Top 5 Applications of Data Science in Supply Chain

Last updated by Divyashree on Oct 04, 2024 at 04:16 PM | Reading time: 11 minutes

Data Science in Supply Chain: Businesses are constantly striving to achieve optimal supply chain operations that result in cost reduction and efficiency enhancement. And that is where data science in supply chains becomes a differentiator. From data analysis and drawing insights from those analyses being made, supply chain professionals make informed decisions that drive better outcomes. Data science in the supply chain enables managers to enhance their comprehension of the underlying processes, smoothen operations, and identify opportunities for improvement.

This blog will cover the inter-relationship between data science in supply chain analysis, with the aim of helping in raising the supply chain performance. Whether from the perspective of a data scientist or a supply chain expert, this guide shall enable you to leverage your skills in a new and dynamic field. Let's dive into some exciting potential of data science for which supply chains are being transformed!

Before talking about the most significant applications of data science, let's first take a glance at the career opportunities in this arena.

Career in Data Science for Supply Chain

Among all the industries, the supply chain is one of those where data science has emerged as a highly demanded skill. Companies are in pursuit of professionals possessing data science acumen in supply chain management to employ them at very critical positions in the organization. According to the Bureau of Labor Statistics, data science and operations research-related jobs in supply chains, such as operations research analysts, are projected to grow 25% from 2020 to 2030, much faster than average.

Data science in supply chains has a wide array of roles, including those of data analysts, supply chain consultants, and demand planners. The main roles of a data analyst entail the collection and interpretation of data. A supply chain consultant supplies strategic support in helping businesses to make the most out of their supply chains.

Other jobs include:

Jobs	Description
Inventory Managers	Leverage data insights to optimize stock levels and minimize waste.
Transport Analysts	Use deep analytics on transportation data to drive logistic efficiencies.
Risk Analysts	Identify potential disruptions and develop mitigation strategies.
Demand Planners	Employ high-order analytics to forecast customer demand.
Supply Chain Consultants	Provide strategic counsel on how to enhance the supply chain performance.
Data Analysts	Collect data and analyze for insight to drive decision-making in supply chain operations.

According to Glassdoor, the average base salary for a Supply Chain Data Scientist in the United States is $100,000 per year, while the average total compensation is roughly around $137,000 - $215,000, including consideration to bonuses and profit-sharing options.

Data Science Applications on Supply Chain

Predictive analytics, machine learning, and artificial intelligence are modern variables of change in how organizations manage their supply chains. The technological capabilities stand to help organizations forecast demand correctly, rationalize inventory levels, reduce transportation costs, and, more importantly, enhance efficiency. Of all, data science in supply chain applications lets business stakeholders have better insights, reduces costs, and improves customer satisfaction in today's technological world. Let's deep dive into the top 6 applications of data science in supply chain and explore how it's transforming supply chain management for companies around the world.

1. Demand Forecasting

Data science plays a very important role in supply chain demand forecasting by analyzing historical and external data that provide a forecast of future demands. It helps businesses ensure accurate inventory, production, and distribution plans due to more precise demand predictions, enabling higher customer satisfaction while reducing costs. Following are some key ways data science in supply chain enhances demand forecasting:

Data sources: Demand forecasting also relies on both qualitative and quantitative data based on which sales data, social media, weather reports, and customer feedback provide rich inputs.
Predictive analytics: Data science in the supply chain enables real-time analysis of data through machine learning algorithms and predictive analytics that allow the company to alter forecasts and respond accordingly to changing market conditions.
Data-driven Collaboration: Sharing of dashboards and analytics across the whole chain of manufacturers, suppliers, and retailers increases collaboration and overall efficiency.

Bullwhip Effect: Data science in the supply chain helps mitigate the bullwhip effect, where small demand fluctuations at the consumer level cause larger swings throughout the supply chain. This effect is explained well in the image below.

Decision Making: Improvement in demand forecasting allows better decision-making about cash flow, risk assessment, capacity planning, and workforce management.

2. Route Optimization

Data science in route optimization of supply chains is crucial. It advocates for better delivery times, reduces costs, and is environmentally friendly. Using algorithms like these:

Dijkstra's Algorithm: The conventional algorithms that have been most suitable for the problem of shortest path finding in a graph. For instance, it is efficient in finding an optimal route.
Genetic Algorithms: Mimics the process of natural selection to solve optimization problems, such as vehicle routing.
Ant Colony Optimization(ACO): It is an inspirational algorithm based on the behavior of ants in the shortest path towards food. ACO is notably effective in solving complex routing problems.
Linear Programming: This is applied because there are multiple constraints in optimizing resources, such as vehicles and drivers.
Simulated Annealing: A probabilistic technique that goes through a great number of route configurations, aiming for near-optimal solutions within a large solution space.
Vehicle Routing Problem(VRP) Solvers: These are special algorithms designed to work out routes for vehicles that must make deliveries at more than one location.
K-Means Clustering: It groups the delivery points geographically, its route being optimized because it minimizes the distance traveled between clusters.

