All posts by AssetTrackr

TPMS – The technology that gives real cost benefits in 2020

As the fleet business is expanding, fuel and tyre expenses tend to grow exponentially for commercial vehicle owners. There is an immense pressure in the market to remain in the race with shrinking profit margins. For commercial vehicle owners, fuel and tyres can affect the TCO(Total Cost of Ownership) by up to 50%. Tyre expenses tend to remain one of the most overlooked aspects of fleet business.

Tires wear out; that is inevitable. However, the longer the tires stay in good shape, the more money you make. Over-Inflation and Under-Inflation tend to wear out the tires faster. A good Tire Pressure Monitoring System(TPMS) can help you monitor this and optimize the tire-life.

How Tyres can affect fuel consumption related expenses

On an average, a commercial vehicle is fitted with 6 tyres. Tyres typically have an average life of 40,000 km. A vehicle covers 300-350 km a day, covering over 6000 km per month on an average. Considering an average mileage of 5 km per litre, fuel expense can average to about Rs. 84,000/month(at Rs 70/litre) for diesel, the commonly used fuel for commercial vehicles in India. Annualized, fuel costs for a vehicle could be up to Rs. 10,08,000!

At 6000kms month, a truck will have to change tyres every 6 months. With an average price of Rs. 20,000/tyre, a 6 tyre vehicle would need to two tyre sets at a total cost of Rs. 2,40,000. Using rethreaded tires, this cost can perhaps be reduced by about 30% to roughly Rs. 1,50,000/ year.

Effects of Under-Inflation

Under-Inflated tyres have a significant impact on fuel efficiency. Increased rolling resistance is the result of under-Inflation, increases the area of the tyre in contact with the road and engines overwork to overcome the resistance. Based on the observation by Singtech, a Singapore based company, under-Inflation causes up to 6% loss in fuel efficiency. Translating that into costs, it amounts of an increased cost of about Rs. 60,480 for fuel. This is a significant amount of wastage that can be avoided with the help of TPMS. 

Effects of Over-Inflation

Over-inflation of tyres is a serious safety hazard and also reduces tyre life by about 25%. Assuming this, over-Inflation can have an annual cost impact of up to Rs. 50,000 for tyres

TPMS is an electronic system designed to provide real-time data about tyre pressure by monitoring the air pressure inside the tyres. TPMS warns the driver about Under-Inflated or Over-Inflated tyres. Using TPMS to monitor tyres continuously and adjusting inflation levels can save fuel costs and extend tire life, while also improving overall vehicle safety. The typical cost of a 6-tyre TPMS system is about Rs.23,000.Based on the potential cost savings in fuel and tyres, TPMS investment can be recovered in less than 5 months.

Conclusion

While there is an upfront investment needed for TPMS, longer-term, return on investment is very clear. Also, monitoring and maintaining tire pressure also is critical for vehicle and driver safety and helps fleet managers avoid unwanted expenses due to accidents, adding up to profits.



Using Machine Learning to manage fuel consumption

Fleet Management, Asset Tracking, Field Force Automation are application areas getting a lot of attention as businesses realize the value of being able to Track their high-value assets, Analyze historical data to derive insights and Optimize business processes for efficiency. Businesses are continuously generating gigabytes of location and other ancillary data which very often get summarized into simple reports.
Machine Learning methodologies can help with developing predictive algorithms and fine-tune them as data is continuously applied against the models. Examples of some such insights are travel time prediction, tire wear and tear, fuel consumption analytics, replacement of disk brakes, belts etc.
AssetTrackr has been collecting telematics data for over 6 years and amassed terabytes of information! The data collected and analyzed has helped many of our customers’ fine-tune vehicle routing to avoid congested areas, schedule travel to avoid peak traffic hours etc. Besides monitoring, many customers have been able to optimize warehouse operations like loading/unloading to minimize wait times at the docks. 

A recent focus has been on addressing fuel consumption and optimization. AssetTrackr offers a non-intrusive Fuel Sensor solution to customers. Besides being able to monitor fuel levels in trucks and preventing pilferage, data is collected and analyzed to effectively predict current & future fuel consumption in optimizing fuel economy for fleet managers. Apart from that, accuracy in fuel consumption report helps fleet managers keep a track of abnormal fuel consumption, giving them an insight into fraudulent activities or other factors.

