Nov 08, 2023

The only thing certain about the future is its uncertainty or at least that has been the common belief until predictive analytics took center stage in today’s forecasting models. Predictive analytics has become an increasingly popular tool in the supply chain in recent years, with about 92% of Fortune 1000 planning to invest in big data models leveraging this emerging system. By utilizing historical data and statistical algorithms, predictive analytics help decision-makers forecast future trends and patterns, allowing them to...

• Assess how much stock is needed to prevent sudden shortages
• Project incoming revenue accurately
• Evaluate potential demand for popular stock
• Manage team schedules and allocate tasks
• Automatically replenish stock and communicate with delivery teams

As we move into the holiday rush and a suspected spike in reverse logistics, a thorough understanding of predictive analytics can help distributors stay a step ahead of consumer demands, regulatory compliance, and surprise shortages.

But first, what is Predictive Analytics?

Predictive analytics is the use of statistical algorithms, machine learning, and data mining techniques to identify the likelihood of future outcomes based on historical data. It involves analyzing current and historical data sets to identify patterns and make predictions about future events or trends. This technique is already used in various industries such as finance, healthcare, and distribution to inform decision-making processes and improve business outcomes.

One of the key benefits of predictive analytics in the warehouse is its ability to improve efficiency and reduce costs. By analyzing data from sensors, RFID tags, and other sources, predictive analytics consolidates data into manageable reports for administrative teams. Supply chain decision-makers have noted the following benefits:

• Accurate Storage Estimates – By analyzing sales data and other factors, predictive analytics can help identify which products are likely to sell well in the future, and which ones are likely to sit on the shelf. This information can be used to verify how much storage space is still available to ensure that the right products are stored properly when customers need them. For manufacturers, this can also help assess how much stock needs to be created, prioritizing budgets for high-demand items.
• Optimized Labor Scheduling for Reduced Costs – Predictive analytics can also help warehouse managers optimize staffing levels. By analyzing data on order volumes, shipping schedules, and other factors, predictive analytics can help managers determine the optimal number of staff needed to meet demand. This can help reduce labor costs while ensuring that orders are fulfilled in a timely and efficient manner.
• Enhanced Picking Zones and Routing – When storage areas are accurately stocked and monitored, picking teams have a clear view of where items are located, facilitating pick routes for a faster throughput rate. High-demand products can be stored more closely to minimize travel time. Moreover, when combined with voice-based picking technologies, collected data can be relayed to your team, optimizing pick routes within a hands-free system for even quicker fulfillment.

Related: Struggling to choose between voice and traditional picking systems? See 5 factors decision-makers consider before finalizing their choice.

• Reduced Product Shrinkage – Whether it be from spoilage or theft, product shrinkage can heavily impact the bottom line if left unchecked. Most facilities now calculate an expected shrinkage rate within their inventory counts to prevent overspending; however, inaccurate estimates may result in overstocking and/or underselling. Consolidated data provides a realistic picture of expected shrinkage based on past patterns. Moreover, digitized data can also help prevent hidden blind spots as inventory is tracked from receiving to shipping.
• Prevented Recalls – Not only can predictive analytics forecast potential demand, but it can also estimate potential malfunctions and breakdowns, enhancing quality checks to prevent future recalls. Advanced models allow engineers to adjust probable scenarios where their products will perform, screening the product’s performance before it leaves the manufacturing plant. Storage fluctuations and transportation scenarios can also be tested to ensure products maintain integrity during the distribution process.  

Related: Check out four ways distributors prevent recalls in the food and beverage industry.

Increased control over inventory movement and labor scheduling proves indispensable during fast-paced workflows, typically occurring during the holiday Peak Season. Managing outgoing shipments, returned goods, and a surplus of online orders, supply chains employing predictive analytics can easily track multiple data points to best preserve customer satisfaction.

How to Incorporate Predictive Analytics in Your Operations

As a warehouse manager, you know that efficiency and accuracy are essential to your operations. Here are some steps to help you implement predictive analytics in your warehouse.

• Step 1: Identify the Problem

The first step is to identify the problem you want to solve. Examples of problems that predictive analytics can help solve in the warehouse include predicting demand for certain products, identifying potential equipment failures, and forecasting inventory levels. Partnering with a qualified support system can help you evaluate operations based on industry standards.

• Step 2: Gather and Clean Data

Once you have identified the problem, you need to gather historical data related to the problem. This data can come from a variety of sources, including your warehouse management system, inventory management system, and sales data. It's important to clean the data and remove any errors or inconsistencies. During this step, facilities employing barcode scanning and computing may find more ease in retrieving data since it's stored digitally.  

• Step 3: Choose the Right Predictive Analytics Software

There are many predictive analytics software options available on the market. It's important to choose one that is tailored to your specific needs and that integrates well with your existing systems. Once again, working alongside a versatile system developer empowers teams with a greater variety of available software for a truly customized system.

• Step 4: Train the Model

Once you have your data and software in place, you need to train the predictive analytics model. This involves using historical data to teach the model what to look for and how to make predictions.

• Step 5: Test the Model

After training the model, you need to test it to ensure it is accurate and effective. Testing involves using new data to see how well the model predicts outcomes. Teams using the model may also need more training to become familiar with the new system. Clear communication with involved parties will also ensure your model captures vital information for all participants.

• Step 6: Implement the Model

Once you have tested the model and are confident in its accuracy, you can implement it in your warehouse operations. This may involve automating certain processes or using the model to make more informed decisions about inventory levels or equipment maintenance.

Overall, predictive analytics is an important tool for any warehouse manager looking to improve efficiency, reduce costs, and make informed decisions about inventory management and staffing. By leveraging historical data and statistical algorithms, predictive analytics provides valuable insights that can help warehouse managers stay ahead of the curve and make better decisions for their daily operations. To learn more about how predictive analytics can fit your facility, reach out to our automation team.