Why Supply Chain Predictive Analytics Are Valuable for Financial Leaders

Enterprises are increasingly turning to supply chain predictive analytics to manage costs, mitigate risk and grow revenue. Here’s why.

Enterprises are increasingly turning to supply chain predictive analytics to manage costs, mitigate risk and grow revenue. Here’s why.

In today’s competitive, technology-driven landscape, consumer expectations are higher than ever. Customers want quality products to be available to them when and where they want. However, meeting these demands is almost impossible if you don’t manage your supply chain effectively. This is why the most successful large enterprises look for ways to optimize supply chain management processes.

In recent years, supply chain predictive analytics has become one of the most powerful tools that enterprises are using to gain better control of their costs, reduce risk and maximize performance. In this post, we’ll explore what supply chain predictive analytics is, how you can harness predictive analytics to your advantage and the technology solutions that can provide you with the right data and analytics. 

What is predictive analytics?

At its core, predictive analytics is a category of data analytics aimed at forecasting future outcomes based on historical data and analytics techniques, such as statistical modeling and machine learning. Such models assess a particular set of data points and conditions to produce fact-based insights that finance teams can apply to guide their business decisions.

How you can harness predictive analytics in your supply chain

The use of predictive analytics in the supply chain has been picking up speed as enterprises look for ways to remain agile and resilient in a shifting economy. By analyzing historical and current data, predictive analytics algorithms can provide your corporate supply chain or finance team with robust information that you can draw on to optimize finances, operations, supplier relationships, decision-making and more. So, how can analytics be employed in the supply chain? Let’s break it down.

Forecast revenue

Past behavior is the best indicator of future experiences. Finance teams use historical supplier and customer data to reveal trends, such as payment history and buying patterns. This allows for better quarterly forecasts and provides data-driven methods for optimizing spend.

Minimize disruptions and respond faster

Predictive analytics can enable you to identify potential risks before they occur. Using past inventory and supplier performance data, you can spot bottlenecks and delays to ensure continued supply. Real-time data and insights allow your team to respond to any issues promptly, improving problem-resolution speed and effectiveness.

Predict demand and optimize inventory

Using predictive analytics can help you improve demand forecasting and inventory management by analyzing sales, seasonality and past inventory turnover data so you can adjust production and stock accordingly. This data can assist with identifying your optimal inventory levels, making it possible for you to reduce instances of insufficient or excess inventory.

Improve customer satisfaction 

By leveraging data-driven insights, you can optimize your supply chain to ensure that products are delivered on time, in good condition and with high quality. Additionally, analyzing customer data can help you provide customers with more personalized experiences, leading to increased loyalty and satisfaction.

The benefits of supply chain predictive analytics

All of the above uses of supply chain predictive analytics provide three key benefits: 

  1. Increased cost control and profitability

Your business can better control costs by analyzing data like demand, supplier invoice history and stockouts to optimize inventory levels, reduce waste and streamline operations. It can also enable your finance leaders to recognize cost drivers and identify areas to improve, so they can make better decisions around procedural changes or new initiatives, like launching an early payment program for suppliers.

  1. Less financial risk

Predictive analytics can help you identify at-risk accounts and prevent supply chain inefficiencies, reducing risks to your company’s working capital. Combined with analysis of historical supplier and customer data, this can aid in better forecasting and data-driven methods for optimizing spend, resulting in long-term improvements.

  1. Greater economic resiliency

Predictive analytics help your business maintain agility in a shifting economy by providing real-time insights and enabling teams to make informed decisions promptly. With improved foresight, your team can anticipate potential roadblocks, modify products and services to meet evolving customer needs, and quickly adapt to new challenges and opportunities.

Technology solutions that provide supply chain data

Numerous systems, tools and programs are available to provide supply chain data that can be used in predictive analytics modeling, including:

  • Supplier Relationship Management (SRM): SRM software is useful for managing relationships with your suppliers. It provides information on supplier performance, lead times and pricing.
  • Business Intelligence (BI): BI software analyzes supply chain data to identify areas for improvement, track performance metrics and trends, and optimize processes.
  • Enterprise Resource Planning (ERP): ERP systems are designed to manage all aspects of your business operations, including the supply chain. They provide detailed information on inventory levels, order status and shipping information.
  • Warehouse Management System (WMS): A WMS helps manage inventory storage and movement within a warehouse. It tracks inventory levels and locations, as well as provides information on inbound and outbound shipments.
  • Radio Frequency Identification (RFID): RFID tags are attached to products or packaging and can be scanned to provide information on location and movement. This technology can track inventory levels and improve supply chain efficiency.
  • Electronic Data Interchange (EDI): EDI is a system that allows the electronic exchange of business documents, such as purchase orders and invoices. This system can automate supply chain processes and provide real-time data.
  • Early Payment Platforms: Financial software and platforms like C2FO’s Early Payment platform can help you segment suppliers and analyze important data, such as payment history and invoice data. These platforms can improve your supplier relationships and provide critical insights.

In summary

Supply chain predictive analytics can provide your operations and finance teams with a powerful resource for making informed decisions based on data and statistical analysis. By collecting and analyzing relevant data, you can identify patterns and trends, and unlock valuable insights.

As a large enterprise, gaining more control over your supply chain and working capital are critical for maintaining your competitive position in the market. Adopting supply chain predictive analytics and using the right technology solutions can help you prepare for potential disruptions, maintain adequate cash flow and achieve faster revenue growth.

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