Paul Whitelam, SVP Global Marketing at ClickSoftware, looks at how to take the guesswork out of scheduling and dispatching with intelligent automation.
In the past, product differentiation was the way to beat the competition. As products become increasingly similar, organisations need a new way to stand out in a crowded playing field. Today, service – especially service that exceeds customer expectations – has become the new competitive battlefield. While there are many types of services organisations provide, your mobile workforce offers the greatest opportunity to tip the competitive scales in your favour. And this begins by increasing the productivity of your field force by optimising your scheduling and dispatching processes.
There is a lot riding on making the best decisions for scheduling. The choices you make impact every interaction with your customers or the assets you are servicing. And margins are under pressure due to multiple factors: the need to accommodate VIP customers with urgent problems, unexpected traffic, issues with site access, customer cancellations, as well as many other challenges that arise on the day of service. Even the best planned schedules are invariably disrupted.
Regardless of the challenges, scheduling still represents an area where companies can achieve a major positive impact on their bottom lines by improving the efficiency of the scheduling and dispatch process. There are two levers to consider in this quest for efficiency. The first is automation – that is, making decisions in an automated fashion that improves response times and can reduce overall labour costs. The second is the use of machine learning to analyse historic data to make better predictions, creating optimal routing and scheduling decisions.
Having the capacity to automate scheduling decisions can be a game changer for field service teams. By using artificial intelligence (AI) to immediately identify the optimal resource allocation, organisations are able to dispatch jobs in a way that maximises the chance of a first time fix, ensuring customer satisfaction, and also reduces the cost of service by minimising travel time and other elements. The ability to continually optimise a schedule as service requirements change also has a major payback. For instance, instead of leaving white space in the schedule when a customer cancels, an automated system will assign an alternative task immediately rather than leave a resource idle. This leads to improved productivity and more satisfied customers.
Another way automation delivers tangible benefits is by understanding the particular urgency of work and SLA’s. This way if an emergency comes up, low priority work like preventative maintenance can be automatically rescheduled to another time within the SLA window without adversely impacting customer experience.
Optimise automation with machine learning
While optimal automation cannot happen without sophisticated artificial intelligence, there is an additional advantage that can be delivered through the use of machine learning (ML).
Machine learning is a type of AI that uses historic data to improve the quality of decision making without being explicitly programmed. One of the greatest attributes of ML is its ability to process large amounts of data from many different sources in a way that the human brain is unable to handle.
Through ML, organisations have the ability to use data about previous disruptions to help with future planning. For example, ML can analyse historical weather conditions throughout the year and, at times when there’s a higher probability for snow, the system can schedule lower priority jobs to preemptively mitigate scheduling disruptions should there be a storm, and therefore cancellations. With a solution that uses AI and ML to handle job planning, scheduling and execution, field service technicians encounter less downtime, fewer work disruptions and are consistently assigned jobs that match their skill sets − which can improve productivity by up to 40%.
When these technologies are strategically applied to connected equipment and sensor devices, valuable data about performance, environmental conditions and more is constantly transmitted and processed. ML can analyse the collected data to preemptively identify issues before they even occur, avoiding downtime and saving time and money for businesses and customers.
Eliminate manual effort
The most experienced dispatchers and service managers still have a limit to the amount of variables they can consider when making scheduling decisions. However, with AI capabilities, calculations and changes are instantaneous, adjusting in real-time to minimise disruptions and maximise the organisations desired outcomes and KPI’s. The majority of these changes and problems are addressed in the background without the need for human intervention. This level of automation enables an organisation to deliver an optimised and differentiated customer experience as compared to a field team that relies solely on manual processes.
AI can also improve customer communication, a key to a good experience. AI enables your team to share an accurate arrival time based on current travel conditions as well as send details about the technician and his real time status and location. This information keeps customers from feeling “in the dark” about the appointment and eliminates variables that can result in customer no-shows and last-minute cancellations.
Lastly, as the use of Internet of Things (IoT) sensors increases, field organisations can be alerted to a problem before the customer even realises that something is amiss. An alert can be sent to your FSM system and the schedule is automatically adjusted in real-time to dispatch a qualified service technician, while also filling in any gaps that might occur due to this change. While providing seamless service like this enhances the customer experience, it also enables field organisations to develop new revenue streams by selling monitoring services or up-time guarantees. Also, studies have shown that customers are willing to pay more for a better experience so this might provide some pricing flexibility for your organisation, helping to stabilise margins.
Let automation handle unexpected variables
Delivering on a service request means having to deal with the unexpected. Factors like last minute cancellations, sick calls, changing weather conditions, and shifting traffic patterns will always remain out of your control, and will inevitably impact field service operations. While these variables can’t be eliminated, they can be managed through technologies like AI and ML. True AI and ML enable a scale and speed of schedule and dispatch optimisation that would be impossible with mostly manual processes.
The benefits of schedule automation through the use of AI and ML are real and only get better over time. The constant stream of inputs and refinements, and the feedback loop created by adherence to or deviation from the optimised routes and schedules, teaches your system to make better decisions in the future. The more data you provide, the more refined and focused your operations will be over time. The ability to run simulations and crunch massive amounts of data empowers service leaders to test drive process changes and weigh variables to understand what will work best.