5 Ways to Discover Your Most Accurate and Actionable Traceability Data
The two central pieces of a traceability program are the proper labeling of products and the submission of data at critical touch points throughout the supply chain. Once your supply chain partners are labeling their products and/or submitting traceability data to an accessible data repository, you now have access to your partners’ traceability data.
But, is this data actionable? How do you know the data is complete and accurate? Would you feel confident in relying on this data for food traceability to guide you through a product recall or food safety incident?
This leads to a crucial step in a traceability implementation: data validation. This often-overlooked backbone of successful traceability will allow your organization to closely monitor your partners’ data and mitigate the risks of making business decisions based on incomplete or inaccurate data.
What is Data Validation?
Data validation is a process used to assure accuracy in your collected data, allowing it to be safely analyzed for patterns and presented to key stakeholders or used to optimize your workflow. Failing to validate data may lead to false patterns or gaps in data during analysis, potentially causing issues later on.
Data validation is most commonly performed through scripting languages or by designated enterprise data validation software. Various checks are executed through these methods, assuring that the data entered is consistent and accurate.
How to Validate Data to Discover Accurate and Actionable Traceability Data
Data integration platforms allow businesses to quickly and easily validate information like traceability data. Analyses from this more trustworthy information can then be used to action new strategies and optimize your business results. Before validating the data with whatever default system your software uses, however, you will need to make sure the validation structure matches your needs. Learn how to validate your traceability data specifically using the five steps below.
1. Establish Which Traceability Data Elements Should be Validate
If your organization is currently implementing a traceability program, there are likely several traceability data elements being submitted by your partners that are new for your organization and not already tracked in an existing internal dataset. Therefore, there will be certain elements of the traceability data, like lot number or production date, that you will not be able to validate.
For validation, you will want to focus on other data elements, such as PO number and item number, which are commonly used for other business functions and are likely already being tracked in an existing internal transactional dataset.
2. Determine Which Internal Data Source Should be Used as the Basis
The dataset used for validating your traceability data should be your organization’s most complete and trusted source of inbound and outbound shipment information. This data should include shipment level detail, such as origin and destination, dates, item numbers, quantities, and a unique identifier associated with each shipment, such as PO number. Since these data elements will also be present in the traceability data submitted by your partners, you can use your internal transactional dataset to validate the traceability data.
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3. Capture the Data Within the Appropriate Time Frame
The appropriate time frame to use for validation depends on the nature of your business. Since you ultimately want to confirm that your partners are submitting accurate and complete data, the time frame used should be long enough to capture a representative sample of your organization’s flow of product. However, it is also important to avoid using too large of a time frame to prevent inactive items or locations from muddling the dataset.
4. Consider the Level of Detail You Want to Validate
You’ll need to consider the standard or level of detail you wish to validate. One way to break down the validation process is by using two different “Levels” of validation. The goals of this approach are to identify high-level issues that can be corrected prior to delving into shipment-level detail and to track the status of each partner’s data progress.
Level 1 Validation
Level 1 validation involves validating the specific items and locations included in a partner’s data within the specified time frame. Possible issues with a partners’ data submission process that can be identified at the Level 1 validation stage include incorrect item setup, incorrect location setup or failure to send data at all.
Level 2 Validation
Once these issues have been resolved and the partner has been validated for Level 1, Level 2 requires that each individual shipment, including case volume, is validated. Potential issues that can be identified at this stage include individual shipments not included in traceability data, case volume discrepancies or delayed submissions of traceability data.
5. Clearly Define What is Considered “Validated”
Finally, it is important to identify the metrics for success. Since achieving 100% case volume match is nearly impossible, your organization needs to clearly define what is considered “validated.” This may vary based on the frequency of shipments in your supply chain or the technological capabilities of your partners.
For example, if the majority of your partners send their traceability data using automated methods, such as EDI or API, a case volume match less than 99% might raise red flags. However, if your partners share their traceability data manually and are more prone to human error, 95% case volume match may be more tolerable and achievable.
Conduct Regular Checks to Maintain Data Accuracy
Regardless of the validation threshold, it is critical that validation of traceability data is maintained continuously even after your partners have been onboarded. This should include regular checks of each partner’s overall case volume match and occasional in-depth analyses into each partner’s data. Although having access to traceability data is the overall goal, a robust validation process is integral to ensure that the data is accurate and actionable for traceability.
Learn more from our blog about the benefits of traceability.
