Leveraging Heat Maps to Find Fast-Movers

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Introduction

In warehousing knowing which products are your fast-movers (high-velocity SKUs) is crucial for improving your inventory management and order fulfillment process. To gain this valuable insight, companies can utilize a 200 year old technique of developing heat maps. In this blog post, we'll explore how heat maps can be used to find the location of your high-velocity SKUs and why this knowledge is beneficial for improving your operation.

Understanding High-Velocity SKUs

Before delving into the role of heat maps, it's essential to grasp the concept of high-velocity SKUs. High-velocity SKUs are products that have a consistent and high rate of sales or movement within your warehouse. These items are in constant demand and are typically the "bread and butter" of your business. Velocity can be defined in many terms such as orders/day, lines/day, units/day, or cube movement/day.

What Are Heat Maps?

Heat maps are visual representations of data that use color-coding to highlight trends, patterns, or concentrations. In the context of warehousing, heat maps are a powerful tool for analyzing the movement of products within your facility. They provide a clear and intuitive way to identify areas of high activity and can be applied to various aspects of warehouse management, including inventory optimization, order picking, and layout design.

How to Construct a Heat Map

Here's how you can use heat maps to determine high-velocity SKUs in your warehouse:

Gather Data: The first step is to collect data on product movement. This may already be captured manually through barcode scanning or automatically with RFID technology and stored in an inventory management system. Ensure that you have a comprehensive dataset that includes the volumes of interest (i.e. orders, lines, units, cube, etc.) and location of each product.

Data Analysis: With your data in hand, it's time to analyze it. Modern warehouse management software often includes built-in tools for creating heat maps. Alternatively, you can use data visualization software like Tableau or Excel to create custom heat maps. You can even create a quick visualization using python! I provide an example of this later on in this article.

Color Coding: Assign colors to represent different levels of activity. Product can be represented by different colors or shades. For high-velocity SKUs, you'll want to use a distinct color to make them easily identifiable.

Actionable Insights: Pay close attention to where you see clusters of high-velocity SKUs and note their proximity to the other areas of the warehouse. This information can guide decisions on bin placement, shelving design, and order picking routes.

Benefits of Heat Map Analysis

Using heat maps to identify high-velocity SKUs offers several key benefits:

Optimized Inventory Management: Heat maps help you evaluate your allocation of prime and reserve storage locations. They offer a quick visual representation of current or proposed slotting plans and the adherence to particular slotting strategies (e.g. sort by unit velocity, zone based on product category, etc.)

Reduced Labor Costs: Efficient placement of high-velocity SKUs minimizes the need for excessive labor to retrieve items, resulting in cost savings.

Faster Order Fulfillment: By strategically locating high-velocity SKUs closer to the picking area, you can expedite order processing, leading to improved customer satisfaction.

Heat Map Example

Creating a heat map of a warehouse to identify high-velocity SKUs in Python requires several libraries, including matplotlib and seaborn for plot visualization and pandas for data manipulation. In this example, I'll provide you with a simplified code snippet to generate a basic warehouse heat map. Note that for a real-world application, you would need to provide your own data.

Assuming you have a CSV file containing columns for SKU, x-coordinate ("xpos"), y-coordinate ("ypos"), and units per day ("velocity"), you can create a heat map as follows:

Make sure you have the required libraries (pandas, matplotlib, and seaborn) installed before running the code. This code reads your warehouse data from a CSV file and generates a heat map to visualize SKU velocities within the warehouse layout.

Remember to replace 'warehouse_data.csv' with the actual path to your CSV file containing warehouse data.

The site in this example has a concentration of high-movers slotted in the center with an even distribution of volume along the x-axis. Assuming this client wants to keep an even distribution of volume horizontally and focus on minimizing vertical trips into aisles, they might notice an opportunity for a re-slot (assigning SKUs to new locations).

A re-slot produces a new heat map (below). Now SKUs are sorted based on their velocity and slot proximity to the center of the building and with an even distribution of volume along the center path, forklift drivers can perform replenishments and pickers can access prime locations with minimal congestion. The result is reduced labor costs due to vertical travel and faster order fulfillment times.

Mapping Other SKU Characteristics

Heat maps can also be used to display other SKU characteristics, including categorical data. In this example, the client created a new heat map, color coded by product category. They noticed that while they reduced travel times, they will need to modify their slotting strategy to account for other constraints like the placement of HAZMAT (hazardous material, e.g. "chemical") items.

Conclusion

Heat maps provide a visual representation of the product slotted in a warehouse. The insights they provide can be used to optimize inventory management, reduce operational costs, and improve order fulfillment times. In today's competitive market, staying ahead often means embracing innovative tools like heat maps to drive success in your warehouse operations.