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Field Service Management

Field services affect a company’s perceived ability to deliver on its promises of product integrity, functionality and availability and hence its brand. Their importance goes therefore far beyond their impact on the bottom line.

Yet most companies fail to leverage field services as a competitive differentiator or to sustainably improve productivity beyond market and customer price pressures. Part of the problem is that field service management tends to be reduced to a logistics and scheduling problem where best practice is standardized and widely emulated, while solutions are provided off the shelf and built into a company’s ERP systems and technological infrastructure. However the ubiquitous presence of such systems has essentially transformed them from strategic resources previously to commodities now, a cost of doing business which is indispensable and must be paid by all, but brings no competitive advantage. Often built into such systems is a reactive nature for field service (after a user calls or equipment fails) and a focus on cost rather than customer needs.

The focus on logistics systems and internal costs often obscures broader field service problems, including customer experience and revenue generation, strategy and organization as well the human resource factor. For example, in many cases installed base data for assessing market potential, benchmarking or planning purposes are inadequate or inconsistent. In many large organizations, field services are run as decentralized, often geographically distinct, operational units with little coordination, often lacking critical mass for specialization and sufficient technical expertise or for developing planning and management capabilities. Inefficiencies along boundaries or interfaces of organizational units or artificial profit and cost allocations between either local and central service or service and product units often provide disincentives for cooperation to achieve customer outcomes. And the psychology of field service employees is often highly individualistic and resentful of central management control. Hence many companies operate with a-priori low expectations for performance.

Nevertheless as installed base ages, including in emerging markets, products become more complex and companies move towards offerings based on outcomes, where risks and costs of intervention are transferred to the service provider, the importance of improving field services will increase. Furthermore technology advances are providing new opportunities for advanced offerings, improved customer outcomes and higher cost efficiencies.

Our systematic approach to field service management goes beyond system implementation and focuses on total performance. We help clients develop strategic approaches for competitive differentiation, methods for managing demand and enhanced revenue generation through improved offerings, smart pricing and new technology implementation. We work with clients to improve logistics, optimize footprints and organizational structures and to design the right incentives and effective management practices.

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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