The role of data analytics in warehouse management systems

Data analytics plays a crucial role in warehouse management systems as it allows for the efficient tracking and analysis of inventory, shipping and receiving, and overall operations. By analyzing data, warehouse managers can make informed decisions about inventory levels, optimize their supply chain, and improve overall efficiency.

With that context, let’s take an in-depth look at the role of data analytics in warehouse management systems:

How Does Data Analytics Impact Warehouse Management Systems?

Inventory Tracking

One key area where data analytics can be used in warehouse management is inventory tracking. By analyzing data on inventory levels, warehouse managers can determine which products are in high demand and which are not, and make adjustments accordingly.

Advanced analysis will indicate increasing or decreasing channels of demand and help differentiate between past and current signals. Tracking management techniques over time, such as shrink, swell, and cycle count accuracies, can focus a firm on where their low hanging fruit or highest pain points reside. This drives improvement efforts to reduce waste and improve overall inventory accuracy and availability.

Shipping and Receiving

By analyzing data on shipping and receiving operations, warehouse managers can identify bottlenecks and inefficiencies, and make changes to improve the process. Finite analytics can pinpoint trending issues with facilities, shifts, or products and focus the team on areas for improvement. 

In addition, advancing beyond purely reporting and KPI analysis, modern facilities can leverage analysis to introduce gamification to their processes, encouraging teams to competitively drive their own metrics for greater gains. This can help to reduce labor spend, shipping and receiving times, lower costs, and improve overall customer satisfaction.

Optimization of the Warehouse’s Layout

Data analytics can also be used to optimize the layout of a warehouse by analyzing data on how products are stored and moved within the warehouse.  Advanced techniques look at products that are ordered together, kit-to-order and kit-to-stock, or regular wave and pick paths that reduce wasted movement in the facility.

In addition, they increase productivity and throughput by reassigning labor to revenue generating activities.  Warehouse managers can determine the most efficient layout and make changes to improve overall efficiency.

Improved Warehouse Operations

Analyzing data on various aspects of the warehouse, such as labor costs and productivity, warehouse managers can identify areas where improvements can be made and make changes to improve overall efficiency.  

Holistic reporting can look at shift efficiency and inter-team productivity across different slices and proactively recommend improvements.  Then by taking a macro look at the warehouse as a series of tasks and handoffs, bottlenecks can be identified and be made the focus of continuous improvement sprints to improve JIT (just-in-time) and continuous flow processes.

Data Process Automations

Analytics for warehouse management assist you in locating time-consuming procedures that produce mistakes as a consequence of manual processes. These procedures may be automated to speed up the process, cut costs, and boost effectiveness.

A primary focus should be placed on repetitive tasks where human entry or audit provides little value or increases error.  This frees up staff for tasks that require creative or dynamic responses and can also improve morale by automating repetitive, low-skill tasks. 

Automation can also give you useful information about these procedures, and how well they are performing, which further improves your operations.

Optimize Inventory Locations

Managing actual warehouses might be difficult when there is little information available (or that information always seems to be out of date). However, with accurate analytics, your entire inventory footprint can be considered as a whole, rather than a series of independent stocking or picking decisions. 

Modern AI-powered recommendations boost productivity and efficiency while lowering operating expenses.

Knowing the precise position of the goods speeds up picking and loading processes, eliminates waste, and increases employee efficiency.

Manufacturers can now identify what produces the greatest outcomes, concentrate on the appropriate KPIs, and manage storage space constraints thanks to improved insights into goods and processes.

Demand Prediction

Businesses are in a better-informed position to plan across channels when employing predictive analytics to estimate demand accurately. To assess customer behavior and demand trends – and forecast demand at various periods – you must rely on precise data.

Inventory optimization relies heavily on real-time insight into goods to drastically reduce the danger of stock-outs and inactive, or obsolete, inventory on the rack.  Both of which are becoming more and more prevalent in the era of e-commerce. 

Many facility issues which firms experience begin with how well their demand signals and response perform. As firms grow, this can become an enabler of, or hindrance to, sustainable growth.

Better Customer Service

Customer buying habits, specifically in an omni-channel world, are incredibly fluid.  Without a centralized, cleansed source of both historical and forecast information, you will inevitably miss opportunities to delight your customers. 

This can be through your own channels, or through your resellers, but ultimately it is a reflection on your ability to deliver.  Combining strong, accurate, real-time inventory information with outstanding replenishment orders and a solid demand forecast ensures your Available-to-Promise levels are accurate when your end customers place their orders. 

While it is impossible to perfectly offer a backorder-free experience, clear communication enhances customer brand loyalty.  Your warehouses are better supplied and more efficient using predictive analytics; this ultimately leads to a better ordering experience and increased customer satisfaction.

Final Thoughts

Overall, data analytics play a crucial role in warehouse management by providing warehouse managers with the information they need to make informed decisions, improve efficiency, and optimize their operations. By implementing data analytics via warehouse management systems, businesses can gain a competitive edge and improve their bottom line.

Learn about the industry’s most modern, effective, economic, flexible, and rapidly growing warehouse management system tools on the market. These solutions streamline crucial warehouse management tasks including paperless picking, packing, shipping and customer service workflows.

Through automation and real-time inventory visibility, best-of-breed warehouse management systems help businesses achieve +99% order accuracy, fulfillment in under 24 hours, and value in a very rapid timeframe.