What being a data scientist in supply chain management entails.
Data science is changing how many industries operate today. They rely on data to make better decisions and track business performance. Manufacturing is one of such industries that are being revolutionized through data science. It helps to optimize costs, enhance quality as well as improve production scale and speed.
Most manufacturing companies leverage big data and data analytics to combat recurring problems such as supply chain management issues, unscheduled downtime, unscheduled maintenance, and equipment breakdowns. This has also helped to create the demand for data scientists in supply chain management.
A supply chain involves a number of activities that are necessary for producing and delivering products or services to a customer. It includes aspects such as logistics, inventory, raw materials, demand and supply, warehouses, freight, suppliers, distributors, retailers, etc.
Managing a supply chain in manufacturing can be very complex and unpredictable. Some factors that influence supply include the cost of production, technology, transport conditions, government policies, and the price of inputs (raw materials, equipment, and machinery).
Data scientists in supply chain management are expected to analyze and predict patterns of inputs and outputs to minimize risk and ensure a smooth-running system. Big data in the supply chain helps manufacturers to improve efficiency and make timely decisions.
The Types of Applications of Data Science in Supply Chain
Every process in the supply chain is different and requires separate attention. But, they are intertwined and depend on each other to function properly. If one or more steps have issues, the entire chain could collapse and lead to significant losses in time and money.
Data scientists in the supply chain industry are responsible for analyzing data to support predictive analysis, make accurate forecasts and inform risk management strategies. Below are some ways in which data science can be applied in supply chain management.
Raw materials are the input goods or inventory that manufacturing companies usually process into finished products. The main types of raw materials include: plant-based (fruits, flowers, latex), animal-based (leather, wool, milk), and mining-based (crude oil, metals, minerals).
Data analytics in materials management can help to optimize processes such as sourcing, quantity, storage, safety, and quality checking. It also analyzes the impact of raw materials on the manufacturing process and measures the quality standards of finished products.
Procurement involves the steps taken to obtain necessary goods and services from suppliers. They often include activities such as: finding suppliers, negotiating terms, issuing purchase orders, making payments, tracking when supplies are received, and maintaining records.
Procurement analytics is all about collecting and analyzing procurement data for business insights and effective decision-making. It helps to observe the procurement process and assess things like the cost of supplies, product quality, and the relationship with suppliers.
- Fuel and shipping costs
Some modes of transportation in the supply chain include cargo vessels, trucks, railcars, and aircraft. Data scientists can help to predict and visualize the best method of transportation. They use various predictive models to calculate shipment scheduling, shipping routes, backhaul routes, and the transport compliances to follow.
Manufacturers who possess truck fleets can use data analytics to lower costs and improve efficiency. They can collect and analyze data related to fuel use from telematics devices and onboard computers. Companies can save fuel costs by promoting good driving behavior and procuring trucks with the best fuel economy.
- Pricing differences and tariffs
Companies that purchase foreign supplies are often affected by certain trade restrictions. For example, tariffs are taxes on imported goods. Some raw materials might be cheaper or better in other countries but government restrictions raise the prices of goods produced from them.
Data analysis can help to understand how the increase or decrease in prices is affecting the business. It also analyzes past performance and acquires insights on customers. Then, it’s used to make pricing decisions that correspond with the product’s value and increase profits.
- Market demand/scarcity
Data scientists can forecast future demand with the help of historical sales data and current sales data. They often use predictive analytics and machine learning tools to evaluate factors that are driving customer demand and their potential impact on the business in the future.
Accurate demand forecasting and planning can help companies to make smarter business decisions. It helps them understand how consumer preferences, competitors’ activities, and their own production or marketing efforts would affect demand at various sales channels.
- Inventory management/challenges
Data analytics help to understand the right inventory to have, the exact amount, and the warehouses to store them in. This makes it easier to calculate inventory budgets, optimize inventory management, and catch up with the demand for materials and finished products.
Data scientists in supply chain management can provide insights on customer behavior as well as the performance of products and sales channels. This helps companies to prevent stock out and overstocking, speed up the process of order fulfilment, maximize sales and profit, and increase customer satisfaction.
- Variable factors like the weather or worker strikes
Some factors in supply chain management can be subject to sudden changes. It could be weather conditions, port congestions, road problems, worker strikes or less manual labor. But, a strong supply chain should be able to optimize, reroute, and resolve issues quickly.
Data analytics can help to predict and prepare for constraints and delays in the processes involved in the supply chain. Before variable factors would disrupt the operations of the supply chain, companies can take preventive measures to avoid unpleasant outcomes.
How To Shift Into a Job as a Supply Chain Data Scientist
As more companies realize the benefits of data science in supply chain management, the demand for data scientists would continue to rise. The average salary for a data scientist is up to $140,000 per year. This shows that companies are offering competitive pay for data science and machine learning talents.
It’s also possible for supply chain professionals to become data scientists. They could start with an online data science certificate which can provide the basic skills. Since they already have supply chain experience, they just need to be proficient in other data science skills such as mathematics, statistics, and coding.
With a certificate in data science and the required skills, anyone can enter a supply chain data science career. They would design, model and prototype data science or machine learning models and algorithms to solve manufacturing, inventory management, and other supply chain management problems.