A sales executive with our company was dealing with a prospect recently who presented an issue that we see over and over in our sales activities. This company has invested in automatic tank-gauging equipment, an aggregation engine to bring that information together, as well as an execution engine for carrying out the decisions. But, when asked about how the actual inventory replenishment decisions are made, the contact explained that he receives a batch of information every day from which he makes a decision.

In his mind, this process is automated. He automatically receives a batch of information which he studies, then makes the necessary ordering decisions, which he then enters into their execution engine to be carried out as orders.
It makes sense that companies often overlook automating the decision-making part of this process, even when all other components of asset requirements planning (ARP) are automated. It makes sense because making a decision is easy. However, making an OPTIMAL decision is hard.

Logically, people can see why they should automate the data collection component of ARP. Data collection is hard. Performing this task manually requires various methods of physically checking the level of inventory in each tank at each site. So, automating this component with automatic tank gauging equipment obviously saves time and effort and reduces the incidence of human error.

Similarly, it is simple to see the benefits of automating the aggregation of this data. Manual aggregation is hard—it requires phone calls, faxes, emails or other methods of communication to centralize the data in one place, often an overwhelming and complicated spreadsheet. Automating the aggregation-- that is, bringing all the data together in one place where it is easily retrievable and viewable from any location-- again reduces the time and effort required by this component.

Lastly, the execution step performed by ERP systems is hard. The manual effort involved with organizing and carrying out the steps of planning resource procurement for the entire organization is precisely the reason ERPs are so pervasive today. It makes logical sense to automate this component as well.

However, the analysis of the data, the actual decision-making part of the process, doesn’t appear to be very difficult. Anyone can make a decision about replenishment, right? “I am almost out of this product. I will order more now, so that I don’t run out.” Decision made, problem solved.

But this third component of wet-stock inventory management isn’t just about making a decision. It’s about making a good decision. And making a good decision requires a lot more information. In addition to the current tank level, good replenishment decisions require information about tank capacities, average daily consumption rates, bottom safety levels that ensure sufficient safety stock, optimal fill levels for regulating excess inventory, information about which tanks that are alike and can be serviced on the same delivery and more.

Without that kind of information, it is impossible to make a good decision. When a decision is not a good decision, it might be:
  Late
  Forgotten
  Wrong Product
  Too Much Product – Risk of Overfill / Haul-back
  Too Little Product – Repeat Orders
  Emergency Order – Disrupts Production Schedule

Unfortunately, many companies automate data collection, aggregation and execution, while still depending on a person (or several people) to perform the analysis of the data and make sure the tanks are serviced properly. Even more unfortunate is that in large companies with hundreds of assets, it is extremely difficult for such analysts to look at and process all the information required for making a good decision about how to service each individual tank. By automating this event, companies can allow software to analyze this necessary information and translate it into suggestions about how the tank can best be serviced.

An important component of good decisions is timeliness of information. Since analysis software is constantly being fed that most updated and accurate information, suggestions are near real-time. And instead of reviewing each tank to find out if it needs service at all, users are presented only with tanks that require attention. All the analyst would have to do then is review and accept these suggestions, or the suggestions can be fed directly into an already-automated ERP system.

Only by automating this part of the inventory management can companies achieve the true benefit of an ARP system. Only then will these companies be able to make good decisions about every asset they own.

This exclamation has become a daily mantra for some. Here in Oklahoma, where gas prices are generally some of the lowest in the nation, we are currently paying $3.87 a gallon for regular unleaded gasoline, down from a high of $3.89 last week. While that is shocking to us, customers in other areas of the country are paying well over $4.10 a gallon, the current national average price.

Drivers, angry at paying over $50 to fill the tank of even an economy vehicle, are looking for someone to blame. All too often, the blame lands squarely on the shoulders of the retailers selling the fuel.

Unfortunately for them, retailers are making less and less per gallon as the price of fuel rises. More frugal driving habits, less in-store purchases and escalating credit card fees keep profits on gasoline to a minimum. With customers already on the verge of mutiny, retailers can’t depend on customer loyalty to keep regulars coming back for fuel if they dared to raise their prices high enough to cover the cost of doing business and bring in a small profit.

So, what is a solution that is good for the (hopefully) short-term crisis and long-term growth of the company? How can a retailer reduce the amount of capital in play without risking run-outs to further incite the mob?

A top priority at this point should be optimizing inventory management. Retail fuel executives have to ask themselves how well they are currently balancing supply and demand. How accurately are they able to forecast future demand, particularly in conjunction with price changes, promotions or upcoming events?

