Prime Vision’s smart warehouse solution uses asset intelligence, computer vision and AI to make its own decisions.
The warehouse is capable of knowing what happened yesterday and today and predicting what tomorrow will be like. Designed to create greater agility, sustainability and cost-efficiency in logistics operations, the warehouse solution works by making operations flexible enough to react to changes in supply and demand.
Think of the following scenario – what if a sleeping mask product in a warehouse told its IT system that it ‘wants’ to go to John’s house, together with a pillow and a copy of the bestseller How to Sleep? What if it also told the system that it wants to be there on Tuesday, November 16, at 06:30pm and knew the fastest and most sustainable carrier to get it there? What if the signal from this item sets a picking device in that warehouse in motion and then a robot that delivers the mask, pillow and book to a packing station? Here an employee puts them together in a well-sized box so no air gets shipped and fewer packing materials are needed. This box then gets shipped by the chosen carrier, all in one van, via a route that is both quick and fuel-efficient.
Predict the Future
All the necessary data to use autonomous decision-making to turn this scenario into reality can be brought together on a label, creating a digital identity for each item that can be read and interpreted with the help of computer vision. With all systems linked – from the online shop to the warehouse management system to shipping/carrier options – not only will an operator have an overview of what is currently going on in its warehouse, it can even go beyond real time and predict what will be happening in the future, so the company can anticipate and be flexible on all levels. Aaron Prather, senior technical advisor at FedEx, said, “The future of warehousing is not about a specific product, but about the data available of that product.”
Maximizing uptime
Companies in e-commerce have been working with data sets for years and those sets are changing more and more from descriptive (such as, “last week there was a lot of congestion in some aisles of the warehouse”) to predictive (“from earlier data we know when a shipment arrives so preparations can be made for unloading and storage”).
In the coming years it is expected this will further develop into prescriptive intelligence and autonomous decision making. For instance, self-driving forklift #6 takes itself out of the operation for maintenance and is automatically replaced by another, so order picking can continue without interruption. Another use case is that the online order system, warehouse management and shipping system are linked so that sleeping mask products can be ordered promptly and be delivered before running out of stock.
Technologies
There is a lot that can already be done on a semi-autonomous level to future-proof warehouses and implement technologies that enable event anticipation. Think of a dashboard that shows a heat map of activities in a warehouse – which aisles are very busy or even congested with roll carts. The same dashboard displays the reduction in distance traveled in the warehouse due to improved product storage and an order counter with the average duration for order completion.
The data and information are designed to enable better decision making and get the most out of a company’s potential, be it in storage space, efficient warehouse routing, digital and physical security or timely maintenance. It’s all about maximizing and improving the quality of uptime to become more cost-efficient, achieve better customer satisfaction and reduce carbon footprints.
Customers and carriers
Customer behavior drives many processes in e-commerce. The Covid-19 pandemic has vastly accelerated the trend of online shopping. Most obviously, that resulted in sharp increases in the number of orders that had to be met in a relatively short time. Customers have also become increasingly demanding in tracking, delivery and return options.
Research by the Baymard Institute has shown that 49% of customers abandon their orders because the extra cost is too high. This includes fees for shipping and delivery. On the other hand, if a delivery option is ‘green’, some customers are willing to pay more or wait longer for their package.
This customer behavior has a profound effect on e-commerce companies’ relationships with parcel carriers. In many cases, carriers that logistics networks have a long-standing relationship with don’t offer the necessary service level anymore, due to – among other factors – lack of flexibility. As a result, these companies have to look for and engage with multiple parcel carriers, and find the right price and service level for different kinds of products and delivery options. Linking the systems of multiple carriers to their own is a big challenge, but it could mean benefits that go further than choosing just the cheapest option. It means transparency to all parties, a more competitive market and it would give insight into trends in customer demand.
By keeping systems up to date through the integration of autonomous decision-making systems, value could also be added for customers. The main key is to convert data into insights so in the future, warehouses can make well-informed decisions.
