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Pilot Flying J Takes Off With Infor Coleman AI & Machine Learning

Travel center boosts fuel margin accuracy to 99.99 percent, anticipates revenue increase of $750,000 a year

Infor, the industry cloud company, announced that Pilot Flying J, a leading network of travel centers in North America, is using Infor Coleman Artificial Intelligence (AI) and Machine Learning (ML) to help boost its fuel margin accuracy to 99.99 percent, which the company estimates could increase its revenues by $750,000 a year.

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With more than 750 locations in 44 states and six Canadian provinces, Pilot Flying J’s fuel margins are particularly important, as they drive much of the bottom line and profit. If there is an error in fuel pricing at a location, the company can lose customers, because fuel prices are too high, or lose revenue because fuel prices are too low.

Previously, the company’s finance team spent five days a month closing the books and a sixth day making sure there were no errors in fuel margins. A one-penny error can have a significant impact on the business, according to David Clothier, vice president of finance, treasurer and controller for Pilot Flying J, because the company sells about seven-and-a-half billion gallons of gas per year.

Clothier knew there had to be a better way, and he envisioned using artificial intelligence, instead of humans, to look for errors. Because Pilot Flying J had already deployed an Infor multi-tenant cloud ERP system, he took advantage of its pre-integrated data, analytic, AI and ML capabilities.

In just less than 90 days, Pilot Flying J’s AI application was tested, validated and operationalized. The Infor cloud ERP system calculated and updated data automatically — and anomalies were available to the Pilot Flying J finance team in an Infor Birst analytic dashboard.

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Instead of the finance team tediously looking at thousands of line items in a report to check for fuel margin errors, the dashboard can visually present anomalies that may need human investigation and resolution. And the dashboard is designed to help the applicable customer teams answer questions more quickly and accurately. Does the data make sense versus budget? Does it make sense versus last year? Are the trend lines okay?

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Pilot Flying J anticipates that the Infor solutions, by helping the company save time and improve fuel margin accuracy, will produce a number of financial and operational results:

  • With up to 99.99 percent accuracy in fuel margins, better pricing and supply decisions can add many basis points to the fuel margin, where an increase of 1/100th of one penny can mean $750,000 a year.
  • With Infor Coleman machine learning checking for fuel margin errors, Pilot Flying J can redeploy the equivalent of two full-time equivalents (FTEs) to other finance work.
  • The company anticipates a 1,000 percent return on investment (ROI), attributed to FTE savings, and a .001 per-gallon increase in revenue without procuring additional hardware, software or specialized skills.
  • Pilot Flying J also expects that the Infor AI & ML application will boost employee satisfaction, because employees can produce more accurate data in significantly less time.

“We didn’t have to purchase any additional software to create an innovative service to deliver to our finance team,” Clothier said. “We leveraged the technology we already had to help solve a business problem. There’s very little incremental cost, so the ROI is fantastic.

“Infor brings to the table a suite of products that we think is as powerful as any ERP system on the market,” he added. “Infor is genuinely interested in serving our needs.”

Going forward, Clothier plans to use Infor Coleman AI to help optimize Pilot Flying J’s workforce scheduling. Pilot Flying J discovered that the more data and feedback provided, the better the AI results. Therefore, with detailed, time-stamped data at the item and store level, Pilot Flying J expects to be able to predict the next 13 weeks of sales revenue, down to the department level, according to Clothier. These predictions can be fed into the workforce management system to help optimize scheduling of the right employees at the right time of day.

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