Service Operations Assessment

Identify actions to improve service performance and customer experience

Empowering Service and Sales teams to increase revenues and drive customer loyalty
A virtual programme for teams looking to deliver service excellence by understanding how well the service processes are meeting customer expectations and what needs to be improved
Why
Company leaders need to better understand how service operations are performing and how service is perceived by customers. A customer survey is usually a very time consuming and costly exercise. A more cost-effective option is the Service Operation Assessment (SOA), which has been designed to involve people from all parts of the company who have customer interactions. This provides valuable feedback and insight into the customer perception of service operations. The results of the SOA allow management to understand which parts of service operations are performing well, where improvement is needed and where the main challenges are. An action plan is recommended, and advice provided on the best way to execute the improvement plan.
Who should be involved

Management, Service operations, Sales, Marketing, Engineering, Commercial

Up to 40 people can be involved in the assessment

How the programme works
A series of virtual meetings or workshops and a web-based assessment:
  1. Planning Workshop: with a small group of key stakeholders in the programme to clarify objectives, participants and timeframe
  2. Assessment Phase: A questionnaire is sent to chosen participants in the local language
  3. Evaluation Phase
    • – Evaluation of feedback and clarification discussions where necessary- Development of report and recommendations
    • Review Workshop- Review report and recommendations with stakeholders – Agree action plan
    Options
    1. Interviews for providing additional richness to the assessment
    2. Customer perspective
    Language
    English / German although assessments are available in a wide range of languages
    Programme Deliverables
    1. Identified Service Operations Strengths and Weaknesses
    2. Agree with key Stakeholders:
      • Objectives to achieve the service excellence goal
      • Specific action plan
    3. Document the process and findings which can be used as part of a Change Management process

    Contact Us

    Interested, contact us at [email protected] for a review of your needs and budget.

    Information Sheet

    Download our information sheet

    Presentation

    View our presentation

    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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