This is the first post in a two-post mini-series about transforming your service department from a reality of unstructured and scattered information to a streamlined organized service experience through digitalisation.
What does your ideal support case look like? I know what mine looks like – if I had all the options that I can think of available to me. If I can choose from any of the ideas that I can come up with around solving my customers issues as quickly and as efficiently as possible. If I don’t think about existing work flows and if I’m not bound by current constraints.
If that was the case I imagine that my field service engineers would start the day with a list of work orders on their preferred device, start the car and drive to the first one on the list. Once on-site, the system would show him a list of previous incident records for this particular asset along with the latest information about hardware and software upgrades, historic and current performance, diagnostic and state data as well as contractual information such as service contract options. The machine itself would say “here I am, here is my state” and based on intelligent automatic evaluation of diagnostic data, error codes and symptoms the system would instantly display the most efficient and effective machine specific troubleshooting step to try out in the exact context of that machine in its particular environment. The system would handle evaluation of complex state data and take the FSE through an optimised sequence of solutions that makes sure the machine gets back into operation as quickly as possible using as few spare parts as possible.
Upon completion, the system would automatically update the work order with all relevant information from the troubleshooting process and make it possible to capture a report from the Field Service Engineer if necessary. After that, we are ready for the next case.
Sure, it’s fun to dream of one service strategy to rule them all that will solve all of our challenges in service and increase customer satisfaction, but in the real world, dreams don’t really come true, do they?
Well, let me tell you what we have seen as key ingredients in service departments rising from printed manuals and DVD’s with troves of PDF’s to technicians armed with mobile devices that analyse real time data and help them make informed decisions on how to best troubleshoot the problem at hand.
As easy as this sounds just as much devil is in the detail. It requires that companies with ambitions to reach new levels of high quality service undergo a transformation in service and support. A transformation driven by Industry 4.0 principles and digitalisation, and a transformation in how knowledge is managed in the organisation. It’s no longer enough to capture sparse knowledge in static documents, articles, manuals, videos and other kinds of media that are hard to update and where the turnaround time from user feedback to actual content update is extremely high. It’s no longer enough to serve knowledge to call centre staff, field service technicians and customers using explicit manual interfaces and through static pre-defined paths. Service organisations need to embrace digitalization and switch to a service oriented architecture enabling proactive, human-centred and data-driven offerings delivered as a true seamless omnichannel user experience providing dynamic and real-time paths.
In this shift, knowledge is currency and key to owning the future of excellent service, and knowledge can be monetized if captured in sound models for knowledge that supports the transformation from “old school” knowledge management to a modern dynamic approach enabling service companies to give stellar service.
However, capturing something so intangible as expert knowledge and sharing it has always been a challenge as knowledge can be considered as both explicit knowledge like formal work procedures and diagnostic maps as well as tacit knowledge such as skills and expertise gained through years of experience, what most people call “know how”.
Knowledge has become the key economic resource and the dominant–and perhaps even the only–source of competitive advantage.
-Peter F. Drucker
But, if we are successful in capturing and structuring the valuable experience within the minds of the best, we are very well set for changing perspective. Combining world class products with world class service is the path to a “whole product“ – not just the core physical product (e.g. a wind turbine or a hauling truck) but the complete package that provides the customer with a 360 degree experience comprised of product, consultancy, extensions/add-ons and services.
But, how do we get from being product oriented to being service oriented?
The Product Service Shift
To get to high levels of service using automation and AI, businesses need to embrace digitalisation on many levels. Digitalisation and Industry 4.0 are no longer options or something that only the major players are doing – it has become critical to stay in front, let alone survive. It’s quickly becoming a do or die thing that needs to be considered and worked into the strategy in all departments. It’s no longer an option whether to use Internet of Things or not as data is becoming a strategic element at the core of service and support. Customers are demanding access to data, and suppliers are weary about giving it out … owning data represents huge revenue opportunities and sales are demanding new services to go along with the products. Profits from a complex asset sale is diminishing and profits are increasing in service. This means that in order to stay ahead, sales need more than a product, they need a product and a range of value adding services – data driven intelligent service solutions is a big part of that offering and customers are coming to expect that they can get access to equipment state data, performance data, diagnostic data and optimal troubleshooting information for their technicians.
