Beating Driver Fatigue on Long Hauls
Business leaders like to back their own judgement. For many of them, gut instinct has played a major part in getting them to where they are today. After thinking that way for many years, making decisions based on data analysis is a difficult leap to make.
A recent report by The Economist Intelligence Unit suggests Australians are more reliant on feeling than their peers, with more than two-thirds (68 per cent) of local executives saying they rely on their own gut instinct or that of a trusted colleague. The global average across 1135 C-level executives and board members who took part in the study was 58 per cent.
Only 29 per cent of global executives, and 22 per cent in Australia, make big decisions primarily on knowledge gained through data analysis. Yet anybody who is remotely progressive realises there are huge benefits in trusting what the numbers say. Let me give you three real-world examples:
Scenario 1
Let's say you're running a construction business. When an earth-moving truck pulls into the site, the driver puts it into waiting mode and slowly reverses back into position. The excavator fills it up and, when the truck is ready to leave, the driver flicks it back into driving mode. With so many trucks on a construction site repeating this cycle at any given time, management has no way of tracking how many hours they're in waiting mode. How does efficiency at one site compare to another? When a lot of trucks come in to be filled at the same time they have to wait in a queue, burning fuel as the engines idle. This could account for 30 minutes per truck, per day. If you're running a fleet of 100 trucks, that's 50 hours per day.
Solution: Data analytics can be used to build a key performance indicator that monitors the performance of individual trucks, shifts or different sites. You can set targets so that management receive alerts every time a truck racks up more than 30 minutes per day in waiting mode. Maybe the data will show you have too many vehicles in one place at the same time. Maybe they can be redirected to an alternative site. Providing more detailed information about operations means you can minimise idle time when planning a big job, making sure the fleet is evenly spread across available sites. Use historical data to better plan your next job.
Scenario 2
Now imagine being the national fleet manager of a transport company. Speeding is a problem for your business because you can incur hefty fines and, if it results in a serious incident, you're open to legal damages that could put you out of business. You also have a responsibility to ensure the safety of your drivers. You install analytics software to measure the problem and find it's worse than you thought ? the fleet is consistently doubling the over-speeding target you've set.
Solution: Using data analytics, you can compare the performance of every driver from one week to the next. If the same driver exceeds the target two weeks running, you can have a chat with them and bring it to their attention. The data can also be used to develop performance management schemes that reward drivers for hitting targets. For example you might set targets for fuel economy, speeding, idling and driving time, using these to develop a scoring system that determines how much bonus each driver takes home every month. All of this can be plugged into the analytics platform.
Scenario 3
What if you were managing a large government car pool, with hundreds of vehicles kept at a main depot. How do you know whether they're being used efficiently? At one end of the scale, some of these vehicles might not have moved for weeks or even months. On the other hand, if an employee wants to travel to another site, there's no incentive for them to find out whether they can share a vehicle with somebody else that is planning to make the same trip.
Solution: Using data analytics software you'll instantly be able to see if 60 of these vehicles weren't used during the past month. The fleet is clearly too big so you might decide to reduce the number of cars in the pool by 30 to see what impact this has. Now you can look at the hundreds of vehicles you still have on the books. What if the data also shows that 50 people travelled to the same location between 8am and 9am on Monday but everybody used a separate vehicle? That's an opportunity to put car sharing policies in place and further increase efficiency.
The metrics that will make the biggest impact on your business vary from one organisation to the next but they exist for every business. Making decisions based on data cuts through office politics, inflated egos and big personalities. Leadership knowledge is crucial to the success of any business but there's a lot of competitive advantage in making more informed decisions.