Today’s “need-it-now” expectations have rendered traditional methods for supply chain planning inefficient. Shipments are still tracked on a point-by-point basis lacking a real-time view of assets, and estimated time of arrivals (ETAs) are calculated using limited information that doesn’t take into account historical patterns of disruption, such as traffic, weather, and poor driver behavior. For organizations that are responsible for storing goods in a warehouse or moving goods from one location to another, it is essential that they receive timely information about where their goods are and when they will arrive in order to maintain an agile, sustainable supply chain.
With today’s technology we have the tools to optimize supply chain planning with accurate ETAs and real-time views of in-transit assets. But it could be argued that the implementation process of a new technology is too cumbersome. Despite the benefits, most organizations don’t have the capabilities and resources to build and develop advanced solutions that provide true visibility and accurately predict ETA. The organizations that do possess these resources are typically using outdated and inefficient solutions that cannot deliver real-time, global ETA. This is because most organizations, for various reasons, cannot effectively manage the robust and complex data generated via the Internet of Things (IoT), or apply advanced, machine-learning analytics to make sense of the data. And even when they have the best technical resources, the organizations don’t have a background in supply chain, which is necessary to develop the right logic to apply to the raw data.
As with any new technology, there are challenges surrounding adopting new technologies because the implementation process requires time, energy, and costs to set-up the right kind of IT infrastructure and IoT-enabled solution. It also involves having the budget to staff these initiatives with the right people and skills, such as having knowledgeable data scientists to develop advanced algorithms that make sense of the data. That’s why a solution that’s purpose-built, like Savi ETA-as-a-Service, is key to obtaining operational benefits quickly and affordably.
It reminds me of the HGTV show, Fixer Upper, that I’ve become slightly obsessed with watching (hence the reference). The show’s hosts, Joanna and Chip Gaines, take poorly designed and neglected homes and turn them into beautiful, functional spaces for new homeowners. They literally tear down walls, forcefully removing previous ways of navigating the home, and they rebuild based on updated floor plans that incorporate modern social conventions of cooking and entertaining in open, bright spaces. Aesthetics aside, the true value in their service resides in their ability to take an existing structure and improve upon the foundational benefits of what a home should be—a place to rest, feed your family, and create memories.
The same value could be said for how new technology has the potential to affect supply chain planning and logistics. The old ways of calculating ETA and knowing where your goods are, is just that—old. While it may still work, it isn’t sustainable in today’s hypercompetitive environment. With a figurative gutting of your traditional supply chain planning methods, you can transform your planning to take into account real-time and historical shipment patterns.
You will know that a truck that leaves at 8:00 a.m. on a Tuesday will arrive at its destination by 3:00 p.m., yet if it rains on that Tuesday, then your shipment will be delayed by 30 minutes and is likely to arrive at 3:30 p.m. This kind of information is transformative. This kind of information allows all parties involved in supply chain planning to proactively solve inventory management, cross docking, and on-time delivery challenges.
Knowing where your goods are at and when they will arrive is what supply chain planning is all about. Now is the time to let technology improve supply chain planning and transform the notion of real-time visibility and accurate ETA despite the potential challenges surrounding implementing new technology.