Let's solve the task. The conditions are the following:
there are 2 bulldozers working on the construction site (manufacturer, model and year of manufacture are the same). The average consumption of the first bulldozer is 18 litre/hour, the average consumption of the second is 14 litre/hour.
The question is Which bulldozer is more profitable for the owner of the machinery?
The answer "second one” looks obvious. But the answer requires much more information, because fuel consumption depends on the course, speed, load and other parameters.
So, in our example, the first bulldozer worked at high engine speeds, burning 18 litres of fuel every hour, and the second one worked without load (at idle) 40% of the time. It travelled a shorter distance and did less work than the first bulldozer.
It seems that now the answer about the efficient operation of the second bulldozer looks wrong. The efficiency was shown by a machine with high fuel consumption.
ICU, a partner of Galileosky, has been using the methodology of building a system of interconnected data and identifying their correlation for several years to assess the efficiency of equipment operation. ICU specialists generate reports based on data received from sensors, structure data, and form mathematical models for indicators computation. This approach allows you to get all the information about the operation of machinery and make the right conclusions based on the data.
How does it work? Ararat Ikilikyan, CEO of ICU, told us.
Restored fair wages and reduced fuel theft
ICU was contacted by a road construction material carrier. The company's management set the task for ICU experts – to evaluate the efficiency of the fleet and to analyse the employee remuneration.
The thing was that the wages of the drivers depended on the length of the route they drove: for each kilometre travelled, the driver received payment. Firstly, data about the length of the route was recorded by the standard odometer of the trucks, and then a monitoring system was installed on the vehicles, so the payments were counted based on mileage reports. The mileage report data from the telematics system and odometer data were not the same. For the same amount of work, drivers received different payments and felt cheated. Both the management and employees of the company understood that there was an error in the data, but what exactly and what its cause was necessary to be clarified.
ICU specialists have found the reason for the difference in the values: some cars had tires that differed from the sizes recommended by the manufacturer by several inches. This led to the fact that the odometer showed less mileage than it was. For the correct calculation of wages, ICU experts recommended the customer check the mileage data from the odometer and from the telematics system and calculate the wage based on the averaged data.
In addition, a special system was developed for the customer. It collected all the data about the fuel consumption from several sensors, processed it and checked it several times. So, the system compared the data from the CAN bus with the readings of the fuel level sensor in the tank. The solution made it possible to obtain correct information about fuel consumption and exclude the possibility of fuel draining from the return line. The company's management received accurate information to encourage conscientious drivers (their wage was increased by 20%).
Ararat Ikilikyan, CEO of ICU
It is important to provide the client with the opportunity to compare data on machinery received from different sensors and processed by different tools in the same report. This allows you to analyse and trace dependencies that were previously invisible. This approach will allow you to look at the problem globally and solve the problem properly.
Saved 500,000 rubles on fuel and reduced the idling of equipment by 3.5 hours without compromising work processes
The ICU analytics also implemented data collection system for a company with a CAT fleet. The transparent picture of the figures made it possible to optimize the costs of the fleet, form profitable and efficient work schemes, track the dynamics of parts wear and find a way to use fuel more efficiently.
The customer's quarry machinery spent more than 4 litres of fuel per hour of idling, which is about 20 litres per day. Multiply by 25 working days, we see that one vehicle spent 500 litres of fuel per month. In monetary terms it is 25,500 rubles if the price is 51 rubles per litre of diesel fuel. ICU specialists also found out that up to 100 inefficient engine hours are accumulated in a month due to idling for some of the vehicles. The regulations for quarry equipment maintenance supposes maintenance every 250 hours with mandatory oil change in the units. Engine of the CAT D6R bulldozer requires 26 litres of oil, the hydraulic system requires 76 litres of oil. In addition to the price of oil (51,000 rubles), you need to add the work of specialists and the cost of down time of the equipment during its maintenance. Taking into account all the expenses, one engine hour costs 300 rubles. If multiply it by 100 engine hours we will get the savings of 30,000 per month on one machine. Add to this figure the hourly payment for the work of the machinery operator and the rental fee if the car is leased. By reducing the number of idling and increasing the efficiency of only one CAT bulldozer from 56% to 95%, the ICU customer eventually saved more than 56,000 rubles. If you multiply this result by 10 inefficient vehicles, the monthly savings will be about half a million. It is more than enough to cover the cost of telematics system installation.
What parameters does ICU use to evaluate efficiency?
Collecting data is just one small thing. The main thing is to structure the data and prepare them for analysis. For example, there are fragments of the productivity report of the CAT bulldozer. It contains real indicators obtained as a result of comparison and recalculation of the initial data.
Let us look at them and determine how to calculate the necessary indicators to analyse the overall efficiency of machinery.
- Engine hours
- Time in motion
- Operating time at high engine speed
- Productivity in motion
- Productivity of work at high speed
- Average productivity
- Mileage
- Initial fuel level by FLS
- Final fuel level by FLS
- Spent by FLS data
- Spent by CAN data
- Difference between FLS and CAN data
- Consumption error (FLS and CAN)
- Average consumption in m/h
Indicators calculation
"Productivity in motion" is obtained as a result of dividing the working hours by the time in motion.
"Productivity at high speed": the engine speed obtained from the CAN bus must be higher than idle. Next, the operating hours are divided by the time at high engine speed.
"Average productivity" is the average figure of productivity in motion at high engine speed.
"Average consumption in m/h" is the fuel consumption by FLS divided by engine hours.
A high error in the consumption of FLS and CAN in the 13th column means that the driver drains fuel from the return line, since the engine had used less fuel than it left from the tank.
Devices
Galileosky 7x C
GPS tracking unit with maximum number of inputs for facing main monitoring challenges
