The Importance of Data Integration in Warehouse Operations

Jacek Warchoł
Mateusz Panek
Warehouse Operations

In today’s digital age and new normal, warehouses are no longer just physical storage sites. They are a multidimensional hub between different parts of the supply chain, brimming with data that businesses are often overwhelmed with. Companies can sail through this sea of data by consolidating and integrating it into a unified system that gives a bird’s-eye view of warehouse operations and what lies ahead of it.

A recent report noted that warehouses around the globe saw an increase in the complexity of their operations in recent years in order to accommodate the needs of e-commerce and other digital services. It’s predicted that in the next five years as businesses expand their warehouse operations, 70% of warehouses will have more automation in their processes, while 66% will equip the staff with more technologies. Not only do these mean pouring more resources to the warehouse — it also means having additional processes to manage these resources.

These complexities are also influenced by growing number of warehouses (and their different locations), longer supply chains, and additional data that can be tracked and monitored. In the past, warehouses relied on Excel workbooks and email communication, with no centralized data systems. Warehouse management is rapidly changing, but many companies are still lagging. Now, companies are not only digitizing their data, but they’re also reaching out for better, more efficient ways to manage and integrate this data within their business processes and use it to make informed business decisions.

Costs are at the forefront of any business, with modern warehouses being no exception. Warehouse managers are always looking for new data and analytics tools to manage costs, maximize profitability, and optimize operations. These tools allow companies to gather and analyze huge quantities of information about their buyers, the products they’re selling, stock maintenance, and other metrics like customer satisfaction. Without proper analytics tools, warehouse managers and business leaders will end up fruitlessly using data which, in turn, obscures their visibility in the warehouse’s operations.

Managing complexities in warehouse operations through data integration

The best way to handle data in a meaningful way is to have a “single source of truth.” This means integrating data through a centralized platform that connects dispersed and multiple sources of data into a single, wide-reaching system. When there are multiple sources of truth, discrepancies and conflicts can (and will) occur. It’s easy for businesses to become compartmentalized and break off into silos with everyone working away in different locations and directions. This can be detrimental in the long term. A single source of truth helps bridge these gaps.

Companies also benefit through improved transparency and collaboration. A consolidated and integrated source of data can also serve as a central hub that can be accessible and visible to people who need it. By allowing them to view, share, and integrate data in parallel, it helps reduce redundancies and increase efficiency across the board.

A warehouse KPI dashboard, for example, can integrate all sorts of data from different sources, such as a company’s depot and transport management systems, enterprise resource planning (ERP) system, warehouse management software, warehouse slotting application, and even from internet-of-things (IoT) devices.

As shown in the following example, a way to manage all these complexities is to integrate all dispersed datasets into a single ecosystem. A warehouse KPI dashboard, for example, can incorporate datasets that are processed in real time through the following:

  • Smartyard: Logistics-related data, such as planning the delivery time of trucks, scheduling their maintenance, and assigning vehicles for specific work
  • Warehouse management system: Data related to inventory, team performance (e.g., workloads, schedules, productivity), costs, and other metrics or KPIs related to planning and monitoring operations
  • Transport management system: Delivery and transit times, palletization of inventory, transport orders, load planning, and fleet and freight management, among others
  • Slotting applications: Data related to labor management, such as the timing of the workforce’s schedules
  • Zoning applications: Identifying warehouse areas — that is, knowing the optimal area within the warehouse for picking and packing inventories
  • IoT: Data that sensors in IoT devices collect, such as real-time location and shipment, working conditions of equipment and machineries, product information through barcodes or radio-frequency identification-enabled (RFID) devices, and product status (particularly for those in the cold chain, like medicines, food, and other perishable products)

A warehouse KPI dashboard can be connected to multiple data sources and even back-end systems. The dashboard becomes the warehouse’s single source of truth, where different kinds of data are gathered, automatically calculated (if needed), and presented in a single view. Warehouse managers don’t have to straddle with different tools and error-prone manual processes to check the warehouse’s performance.

Warehouse Operations
A visualization of a single ecosystem where different kinds of data from various sources can be aggregated and integrated into, for instance, a warehouse KPI dashboard

Incorporating advanced analytics in warehouse operations

The importance of data and analytics in supply chains is well-known. In fact, 91% of surveyed decision-makers said they are actively investing in capabilities that can analyze large amounts of data and automate business processes. Data and analytics gives warehouse managers the ability to find areas in warehouse operations that can be improved.

