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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.
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.
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.
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.
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.
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.
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.
All of the above uses of supply chain predictive analytics provide three key benefits:
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.
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.
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.
Numerous systems, tools and programs are available to provide supply chain data that can be used in predictive analytics modeling, including:
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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