"The launch of our upgraded COE marks a significant milestone for Unitrans and the industry as a whole," says Jacques Greeff, executive of solutions at Unitrans. "We are thrilled to unveil this state-of-the-art facility that will drive value creation and operational excellence for our clients across Africa."
The modern supply chain is driven by ‘big data’ with information coming directly from trucks on the road, fleet management systems and other services. The COE collects and analyses this data and provides real-time solutions and information to all relevant departments, and drivers on the road – for example, instant route changes to avoid traffic jams, etc.
Through the COE, Unitrans can process large-scale data into actionable business intelligence.
"Our focus is on empowering our clients with real-time insights and predictive analytics that drive informed decision-making and enhance overall operational performance," remarks Greeff. "Through our centralised platform and data-driven approach, we are unlocking new possibilities for supply chain optimisation and cost efficiency.”
"We believe that the future of supply chain management lies in the integration of data-driven insights and advanced technology," continues Greeff. "Our COE is designed to be a game-changer, offering our clients a competitive edge in a rapidly evolving marketplace."
Greeff describes the COE as an enabling tool that serves to optimise operations, mitigate risks and enhance safety and security measures – ultimately contributing to the increased efficiencies of our customers’ supply chains.
Some tangible benefits of the COE include fleet optimisation, reduced standing times and an overall boost in vehicle efficiency. While none of these concepts are new, the Unitrans COE uniquely balances risk mitigation imperatives and efficiency targets with sustainability objectives.
Predictive analytics means leveraging historical and real-time data to forecast future outcomes. By analysing large-scale data sets, businesses can gain valuable insights into patterns, trends and potential risks, allowing them to make informed decisions and mitigate operational challenges effectively.
In the context of a logistics business, predictive analytics can revolutionise risk management practices by identifying potential issues before they escalate, reducing operational risks and improving operational cost efficiency significantly.
Through the continuous analysis of data related to factors such as weather conditions, traffic patterns, equipment maintenance and driver behaviour, transport and logistics companies can proactively address potential problems and optimise their operations for efficiency, safety and sustainability.
“Ultimately, with the integration of advanced predictive analytics tools and large-scale data analysis, it is conceivable the supply chain industry could not only reduce operational risks, but potentially eradicate them,” says Greeff.
