Data science is one of the most coveted job functions of this century. Most data scientists flock to the world of finance, business technology, and government. What about supply chain logistics? Here’s what a career as a data science professional in the logistics industry promises.
Developing a vaccine to fight the COVID-19 virus was one of the biggest challenges the world has faced in recent memory. In the time that has passed since the vaccines first started rolling out in early 2021, healthcare companies and public health organizations have risen to the task admirably and have administered some 11.55 billion doses.
The focus has now shifted to a seemingly less interesting industry: Supply chain management.
Supply chain companies have always been silent contributors to the world’s economy. Mention “logistics,” and the first thing people think of is ports, containers of cargo, and ships the size of small towns. But there’s another phrase that is increasingly being associated with the industry these days: data science.
Analytics have begun disrupting traditional supply chain processes, and there has never been a better time for a data science professional to enter the supply chain industry.
Not convinced? Here are three amazing data science applications that exist in the world of supply chain analytics.
1. Network Planning.
The supply chain is a delicately balanced network of manufacturers, suppliers, logistics vendors, all of whom must comply with changing government regulations. Delays in one portion of the supply chain can disrupt the entire delivery schedule and even damage the product, which makes.
This is where logistics firms depend on data scientists, to assist in condition monitoring, to optimize strategies and to iterate on predictive models. For example, the COVID vaccine will rely on sophisticated cold chain temperature monitoring systems to maintain storage conditions at exact temperatures. The various vaccines that are now available have differing storage requirements, with one vaccine requiring storage at temperatures as low as -60 degrees Celsius.
If deliveries aren’t compliant with customs regulations, or if the supply vehicle finds itself stuck along a route that has experienced geopolitical instability, the efficacy of the vaccines being delivered will be thrown into question.
Cross-border supply routes are littered with differing regulations and potential politically induced instability. The shortest route isn’t always the best, and different times of the year can influence delivery route choices. Analytics platforms in the industry take all of these factors into account to determine the best route. Given the different variables in play, the supply chain data science professional has to be aware of several real-world factors instead of confining themselves merely to a few technical issues within their industry.
Those wishing for a challenging and fulfilling career couldn’t ask for a better environment to work in, with underlying conditions constantly changing and models having to constantly assimilate new information.
2. Replenishment Analysis.
Replenishment analysis is a major function in the supply chain industry. With regards to the COVID-19 vaccine, it’s even more critical because the most effective vaccines require two shots, spaced at least three weeks apart. During this time, doctors will monitor recipients for adverse symptoms and side effects that require further treatment. As a result, the delivery of the second dose of the vaccine needs to be coordinated with a large number of hospitals and clinics.
Given the urgency with which the vaccine has been developed and administered in clinical trials, getting the number of doses that have to be delivered correct is critical. Excess deliveries in one area might create a shortfall in another. Data regarding side-effects need to be monitored as well. Health experts have mentioned that the number of observable side-effects will increase once the vaccine is administered to the general public.
All of these situations mean supply chain companies need to be ready to deliver or withdraw shipments based on observable patterns. Data science professionals will be at the center of these analyses, defining relevant data sets, and cleaning raw data. The delivery models will have to constantly adapt and change to meet demand from consumers.
Replenishment analysis is an exciting field in non-COVID situations as well, with data scientists having to analyze consumer buying patterns and define production flows. Seasonality and consumer trends are the two most basic variables you’ll need to adjust for. Other variables such as competing product features, trends in the industry, and the prospect of product upgrades make replenishment analysis a challenging and rewarding job function.
3. Predictive Analytics.
AI is taking over the field of analytics, and supply chain systems are no exception. Companies in the field are increasingly investing in IoT and cross-platform software, which means the quantity of data being collected is greater than ever before. AI is playing a crucial role in developing pricing strategies and predicting demand.
A few manufacturers have adopted a pricing model that is similar to Uber’s surge pricing model, where prices adjust dynamically to match demand. While industrial product prices won’t fluctuate as much as Uber’s fares do, the applications for AI in this field are immense. Currently, the majority of AI applications are focused on augmentation, with investment being directed towards assimilation and automation.
AI will eventually integrate itself throughout various functions in the supply chain. As a data science professional, the future is exciting with predictive analytics expected to take over inventory management, supply scheduling, procurement, and demand forecasting.
Wide and Critical Applications.
The management consulting company Robert Half notes that the distribution and manufacturing industries will witness the most demand for data scientists in the coming years. While the current focus of the world’s supply chain companies is on delivering the COVID-19 vaccine safely, supply chain processes are critical to every industry.
A data science professional working in a supply chain focused role will have their finger on the pulse of their industry. The future of supply chain data analytics applications has never been more exciting.
