December/January 2019

| 30 | December/January 2020 | SOLUTIONS | I n 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, Intelligent automation Paul Whitelam, SVP Global Marketing at ClickSoftware, looks at how to take the guesswork out of scheduling and dispatching with intelligent automation. creating optimal routing and scheduling decisions. Boost productivity 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