Discover Methodologies to Ensure Accuracy, Consistency and Completeness of Supply Chain Data
Data Quality defines how data sets can be trusted, understood and utilized effectively for their intended purpose.
In Supply Chain Management, data plays a crucial role in detecting issues and making informed decisions.
Ensuring that data is accurate, consistent and fit for its intended purpose is a critical task to ensure smooth and efficient operational management.
In this article, we will delve into the concept of data quality by exploring its dimensions and understanding its importance in the context of supply chain management.
Summary
I. The Pillars of Data Management
1. Why is it key?
2. Quality vs. Integrity vs. Profiling
II. Dimensions of Data Quality
1. Completeness: do we have all the data?
2. Uniqueness: are features all unique?
3. Validity: is the format respecting business requirements?
4. Timeliness: up-to-date data
5. Accuracy: does it reflect reality correctly?
III. Why is Business Intelligence Significant?
1. What is the environmental impact of your operations?
2. Become a data-driven green organization
IV. Conclusion
1. Why is it key?
High-quality data can be the difference between the success and failure of your supply chain operations.
From planning and forecasting to procurement and logistics, every facet of supply chain management relies on data to function effectively.
- Planning and forecasting algorithms rely on WMS and ERP data for historical sales, inventory levels and stores orders
- Transportation Management Systems rely on WMS data to properly track shipments from warehouses to stores
This post originally appeared on TechToday.