Data Science in Supply Chain Management – Key Applications

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    Today, data science is altering how many industries function. To improve decisions and monitor corporate success, they rely on data. One of these sectors being changed by data science is manufacturing. It aids in cost optimization, quality improvement, scaling, and accelerating production.


    Industrial organizations frequently use big data and data analytics to address recurring challenges such as supply chain management problems, unplanned downtime, unscheduled maintenance, and equipment breakdowns. Additionally, this has contributed to the growth of the need for data scientists in supply chain management.




    Several tasks required for producing and providing goods or services to a customer are included in a supply chain. The components include logistics, inventory, raw materials, demand and supply, warehouses, freight, suppliers, distributors, retailers, etc.


    Managing a supply chain in the manufacturing industry can be challenging and unexpected. The cost of production, technology, transportation conditions, governmental regulations, and the cost of inputs are some factors that affect supply (raw materials, equipment, and machinery).


    In order to reduce risk and guarantee a smooth system, data scientists in supply chain management are expected to examine and forecast trends of inputs and outputs. Big data in the supply chain enables producers to increase productivity and take prompt action.


    Key Data Science Applications in Supply Chain 


    Each step in the supply chain is unique and needs to be handled separately. However, they are linked and rely on one another to work effectively. The entire chain could break down and result in major time and financial losses if one or more steps have problems.


    In the supply chain sector, data scientists evaluate data to enable predictive analysis, produce precise projections, and influence risk management strategies. Several ways data science can be used in supply chain management are listed below:


    • Material

    Raw materials are the inventory or input goods that manufacturing enterprises typically use to create finished goods. The three primary categories of raw materials are those derived from plants (fruits, flowers, latex), animals (leather, wool, milk), and mines (crude oil, metals, minerals).


    Materials management operations, including sourcing, quantity, storage, safety, and quality assurance, can all be improved with data analytics. Additionally, it evaluates how raw materials affect the production process and assesses the quality standards for produced goods.


    • Procurement

    The actions taken to get essential products and services from vendors are referred to as procurement. Finding suppliers, negotiating terms, issuing purchase orders, making payments, keeping track of when supplies are received, and maintaining records are frequently included in them.


    Procurement analytics aims to gather and analyze procurement data for corporate understanding and wise decision-making. It is beneficial to see the purchasing process and evaluate aspects like supply cost, product caliber, and supplier relations. For further details, refer to the data analytics course in Pune, and learn the in-demand skills to get certified by IBM.  


    • Costs of freight and fuel

    The supply chain uses various transportation methods, such as trucks, railcars, cargo ships, and airplanes. The optimum mode of transportation can be predicted and visualized with the aid of data scientists. They employ various predictive algorithms to determine shipment schedules, shipping routes, backhaul routes, and the required transport compliances.


    Manufacturers can use data analytics with vehicle fleets to reduce costs and boost productivity. They can gather and examine fuel consumption data through telematics systems and onboard computers. Businesses can reduce their fuel expenditures by encouraging safe driving practices and purchasing trucks with maximum fuel efficiency.


    • Tariffs and price variations

    Certain trade restrictions frequently impact businesses that acquire materials from abroad. Tariffs, for instance, are taxes imposed on imported goods. Even if some raw resources may be more affordable or superior in other nations, government regulations increase the cost of the products made with them.


    Understanding the impact of price changes on the firm can be aided by data analysis. Additionally, it gathers client information and analyzes historical performance. Then, it is applied to pricing choices that reflect the worth of the product and boost earnings.


    • Market supply and demand

    With the aid of recent and previous sales data, data scientists can predict future demand. They frequently employ predictive analytics and machine learning methods to assess the elements influencing client demand and their potential future influence on the organization.


    Planning and anticipating demand accurately can aid businesses in making more informed decisions. It aids them in comprehending how demand across multiple sales channels might be impacted by consumer preferences, rival activity, and their own production or marketing efforts.


    • Problems of inventory management

    Data analytics makes it possible to understand the ideal inventory to have, the precise quantity, and the warehouses to put them in. As a result, it is simpler to determine inventory budgets, improve inventory control, and keep up with the demand for raw materials and finished goods.


    In supply chain management, data scientists can offer insights into customer behavior and the effectiveness of the products and sales channels. This aids businesses in avoiding stockouts and overstocking, expediting the order fulfillment process, maximizing sales and profit, and elevating customer pleasure.


    • Variable elements such as the climate or worker strikes

    Supply chain management variables might experience abrupt shifts. Weather patterns, port backlogs, traffic issues, labor strikes, or a decrease in physical labor are a few possible causes. However, a strong supply chain should be able to immediately optimize, reroute, and address problems.


    Data analytics can anticipate and plan for limits and delays in the supply chain processes. Variable circumstances can disrupt the supply chain's operations, but businesses can take proactive action to minimize negative effects.


    How to Make a Career Change and Become a Supply Chain Data Scientist


    The need for data scientists will increase as more businesses understand the advantages of data science in supply chain management. A data scientist can earn up to $140,000 annually on average. This shows that businesses are paying competitively for data science and machine learning experts.


    Professionals in the supply chain who want to become data scientists can also do so. They could begin by earning a data science course in Pune, which can give them fundamental knowledge. The only additional data science skills they need are to be strong in arithmetic, statistics, and coding, given that they already have familiarity with the supply chain.


    Anyone can start a career in supply chain data science if they have a qualification in data science and the necessary skills. In order to solve the problem in manufacturing, inventory control, and other supply chain management issues, they would design, model, and prototype data science or machine learning models and algorithms.