By assessing how its fleet of vehicles and lighting towers were being used over a six-month period, a large mining company in Australia implemented changes which saved more than 780 labour hours. This greatly increased operational efficiency and meant some of its workforce could be allocated elsewhere.
Civil Construction Company
An Australian civil construction company reduced its fuel bill by 11 per cent, by using data analytic to identify the problem of fuel theft - drivers fill up their private cars on the company dollar, by tracking individual vehicles and drivers to monitor personal use and comparing this against company policy.
Australian Taxation Office - Reporting Requirement
In Australia, companies report Fringe Benefit Tax (FBT) to the Australian Taxation Office, and this usually involves the complicated process of collating data manually for FBT purposes. Data analytics enables more data to be collated seamlessly thus freeing up the resources to collate manually. The data can be accessed to accurately work out the private use of every vehicle in the fleet, to reduce the FBT liability to companies. This also helps to create policies, set targets and negotiate employment contracts. Studies show that by building an accurate report of operational costs instead of relying on statutory fraction has helped many companies reduce FBT liability by over 40%.
UPS - Minimise Left Turns Policy
In 2004, UPS turned to data analytic on its fleet to improve efficiency, and discovered that making left turns against oncoming traffic (equivalent to making right turns against oncoming traffic in countries where drivers travel on the left lanes of a 2-way streets) wasted time and fuel, and led to an increased number of accidents. The ‘minimise left turns policy’ ,which was a direct result, saves UPS over a million gallons of fuel every year. Ten years later, valuable data-driven insights and optimisation are available to almost all fleet operators regardless of sizes.
Efficient and flexible tolling on a global scale
An end-of-life technology with increasing maintenance costs and an inability to augment in response to changing market conditions and customer expectations.
A new tolling solution that consolidated best practice from 15 years of toll road operations across seven roads in three jurisdictions into a single platform.
The new system introduced a high level of smart automation, a steep increase in the trip construction and a reduction in manual processing. These achievements led to significant cost reductions and an increase in accuracy in the event to cash process. Customers are able to interact with core business processes in near-real-time via any internet enabled device.
Big Data at UPS
UPS is no stranger to big data, having begun to capture and track a variety of package movements and transactions as early as the 1980s. The company now tracks data on 16.3 million packages per day for 8.8 million customers, with an average of 39.5 million tracking requests from customers per day. The company stores over 16 petabytes of data.
Big Data at Schneider National
Schneider National, one of North America’s largest truckload, logistics and intermodal services providers, has been pursuing various forms of analytical optimization for a couple of decades. What has changed in Schneider’s business over the past several years is the availability of low cost sensors for its trucks, trailers and intermodal containers. The sensors monitor location, driving behaviors, fuel levels and whether a trailer/container is loaded or empty.
A European Courier Company Embarked On Cloud-based Analytics
This leading courier company in Europe with over 4000 vehicles and over 12,000 employees embarked on its cloud journey with the help of Striim. The company is moving its data warehousing and analytics solutions to the cloud and uses Striim to move real-time data from transactional systems running on Oracle databases to Google BigQuery to enable cloud-based analytics.
Google BigQuery serves as the operational data store supporting real-time reporting and ad-hoc queries. By using real-time data in its analytical environment, the company can provide fast operational reports with up-to-date data. The company plans to use real-time transactional data for fleet optimization and real-time shipment status notifications to customers.
Set up the operational data store (ODS) in the cloud by ensuring up-to-date transactional data is available in the cloud.
Eliminated the performance impact of running ad-hoc queries on the production OLTP systems by offloading reporting to real-time ODS in the cloud.
Gained the ability to optimize fleet management and improve customer service using real-time GPS data.