Getting Smart With Logistics Big Data
Here at Freightos, we’re big fans of making global logistics work better by bringing data online (and especially instant freight quotes!). And, like many other logistics startups, we’re particularly enthusiastic about how Big Data can change the way goods are moved around the world. In this guest post, Kerwin Everson the Vice-President of Supply Chain Solutions at RMG Networks, investigates two examples of driving more supply chain value with logistics big data and data visualization.
Much like IT, the supply chain is no longer a cost center … but instead a profit driver. As noted by Logistics Manager, citing a new DHL report, companies are sitting on a “gold mine” of supply chain data that offers a huge competitive advantage — if used properly. However, many companies struggle with turning good data into actionable insights, especially when it comes to the ebb and flow of supply chain demands.
Put simply, corporate logistics are at a crossroads: How do you choose wisely?
According to a recent Supply Chain Digest whitepaper, excellence in global logistics and supply chain management no longer sets companies apart from their peers — instead, superior performance is a must simply to stay competitive. It makes sense. The advent of cost-effective, outsourced suppliers and real-time supply chain data generated by public cloud architecture and wirelessly connected sensors create a kind of information overload. Companies able to manage this unchecked flow of data can gain an advantage on the competition and establish themselves as leaders in a global marketplace.
Data Visualization Example #1: Mobile Mastery
To tap this flow of logistics data, many companies are turning to mobile devices. Samsung’s Insights blog notes that while many organizations have embraced the idea of sensor-driven technology to capture product data and deliver a continuous view of stock levels, there’s often a gap — users. For example, truck fleets are often equipped with a range of telematics and temperature-measuring devices that send their findings to a central database for processing. Drivers, meanwhile, are left in the dark — while data is analyzed by IT professionals, employees in the field don’t get any feedback on how to improve their driving or handling practices to increase supply chain efficiency. By leveraging in-cab mobile devices such as smartphones or tablets that are connected to the network at large, drivers can get the data they need to make moment-by-moment adjustments.
Data Visualization Example #2: Seeing is Believing
Before the advent of computerized inventory tracking systems, companies had little choice but to produce based on “best guesses” and hope that demand didn’t exceed — or fall significantly short of — supply. More advanced tools allowed business to transition and embrace a JIT (just in time) model where supply is driven by emerging demand. The result? A more stressful environment for manufacturers and shipping companies.
The advent of on-demand social media and e-commerce shopping has refined this model even further: Customers want items to be in stock and ready to ship no matter where or when they choose to make a purchase.
While existing back-end tools can help make sense of incoming orders and predict necessary stock levels, there is a finite threshold of complexity, a barrier beyond which even supply experts can’t make heads or tails of incoming orders, outgoing shipments and necessary product volumes. This naturally leads to a next step of the big data/logistics pairing — visualization. Instead of seeing only the input or output values of a supply chain system, data visualization tools give logistics experts the ability to “see” the chain end to end, in turn allowing them to predict emerging bottlenecks or address minor concerns before they become big issues. They now have a “scoreboard” to track performance in real time.
When companies arrive at their logistics crossroads, there’s often a temptation to make big changes in pursuit of opportunistic cost savings. After all, the new landscape bears so little resemblance to the old that large shifts seem the best course of action. In fact, tapping big data in the supply chain is easier done with small changes that leverage the right technology in the right place — mobile devices in fleet trucks, for example, or the use of data visualization on large screen digital signage or desktop dashboards to track stock from start to finish.
Bottom line? Choosing wisely means embracing big data on its own terms by deploying technology to tap its latent potential.
Kerwin Everson understands how important technology is to the supply chain as the Vice-President of Supply Chain Solutions at RMG Networks. Kerwin’s goal is to educate supply chain operations on the value of visualizing real-time performance management to improve productivity and efficiency.