How Location Intelligence Can Help Solve Supply Chain Issues
The outlook on our current global supply chain problem seems grim.
Since 2019, shipping rates for global containers have more than quadrupled, and schedule delays have increased. This has led to abrupt price increases. The reason isn’t solely the pandemic—it’s labor shortages, unavailability of key equipment, and the effect of global bottlenecks.
The impact on brands is steep. Consumers increasingly can’t count on finding the products they want at the right price point or that product getting to them in a timely fashion. As more brands face inventory shortages, some cut advertising spending by as much as 50% in late 2021.
Many brands might feel their hands are tied when it comes to these macroeconomic factors. But there is a way to mitigate supply chain disruption, and it starts with data—location intelligence, or geospatial data, to be specific. Here’s how.
Dig into Demand Modeling
Surging demand is putting strain on container groups, suppliers, and logistics companies. Given consumer appetite is expected to continue rising and there are fears of more disruption in the run-up to the Q4 peak shopping season, more accurate demand modeling is critical. If logistics managers can get better visibility into the different areas of the supply chain and improve maneuverability when issues occur, they’ll be able to rebound.
Geospatial data bridges the gap between offline and online intelligence, providing in-depth analytics of an area’s demand trends. Models created with this data can be used to simulate demand and then enriched with other data streams related to key trigger events such as weather or traffic. This provides insights that help predict, test, and protect against future demand scenarios.
Right-Size Route Optimization
For a relatively small number of deliveries, there are trillions of possible routes. The vehicle routing problem isn’t just about finding shorter or fewer routes. It’s about a more strategic way to reduce costs, curb your carbon footprint, and maximize pick-up and delivery sites.
The challenge is finding the best van routes or the way to assign orders to vans and then determining the sequence for each van to deliver its assigned orders. Routing engines use heuristics and custom-designed algorithms to find the shortest and most efficient path, allowing drivers to visit a number of locations only once. What’s more, these engines get smarter over time because they learn from previous experiences.
Leverage Last-Mile Logistics
As e-commerce shopping continues to rise, consumers expect an immediate response from brands as well as accurate estimates of delivery times. In essence, they’re looking for the Amazon experience no matter where they shop. But guaranteeing, same-day, one-day, or two-day shipping requires goods to be physically closer to customers so you can deliver their items in a seamless, cost-efficient way. It also requires being able to load and find deliveries quickly.
The gig economy has had a big impact on last-mile logistics. Not only has gig work helped plug gaps in the workforce, but it’s also resulted in many postal and logistics providers moving from using dedicated scanning devices to smartphones. This enables delivery drivers to quickly download an app allowing them to scan multiple items in one go, read instructions, and review information. The result is speedier loading at depots and less time required to find packages in vans.
Streamline Site Selection
Another method for reducing delivery times is encouraging last-mile deliveries to use parcel lockers or using click-and-collect solutions in which drivers can complete multiple deliveries at a single location. Networks of stores or self-serve lockers that function as pick-up and drop-off points can also reduce the number of failed deliveries. With so much online shopping still happening, increasing the geospatial coverage of such networks is fundamental to retaining market share and profitability—especially with higher-value items.
Using spatial modeling enables supply chain firms to predict demand using new data streams (such as human mobility or e-commerce propensity). It can also help pinpoint locations and causes for bottlenecks in your processes and routes.
Make Data Your Differentiator
Spatial platforms capable of handling large amounts of data give brands full, end-to-end visibility of supply chains so they can stay informed, allocate resources during demand peaks, and optimize supply chain strategies when needed. This ability to both analyze and visualize entire supply chains allows companies to understand where risk exists and how to re-route goods in a cost-efficient way when sudden changes or disruptions occur.
Rus Ackner is CMO at Spectus.