Intelligent work-order forecasting

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Leveraging artificial intelligence and machine learning to improve field-service operations

From addressing outages to repairing equipment, successful field-service operations are critical for telco, healthcare, and manufacturing companies. In these industries, providing a great customer experience is dependent on field services quickly and efficiently responding to problems or proactively working to prevent them.

Demand and capacity planning are the most crucial elements of successful field-service operations, ensuring that when there’s an issue it can be resolved quickly. However, companies face many challenges in planning adequate capacity of staff and resources, so technical resource utilization often falls well below optimal rates.

Challenges with resource planning and demand management

Too often, field-service operations are bogged down by manual, inefficient processes that prevent effective problem resolution. When they are unable to resolve the issue right away, material and logistics costs increase. Additionally, without appropriate planning, unexpected circumstances may mean missed appointments or failure to meet service commitments, which can result in costly penalties. Advances in technology have only added to the challenge by introducing additional complexity to these operations.

For effective demand and capacity planning, organizations need a better view into what drives fluctuations in requirements and to analyze data quickly enough to make timely and informed decisions. Though there are a variety of tools available to help, they generally aren’t sufficient for accurately estimating workloads. Typical limitations include volume caps, inability to scale, ineffectiveness at responding to business demands, and lack of automation. Current tools tend to focus on available data points, but accuracy is tougher to achieve when predicting new data points. Additionally, experts are generally needed to validate forecasts, which can be expensive and time-consuming.

AI/ML: The key to successful resource planning and demand management

With the rise of artificial intelligence and machine learning, organizations can dramatically improve their approach to resource planning and demand forecasting for better field-service operations. With AI/ML, organizations can build robust forecasting solutions that are much more accurate for short- and long-term planning. In fact, we’ve found it’s possible to achieve a forecast accuracy of up to 95 percent using AI/ML. Additionally, algorithms can be trained on new data fed into the system, which means that, as more data is captured, planning becomes more accurate. Additionally, AI/ML solutions can be implemented on premises or in the cloud and be configured to accept data from various sources.

 Better work-order forecasting

Thanks to increased accuracy, AI/ML can help organizations improve productivity, optimize resources, and improve both scheduling and response time. Capgemini recently worked with a geographically dispersed telco organization offering different types of services to leverage AI for better work-order forecasting. As a result, the company boosted productivity by 40 percent, reduced costs by 10 percent, and decreased both call volumes and resource utilization.

AI/ML isn’t currently being leveraged by many field-service providers but, given the widespread uptake of AI/ML, the time is right for organizations to take advantage. We recommend beginning with a pilot and then scaling to other sites. Additionally, it’s important to include both technical and process considerations when planning to use AI/ML. Because of the newness of the technology, organizations can face challenges with buy-in, talent, compliance, and security. However, the right partnership can solve these problems. Capgemini brings the experience and expertise in cloud and AI/ML to help you build and then optimize a solution that works for you.

Authors:

Pushkar Ghatole Pradyumna Pendse

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