Exploring Different Dimensions of Analytics: LOGISTICS
All in all, logistics is everything related to the movement of goods or resources from one place to another. Logistic tasks solve everything from large carrier companies to marketplaces and online stores.
Most processes in logistics are very complex: with many stakeholders, performers, intermediaries, conditions — and at the same time require prompt decisions. In general, machine learning and analytics are here to the point.
What type of data does logistics analytics work on?
It all depends on the complexity and specifics of the logistics system. Information can come from GPS trackers, from built-in sensors in warehouses, from smartphones of employees, from manufacturers, distributors or dealers of products.
What tasks can ML and analytics solve in logistics?
1. Supply chain planning
Pattern/correlation detection and predictive analytics based on dynamic data allow you to plan deliveries and even adjust production plans in real time. The goal is not to miss the hype, but also not to lose money due to illiquid assets.
2. Process Automation
Autopiloted vehicles insure drivers over long distances. Delivery drones are roaming the cities (or even flying over them). Robots also work in warehouses: they collect and pack orders faster than humans.
3. Smart Cameras
Neural networks with computer vision independently check from photos and videos whether all and those boxes were loaded into the truck. And automated video surveillance systems monitor the safety in warehouses without days off and breaks.
4. Route optimization
Predicting the time and delivery cost by different routes (taking into account not only historical, but also the most recent data!) is critical to reduce logistics costs. Especially if we are talking about big business and the movement of goods over long distances.
5. Quality control
Analytical systems help to find the causes of mechanical damage to goods during transportation and therefore eliminate them. At the same time, damaged packages themselves can be detected, for example, using visual recognition by a neural network.
What skills and qualities do you need to have to become a successful logistics analyst?
As in most other areas, a lot depends on the grade and place of work. The standard inputs are:
● First, you need solid hard skills: a deep understanding of statistics, the ability to work with Big Data and ML models.
● Secondly, it is important to love logistics (or at least be sincerely interested in it), to understand the specifics of processes, rules and problems.
● Thirdly, it is better to initially be aimed at working in a large corporation. Rarely does a young startup need logistics analysts.
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