It is the day after a pleasant dinner evening with friends. As usual there are quite a few dishes to wash. Just that your high-end dishwasher refuses to start up. You call the service line and the friendly person on the phone tells you that she needs to talk to the technician and will come back to you as soon as possible. “Do you have a cell phone number on which I can reach you?” An hour later she informs you that the next possible repair appointment is a week later, between 10 am and 4 pm, after asking a few questions about the nature of the problem. “Sorry, I cannot be more precise, but the technician can call you about half an hour before he arrives”.
The technician who arrives at noon on the announced day is in his first week. He disassembles the machine, looks confused at one part, “uhm, what is this?”, finds the broken one “oh, I do not have this, need to return another day” and then has difficulties reassembling the machine.
This is admittedly an extreme scenario but surely not outside the realm of possibilities. We have seen one part or another of it, b2b or b2c. Field service scenarios are notoriously difficult, with challenges throughout the process.
Time is Money
On the other hand the most valuable asset that people have is their time. So spending little time in coordinating a repair, having an accurate arrival time of the service technician, and a fast and efficient repair in one visit are paramount traits of good customer service. This is something that e.g. Forrester’s Future of Customer Service Study found. And this is true for businesses, too. Doubly so, as machine downtime equals loss of income and perhaps even penalties.
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All service technicians have different skills and specializations.
Minimizing the travel time to maximize time on site remains challenging. Similarly it is hard to accurately determine the time needed for an actual repair.
Even if the service technician has all necessary skills to action a repair he might not have the right spare parts in the van.
Similarly, on the corporate and management side there are regularly questions like: Who takes how long on repair jobs? Who of my technicians spends lots of time on the road? Why are there so many repeat visits necessary? Where can I optimize my processes to become more effective and efficient? Plan ability is another factor here. That is why preventive maintenance got introduced. Now, with the help of AI and IoT, maintenance can even become preventive.
Of course, there is the technician’s view as well, as the technician is the one who ultimately might the stress levels of an unknown issue, a customer in an aggravated state of mood, traffic challenges, and perhaps the need to admit a the need for a return visit.
These challenges get compounded if there is no appropriate, integrated system landscape that helps in identifying possible root causes, dispatching a technician with the necessary skill set, keeping travel time short and making sure that the technician has all relevant parts in the van.
The Ideal World
Field service needs to accommodate the same hierarchy of customer expectations as general customer service. It first needs to be effective, then efficient, and ultimately have a joyful component to it.
Having said this effectiveness is first. A service technician can be as competent, good natured and possibly charming as – if he cannot get the job done this is worthless for the customer.
AI- and IoT technologies can help meeting the two base layers of this pyramid.
Let us revisit the scenario I started off with.
While not being AI, the incoming call can be translated directly into a service request that can get assigned to a service technician efficiently. Natural Language Processing helps in identifying the issue and can trigger an automatic search for a likely root cause. This might even result in the incident being resolved right away if the system finds a document describing a solution and presents it to the phone operator along with a confidence level.
If the issue cannot get resolved, the integrated intelligent system determines the right service technician and, with a high likelihood, the necessary repair time and the parts that the technician needs to have when on site. Due to an optimizing route planner the wait time until repair for the customer is minimized and more accurate, too. Time in traffic is minimized and therefore the technician’s stress levels. It is even possible to automatically send schedule updates to the customer.
On site the technician himself can get help by using AR-enabled glasses that project maintenance and assembly instructions as an overlay to the machine that he sees into his view. Besides keeping the process efficient this also helps identifying parts.
What it Takes
The ideal world is not so far off. The ingredients for delivering this scenario are available now, albeit often not yet pieced into one coherent solution. Microsoft is strong where it comes to predictive maintenance and combining IoT with business process. So is SAP, but less in Field Service scenarios at the moment, although recognized as a leader in Gartner’s 2016 MQ on Field Service. Salesforce has embedded Einstein Vision into their Field Service Lightning solution. Train the model, take a picture of an equipment, and Einstein tells you what it is via chatter.
Or Oracle with its Field Service solution that looks at optimizing timings to create better results
Of course there are a number of smaller vendors in this area, too, like Servicemax, IFS, Clicksoftware, OverIT, or Servicepower, to name but a few. All of them, and other unnamed competitors, too, have good solutions for a part of the ideal world.
To be prepared and to already improve capabilities, executives need to consider a few points.
- Develop a solid strategy on how you want to make Field Service more efficient without placing undue pressure on the technicians.
- Keep current skill matrix of service personnel. This helps in efficiently dispatching the right technician
- Employ a smart route planning software that takes traffic into account as part of the dispatching process to minimize travel time and to improve predictability
- Use geo-location services and electronic signatures as a trigger to keep staff and customers informed automatically.
- Agents and technicians researching documentation can already now be used to train intelligent service systems
- Data is key. It will first tell you where the low hanging fruit lie and then help in training the business systems to better support personnel and customers
Last, but not least, start with limited experiments using smart automation technologies like the ones mentioned above that may help improving the process – or building a better one. This will become a platform- and ecosystem play. Don’t commit to an ecosystem yet but to technologies that may help. If they do, choose a platform.