An omnipresent problem in several metropolitan cities which adversely affects most of us is the stalling of mass transport vehicles, which many a times do not undergo timely interventions via predictive maintenance or have no a priori indicators reflecting the true state of the engine’s condition. Even if there are early signs of potential malfunction, they are at best neglected or are not discernible to skilled operators, thereby resulting in sudden failures and many a times causing inconvenience to the masses by creating traffic jams or blocking the roads.
The number of failures is higher among the buses that operate on a supposedly cleaner CNG fuel, mainly because of the fleet operator’s compulsion to adhere to stricter emission norms set by the government. These buses are powered by Bharat Stage IV engines with electronic management systems that creates new opportunities to streamline fleet management by constantly drawing inferences and correlations from ‘multiple sensor data’ tapped in real time and pushed to the cloud for predicting potential failure modes. Failures is a reality and to bring some perspective in terms of numbers, as per a report published in one of the articles of Times of India dated Apr 20, 2016, the situation in Pune, India, was as follows:
- Number of breakdowns in Oct 2015 were 5505 and in Mar 2016 were 4404
- Daily Average breakdowns were around 177 in the month of Oct 2015 and 142 in Mar 2016
- Accounted breakdowns imply the vehicle stopped completely
- Some of the common reasons for most of the breakdowns were:
- Lack of Preventive Maintenance
- Engine Overheating
- Lack of technical staff
We at Intangles decided to look into the problem by working with one of the largest private fleet operators tendering vehicles for municipal transport and assessing if we could improve their operational efficiency with our technology. The fleet operator has made the best effort to streamline operations by dividing his maintenance operations at multiple sites scattered across different locations within the city. However, despite their best optimisation of resources, there are some complex maintenance paradigms that create a bottleneck and impact top-line margins.
Our approach towards solving the problem was isolating the wastage in the system and focusing on how we could impact the bottom line. Before we delve into how we solved the problem, let us understand the complexity associated with the current ‘maintenance’ paradigm. We see that there are some common issues and operational constraints faced by all major fleet operators in India.
Each operation is divided into multiple Hubs/Branches. Each Hub has a maintenance team working in three shifts. A hub has around 50 buses attached to it. These hubs are spread across the cities to ensure good coverage for timely maintenance. For a maintenance team to do preventive maintenance they have a scan tool which costs north of Rs 1 Lakh and needs to be connected to a laptop. A Vehicle scan typically takes around 15 mins during which potential engine malfunction needs to be identified.
With 50 buses it would take around 12 to 13 hours every day. A team typically gets a maintenance window of 2-3 hours in a day for a healthy vehicle and a whole day for fixing a vehicle which is malfunctioning and has stalled. The priority always goes to stalled vehicles as every hour of non-operation can result in a penalty of about Rs. 500 per hour in addition to revenue losses incurred by the operator.
We started the pilot by deploying devices on the worst performing vehicles. Vehicles were categorised as poor performing based on low mileage and maximum downtime due to breakdowns. We deployed ‘Ingenious’, our proprietary telematics system on these poor performing vehicles. Ingenious is one of the most advanced Vehicle Health Monitoring Systems with capabilities to predict and benchmark vehicle performance. Its proprietary algorithms can help predict vehicle performance degradation well in advance. Ingenious also helps benchmark the vehicle performance with its pears thereby helping individual owners and fleet operators take informed decisions pertaining to maintenance.
To quantify the efficacy of Ingenious, the following vehicle performance indicators were implemented.
1.’As Is’, which means logging shortcomings for seven days without undertaking repair
2.‘Post maintenance improvement’ based on the recommendations by Ingenious
Vehicle performance was measured in KMPKG (CNG fuel is measured in KG’s). The ten worst performing vehicles from two Hubs, namely, Nigdi and Bhosari in Pune were selected.
We then opened up our vehicle health monitoring platform to their maintenance team. At Intangles, we constantly strive to make the ‘mined data’ self-explanatory and easy to consume so that the maintenance team feels empowered and well informed to perform their task in the most efficient way.
A plethora of parameters and corresponding analysis were exposed to the fleet managers and the technical team. The maintenance team performed all the repairs based on the repair strategies recommended by Ingenious. The following is a brief subset of all the performance parameters that were exposed.
- Mileage in km/kg
- Fuel rate of the vehicles at peak performance limits
- Engine temperature as a function of engine load
- Engine control unit error codes with causes, symptoms and repair strategies
- Engine air intake characteristics
- Engine fuel intake characteristics
- Characteristics of combustion by products
- Engine electrical system output
- Engine torque output
- Driver profile dependencies like gear utilisation and idling
Ingenious infuses ‘Artificial Intelligence’ into the vehicle upkeep process by suggesting repair strategies after correlating causes and symptoms and regressively mapping it with vehicle performance pertaining to various sensors.
Post maintenance the vehicles have been on road for more than 2 months now and the statistics of the improved performances are as follows
We see that vehicles have shown remarkable performance improvement in Nigdi depot and marginal improvement in Bhosari Depot. Where Nigdi depot had a performance improvement of Rs 0.76 per KM, Bhosari depot had an improvement of Rs 0.25 per KM only. In terms of fuel consumed, Nigdi Depot saved Rs 760 per day per vehicle whereas Bhosari depot saved Rs 257 per day per vehicle. Less than appreciable improvement in Bhosari depot can be attributed towards driver behaviour and certain areas of improvement have been identified by the maintenance team and work is under way to raise the performance.
However, the point noteworthy is that Ingenious has resulted in predictive maintenance with absolutely no downtime, which has lead to improvement in overall operational efficiency. The savings are staggering if we consider the large scale of operations. For instance, savings of about Rs 760 per day can fetch you Rs 277,400 in saving per bus per year and Rs 257 per day in savings yields Rs 93,805 per vehicle per year.
While one can do the maths for the entire fleet of 2000 Municipal transport vehicles plying in Pune, we also found that the fleet owner rewarded the Nigdi depot with bonuses for the maintenance team while the Bhosari Depot was deprived of the same. As we move ahead, it is becoming increasing clear that data is the key, which can help streamline operations and Internet of Things (IOT) as a means to this data has a vital role to play.