By James Fisher, Chief Strategy Officer, Qlik
Consumers today expect online deliveries to arrive in two days or less – especially when buying from suppliers like Amazon. In fact, research this year found that 80% of organisations reported higher customer satisfaction levels, and 70% experienced higher sales, when able to offer same-day delivery.
But climate disasters and severe weather conditions, which are unfortunately becoming increasingly prevalent as we battle with climate change, pose a significant challenge for shipping and logistics companies to meet customer expectations.
Drought conditions in the Panama Canal have disrupted shipping, snowstorms have closed roads, and heatwaves and hurricanes have also made guaranteed fast deliveries harder. Overall, weather related supply chain disruptions are expected to cost the shipping industry $100 billion in 2024.
While we can’t put an immediate stop to severe weather conditions, we can take steps to help minimise disruption and support the shipping and logistics industry to keep customers happy and business profitable. It all comes down to predictive analytics and automation.
Traditional ML models can’t handle today’s climate impact
Traditional machine learning models learn from existing data, and map potential outcomes based on this information. But when it comes to the type of climate impacts we are facing today, we cannot just rely on replicating previous scenarios. Past data doesn’t accommodate for all the new possibilities that could, and are more likely, to happen in the future.
Temperatures are changing, and ‘freak’ weather events are happening more than ever before. The Covid-19 pandemic offers a good parallel to this – the way it impacted the modern world was unlike any health crisis we had seen before, and therefore using past information was not useful to map its potential trajectory to determine how to react.
When we are faced with net-new challenges, we can’t look backwards.
The role of real-time data and automation
To navigate new or unexpected challenges, like emerging climate disasters, we need other ways to be better prepared to remain operational and meet customer needs. This is where real-time data and generative AI becomes vital.
With access to real-time data, like emerging weather or traffic conditions, logistics, shipping and retail businesses can build more resilient operations.
If you can apply AI to model and predict how a scenario may play out based on information that is correct up to the minute, you can make well-informed decisions that reflect the exact scenario you face. This could be changing delivery routes, shipping products from a different warehouse in an unaffected area, or even just having information early enough to let customers know ahead of time that their delivery will be delayed. Combine that with automation and AI powered agents and we can give customers warning about possible delays and offer alternatives, all can help to improve their overall experience..
Putting predictive analytics into practice
One example of how AI and predictive analytics is helping to manage supply chains and minimise disruption is Penske, the company which operates and maintains more than 422,000 logistics vehicles across the USA and Canada.
Penske worked with Qlik to develop Fleet Portal, which provides information and analytics around a vehicle’s operation and how it is being used and maintained in near-real time. The company is also implementing AI to reduce repair time for vehicles, and in some cases, predict maintenance events before they become a problem, helping to reduce any potential delays or disruptions from broken down vehicles.
It’s clear to see how this type of sophisticated real-time data insight can also be applied to react to everything from adverse weather conditions to real climate crises like wildfires or hurricanes.
Unfortunately, we are faced today with many climate challenges and severe weather conditions. There is a lot of pressure on logistics and shipping companies to meet customer expectations and drive business success, even in the face of these obstacles.
Understanding and harnessing data in the correct way means businesses will be better equipped to predict the potential impact of climate events in as near to real-time as possible, thus mitigating business disruption and, critically, keeping loyal and new customers happy.
