Published on
February 14, 2024
Read time
5 minutes

Clean data isn't a one-time achievement; it's an ongoing commitment. As your business evolves, there will always be a steady influx of new spare parts to add and the existing data that needs regular updating. Since we know this process can be challenging (that’s why we created Sparrow.Clean), we want to share what we learned.

When adding new spare parts data, consistency is key. Follow the same practices for data cleaning that we have established in the previous steps. Ensure that new entries align seamlessly with existing ones.

The evolving nature of spare parts, be it upgrades, replacements, or discontinuations, means that you’ll also need to update existing data regularly. Have a clear protocol for this, and make sure you have mechanisms in place for tracking changes made over time.

In essence, think of your spare parts database as a living entity that evolves alongside your organization. By maintaining your data with care and consistent practices, you can ensure that it will remain a reliable and efficient tool for all your operations.

Maintaining Your Spare Parts Data: 4 Best Practices for Clean Spare Parts Data and Operational Efficiency

Best Practices for Spare Parts Data Management and Maintenance

Managing and maintaining clean spare parts data takes consistent effort. To ensure that your data remains pristine, follow these best practices from our playbook at Sparrow:

Best Practice 1: Establish Your Data Model

An additional important aspect of maintaining clean spare parts data is agreeing on the data model for your company. 

A data model is like a well-organized file cabinet for your spare parts information. It ensures that all your data is easily retrievable, simplifying analysis and updates.

Here are the steps to follow to do this:

  1. key fields: Determine what essential information needs to be captured for every spare part (e.g., Manufacturer Part Number, Manufacturer, Description, Specific Technical Attributes). Also, make sure that the definitions of each field are clearly presented.
  2. Standardize field formats: Ensure that data within each field follows a consistent format. For instance, date fields should always be in the format DD/MM/YYYY.
  3. Incorporate hierarchies: If there are relationships between parts (e.g., assemblies and sub-assemblies), make sure this is captured in your data model.
  4. ERP integration: Align your data model with your ERP system to ensure seamless data flow. This involves reserving specific fields, like for the manufacturer's name and the norm.
  5. Review and revise: As your operations evolve, regularly review your data model to accommodate new requirements or optimize existing ones.

Best Practice 2: Create Naming Conventions

This one can’t be stressed enough. Having naming conventions ensures that information is entered into your system consistently and predictably. This reduces errors and makes data retrieval more straightforward. Consider things like: How do you want your short description to look? Which data belongs in additional description fields? How do you enter technical values?

Here are the most important steps to accomplish this:

  1. Create short description conventions: Decide on a fixed format. For instance, [Part Type]-[Dimensions]-[Material]. An example could be "Bearing-Radial-8mm". This gets a lot easier when you use Sparrow, where these conventions are baked in.
  2. Create additional conventions: Outline what supplementary information should be included here, such as material type, usage guidelines, or compatibility notes.
  3. Consider technical values: Establish how technical values are to be entered. For instance, ensure consistent units of measurement (e.g., always using "mm" instead of "millimeters").
  4. Ensure feedback loops: Encourage team members to provide feedback on the conventions to help you identify areas you can improve.

Best Practice 3:  Create an Internal Glossary of Part Designations

Setting up an internal glossary for your spare parts data can alleviate confusion and discrepancies that arise from using different terminologies for the same component. This is especially important in large organizations where various departments or regional branches might use different naming conventions.

Here are the steps to implement:

  1. Gather common terms: Start with your now-cleaned parts list. Go through all previous descriptions and extract the most common part designations. Next, workshop these as a team: which should be merged? Which ones need changing?, etc.
  2. Standardize definitions: Define each part clearly. For example, a "belt" in one department might be a "drive belt" in another. Make sure there is a clear, universally accepted definition.
  3. Code your glossary into a tool: You can use a data management tool for this, but also Excel (e.g. an Excel template that enables users to choose from a  pre-established selection of designations when creating a material).
  4. Ensure access: Make sure the dictionary is easily accessible to all relevant personnel.
  5. Update regularly: Continuously update the dictionary as new parts are introduced or old ones are phased out. This ensures that the dictionary remains current and in line with any industry changes or internal product developments.
  6. Educate the team: Organize training sessions to familiarize the team with the dictionary and encourage its usage.

By maintaining a robust internal dictionary and ensuring it's always up-to-date and accessible, you'll prevent misunderstandings and ensure that everyone is on the same page when referring to specific spare parts.

Best Practice 4: Run Scheduled Audits

Think of this as doing a health check-up for your data. It can feel daunting, but doing it can identify issues before they become problems. Luckily, if you’ve done your initial data cleaning, you only need to use these audits to determine if processes have deteriorated (and if they have, to come up with a plan to fix this).

Engage a dedicated team or use automated tools to periodically review and verify the accuracy and relevance of your data entries. This systematic approach not only ensures data accuracy but also helps in assessing the effectiveness of your data management strategies, allowing you to adjust and optimize as necessary.

Here are the recommended steps to follow when running scheduled audits:

  1. Determine the frequency: Decide how often audits should be conducted. This could be monthly, quarterly, or annually based on your organization's needs.
  2. Choose a team or tool: Assign a dedicated team or use specialized software tools to execute the audits. Ensure the team or tool is equipped with the necessary expertise and resources.
  3. Set clear criteria: Define what constitutes a discrepancy or error in your data, like outdated part information, missing entries, or inconsistent naming conventions.
  4. Document findings: After each audit, note down the findings and any discrepancies detected, which aids in tracking patterns or recurring issues.
  1. Make corrections: Rectify the data based on the audit findings. Ensure all changes are validated to avoid introducing new errors.
  2. Review & improve: Post-audit, re-evaluate the process for potential improvements for future audits, such as areas needing more focus or adjusting the audit frequency. Also, make sure to document any new processes for part creation and updates. Archive older versions for reference, offering a snapshot of the database's evolution and the rationale behind certain changes. This way, it’s easy for anyone to replicate your process when adding a new part or material. 

Wrapping Up

By diligently following these practices, you can elevate the quality of your spare parts data. It's not just about clean data; it's about ensuring that your data aids in effective decision-making. The good news: we have software that does this for you. Using it, and managing your data consistently, leads to smoother operations and provides valuable insights for strategic planning. Remember, the strength of your operations often rests on the quality of your data. Ultimately, your data should serve as a powerful catalyst in decision-making, driving both operational efficiency and strategic insights.

Interested in learning more about how Sparrow can help you maintain clean spare parts data? Get in touch with us.