Service is indeed shifting from a cost consuming necessity to a revenue generating business unit that opens op for new business opportunities.
Service customers don’t want features, they want outcomes
Customers no longer wants to buy just a product, they want to buy a complete experience. Energy companies don’t buy Wind Turbines, Gas Turbines and Nuclear Power Plants, they buy energy. This means that when they procure a Wind Turbine, they want 99% uptime that turns into an expected amount og megawatts of energy at the lowest possible cost. It’s the job of the Wind turbine manufacturer to provide a complete package consisting of the actual machine, the installation, commissioning, service & maintenance and troubleshooting in case of unscheduled maintenance. Telecommunications customers don’t buy modems, routers, WiFi access points, cables and mbit up/down speeds, they buy an internet connection that needs to be available 24 hours a day throughout the entire house and when downtime happens, the vendor needs to be available on the channels that the customer happens to use at the time and the customer expects a personalized support experience.
Industrie 4.0 and the Service Challenges
The term “Industrie 4.0” was coined as part of a long term high-tech strategy of the German government promoting digitalisation within manufacturing. The purpose of the strategy was to formulate a set of design principles that support companies in the computerisation of their operations. The term has since spread to the rest of the world and today we see many companies adopting some of the principles of Industry 4.0 in their own journey to become digital leaders in service and support within their field.
The 4 basic design principles of Industry 4.0 are:
- Interoperability: The ability of machines, devices, sensors, and people to connect and communicate with each other via the Internet of Things (IoT) or the Internet of People (IoP).
- Information transparency: The ability of information systems to create a virtual copy of the physical world by enriching digital plant models with sensor data.
- Technical assistance: The ability of assistance systems to support humans by aggregating and visualising information comprehensibly for making informed decisions and solving urgent problems on short notice.
- Decentralised decisions: The ability of cyber physical systems to make decisions on their own and to perform their tasks as autonomously as possible.
Of these four, the most interesting principles from a service stand point is “Interoperability” and “technical assistance”.
For service organisations aspiring to maximising performance and increase profits by improving the service offerings and be able to provide high-end quality of service with the least cost, knowledge management is key in getting there. The interoperability of machines and software is an essential part of the equation and adding experience and pre-existing knowledge to integrated systems makes an incredible efficient combination.
The ability to connect machine state date and human experience is incredibly powerful in troubleshooting scenarios as there are tremendous amounts of time to be saved when the machines are able to “speak” to the technician and the supporting software tools that the technicians are using is capable of consuming that data stream and transform it into meaningful answers to questions that are necessary to answer to get closer to a solution to the problem. This implies that the software systems are able to pick up and interpret machine data that in turn is used for automatically answering questions. Every time there is a question in a troubleshooting process there is an opportunity to consider automation as automatically answered questions provides a faster, more accurate and more concise troubleshooting experience every time. It also ensures that the same answer is provided every time as it is the software making the interpretation and not the human – even the two most skilled engineers troubleshooting the same device are unable to make the same evaluation of complex data in the same way every single time. But, capturing the knowledge of those engineers and adding automation to handling the sensor data readings through IoT, results in a very powerful approach.
The design principle reads:
- The ability of assistance systems to support humans by aggregating and visualising information comprehensibly for making informed decisions and solving urgent problems on short notice.
This plays very well with the interoperability of humans and machine state data. Aggregating data, information and knowledge in a troubleshooting scenario using a sound model for knowledge and combining it with automation is the best way to support call centre agents, field services engineers and even end customers when solving urgent problems on short notice. The opportunity to inject equipment state data or customer information into a troubleshooting scenario provides a dynamic effective and efficient experience for the user.
However, it all requires knowledge. Good old-fashioned knowledge. From the man in the van with nothing but his tool box to the hyper connected field service technicians with access to big data, analytics, machine learning and an abundance of information. No matter how high tech a tool box the technician has, knowledge remains the key ingredient when assigning the best possible man for any given problem situation. With a good knowledge base and automation, you can even send a less skilled technician to solve the problem with a high likelihood using new advanced digital technology.
While this all sounds fine and dandy, it does raise a lot of questions as to how we can approach and undertake a knowledge management project and succeed in doing so.
In our next post, we will shed some light on the approaches that we have seen working.