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Return on Investment is calculated by comparing the cash flows of alternative decisions. More simply put, ROI is based on the analysis of differential cash flows. In the case of remote data acquisition and aggregation systems for fuel tank operators, it is based on calculating the cost of acquiring and aggregating the data manually and compared to the total cost of owning, maintaining and operating an automated data acquisition and aggregation system. ROI is essentially the comparison of costs associated with a labor-intensive process versus the costs associated with an automated process, and determining how long it takes this cost difference to account for the investment in the automated system.
Site Level Data
Calculating the Cost
At each of a company's remote sites, there are multiple activities that, taken individually, do not seem to add up to much. However, when spread over dozens of sites, the total expenditure mounts quickly. Tank levels are measured manually ? either by "sticking" a tank or by visually inspecting a tank gauge and writing down a reading. This task is often performed several times a day, plus additional readings if a delivery to the tank in question takes place. The cost of the tank level readings (Cl) is therefore the time (t) it takes to complete each reading multiplied by the associated cost - salary and benefits ? of the individual that takes the readings (Sl), further multiplied by the number of readings (n) and again multiplied by the number of sites (N) as the same activity is repeated throughout.
Cl = t*Sl*n*N
At specified intervals, the tank level data must be compiled and transmitted. Depending on the type of facility, this occurs once a week up to two or three times a day. This compilation and transmission again consumes some time, the cost of which must be calculated in a similar way as the level readings. In addition, there may be costs associated with the transmission of this data that should also be taken into account.
The Risk of Human Error
Each time there is human intervention in the acquisition, compilation and transmission of data, the chances of error increase exponentially. In addition, when depending on personnel to complete these tasks, there is a risk of delayed or missed transmissions. The result of these problems is that the data received at the other end will frequently be either erroneous or outdated; therefore, decisions based on it will also be prone to errors.
Automated data acquisition and aggregation systems do each of these tasks without human intervention, hence greatly reducing or eliminating data errors and freeing up resources for other activities or eliminating the need for the resource altogether. This ability to reduce or redeploy resources is incremental cash flow.
Optimal Performance
There are multiple reasons for companies to aggregate site specific fuel tank data at a centralized location. One desire is to attempt to optimize the logistics of fuel distribution to multiple sites. This is true to both vendors and buyers of fuel. Both need to know what products to buy or sell and how much for each location. They want to deliver it at the lowest possible cost (which translates to the least amount of deliveries) without incurring any run-outs. If the data used to make these operational decisions is corrupt (see The Risk of Human Error), the operational decisions themselves will be erroneous. Knowing that their data is less than perfect, in order to avoid run outs, many companies opt for overservicing a tank or location, which is also inefficient.
Automated data acquisition and aggregation systems provide accurate and timely data. By ensuring the accuracy of the data, errors in decision-making processes are greatly reduced. The result is more effective decision- making that leads to more efficient performance: elimination of run-outs alongside a reduction in service trips. These associated savings go directly to the bottom line and are a fundamental component of the ROI calculation.
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