Data scientists analyze a number of causes that may lead to an improvement in the routes of delivery using these algorithms and act accordingly. Data science in supply chain route optimization serves many industries, such as retail store deliveries, home grocery services, the distribution of parcels, waste collection, and equipment installation, making operations more efficient and cost-effective.

Also read: Data Science in Supply Chain Management: Optimizing Operations

3. Inventory Optimization

Inventory management is the most crucial and complex part of the data science in supply chain management.Too much inventory may result in overcapitalization and eventual waste, while too little inventory leads to stockouts and loss of revenue. Using data science in supply chain analytics helps organizations apply various algorithms to optimize inventory levels by reducing waste and increasing overall efficiency.

A few of the key algorithms which can be used are mentioned:

Economic Order Quantity(EOQ): It calculates the quantity of orders that would minimize the total inventory costs, which involves ordering and holding costs. The graphical representation is shown here:
Reorder Point (ROP) Algorithm: This is the algorithm specifying the level beyond which the acquisition of new inventory becomes necessary to avoid a stockout situation in an effective running of operations.

This formulates to: ROP = (daily sales velocity) × (lead time in days) + safety stock

ABC Analysis: This is done by segmenting the inventory into three categories: A, B, and C. The classification will be based on the value and importance of items in the Inventories. It is useful in prioritizing inventories for management purposes.
Machine Learning Models: Algorithms such as regression analysis, time series forecasting, and neural networks are used to predict demand, optimize inventory levels, and adjust to real-time data and trends.

Advanced algorithms in these tools let organizations do data science in the supply chain on the basis of sales data analysis, supplier lead times, and demand forecasts. The businesses can, therefore, maintain optimal levels of inventory, and avoid stockouts, and excess inventory. It's holistic hence, better profitability and smoothing of supply chain operations are enabled.

4. Risk Management

Risk management calculates the overall amount of risks involved in the supply chain and forms contingency plans accordingly. Data science in supply chains helps to a great extent in identifying and predicting different types of risk factors, which may include:

Disruptions in the Flow of Goods: Discontinuation of smooth flow of goods due to disruption in transport or distribution that might affect the delivery schedule.

Delays in Delivery: Delays in delivering goods, often due to problems in transportation delays in the supply of raw materials.
Supply Shortages: These are insufficient supplies of raw materials or products that can even cause a stop in production or sales.
Demand Fluctuations: These are sudden changes in customers that may lead to overstocking or stockouts.
Supplier Reliability: These refer to the risks of dependence on suppliers concerning performance or quality and even financial instability.

Data science in the supply chain helps organizations predict such risks by analyzing data from different sources and providing the ability to develop mitigation strategies that make business continuity possible. It also improves quality control processes and reduces downtime, hence lowering the occurrence of lost production and revenue. With data science in the supply chain, an organization can manage such risks proactively and maintain seamless operations.

5. Supplier Management

One of the crucial factors within data science in supply chain management is supplier management. Data science in the supply chain helps organizations understand where their decisions lie in terms of the performance appraisal of suppliers and finding enhancement opportunities. From examining delivery times to quality metrics and pricing, supplier data analysis can give a data scientist an idea about which suppliers excel and which do not.

This insight can help organizations negotiate better contracts and refine their supply chain processes, bringing down costs and improving efficiency. Data science in the supply chain thus lets an organization optimize its supplier relationships and apply continuous improvement in its supply chain operations.

Also read: Data Engineering: Building the Foundation for Data Science

Future of Data Science in Supply Chain

The future of data science in supply chains is considered to bring huge changes to the industry. For instance, in a recent survey, Gartner reports that 48% of the supply chain companies would invest in machine learning, artificial intelligence, and data science in the supply chain for full realization of those technologies in bringing efficiency to the management of the supply chain.

Data science in the supply chain, therefore, will continue to grow and promises to revolutionize supply chain management. It will keep providing a competitive advantage for companies that can exploit such technologies over their competitors. Supply chain management will continue to evolve rapidly in the next few years with continuous investments in data science and analytics capabilities.

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FAQs: Your Questions on Data Science in the Supply Chain Answered

Q1: What role does data science play in supply chain management?

A1: In a supply chain, it improves efficiency by optimizing inventories, enhancing routes, managing risks, and assessing suppliers, which then enables cost reduction and improved customer satisfaction.

Q2: How does data science improve demand forecasting?

A2: By processing information and using machine learning for real-time adjustments to provide better accuracy and collaboration.

Q3: Which algorithms are applied in route optimization?

A3: Dijkstra's, Genetic Algorithms, Ant Colony Optimization, Linear Programming, Simulated Annealing, VRP Solvers, and K-Means Clustering.

Q4: How does data science help in inventory management?

A4: It takes the help of algorithms, such as EOQ, ROP, ABC Analysis, and machine learning to see the right stock levels and prevent shortage or overstocking.