In today’s business environment, data is considered one of the most valuable assets of any organization. It enables leaders to make informed decisions, measure performance, and identify trends that drive growth.
In warehousing and distribution, data plays a critical role in driving competitive advantage in efficiency, labour productivity and accuracy, among other areas. However, capturing and managing data can be a challenging task, especially when dealing with large amounts of information from multiple sources.
In fact, I would say the way it is collected, its “cleanliness,” and the way it’s presented and utilized, are some of the most critical hurdles we face in working with software implementations with customers. That’s where data engineering comes into play.
Data engineering is ”the process of designing and building systems that let people collect and analyze raw data from multiple sources and formats,” according to Dremio. It enables businesses to capture and manage data from multiple sources, as well as organize and structure it, to ensure that the information is accurate, consistent, and reliable.
In warehousing and distribution, good data engineering can help businesses improve efficiency, reduce errors, and increase visibility into operations. Here are some best practices for driving optimized data engineering in the warehouse or distribution centre.
Define clear data requirements
It’s essential to have a clear understanding of the data requirements for your business. This means defining what data is needed, where it’s coming from, and how it will be used. This could include the existing warehouse management system (WMS), barcode scanners, RFID readers, ERP (enterprise resource planning) systems, and any other relevant sources.
Define the data format, frequency of updates, and the method of data transmission or synchronization. For SKU information it could include product descriptions, consistent unit weights, dimensions, storage locations, quantities on hand, and any other relevant attributes. Having a clear understanding of the data requirements enables businesses to capture and manage the right data, and ensures that the information is accurate and relevant to the business needs.
Use standard data formats
Standard formats are essential for the efficient use of data. Using standard formats such as XML, CSV, or JSON ensures that data can be easily integrated and shared across different systems and applications. It also makes it easier to analyze and report on data, enabling businesses to identify trends and patterns that drive performance.
Also, when gathering data, make sure it follows the same pattern and format across all your locations. In terms of locations, are identifiers for zone, aisle, slot level and bay the same in all your DCs?
Identify your key performance indicators (KPIs)
Definitively identifying your KPIs will ensure you are capturing the data that is most relevant to your business goals. For example, you might use order fulfillment rate, inventory turnover, and shipping accuracy as KPIs for your warehouse or distribution centre.
In terms of productivity, you could identify:
- How efficient is each of my pickers in terms of productivity?
- How frequently did our order picking lag behind the schedule, leading to missed shipping deadlines?
- What are the velocities of our products and the current productivity of order picking?
Capture data in a timely way
Timely data capture is essential for businesses that require up-to-date information to manage operations effectively. This means capturing data as it’s generated, rather than waiting for batch processing or manual input. Timeliness in data capture enables businesses to respond quickly to changes in demand or supply chain disruptions and ensures that operations are always running smoothly.
For example, Lucas customer Apex Tool Group used timely data on order, inventory and task information from the WMS, including work status updates and exception notifications to respond to stock outs immediately, which drove better quality, workflow and completed orders.
Automate data capture and processing
Manual data capture and processing can be time-consuming and prone to errors. Automating data capture and processing using technologies such as barcode scanners, RFID, and warehouse automation software can help businesses streamline operations, reduce errors, and improve data accuracy. Automating data capture also frees up staff to focus on other tasks.
For example, one DC was manually batching orders based on ship times and picking three to four orders at a time. Implementing an automated solution in tandem with a voice-directed picking process, allowed them to gain the benefit of optimized batches, without sacrificing on-time delivery. They were able to reduce person-hours for picking dramatically while significantly increasing throughput per hour.
Implement data quality checks and a data governance framework
These best practices often go hand in hand. Data quality checks are essential for ensuring that data is accurate, consistent, and reliable. Implementing data quality checks at various stages of data capture and processing can help identify errors and inconsistencies before they impact operations.
Data quality checks can include validating data against predefined rules, checking for duplicates or missing data, and data cleansing to remove invalid or irrelevant data. On e-commerce fulfillment, for instance, regularly ensuring that customer information, including shipping addresses and contact details, is consistent across all systems will minimize shipping errors and improve customer satisfaction.
Data governance is the process of managing the availability, usability, integrity, and security of data. Implementing a data governance framework enables businesses to ensure that it is managed effectively and consistently across the organization. This includes defining data ownership, data access policies, data quality standards, and data security protocols.
Implementing a data governance framework also helps businesses ensure compliance with regulatory requirements. For product information, setting responsibilities and a regular process to ensure product information – such as SKU numbers, descriptions, and dimensions – are regularly validated and updated will prevent errors during automated handling and order fulfillment.
In a pharmaceutical warehouse automation project, data governance ensures that patient-related information and drug tracking data comply with strict health regulations like HIPAA or GDPR. It includes access controls, encryption, and protocols for handling and anonymizing patient data.
Leverage data visualization tools
Data visualization tools such as management dashboards, charts, and graphs can help businesses make sense of large amounts of data and identify trends and patterns that drive performance. Data visualization tools enable businesses to create customized reports and dashboards that provide real-time insights into operations and enable leaders to make informed decisions quickly.
Several Lucas customers for instance, utilize real-time productivity dashboards and alerts as part of the workforce management module that allows managers to be more proactive in coaching and training and helps workers to stay on track with real-time feedback
Integrate data from multiple sources
Your warehouse or distribution centre likely generates data from multiple sources, including your ERP or WMS, inventory management system, order management system, and shipping and receiving systems. Conveyors or sorters with sensors, and devices like printers and mobile computers, can also be a great source of data, especially if it’s timely. Integrating data from these sources can provide a more comprehensive view of your operations and help you identify opportunities for improvement.
In conclusion, data engineering is a crucial process that can help improve the accuracy and efficiency of warehouse or distribution centre operations. By adopting the best practices outlined in this article, executives and leaders in the industry can ensure the data they capture is both relevant and reliable.
This, in turn, can help drive better decision-making, reduce costs, and enhance overall productivity. From ensuring data completeness and consistency to leveraging automation tools and integrating data from various sources, these strategies can help organizations stay ahead in an increasingly competitive market.
Ben Smeland is senior software engineer at Lucas Systems.