What affects fuel consumption?

Fuel consumption of a vehicle depends on several internal factors such as distance, load, engine tuning, vehicle characteristics, and driver behaviour, as well as external factors such as road conditions, traffic and weather. However, not all these factors may be measured or available for fuel consumption analysis. Hence, it’s a challenge to model fuel consumption with limited data while taking all other influences into consideration. This is where Machine Learning provides excellent value by studying and using various data and patterns to create highly accurate customized fuel consumption analysis.

So, what is Machine Learning?

Machine Learning is a field of computer science which gives the computer the ability to learn without being explicitly programmed. It is defined as the practice of using algorithms to extract data, learn from it, and then forecast future trends for that topic. It is closely related to the field of computational statistics where we build a mathematical model for data prediction. Building this model can be done either by supervised learning or unsupervised learning. 

How AssetTrackr uses Machine Learning

AssetTrackr uses smart principles of machine learning to track, manage and analyze fuel consumption of your vehicle. Fuel tanks are generally irregular in shapes and projecting fuel level at any instance is difficult not just for these kinds but also for regular-shaped tanks. Also, inside the tank, often there are reinforcement rings, partitions, mechanical fuel level floats etc. which makes it difficult to use a mathematical formula.
 AssetTrackr uses machine learning principles where the model is trained using supervised learning. During installation, we take calibration data in terms of fuel volume and sensor height. Sometimes it is not feasible to do the calibration of the tank because of it’s large holding capacity. For those cases, we apply unsupervised learning where we start off with mathematical formula based on structural mechanics to calculate fuel and iteratively update the formula to reduce error over a period of 2-3 weeks until we get the desired accuracy. This process is carried out in unison with the customer where we require data related to refills and pilferage during those intervals. 
We then use this data to train our model using regression with gradient descent(a process of reducing error from the model). Amongst various regression models, we choose the best model for your tank with minimum error percentage. Once the model is trained we do validation to check the correctness of the model. 

We have also studied the consumption pattern of the vehicle across segments and we currently classify vehicles in three categories:

  • Low consumption, usually 4 tyre vehicles.
  • Medium consumption, usually 6-10 tyre vehicles.
  • Heavy consumption, usually 14-18 tyre vehicles.

Across these segments, we have studied the consumption rate and has successfully developed a smart mechanism to detect pilferage, refills and even abnormalities in fuel consumption during trips. Since our solution is customer-oriented we also provide in-depth analysis of mileage using speed profiling. We study the consumption vs speed pattern of a vehicle during a trip and categorize it into various speed categories along with the consumption rate. This gives us insight about how speed affects the consumption rate.

Machine Learning in Telematics Industry can have a significant impact on fleet management, showing the way to more detailed predictive analysis and add tremendous value to Total Cost Of Operation (TCO), Fuel Consumption Management, Vehicle Safety, & Predictive maintenance in fleet operations. 




AssetTrackr Helps Daily/Dina Thanthi Streamline Their Newspaper Distribution and Address Missing Newspaper Bundles Issue

Daily Thanthi Uses AssetTrackr GPS Tracking Device

Daily/Dina Thanthi, which was started in 1942, is one of the most popular and largest Tamil daily newspaper in the country. It is printed in more than 16 cities across India including Chennai, Madurai, Dindigul, Tiruchirapalli, Tiruppur, Thanjavur, Tirunelveli, Nagercoil, Coimbatore, Salem, Erode, Vellore, Cuddalore, Pondicherry, Bangalore and Mumbai. Everyday about 17 Lakh copies are sold and it has been recognised as India’s no.1 Tamil newspaper. However the main concern for the Daily Thanthi has been to ensure that on daily basis all their copies reach their readers in time. So, they are in need of a vehicle tracking software to keep track of their deliveries. Continue reading AssetTrackr Helps Daily/Dina Thanthi Streamline Their Newspaper Distribution and Address Missing Newspaper Bundles Issue