Service, and the quality of the customer experience, are increasingly the key points of differentiation for businesses worldwide. In a recent consumer survey by PwC, 55 percent of respondents said they would stop buying from a company that they otherwise liked after several bad experiences, and 8 percent said they would stop after just one bad experience.
In today’s service economy, customers demand rapid response, flexible appointment slots and guaranteed first-time fixes. Yet, with skills shortages continuing to impact service-focused businesses, field service teams often find themselves stretched more than ever before. Dispatchers are frequently overburdened and under a lot of pressure.
They have to manage a lot of different scenarios. The intensity and complexity of their work means that dispatchers need to move from decision to decision very quickly and often have to switch context to do so. They are only too aware that field workers’ time is precious. In line with that, they need to ensure that each worker is at the right job at the right time in the right place in order to maximise efficiency.
A difficult balancing act
To complicate matters further, dispatchers are having to manage planned maintenance with new jobs coming on stream in real time. And they are trying to optimise the workforce to do this based on traditional processes, which is time-consuming and error prone.
Prioritising jobs is difficult too, when multiple tasks are coming in continuously, often encompassing a wide range of different geographical regions. Dispatchers are forced to firefight, which can be highly demotivating and stressful. This, in turn, is likely to negatively impact employee retention.
Service managers also may struggle in this kind of scenario to navigate uncertainty and plan ahead with any degree of precision. Added to all this, field workers themselves may become disillusioned, having to deal with significant travel requirements, short notice changes to job requirements and problems in completing allocated work. Morale across the entire field workforce is likely to suffer as a result.
Finding a solution
That’s a serious concern for businesses, of course, most of which are well aware of the requirement to focus on employee engagement and keep dispatcher, service manager and field worker stress levels to a minimum. At the same time, they must never lose sight of the need to optimise the customer experience, and meet SLAs, while minimising bottom line costs. But how do they best address these complex challenges?
What is needed here is a way of harnessing the latest advanced technologies like AI and machine learning (ML) to optimise the scheduling and allocation of planned and real-time maintenance jobs, enabling the dispatchers to focus on reactive jobs that come in.
This AI-powered scheduling can schedule large amounts of jobs, in real-time, to ensure the right engineer, is in the right place at the right time and with the right skills and parts. This kind of technology has the capability to continuously analyse real-time events, taking into account everything from job location to duration, traffic technician availability, skills, parts, tools and other dependent tasks to automatically deliver highly-optimised plans in seconds.
Beyond this, it can tailor or customise the chosen approach to meet the precise needs of each business. There will typically be a need to efficiently blend appointments with reactive and planned work, so businesses will, of course, need an effective way of aligning appointment times around existing committed work. But that is not sufficient in itself. Organisations will need to go beyond this to deliver something known as target-based scheduling.
This approach allows the organisations that deploy it to focus their scheduling directly on the key performance indicators (KPIs) that matter most to their business. That could be a reduction in the average cost per job for a white goods repair firm, for example, or an increase in the percentage of calls responded to within the target SLA (time window) by a regional ambulance service.
Typically, it is a question of managing complex and even competing priorities to ensure SLA compliance and maximise profit. In line with this, organisations are increasingly looking to minimise risk by trying out ‘what if scenarios’ to understand which available option is most likely to drive revenues and minimise costs.
What makes AI and machine learning the answer
AI and machine learning capabilities are well-suited to driving all the capabilities outlined above. They can manage data at scale much more quickly and accurately than a human and therefore adjust schedules rapidly to optimise plans. Equally, they can deliver the predictive analytics capabilities needed to help businesses evaluate how they are likely to be impacted by a wide range of likely scenarios and therefore pinpoint the best action to take in a given situation.
AI is also key in enhancing the experience of the dispatcher by giving them information they can understand, particularly when something goes wrong, Dispatchers will want to get an idea why certain decisions are being made.
That’s increasingly possible today. The big advance in AI over the past few years has been around this growing focus on ‘explainability.’ This kind of explainability is increasingly key in terms of regulatory compliance but also simply in building trust and communication with dispatchers.
It is important to note, AI must always play a supporting role rather than dictating the final decision. In line with that, it can help get the right intelligence at the right time, in the right form to the dispatcher, so that they can consume it quickly and easily and then use it, alongside their own experience and expertise, to take the final decision.
The benefits of AI extend much further than simply streamlining the work of the dispatcher, however. Organisations also need to think about how this kind of technology can impact the other teams they are responsible for, the business itself and its customers.
There are benefits across the board. The greater precision delivered by AI-driven workforce scheduling enables service managers to plan for the future more accurately. It can also make field workers more productive, reduce their travel requirements and allow them to complete more jobs, thereby raising their morale.
More broadly, the business gains by better meeting SLAs; reducing costs and increasing profits. They will also benefit from a happier workforce. But it is in their ability to support the delivery of enhanced customer service that the most far-reaching benefits of AI-driven workforce scheduling lie.
Ultimately, every service-based business and field service team operating today needs to be focused on enhancing the customer experience. The use of AI allows them to achieve this goal by enabling dispatchers to make workforce scheduling decisions that help ensure every customer receives a high-quality service.