Collecting and understanding that data has never been more important.
Jason Cassidy, Shinydocs
From tracking sales to managing loss prevention and keeping updated maintenance logs, the amount of data and information required to successfully run retail gas stations is daunting. However, with the industry in flux, the importance of collecting and understanding that data has never been more important.
It is no secret that there are fewer gas stations in Canada as a result of rising land costs, urban density, the availability of public transit and the popularity of electric vehicles. According to a survey conducted by MJ Ervin & Associates, there are fewer than 11,850 gas stations in Canada, down from more than 20,000 in 1989. Those that do remain are larger and offer more enhanced services like car washes, retail stores and coffee shops.
These services are critical to increasing profit margins, but they come with additional data points and paperwork that need to be managed such as inventory logs, point of sale transactions, loss prevention, competitor pricing, utilization rates, loyalty programs, supply chain and distribution logistics and more. This is on top of the information traditionally collected by gas stations, such as fuel measurements, leak detection, washroom maintenance logs and safety inspection reports.
Management of that data is key to make informed business decisions and drive growth and revenue. Unfortunately, the sheer volume of information and the silos in which it is collected and stored is becoming increasingly problematic. For example, product inventory data often lives in a back office system, while sales data is collected in the point-of-sale system where all transactions are recorded. If these systems are incompatible or the data is not easily reconciled, it is difficult to determine the most popular sale items.
Artificial intelligence can help businesses make sense of data. For example, it can automate order processing and inventory management by integrating all data sources. It can provide managers with an optimal purchasing plan by analyzing all available information including demand forecasting and supply chain constraints.
Outside of the retail environment, AI can also help improve facility maintenance by collecting and interpreting data from cameras, maintenance logs, sensors and other sources. The technology can then detect any unusual patterns in equipment behaviour, identifying what needs to be repaired or replaced. AI can also be trained to look for high-risk behaviour at gas stations, such as people driving recklessly, theft at the pump or improper fuelling. For example, if a person is smoking at a gas station, an AI model can identify that as high-risk behaviour and take action to shut down the machine.
But even AI needs a little assistance when it comes to determining what data is useful. If data is collected and carelessly stored in different programs, across different platforms and tagged using different keywords, the data will more than likely be inconsistent, inaccessible and saved in different file formats that need to be reconciled. There is no AI tool in the world that will be able to interpret that data in a way that drives sound business decisions.
In order for business owners to be in a position to successfully incorporate AI solutions into their operations, we need to create a model personalized to their data. In other words, we need to prepare data in a way that allows it to be analyzed or leveraged to provide business insights.
While this task may seem daunting, it has never been more important. The amount of information business owners or managers have to sift through is beyond human consumption and it is only going to get worse. Adopting AI solutions will be necessary for businesses to thrive in the future. Right now, we are at the critical “preparation” phase, requiring a deep dive into our internal data ecosystems.
It is important to note that another “tech solution” may not be the answer. In fact, downloading another app or data management platform may make things worse. Instead, we have to take a step back and determine what decisions we are trying to make as a business - are we struggling with purchase agreements? Competitor pricing analysis? Detecting infrastructure vulnerabilities? Determining how supply chain disruptions are impacting our day-to-day operations? Once you have determined what decisions we are trying to make, we can begin to structure data in a way that allows us to leverage its contents.
The crisis for retail gas stations is real. Car-centric cultures are becoming an unsustainable reality, contributing to an uncertain future for the retail gas industry. We need to make sure that now, more than ever, we are using all of the tools at our disposal to ensure retail gas stations remain relevant and profitable.
Jason Cassidy is the CEO of Shinydocs, an information management software solution that automates the process of finding, identifying and actioning data.