Predictive analytics in supply chain management has been around for decades, but recently it has become mainstream and affordable for businesses of all sizes. With the latest technologies, like predictive data analytics, companies are fine-tuning their supply chains and reducing inventory expenses.
With competition increasing and product margins shrinking by the day, many manufacturers are turning to improving customer service. Supply chain predictive analytics is one-way businesses can enrich customer relationships and enhance customer responsiveness through supply and demand forecasting.
In this article, we will reveal what a predictive supply chain is, its use cases, and how predictive analytics solutions are transforming logistics and supply chains.
What is Predictive Analytics in Supply Chain?
As the name implies, the term “predictive analytics” helps businesses predict future trends, such as exchange rates, sales demand, and other supply chain metrics. The primary goal of predictive tools is to analyze historical data to determine and formulate future trends.
Predictive analytics solutions and advanced mining techniques are capable of analyzing large structured and unstructured datasets. However, one important thing to remember is that predictive analytics does not predict the future. Instead, it lets you understand patterns and trends by analyzing historical data.
There are three types of predictive analytics tools and techniques for businesses:
- Predictive models
- Descriptive models
- Decision models
How is Predictive Analysis Transforming Logistics and Supply Chain?
The rapid emergence of advanced predictive solutions and artificial intelligence has led supply chain leaders to explore potential use cases of the technology. According to a survey by Gartner, 96% of respondents use predictive analytics, while 85% use perspective analytics to predict future market trends and supply chain models.
The reason behind the adoption of the technology is that it helps businesses make smarter decisions about their supply chains. Big data analytics applications within the business supply chain help suppliers through procurement, production, logistics, sales, and end customer. Businesses that adopt predictive analysis experience the highest return on their investments and improve supply chain operations.
Use Cases of Predictive Analytics in Supply Chain Management
Predictive analytics models are one of the most critical technologies that help companies anticipate future trends, manage big data, and identify potential risks and opportunities. This allows organizations to optimize overall supply chain operations to streamline business operations.
Below we will reveal some practical predictive analytics use cases for supply chain managers.
Supply and Demand Forecasting
Precise demand prediction is one of the most important ways to improve the supply chain management process by monitoring important supply chain metrics. When supply chain leaders use predictive analytics, it helps them satisfy customer demand while minimizing inventory expenses. Historical data can help supply chain managers look at past trends and forecast demand.
Logistics/Transportation Optimization
The role of logistics is to manage the company’s goods from origin to consumption. Implementing predictive analytics solutions ensures the right product is delivered to the right place at the right time. By utilizing advanced data analytics techniques, companies can optimize their transportation networks, traffic patterns, and other relevant factors.
Pricing Optimization
Forecasting the product’s demand is one of the best ways to adjust the price that the market can bear. The latest technologies can help identify ideal costs depending on historical data about product sales volume. Predictive analytics optimizes promotion and price by lowering the risk of potential pricing mistakes.
Customer Experience
Predictive analytics for eCommerce can also help supply chain companies gain insights into customer behavior and improve customer experience. Computer models help understand which products customers will buy next or when they will cancel the order. Predictive analytics in supply chain management algorithms also offer differentiated product recommendations to retain existing customers and attract new ones.
If you want to integrate big data solutions or predictive analytics into your business, feel free to reach out to Inferenz experts. The professional team has years of experience in helping companies stand ahead of the curve with advanced technologies.
Benefits of Implementing Predictive Analytics Solutions
Using predictive solutions and big data helps companies rebuild their supply chains and generate better revenue. Here are a few benefits of introducing predictive analytics into the operational lifecycle:
- Reduces chances of production overruns
- Improves customer engagement
- Strengthens decision-making process using data and automated algorithms
- Reduces maintenance costs
- Decreases customer churn
- Prevents supply chain interruptions
- Makes your supply chain more responsive to changes happening across the supply chain
Future of Supply Chain Predictive Analytics for Business
Predictive analytics solutions in the supply chain industry are based on machine learning algorithms to help businesses predict price and demand. Artificial intelligence in predictive analytics can help supply chain partners automate demand forecasting and optimize inventory levels and production planning. The complete supply chain process is automated, ensuring there is limited or no human intervention.
In the near future, we can expect artificial intelligence-powered predictive analytics tools to be widely adopted by businesses. If you want to take your supply chain management to the next level with predictive and prescriptive machine learning, contact Inferenz experts. Our team of AI experts helps companies develop predictive analytics solutions for logistics and supply chains.
Get in touch with Inferenz experts to increase your business revenue by adopting predictive analytics in supply chain management.
