Supply Chain Management (SCM) is a complex process that poses several challenges for businesses including lack of transparency, schedule production, and synchronization between various teams. Manufacturing, retail, and Fast-Moving Consumer Goods (FMCG) companies use intelligent data analytics to tackle these challenges. This is termed supply chain analytics. It enables companies to figure out consumer preferences and predict future demand for goods or services. Let’s dig deeper into what is supply chain analytics.
What is Supply Chain Analytics?
It refers to the use of big data analytics to enhance supply chain processes. In simple terms, it means analyzing the data collected in the SCM process through various tools, and drawing real-time insights from such data to improve logistics and the SCM environment.
Supply chain analytics has three core components – data analytics, data visualization, and a tech platform. Data analytics facilitates drawing valuable market insights while data visualization enables a better understanding of insights through graphical representations. These two processes are carried out with the help of a tech platform or an engine that captures, stores, and analyzes supply chain transactions done by businesses.
Why is Analytics Important in Supply Chains?
The SCM environment is said to have a domino effect, which means that every step in the SCM process is impacted by the previous step. Hence, companies must get real-time data insights to improve the overall process.
- Facilitate planning and scheduling supply chain events by providing real-time insights about inventory
- Predict consumer demand, manage resources, and stay updated with the latest market trends
- Optimize SCM process by allowing businesses to keep a track of production material, and reducing purchase cost
- Improve logistics by providing real-time data, and increasing customer satisfaction.
Supply Chain Analytics Examples
- Designing supply chain networks to determine the business production and supply capacity
- Configuring the placement of SCM facilities and the flow of products in the supply chain
- Inventory optimization by facilitating transparency in products purchased and sold
Features of Supply Chain Analytics
- Process and stream real-time data generated from different SCM tools
- Analyze customer responses from different digital platforms to improve inventory planning and scheduling processes
- Extract and organize unstructured data from various sources
- Reduce siloed data (data that cannot be accessed by cross-functional teams in an organization), and allow companies to identify issues and fix them
What Kind of Data is Used for Supply Chain Analytics?
The greatest advantage of using big data analytics in SCM is its ability to extract data from multiple sources to optimize supply chain processes. Here are the various kinds:
1. Product Data
It shows how different products in the inventory are related.
2. Financial Data
It includes the cost of production and logistics.
3. Sales and Demand Data
It indicates the historical sales of a product and predicts future demand.
4. Warehouse Data
It includes contracts with suppliers and vendors and the availability of staff and storage space.
5. Inventory data
It contains details of products in stock.
6. Logistics Data
It comprises customer and transport information.
What Supply Chain Analytics Can Do for Your Business?
The use of real-time data has transformed business functions and facilitated immense growth. The most significant advantages of using data analytics in SCM are:
- Demand Forecasting: It helps identify customer purchase patterns and predict future demand for products in the market
- Inventory Visibility: Such analytics promotes transparency in inventory management by allowing businesses to keep track of suppliers
- Invoice Reporting: It helps businesses analyze and manage supply chain costs and transactions with different suppliers and customers
5 Types of Supply Chain Analytics
1. Descriptive Analytics
Descriptive Analytics refers to the use of KPIs and metrics to evaluate performance by using various statistical tools. For example, how have inventory costs been reduced in the past six months, or how much revenue has been generated?
2. Predictive Analytics
Predictive analytics uses real-time data from multiple sources to foresee how different factors will affect supply or demand in the market. For example, how inflation will impact customer preferences?
3. Prescriptive Analytics
Prescriptive analytics refers to the use of supply chain analytics to configure and automate suitable approaches for optimizing the SCM environment. For instance, when should you rent a new warehouse, open a new factory, or buy more raw materials? In simple terms, it means making data-driven decisions to scale
4. Diagnostic Analytics
Diagnostic supply chain analytics is used to find out or diagnose issues in supply chain processes like delays in production or shipping, or missing items from inventory.
5. Cognitive Analytics
This is similar to diagnostic analytics. It involves unearthing the causes of issues faced in SCM processes and taking appropriate automated measures to resolve them.
Tips and Best Practices
Understanding what is supply chain analytics is relatively easier but implementing it in an organization can be tough. The following are some effective practices:
1. Establish a Cross-Functional Analytics Team
Integrate various teams in your organization to optimize supply chain analytics. You can bring together people who are skilled in planning, finance, delivery, and analyzing metrics.
2. Set Out Clear Business Goals
It is essential to set clear and reasonable business goals to make the best use of analytics. You can consider goals like improving customer service and reducing production costs. Also, you should start with fewer KPIs (key performance indicators) to measure progress.
3. Ensure Flexibility and Agility
You must ensure flexibility in cross-departmental teams so that new metrics can be added and members can take prompt action.
4. Minimize IT Infrastructure Investment
Implementing supply chain analytics requires smart investment in technological infrastructure. You should initially invest only in the necessary tools to allow team members to get acquainted with the process.
Businesses rely on data analytics strategies to boost their supply chain operations. However, the availability of a plethora of analytics tools and massive amounts of data makes it difficult to extract value and real-time insights from supply chain data. This has resulted in high demand for data analytics professionals. If you are planning to pursue a career in supply chain management, check out Emeritus online courses on strategy and innovation. These courses will help you gain the relevant skills and practical experience required to pursue a fruitful career.
By Sneha Chugh
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