The growing number of connected devices has come to be referred to as the Internet of Things (IoT). The IoT is not just a random grouping of internet-enabled gadgets; it is a rapidly growing network able to capture vast amounts of data with fixed and moving sensors. Big data processing of these data—whether in real time or on data-at-rest—is the crucial component of extracting business value from modern analytics, in private or public services. Location plays an essential part of this. Spatial analytics technology gives us the ability to tie these massive amounts of information together by placing them within the critical context of where.
The subsequent spatial analysis can provide unique insights, revealing previously hidden patterns and relationships that drive stronger decision-making for businesses. Fed by spatial analytics and real-time data, location technology’s applications are broad, ranging from optimizing supply chain management to using real-time asset tracking for logistics to customer analytics in retail.
Meeting the Demands of Supply Chain
We tend to think of the Internet of Things as a network of sensors, but it can also as easily be the barcode on a clamshell package that holds produce. This creates visibility, not just of a shipment, but down to an individual stock keeping unit (SKU). IoT data in this respect can give a company fine-grain tracking and understanding of its assets, including perishable goods.
Packaged berries are enjoyed every day by millions of people. It is taken for granted that the strawberries and raspberries are fresh and of high-quality once they reach the supermarket in their familiar plastic containers. But the process of ensuring that only the best berries make it from the field to the grocer in top condition involves multiple locations, often spanning several different countries. Because products need to go from a farm all the way to a retail location in a limited period of time, agricultural companies must track information about their product as it moves from the field to a processing center, a distribution center, and then a retail location.
This information is used to understand not only where the product went in the supply chain and how long it took to get there, but also to analyze produce quality. For instance, this process enables one agricultural producer to understand why a particular batch of strawberries was superior. The company can see where that batch came from, right down to what part of the field. They can then look at how they treated that field differently so they can repeat this success in the future. The company is essentially performing analytics on the back end to help improve the product that they deliver to their customers. This application of IoT also pays dividends when it comes to reverse logistics. If a specific product needs to be recalled, a company that knows the product’s origins down to the SKU level can perform more selective recalls and avoid waste.