The Imperative of High-Quality Data in Supply Chain Operations

Amir Taichman
Founder & CEO
April 17, 2024

In the complex world of supply chain management, the importance of high-quality data cannot be overstated. Accurate and reliable data is not merely advantageous; it is essential for efficient operation. With subpar data quality, the reliability of information needed to support insights, trade-offs, and critical decisions is compromised. This can lead to increased operational costs, reduced service levels, and considerable stress across teams.

Challenges in Achieving High-Quality Supply Chain Data

Despite its critical importance, many organizations struggle with poor data quality within their supply chains. The root causes are multifaceted:

  • Manual Data Entry: Human error in manual inputs—such as incorrect SKU entries by customers or supplier typos in master bill numbers—is a significant challenge. While humans are adaptable, machines excel in consistency and accuracy, particularly in repetitive tasks like data entry.
  • System Discrepancies: Different systems often operate with unique codes, databases, and models, requiring complex transformations during data exchanges. This is particularly evident in systems like EDI, where a deep understanding of the communication protocol is necessary on both sides.
  • Inter-organizational Data Management: Coordinating master data across various companies is inherently challenging. Differing standards, units of measure, and naming conventions complicate this process further.
  • Batch Processing: Diverse reporting timelines—such as hourly reporting versus daily aggregation—can lead to inconsistencies that are difficult to reconcile.
  • Customized Systems: Many organizations rely on bespoke or heavily customized supply chain software. These systems can exacerbate the issues mentioned above, leading to further complications in data management.

Key Considerations for Improving Data Quality

To begin addressing these issues, organizations must first identify and measure them. Common Key Performance Indicators (KPIs) for data quality include:

  • Accuracy: Ensuring data correctness against predefined expectations, such as SKU accuracy in catalog listings or proper structuring of tracking numbers.
  • Completeness: Data must be comprehensive, though this often varies depending on the specific use case—what "all" encompasses can differ significantly from one context to another.
  • Timeliness: The utility of data is also dictated by its timeliness. Data about delays that arrives after the decision point is of little use.

UnitySCM’s Approach to Data Quality Management

At UnitySCM, we recognize that supply chain data is inherently messy. Unlike conventional systems that require perfect data to function optimally, our Data Versatility platform is designed to manage and improve imperfect data. Our platform not only identifies data quality issues but also assists users in understanding which data can be trusted and how to address underlying problems.

This proactive approach allows users to derive value from their data immediately, even during the initial stages of integration. As the platform continuously analyzes data to identify and rectify quality issues, it enhances trust and reliance on the data, gradually improving data-driven decision-making processes.

The Benefits of High-Quality Data

Investing in high-quality data yields substantial benefits:

  • Certainty in Execution: Data-driven operations enhance the accuracy of business outcomes, allowing companies to execute strategies with confidence.
  • Operational Efficiency: Improved data quality can significantly reduce the time spent by teams in verifying data, equivalent to gaining several additional working days per week.
  • Alignment: When all teams base their decisions on the same high-quality data, it eliminates the need for lengthy preliminary discussions in meetings, fostering better collaboration.
  • Trust: Reliable data dispels doubts and reduces reliance on guesswork, supporting informed decision-making.
  • Broad Implications: Enhanced data quality not only benefits the company but also positively impacts customers and partners, leading to improved service delivery and increased revenue.

By prioritizing data quality, organizations can transform their supply chain operations into a strategic asset that drives continuous improvement and competitive advantage.