It’s all well and good for companies to have access to their data in one convenient place, but what can they do with all that information? How can they effectively use it to improve the warehouse’s efficiency? This is where a warehouse KPI dashboard can also help. It can track the movement of goods, schedule workloads, and monitor the performance of machineries and equipment, to name a few — all in a single pane that warehouse managers can view wherever and whenever they are.

These can be complemented by business intelligence and advanced analytics capabilities, which can help analyze data to, among others:

  • Estimate the capacity needed for operations.
  • Identify risks, such knowing temperature-sensitive products or separating expiring or obsolete items.
  • Plan a better course of action, such as taking alternative routes during high-traffic periods.
  • Compare the procurement costs and quality of products from vendors and suppliers.

Using analytics to optimize key areas in warehouse operations

Predictive analytics is gaining ground in the supply chain, particularly in warehouse management. It uses different techniques like machine learning (ML) to analyze current and historical data to project future trends or events, estimate their impact, and assess if they pose risks or open new opportunities.

Predictive analytics doesn’t just help warehouse managers plan a strategy for responding to market fluctuations. It can also compute and rationalize data that can enhance key areas in warehouse operations — predicting demand, optimizing inventories, managing data, and improving customer service. For example, it can give warehouse managers a good idea of what kind of stocks the company might likely need (and reserve), which will give the team a practical safety margin.

Here are some key areas of warehouse operations that benefit from predictive analytics:

Demand forecasting: Predictive analytics looks at historical and current data and uses this information to predict demand for a product or service based on patterns in customer behaviors. This is extremely useful, especially in business settings where there is a strong seasonal demand for products. These predicted patterns are an enormous asset to companies when it comes to stock control and manufacturing.

Inventory optimization: Predictive analytics can be used to manage assets and help companies avoid running out of stock. By enabling warehouse managers to understand patterns in how customers buy products, they can plan ahead and maintain the necessary levels of inventory.

Process automation: Predictive analytics helps minimize labor-intensive tasks that entail repetitive work and manual data processing and analysis. By automating analytics functions, certain business processes can be streamlined, like in inventory and space utilization management.

Data customization: Predictive analytics correlates different kinds of data across various functional teams. This comprehensive approach means companies can make holistic, long-term decisions about their finances, daily operations, and inventories, all of which have an impact throughout the business.

Customer service: Predictive analytics helps improve customer satisfaction. How so? Warehouses will stay stocked up and running as efficiently as possible, customers will experience minimal delays, and products will remain readily available.

Indeed, data is key to effective warehouse management. However, complexities in warehouses operations — from digitization and digitalization to multiple, dispersed sources of truth — are hindering businesses to effectively use data. Developing a platform that can integrate and simplify these complexities into single source of truth can empower warehouse managers and decision-makers to gain more visibility into the warehouse. Complementing it with advanced analytics can open windows of opportunities and enable businesses to look and expand beyond the warehouse’s four walls.

 

Lingaro All-in-One Warehousing KPI Dashboard

Lingaro provides comprehensive warehouse analytics solutions that empower businesses to build custom warehouse dashboards and use data to identify key processes that can be automated, make capacity improvements, minimize pallet routes, monitor workforce performance, and plan workloads.

Lingaro also provides supply chain analytics to help businesses gain full visibility across their supply chain, make informed decisions, and achieve operational excellence. By optimizing processes and harnessing cutting-edge technologies, Lingaro delivers tailored AI- and ML-powered solutions that map new opportunities and improve key areas in the supply chain — from demand forecasting, logistics networks, and warehousing to inventory management.

To learn more about how to effectively define and track KPIs for warehouse operations, download our guide by filling the form below

Best Practices for Tracking Warehouse KPIs

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Jacek Warchoł
Head of Supply Chain Practice

Expert in Digitalization of Supply Chains, Program Manager, Leading Supply Chain Practice in terms of Business Development and driving project delivery. Over 20 years of professional experience as a Manager in big4 management consulting as well as SCM Manager working for global manufacturers. He managed projects related to strategy development, business process improvement (process mapping, redesign, optimization) and technology optimization (feasibility study, blueprint design, implementation) for global clients from different business sectors and worked within multinational and cross functional teams.

Mateusz Panek
Enterprise and Business Solutions Architect

Doctor of Business Administration and Doctor of Laws. University Professor with almost 14+ years of professional experience in managing projects, building logistics processes, S&OP, business development and cost optimization. His expertice is related to logistics and operations in international multichannel retail companies, FMCG, fashion, e-commerce companies and business law.

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