There’s no escaping it—service delivery is a key business driver and not just for Amazon, but across asset-intensive industries where getting day-to-day planning, scheduling and routing managed competently, and fully optimized, is becoming the make-or-break point for many businesses. According to a PwC report, 55% of customers would stop buying from a company that they otherwise liked after several bad experiences. It’s become a tough balancing act for businesses to optimize this need for customer experience with employee engagement, meeting SLAs, and not impacting bottom line costs.

For Bob De Caux, VP Automation at IFS, it’s all about harnessing a PSO (Planning and Scheduling Optimization) system, powered by the latest AI and ML technologies to take the employee and customer experience to the next level. Excellent service delivered on time, and with a smile on the face of customers and employees alike, is key.

In today’s service economy, customers demand rapid responses, flexible appointment slots and guaranteed first-time fixes. Yet with skill shortages continuing to impact businesses attempting to roll out servitization initiatives and organizations looking to support outcome-based services, field service teams often find themselves seriously stretched. Dispatchers are frequently overburdened and under a lot of pressure, as they have to manage many different scenarios. But service delivery doesn’t exist in a vacuum.

Service optimization becomes even more essential when it comes to assets, where service delivery, parts and logistics often require complicated planning and scheduling, and the bellwether of successful management of all of those pieces is a powerful AI-enhanced PSO (Planning and Scheduling Optimization) solution.

A truly optimized schedule can mean the difference between operating profit and loss, so it’s important that businesses pinpoint crucial areas for improvement. Asset-intensive businesses that leverage the capabilities of AI-enhanced PSOs can streamline operations, enhance service delivery, optimize resource allocation and improve customer satisfaction.

When time is of the essence, ensure the right technician is always on hand.

The intensity and complexity of a service dispatcher’s work means decisions with different contexts need to be made quickly in order to maximize efficiency. The primary reason why optimization in the moment matters comes down to the impact of delays on customer experience—such as when customers cancel, appointments run over and parts need to be allocated. Businesses need a system that can react in minutes, not hours. This is where the importance of AI-powered optimization demonstrates its value, as an effective system can do in fifteen minutes what some systems need overnight to compute.

An AI-enabled PSO system can schedule large amounts of jobs in real-time to ensure the right engineer or field worker is in the right place at the right time and with the right skills and parts to successfully complete any job. AI PSO technology has the capability to continuously analyze real-time events, taking into account everything from job location to duration, technician availability, skills, parts, tools and other dependent tasks to automatically deliver highly-optimized plans in seconds. AI can go one step further to enhance the experience of the dispatcher by giving them information they can understand, particularly when something goes wrong.

The dynamic route optimization function of PSO technology assigns jobs to technicians that will optimize drive time by taking the most efficient route and assign jobs that are as close together as resource availability will allow. The system achieves this by using AI to calculate time needed to complete each task based on existing data for each technician, so that an appropriate timeframe is given to jobs that are more complex or have a larger scale. This guarantees that there is enough time for completion and prevents costly overruns.

Prioritize where it counts to keep employee morale high.

Prioritizing jobs is difficult when multiple tasks are coming in continuously and encompass a wide range of different geographical regions. Service dispatchers are forced to firefight, which can be highly stressful and likely to negatively impact employee retention. Added to this, field workers 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—but asset-intensive businesses can turn the tide with AI.

The right AI-powered scheduling tool can tailor 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 need an effective way of aligning appointment times around existing committed work. But that is not sufficient in itself. Organizations need to go beyond this to deliver target-based or value-based scheduling. This approach allows the organizations to focus their scheduling directly on the key performance indicators (KPIs) that matter most to the business.

An AI-powered PSO system for instance, allows organizations to layer specific values, such as company rules (KPIs) or regional rules (regulatory) over the engine powering its planning optimization to ensure that appointments are triaged effectively. This could be a reduction in the average cost per job for a white goods repair firm 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 maximize profit.

Cut down on wasted time and eliminate human error.

Service dispatchers often have to manage planned maintenance with new jobs coming on stream in real-time. To complicate matters further, many try to optimize the workforce using traditional processes, which is time-consuming and error prone. So where can businesses cut down on inefficient and time-killer tasks?

Today’s enterprises continuously collect asset performance data but industries from manufacturing to service all struggle with similar dilemmas: How to put data collected in the right context and take action in real-time. Autonomous enterprises that incorporate AI and ML into their processes can manage data at scale more quickly and accurately than a solely human workforce. Equally, AI and ML models with self-learning asset performance anomaly detection 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 in order to pinpoint the best action to take in any given situation.

“What-if” scenario forecasting capabilities available in advanced AI PSO systems can run a variety of models for businesses to prepare for any eventuality. Here’s an example: What if a business expects a 50% spike in appointments? Scenario planning software can run a variety of models to ensure that the business is staffed up or help connect the business to the tools to leverage contingent labor.

Just don’t cut out the human.

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 without as much hassle—factors that all contribute towards a happier workforce.

But it’s important for businesses to not go too far down the AI well. Yes, AI has the potential to bring new efficiencies and unlock business value across asset-intensive industries but AI must always play a supporting role rather than dictating the final decision. AI can provide businesses with the right intelligence at the right time but it is in the ability to support the delivery of enhanced customer service that the most far-reaching benefits of AI-driven workforce scheduling lie. For instance, the information collected by an AI PSO system must be shared 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.

Service optimization delivers on long-term CX success.

Every service-based business and field service team operating today needs to be focused on enhancing the customer experience. But this must not mean sacrificing employee experience and bottom line costs in the process. 

AI-powered optimization combines the ability to analyze data at scale in real-time with the capability to serve information up to decision-makers in an explainable format to drive service success. AI-driven service management tools are increasingly vital to any service-based organization—and those businesses that harness them most effectively will be best placed to achieve an edge in this highly-competitive arena.