Big data and AI play a part in improving field service work

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Learn how big data and artificial intelligence are helping field service employees be more efficient and effective.

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ManpowerGroup reported in 2018 that 70% of companies interviewed in a survey said that they expected a skills shortage in field service personnel over the next 10 years. Field service workers are those who travel to locations to repair goods and services onsite. Among the contributing factors were an aging (and retiring) workforce and a lack of enthusiasm among millennials for field service careers.

“For large enterprises, field service must address an entire lifecycle of a product or service,” said Arka Prava Dhar, CEO and founder of Zinier, which provides field service AI solutions. Field service starts with work orders, then the work is done by the field service worker, then it’s either verified or forwarded to an expert, Dhar said.  Large companies that have 100,000 field service workers are often still working on paper. This is where AI can help, he said. 

SEE: 60 ways to get the most value from your big data initiatives (free PDF) (TechRepublic)

Dhar said many companies no longer have the luxury of fully training new staff because they are facing the challenges of a retiring workforce and attrition rates that can exceed 30%.

“These organizations are finding that they can take advantage of AI by implementing workflows that can guide and provide help to an inexperienced field engineer throughout a problem resolution process in the field,” Dhar said.

The field service challenge also exists at the home office, where a single work order dispatcher might be taxed with issuing orders for many different field engineers at one time.

“An ‘intelligent’ ticketing process is needed for this,” Dhar said. “We can address this with AI by having AI interpret the data, do some troubleshooting of the situation, and then present an outcome to the dispatcher, who is then in a better position to find the best person to address the problem.”

Big data analytics also plays a major role. For instance, if a technician is in the field and is stumped by a problem, he or she can snap a photo of a part with a mobile device and forward the photo to headquarters, where either the AI or a subject matter expert can make a diagnosis and assist the technician in real time.

The AI operates on the big data based on a set of rules and intelligence that have been gleaned from company experts.

“Developing a comprehensive set of rules and field troubleshooting techniques can take three to six months to accomplish because you have to train the AI with what you learn from your experts,” Dhar said. “We develop algorithms based on the information that we obtain from many different experts and keep refining these algorithms and how they operate on big data such as an image until we attain a level of 95% accuracy. At this point, we are ready to deploy the AI for field service support.”

Here’s what companies struggling with their field service support can learn from this.

1. AI and big data can be used for field service efficiencies

Too many organizations still mainly use their unstructured data for management analytics and decision making; however, there is also a real opportunity to include big data such as images and photos in everyday workflows because it can help the operational effectiveness of teams in the field.

2. A combination of AI and big data reduce time to resolution

Customer satisfaction improves when problems are fully and quickly addressed. With so many experienced field service technicians retiring from the workforce, companies can use AI and big data analytics pick up the slack.

3. Introducing AI doesn’t have to be career threatening

“We do find employees initially apprehensive about whether AI is going to take their jobs when we first start working with a company,” Dhar said. “But after the employees see that the AI is there to augment them so they can focus on the more difficult tasks, they are very supportive.”

This is key to any successful AI implementation: Employees need to see a “life after AI” where what they do is still relevant to their employers.

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