This is part four in our series designed to help organizations gain efficiencies, cut unnecessary costs, and ultimately protect their bottom line in today’s uncertain landscape. The Kepler platform was developed to address a vast array of Machine Learning (ML) use cases in the Transportation & Logistics industries, and its easy-to-use Automated Data Science Workflows ensure all transportation leaders and their teams, no matter the department, can leverage the benefits of ML-powered insights within days, not weeks. Below, we outline how to leverage Kepler’s advanced capabilities for AI in transportation to overcome some of the complex, multifaceted challenges that are putting businesses at risk today.
As COVID-19 impacts every person, community and industry across the globe, experts are digging for solutions on how to ensure our economy can rebound while keeping citizens safe and healthy. The social distancing, or “shelter-in-place” regulations enacted by governments worldwide have left a significant mark on global transportation, leading to canceled trips, border shutdowns, quiet airports, and, consequently, mass layoffs.
While tourism and business travel has come to a halt across the globe, the impacts on commercial transportation are varied. Governments have worked to maintain healthy trade relationships to ensure goods pass through borders, and businesses that typically rely on in-store sales are seeing a surge in ecommerce activity, which increases demand for certain types of goods. This has added measurable strain on certain supply chains, especially as consumer demands have rapidly changing peaks and valleys due to panic purchasing, decreased spending, and increased demand for health and wellness items. On the other side of the coin, countries undergoing strict lockdown measures are seeing transportation needs massively decrease, despite exemptions from governments.
In the Canadian-US context, a recent report from Geotab concludes there is a decline of roughly 36% of commercial border activity between both countries, with airports exhibiting the sharpest decline (including a 57% decrease at Toronto Pearson), while heavy duty trucking industries and seaports show steadier figures. Overall, the data illustrates that commercial transportation in the context of COVID-19 is a complex web of governments, businesses, consumers and communities that’s in constant flux.
While transportation companies are still seeing up to 82% of normal activity to warehouses, there is a sharp decline in activity to retail and industrial sectors. As such, leaders are pressured to find and capitalize on opportunities to find efficiencies throughout the supply chain, effectively offsetting lost activity and subsequent revenues. Acting quickly while staying adaptive and agile is paramount.
PwC released a report that urges leaders throughout the entire supply chain – from manufacturing to CPG to transportation – to increase visibility on end-to-end supply chain activities to allow for better workforce capacity planning. And, crucially, PWC urges leaders to employ tools that augment understanding of key patterns in transportation on a global scale: by integrating data sources from different geographic, regulation, economic, and health sources, leaders can react wisely to ever-evolving consumer demands, and possible shifts in governmental restrictions.
Implementing Machine Learning that leads to quick, accurate predictions is a way that leaders can intelligently plan and manage the uncertainties in today’s landscape. Crucially, ML that’s usable by anyone on your team is ideal, as it won’t impact other key projects your Data Science teams are focusing on right now – and it can also provide an opportunity to upskill your analyst teams with ML capabilities. In fact, McKinsey has cited upskilling or reskilling workers as one of the key items that organizations should be focused on in order to protect the economy, and their businesses, from further downfall. Below, we outline a few of the key ways our ML platform, Kepler, can help incorporate AI in your transportation organization.
With effective demand forecasting, transportation and logistics organizations can better understand how shifts in buying behavior, regulations and border restrictions, and impacts on key industries (for example, health and wellness supplies) can influence demand. With Machine Learning, leaders can proactively plan for the short to medium terms by using multiple data sources to make effective decisions. This planning can significantly improve efficiencies related to inventory holding costs, and help you better anticipate timing of delivery of goods from suppliers and partners.
Kepler Automated Data Science Workflow: Time Series Forecasting
Whether you’re managing how to allocate your operators on the ground, maintenance workers, or in-office team, strategically approaching the peaks and valleys of your hiring needs brought on by a reduced workforce is key. With ML-powered workforce planning, you can gain efficiencies related to several aspects of human resources, including improved predictions for capacities needed to fulfill objectives, or reallocation of resources to ensure you have the right manpower in the right place at the right time.
Kepler Automated Data Science Workflow: Regression or Time Series Forecasting
Maintenance of equipment has always been a key aspect of smooth transportation operations, but now more than ever, having a clear understanding of what needs to be serviced and when can help your organization better allocate resources. Not only will this help you plan for maintenance costs and needs with greater precision, predictive maintenance also ensures your workers are protected on the roads, skies and waters with the most optimal, safest fleet possible.
Kepler Automated Data Science Workflow: Anomaly Detection or Time Series Forecasting
