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4 Ways to Enhance Warehouse Efficiency with Prescriptive and Predictive Analytics

By February 8, 2024Uncategorized

In today’s fast-paced and competitive business landscape, optimizing warehouse operations is crucial for achieving operational efficiency. Predictive and prescriptive analytics have emerged as valuable tools that enable warehouse operations managers to streamline processes, minimize costs, and enhance productivity. While predictive analytics seeks to answer questions such as “what might happen in the future,” prescriptive analytics goes beyond by recommending the best course of action going forward.

In this article, we will actively explore how predictive and prescriptive analytics can significantly enhance efficiency in several diverse aspects of warehouse operations. Not only can you optimize performance but you’ll also help create needed agility, make smarter decisions and be future ready.

Slotting Optimization – Product slotting is a complex problem. It involves many input factors and many goals (which are sometimes at odds with each other). Traditional slotting solutions require customized models, extensive engineering, measurement, and data collection. Newer, more dynamic slotting models use AI to eliminate the need for manual engineering and measurement. They learn the spatial characteristics of the DC, and predict task time based on activity-level data captured in the DC. Better still, the model adapts to your DC and evolves as conditions change, providing continuous optimization. The models offer prescriptive analytics, leveraging data on order history, batching and pathing patterns, and warehouse layout to recommend optimal slotting strategies. By identifying the most efficient placement of products based on their popularity, size, weight, and other relevant factors, warehouse managers can minimize travel time, reduce replenishment jobs, and improve warehouse space utilization.
Restock/Replenishment Planning – Maintaining optimal inventory levels is crucial to meet customer demands without overstocking. Predictive analytics can forecast future demand based on historical sales data, seasonal patterns, market trends, and other factors. By analyzing this data, warehouse managers can accurately plan restock and replenishment activities, ensuring that inventory levels are optimally maintained, minimizing stockouts and excess inventory carrying costs. With more and more ecom orders, which are more labor-intensive than traditional store replenishment, operators need to continue to leverage solutions that more proactively can minimize stockouts. On the other size of the coin is overstock; bloated inventories are one of the biggest inflationary pressures on warehouse costs and negatively impact the bottom line as well.
Flow Improvement and Work Order Task Optimization – Value stream mapping is a powerful technique used to analyze and optimize the flow of materials and information within a warehouse, identifying inefficiencies and detecting improvement opportunities for an order’s voyage through the distribution process. By combining historical data with predictive analytics, managers can identify bottlenecks, process inefficiencies, and areas for improvement. This enables them to restructure work order tasks, optimize picking routes, and develop user-configurable workflows for all major material handling processes, from receiving to picking to shipping, including audit, cycle count, and returns.
Pick Error Prediction – Mispicks – when an associate picks the wrong product – can have serious detrimental effects on a company’s bottom line and their reputation with customers. Frequently, distribution center operators will audit a certain percentage of their orders to keep an eye on their mispick rate and ensure it doesn’t rise above unacceptable levels. Predictive analytics can inject some intelligence into this process. By analyzing data on a package, such as the products that are supposed to be in it and who picked those products, the probability of a package containing an error can be predicted, and the decision can be made to audit that package if the probability is high enough. Taking this even further, we can estimate the cost of an error to the business and use prescriptive analytics to automatically make the decision to audit a package or not. This can decrease the number of packages that leave the warehouse with errors while reducing the required auditing workforce, lowering costs and improving customer satisfaction.

Starting your analytics journey

Because of the complexity of the warehouse optimization process, it’s a very difficult problem to solve with traditional approaches. That’s why many warehouse managers are turning to things like software to help optimize the process. While many warehouse management systems can provide a fair amount of the data necessary to help drive improvements in slotting, replen, workflow and the other areas mentioned above, you can only get so far through general data analysis and higher level insights derived via spreadsheet calculations.

For that reason, warehouse execution software that combines AI technology, flexible workflows, and modular IT architecture to adapt to each DC’s unique process and technology requirements, is worth exploring. They can give you the extra layer of optimization that can transform your operations and easily be scaled up as your products, customers, and business grow. These solutions can often be implemented quickly without much disruption, and give a quick ROI as compared to larger scale automation implementations.

The real goal of analytics and their utilization is ultimately to enable smarter decision-making for the organization and its leaders. Having access to robust management dashboards through your WMS or other software is also an excellent resource to enable your team to leverage available data to its fullest. For instance, for one Lucas customer utilizing a management dashboard, managers could get real-time updates on work status, with insight into what percent of orders were completed, across all picking zones. If one area fell behind, workers could be moved to that area to complete the shipping to meet dispatch times. This resulted in more predictable, reliable dispatch and delivery times for their facilities, helping managers improve workforce scheduling for current and future shifts.

In today’s competitive warehousing landscape, efficiency is paramount. Predictive and prescriptive analytics offer warehouse operations managers powerful tools to optimize various aspects of their operations. From slotting optimization and restock/replenishment planning to warehouse layout improvement and flow optimization, these analytics-driven approaches provide valuable insights, enabling managers to make informed decisions and streamline processes. By embracing predictive and prescriptive analytics, warehouse operations managers can enhance efficiency, reduce costs, and ultimately deliver superior customer service.

Rob Mitchell leads Lucas Systems in the development of data science products and solutions that allow its customers to extract more value from their warehouse and distribution center operations. Inspired by a commitment to improving the lives of our customers by making them more efficient and making their jobs easier through data, he showcases a unique skill set driven by superior knowledge in data engineering, machine learning, data visualization and Python programming. Rob excels at creating data pipelines, training machine learning models, and building simulations that enhance value for customers, while also utilizing his knowledge of cloud computing to simplify data processes and improve performance and accessibility. He is a graduate of the Harris School of Public Policy at the University of Chicago, where he earned a Master of Science degree in Computational Analysis and Public Policy. Rob also holds a Bachelor of Science degree in Mathematics & Political Science from the University of Alabama.

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