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.