The challenge
In 2019, Green Cargo rolled out the Synergi Life Connect mobile app to their employees. The goal of the roll-out was to allow employees to file new cases more easily if they were involved in an accident, near-miss or saw any unsafe conditions. Previously, employees only filed reports for accidents that had already happened, which prevented Green Cargo from having a proactive risk management approach. As the mobile app can be accessed from any mobile device as well as offline, it was much easier for employees than waiting to return to the office to file the case after a shift.
“After the introduction of the mobile app, there was an immediate increase in the number of cases that were filed,” said Jimmy Johansson, Safety and Security Developer at Green Cargo. “We were happy to see the new level of engagement in our safety program, but we didn’t have the resources to handle all the new reports.” Although the quantity of the reports had increased, their quality also became an issue. Many reports were now being incorrectly categorized as quality issues instead of safety issues, making the investigation more difficult.
The solution
When Green Cargo flagged the new data volume and quality concerns to the Synergi Life team, it was promptly suggested to try DNV's machine learning tool, Cato, to improve the categorization of the reports. Green Cargo was enthusiastic about trying to solve the problem without requiring additional resources to process the cases. During an exploratory workshop, the use of Cato and the goals of the program were discussed, and it was agreed that Cato should be used initially in the classification of cases. As a result, the team generated a list of possible entries and taught Cato how to properly classify them, while also ensuring that Cato’s performance was continuously improving.
The results
Soon after launch, Green Cargo saw that the data quality started to improve. Previously, about 50% of all cases were categorized as a quality issue. This level decreased to about 33% and the classifications were becoming more accurate.
Green Cargo employees were also happy with the introduction of AI since it meant that the case filing process took less time and it was easier for new employees to do it correctly. Before using Cato, the HSE (Health, Safety and Environment) team used about 5-10 minutes to investigate each case. With an average of about 1,000 cases each month, they saw a time savings of between 5,000 and 10,000 minutes (about 7 days) each month. “Using Cato has really freed up more time for us to help the organisation find the root causes of the incidents and increase the safety for all employees. We can spend more time doing impactful things and less time on administrative tasks,” said Johansson.
Cato has also made monthly reporting easier. Instead of manually searching for the necessary information to verify and report, it's simply about sharing the latest reports. “We now trust our data now more than ever. Since the quality check is being done daily, it’s not the same once-a-year heavy lift that needs to be done,” said Johansson. Green Cargo has seen that both monthly external reporting and compiling information for annual reports takes much less time to complete.
The next step in the AI journey will be to introduce Cato to barrier management. “We’d like to train Cato to help identify the top events and help define broken barriers for us,” he said . For Green Cargo, this is just the start of working with AI to improve health and safety. “We’ve seen how AI has helped us improve stakeholder engagement and the credibility of the data.”
When asked if he had any advice for other organisations looking to get started with AI, Johansson had a few tips. “First, you need to do your homework. Take the time to understand what you want Cato to learn and define all the data possibilities. Second, take the time to continue to teach Cato to improve. We’ve all learned a lot about our data and processes by dedicating a bit of time every day to improvements and we’re happy with the results.”
Are you ready to learn more about how your organisation can start to use AI in their QHSE (Quality, Health, Safety and Environment) program? Please reach out to us directly in the contact field.
About Green Cargo
Green Cargo is a Swedish state-owned freight transportation company specializing in rail logistics. The company was established in 2001 and is the largest rail freight operator in Sweden, playing a significant role in the country's transportation infrastructure. Green Cargo focuses on sustainable and environmentally friendly freight transportation solutions and offers both domestic and international transportation, collaborating with other railway operators for cross-border freight movements. The company also provides value-added logistics solutions such as warehousing, distribution and terminal operations.
Green Cargo has been a Synergi Life customer since 2014 and is currently using Synergi Life to support Incident Management, Audit Management, Risk Management and Inspection Management processes.
Profile
Customer name: Green Cargo
Website: https://www.greencargo.com/en
Market: Sweden/Europe
Employees: 1,900
Users: 2,000+
Product: Synergi Life - Incident Management, Risk Management, Audit Management, Inspection Management, Cato Machine Learning, Synergi Life Connect mobile app
Brief account
Why we chose Synergi Life:
- Enables a proactive risk management approach
- A flexible system that can be adapted to our future needs
- Close collaboration with DNV as a trusted partner to help solve arising concerns
This is what we gained:
- Increased number of submitted cases with the mobile app
- Improved data quality on reported cases
- More efficient case management
- Simplified external reporting and information collection
- Growing engagement in the safety program from all employees
