Supply Chain Predictive Analytics: What is it?

Supply chain predictive analytics: What is it and how is it shaping logistics?

Predictive analytics has been quietly transforming the logistics industry in big ways over the past decade. Enabling everything from faster delivery times to improved demand planning, using big data for predictive modeling and smarter decision making is here to stay.

In fact, with the acceleration of eCommerce and omni-channel strategies during the COVID pandemic, big data and predictive analytics will play an even larger role in the future. The good news is, everyone stands to gain – manufacturers, warehouses, shipping companies, online retailers, and consumers.

So, what is supply chain analytics? What impact has it already made, and how is it shaping the future?

What is predictive analytics?

Predictive analytics takes historical and current data and analytical techniques to generate predictions about the future. Before recent advancements in artificial intelligence (AI) technology, data scientists relied on statistical models to gain insights into future trends.

Now, predictive analytics systems can generate faster and more accurate insights using AI-driven techniques such as machine learning. As a result, companies are using data to accurately forecast trends and behaviors as far out as months and years into the future. And as close up as the next few days or even the next few seconds.

What industries are using predictive analytics?

From financial services and energy to logistics and manufacturing, every industry is leveraging the power of predictive analytics. The global market for predictive analytics is expected to reach $10.95 billion by 2022, with a CAGR (compound annual growth rate) of 21 percent from 2016 to 2022.

Why is this technology so indispensable?

It provides increased visibility. With vast amounts of data fed into sophisticated statistical models and machine learning algorithms, businesses across the board are benefiting from accurate forecasts and actionable insights.

The impact of big data in logistics

According to a study conducted by the Council of Supply Chain Management Professionals, 98 percent of third-party logistics firms believe data-driven decision-making is crucial to supply chain activities. Of shippers and logistics firms, 71 percent think big data enhances quality and performance.

By feeding reams of data into analytical systems, the logistics industry can leverage real-time visibility to make smarter decisions. Where bottlenecks might arise. How to better streamline fulfillment. Cut costs. Improve outcomes. Ship faster. It’s all in the data.

Big data offers key benefits to the logistics sector:

Forecasting customer demand

With insights on how consumer behavior is likely to shift, warehouses can prevent inventory shortages. Manufacturers analyze big data to learn when they should increase or decrease production to meet expected demand, thereby optimizing resource consumption.

Streamline shipping and fulfillment

Logistics firms integrate road maintenance data, weather data, personnel schedules, and other route data into a predictive analytics system to determine how best to optimize routes. This can help businesses achieve goals such as faster delivery, reduced shipping costs, and even a reduction in carbon emissions.

Better inventory management 

Warehouses get a real-time understanding of what’s on the shelves, what’s moving, and how stock levels are changing. This information is used for making smarter labor decisions and ensuring stock levels are always primed to serve evolving consumer trends.

How supply chain predictive analytics is being used today

Warehouses, fulfillment centers, shipping firms, and other logistics companies all over the globe are using predictive analytics to spot patterns, forecast behavior, and, essentially, predict the future. Here are some examples of supply chain predictive analytics in action:

  • Amazon uses predictive analytics and big data for its innovative fulfillment process known as “anticipatory shipping.” AI determines which items customers are likely to order before they order them. Then, Amazon sends those anticipated items to a specific shipping hub or strategically places them in warehouses, so customer orders are fulfilled much faster.
  • FoodServiceCo, a leading online food service retailer in the UK, uses IoT technology to collect data on its fleet of drivers. Everything from information on the temperatures inside the trucks to real-time location data is tracked and analyzed, giving fleet managers crystal clear visibility over the entire supply chain. This has helped the company improve routes and shipping quality while drastically cutting down on driver hours. Since using predictive analytics for their shipping fleet, they save 18,000 driver hours each month.
  • With 46 markets in different regions all over the world, supply chain management for global food giant Nestle is highly complex. The company uses AI, machine learning, and other advanced analytical tools to better predict consumer demand, driving smarter planning decisions and boosting efficiencies.

Emerging trends in how analytics are used in supply chain management

Predictive analytics is now the norm for third-party logistics firms. This is increasing competition as consumers grow accustomed to faster shipping times and better services and as manufacturers and retailers expect more streamlined processes.

Also, eCommerce and omnichannel are growing faster than traditional retail, putting even more pressure on third-party logistics companies to offer quicker, more efficient, and better-optimized solutions.

To compete, firms will have to rely even more heavily on technology and big data. So, what future trends can we expect?

  • More warehouse automation for picking, packing, and shipping, including the use of autonomous robots
  • Drones and autonomous mobile robots playing a role in the delivery process
  • Increased adoption in cloud-based supply chain management software as companies look for ways to boost resilience in the wake of the pandemic
  • More granular predictive modeling to help firms gain even more clarity on logistics challenges and how to solve them
  • Segmented supply chain strategies, which is where companies will use a higher service level for certain customer segments or product groupings
  • Interest in sustainable shipping methods – as pollution becomes an increasingly pressing issue, consumers are likely to prefer buying products from companies that embrace sustainability

The reality is, predictive analytics and big data have improved the logistics industry. They’ve enabled lightning-fast shipping, reduced costs at every stage of the supply chain, and have made the rapid rise in eCommerce feel effortless to consumers.

How can businesses take advantage of this shift and remain competitive? By partnering with a logistics team that uses advanced predictive analytics to streamline your supply chain. 

Combining industry-leading technology with analytical expertise, the logistics experts at FTDI West help businesses make smarter decisions that drive measurable value. Contact us today to learn more.