous war-stories that this is often where managers have the most challenges.

  2. People are the enabler: We heard a variety of reasons why both employees and customers are critical enablers as well as some tips for engaging with both these groups:
    1. Make it easy for people to use solutions. A good example would be to tailor data visualizations to individual needs
    2. Knowledge/data projects are often part of a wider change management process. So explaining the reason for data and process within the context of the larger change is important to communicating the WHY to employees and customers.
    3. Change often requires the development of new digital capabilities & knowledge. This should be actively managed. Make sure you have a budget!
    4. When talking about people don’t forget the customer! Communicate value of the data and what you are doing with it to your customer.
    5. Successfully incorporating data and knowledge into processes is a team game. This needs to be stressed as part of any communication plan.

What was surprising was that there was only one group who talked about the importance of documenting the process, so as to integrate the data & knowledge solution into the organization’s way of working. They mentioned that the fact that how the solution works within the company has been thought through and is accompanied by training, is an important factor in overcoming the key challenge of how to get your people to do the actions required for successful implementation. Having an understanding of the nature of the data to be used was not mentioned at all. Whether this is because the professionals in the discussion were mainly from operational backgrounds and so were not too concerned with knowledge application, or perhaps we did not allow enough time for the discussion. Either way this appears to be an interesting follow-on conversation to have and we will be organizing another experience exchange on this topic in the coming months.

Other comments and lessons learned when implementing Knowledge and Data Solutions were:

  • Future proof the tools you are using.
  • It’s not just about changing operational processes, management processes also need to change. Hence change management is a key element of any project especially the impact of organizational culture on the outcome.
  • Don’t underestimate the effort required within field support processes such as installation, commissioning and problem solving.

A couple of days after this experience exchange, we had an opportunity to have a deeper conversation with professionals from a very large industrial technology business about the implementation of a corporate learning program. Although they are in the early days of their project, a very similar emphasis was seen. First get clarity on ‘why do the project’ and ‘what is its value’. Then you can start to identify about where you get the expertise from within the organization to deliver a sophisticated data solution. And don’t forget the people implications. Their observations were that only too often project teams miss one of these areas which slows down the implementation and in the worst case leads to failure.

 If you want to know more the Knowledge and Data Implementation framework, you can contact [email protected] and he can support you with engaging workshops that will help you and your team identify how to integrate data into your business processes. Si2 also have run a series of workshops that help service professionals become more data savvy. To date more than 200 professionals have participated in these programs which aim to raise the bar in terms of how to use data.

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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:


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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