The Growing Role of Supply Chain Data
Infosys reported that businesses are beginning to approach data in a different way than they have in the past. Organizations are increasingly relying on data for decision-making and to ramp up operational efficiency. The problem is many companies have been unable to update their data workflows around the new reliance on information, leading to escalating problems.
Businesses using legacy methods for data collection and system integration end up with data quality issues that bog operations down with inaccurate or unnecessary information. Companies that get data quality processes in order set themselves up for greater returns from their technology investments across the business, the news source explained.
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Establishing systems to improve data quality is critical in supply chain ecosystems, and a TechTarget report highlights the way the industrial internet of things (IIoT) is setting a new foundation for operational success. In many cases, businesses have outdated models for data collection within their inventory management systems. In these cases, companies rely on review-and-stock processes that use historical data to project ideal inventory levels. At the same time, companies use algorithms to try and account for any variance in historic data. The IIoT offers a solution in simplifying data collection.
Most companies don’t fully track assets across the supply chain.
The problem with these strategies, according to TechTarget, is that most companies don’t fully track assets across the supply chain. With review-and-stock processes in place, the view of inventory levels is static and can lead to delays between when goods are used and when the system identifies the change in the stock level. Furthermore, many organizations do not account for failed parts that go down in the field, limiting awareness of stock levels. These types of data quality gaps hold back innovation and efficiency in warehouse settings.
Businesses have opportunities to move forward to resolve these issues and get data under control.
Six Tips for Ramping Up Data Quality
There isn’t any one thing organizations can do to immediately solve data quality issues. However, the following six tips can help businesses get moving in a positive direction:
- Leverage IIoT sensors and monitoring devices, something TechTarget said could close gaps in legacy inventory management processes.
- Take advantage of mobile data collection solutions, such as wireless barcode scanners, to make data gathering easy for users and limit human error.
- Invest in ERP integration to ensure that backend systems can be updated in real time when users log information on the warehouse floor.
- Train employees regularly to ensure they stay on top of data collection best practices and don’t let quality standards slip.
- Automate data workflows to eliminate human error, particularly when it comes to issues such as manual data entry.
- Create a system of checks and balances so no one user can create significant problems within the data ecosystem.
These types of strategic investments give businesses the tools they need to establish a strong basis for high levels of data quality. Simplifying and streamlining data collection ensures that key information doesn’t slip through the cracks. Integration between disparate systems allows for easier updating without creating data redundancies that generate complexity and potential for errors. User error and technical limitations can hold back data quality, but the right technological systems can eliminate those problems and establish the high levels of data quality needed to drive better decision-making.
At RFgen, we are among the leaders in establishing mobile data collection in the supply chain. We offer a full ecosystem of barcode scanners, remote management tools and data integration software systems that help companies gain control of their essential warehouse data.
