6 Smart Ways to Use Predictive Analytics for Propane Inventory Management

Game plan. Treasure map. Magic 8 Ball. What are you using to plan for your business today and into the future? And how do innovative technologies help you make smarter, more efficient and more profitable decisions?

Whether you’re already using predictive analytics for propane inventory management or starting to evaluate how they can drive your operations — and whether you have a team dedicated to inventory management or are running on the leaner side — you likely have questions on making the most of this critical modeling tool. Let’s dig in to the top six questions we hear most often from operators who want to get ahead in the propane industry.

1. How Predictive Analytics for Propane Inventory Management Improves Accuracy

Show me the data! The key to precision, resource management and forecasting in any season is reporting. Reporting allows you to analyze inventory on a dedicated cadence, and successful teams are both specific in the reports they run and consistent with running them.

Reports are a helpful tool to explore customer changes — including year-over-year service contract adoption, adding new customers and accounting for loss. Predictive analytics will then help to drive how much inventory is needed and prepare for surplus in colder months based on historical actuals and customer retention.

Want to make sure you’re getting the most out of your reports? Work with your fuel oil and propane management software provider for tips and tricks. Educational classes and webinars are a valuable resource of how-to information, new techniques and best practices that help report novices and seasoned veterans win in the marketplace.

2. Using Customer Data to Forecast Future Propane Demand

For companies with growth goals (and isn’t that every company!), you need to plan for increased customer counts and their usage. Planning one heating season ahead is a good place to start. Look at customer additions in your current year and those prior to forecast for next season. Layer on gallons delivered, parts used and total sales. With predictive analytics from this data, you can arrive at a growth rate that determines future inventory needs.

The growth planning doesn’t end there. Make sure you’re considering any planned marketing and promotional efforts that could mean additional committed gallons and service components, for example.

3. Integrating Predictive Analytics Into Inventory Management

This is indeed a hot topic, and one that was deeply discussed at the customer roundtable at the annual Connections Live conference. Reporting, insights and predictive analytics are only as good as the data being fed into them. Successfully using predictive analytics for propane inventory management requires the discipline of inventory management to be adopted by the entire company. From bulk tanks to fuel delivery trucks to service and many other aspects, it all needs to be accounted for and accurately recorded.

Each delivery should have a truck assigned to it and include where the fuel originated. Every service appointment should have parts associated with it. For example, it only takes a few (and common) instances of Jim getting a nozzle from Steve’s truck without accounting for it to throw off your inventory. Once the whole team is 100% bought in, you’ll see the trends in usage reflected and minimize risks, like having too much or too little when it comes to inventory and stock levels as impacted by varying demand.

4. Improving Collaboration With Propane Data & Analytics

We’ve discussed the strength of predictive analysis when data and reporting are a part of everyone’s mantra across the organization. Having a data-first mindset for all can then help to guide decision-making cross-functionally and throughout the ranks. There also may be some data “leaders in the making” in less expected parts of the business. Giving rising talent a chance to broaden their skills with analysis opportunities can help in retention and succession planning — a valuable add considering ongoing labor challenges.

5. Using Weather Data to Forecast Propane Demand

Successful dealers, distributors, marketers and other leaders in the industry adhere to the degree day system. Weather data can also strengthen predictive analytics for propane inventory management by helping marketers anticipate changes in demand and plan inventory levels accordingly.

This approach compares the average of the high and low outdoor temperatures recorded for a location to a standard temperature — usually 65 F in the United States. When the outside temperature is more extreme, there is a higher number of high degree days and thus higher energy use for heating. Running system checks as part of predictive analysis ensures that no customers have a K-factor of zero, and that there is supply for the demand — even if that means a cold snap or other potential shift in weather.

6. Protecting Data Used in Predictive Analytics

Working with a cybersecurity provider can help ensure that your business and its assets are protected from bad actors, as well as protecting your customers and your customers’ data. Security experts also can recommend ways to simplify managed network services so you can reduce complexity and save money while also increasing security.

If you’re going at it on your own, keep in mind best practices for security like permission-based access. Multifactor Authentication (MFA) is also a strong component of security. Since MFA requires users to provide two or more verification factors to gain access, it helps protect against cyberthreats. The decision to use hosted servers, rather than local servers in the office, is good since hosted servers typically offer robust data backup. This minimizes the risk of data loss in cyberattacks, but it also gives some peace of mind if there’s hardware failure or natural disasters. In any case, make sure your system is set to back up your data regularly.

Ultimately, predictive analytics for propane inventory management can give marketers greater visibility into demand, inventory needs and operational trends when it is supported by accurate, consistent data.