I noticed that Jim Hayden, Savi’s SVP of Data Science, sometimes wears a red cape around the office; but I was curious about his specific role and what it actually means to be a Data Scientist. When I asked, Hayden explained how he uses his technology background, Internet of Things platform, and subject matter expertise in supply chains. The more complex our global supply chain networks become, the more we will need to depend on the algorithmic power used by Data Scientists like Jim Hayden.
Alright, so maybe Jim doesn’t actually wear a red cape, but here are three ways the skills required by Savi Data Scientists resemble those of superheroes.
Data Scientists have “superpowers”
A secret to their superpowers is the combination of business knowledge and technology knowledge. Data Scientists can listen to a logistics problem, understand the data available to solve that problem, then figure out exactly which machine learning algorithms should be applied to solve it. A lot of people can look at data and reports, but the true science, or superpower, is the power of the algorithm. Algorithms give us a sense of direction within the complexity of the supply chain data and allow us to make pivotal decisions on the spot. Data Scientists are critical to implementing the solutions that will keep the supply chain running smoothly and efficiently.
Data Scientists anticipate problems
Data Scientists use machine learning algorithms to show us exactly where our shipments are in real-time, then use them to predict when exactly they are going to reach their destination. This is far beyond the range of normal human ability. The process of using algorithms to generate models that predict future outcomes is called predictive analytics. Data Scientists analyze historical data on shipments collected from all over the globe and determine how factors, such as time of day, season, specific lane and carrier, affect a shipment’s transport time. They then use this analysis to determine what the most efficient shipment strategy will be. Using the combination of real-time in-transit visibility, predictive analytics, and prescriptive analytics, Data Scientists can help us to understand where there is slack in our supply chain so that we can make changes to tighten it. They can optimize cross docking, reduce transportation costs and inventory on hand, and even anticipate customer service problems.
Data Scientists strive for good to prevail
Data Scientists are an excellent resource for building solutions to manage the risk, cost and complexity that comes with global logistics. Companies are becoming aware that they can acquire real-time logistics information to improve their supply chain networks with the Internet of Things (IoT) technology. But companies are only recently realizing the importance of data quality. Data quality is important so that “dirty data” can be captured before it corrupts the models. Like superheroes, Data Scientists need to identify and protect the good [data] and remove the bad [data]. With their knowledge and experience, Savi’s Data Scientists are experts at saving the good IoT data so that they can implement predictive and prescriptive analytics, help companies all around the world, and maintain their status as the superheroes of the supply chain.