The bottom line: Efficiency and clear, well-informed decisions are the keys to surviving and even thriving in the current economy.

Business Intelligence is the ideal solution for a retail fuel executive to be able to take control in a market that many deem uncontrollable. There is a strong need for analytical tools that provide forecasting, optimal tank level information and even suggested orders that can be integrated with an existing ERP system. In other words, Business Intelligence in a retail fuel environment takes all the effort and guesswork out of optimizing inventory management and allows business to perform at top levels, even in today’s economy.

Analytics?

One part of the “whole product” we noticed that Reid didn’t address at all is the previously mentioned need for analytic software. As he is limited by the technology Veeder-Root has to offer, one might guess why mention of this important tool escaped his attention.

Analytics software has become critically important to any organization looking to optimize its inventory levels and create a smooth supply chain. As Tony Mills of OPW asserts in the NPN article, “You want fuel to be constantly available so that you never run out. But with the volatility of fuel prices today, you might not want to completely fill the tank every time you get a delivery. When prices are high, you can have a lot of dead money sitting in a full storage tank of gasoline or diesel.”

We absolutely could not have put it better ourselves. Sure, it’s great to know how much fuel is in your tanks at the touch of a button, assuming you are on-site and able to do so. But how much more valuable would it be to have an extremely tight forecast of what your fuel usage will be over the next 24, 48 or 72 hours? How much more informed would all of your P & L actions be with the decision support of such a tool? How much value could you create by keeping cash in the bank and not in the tank? How much better would you be able to meet customer demand while doing so at the least cost?

For instance, in a c-store environment, managers take great care to track dry goods inventory. They use enabling technologies such as UPC scanners and ERP systems to effectively manage their in-store inventory by tracking sales levels and patterns to forecast inventory needs. Many use these systems to enable vendor-managed inventory, which allows the product supplier to track inventory levels and make delivery decisions to best serve the c-store. It makes sense, then, for these retail locations to make the same effort in managing their wet-stock inventory, since a company that doesn’t know in advance that they are facing a impending run-out situation will suffer not only wet-stock sales losses, but also a big drop in dry sales.

The whole product—data collection, aggregation, analysis and execution—carries a huge amount of potential for companies to optimize fuel management by reducing inventory while increasing available cash. The trouble is, our competitors don’t appear to have entirely caught up yet. Some tout data collection as the most important investment for their customers, with no consideration for what will be done with the data after it is collected. Others are beginning to see the value in aggregation and analysis, but focus on environmental compliance and dispenser monitoring—selling on fear rather than by the huge amount of value that inventory optimization can bring to an organization.

How can a company possibly bring you the enormous potential that the whole product brings if they have failed to even recognize the need for a whole product?

At WorldTelemetry, we know we have great wireless data collection hardware that is intrinsically safe. We’ve spent a lot of time and money to make sure it is extremely reliable. It’s easy to install, simple to move if necessary and it doesn’t require any on-site downtime due to digging and trenching. However, if you have already made an investment in an ATG system, we are certainly not going to ask you to buy our equipment.

Our managed services include a leading-edge datacenter that aggregates collected tank information securely and a web interface that allows you to access tank information from anywhere in the world with a connection to the Internet. Yet, again, if you already have a successful data aggregation hub, we’re not going attempt to sell you ours.

WorldTelemetry’s analytics software is top-of-the-line and is developed further every day to give our customers the absolute best decision support tools for fuel management. However, if you feel your analytics software is good enough, we won’t ask you to buy ours.

Ours is not a business of selling a widget and walking away. Our intent is to explore your current fuel management situation with respect to the whole product and see where we can fill gaps. Where can we bring value to the organization by protecting its existing investments and making them work harder for the company?

Separately, the pieces of the whole product are simply time-savers that sometimes remove instances of human error. It’s faster to read a console than to manually stick multiple tanks. It’s faster to automatically aggregate the data than it is to manually enter data into a spreadsheet. But do these pieces really help optimize inventory? It’s the whole product—machine-to-machine feeding data from the edge of the network through an aggregation hub into an analytics engine-- that delivers the support for quality decisions leading to optimized wet stock management.

We agree that companies need data collection. They need timely, accurate data about their assets. We agree that companies need data aggregation for visibility, but we believe it should be managed at a centralized location, with few decision-makers rather than many autonomous remote locations. We also believe that companies need analytics software in order to have premium decision support and to create rules at a micro level—managing each remote location (even each tank) based on its individual needs.

When you put it all together—data collection, aggregation, analysis and execution—the result is the “whole product” with huge potential for turning excess inventory into working capital and truly optimizing inventory management.