We’ve given you a run down of what materials data is and what it means to clean it. However, we have yet to put those two things together and give you a more holistic step-by-step guide to cleansing spare parts data. A cleansing project can take months, and knowing where to start and how to end can be tricky, especially when handling mountains of spare parts information. The list of steps below isn’t exhaustive, but it may give you a sense of the scope of a materials cleansing project.
A general guide to your materials cleanse, from start to end
Step 1: Setting up – Before you begin, a careful analysis of your existing spare parts data will be needed. What poor quality source data is costing you the most? What tasks should take priority? How are you going to report on your progress, and what will be your Key Performance Indicators (KPIs)? These questions will help you determine the best processes and tools to use as you pursue your cleanse.
Step 2: Building a dictionary – Before you can begin cleansing your spare parts data, you may need to decide what naming conventions in your EAM system need to be changed or updated so that all your spare parts can be easily identified according to a uniform naming system. This may mean developing best practices for creating abbreviations, classifications, or descriptions of spare parts, or later creating a rule book to help workers understand your dictionary. (Learn more about spare parts dictionaries with this blog post!)
Step 3: Set your standards – Similar to a dictionary, standardizing your materials data means adhering to a uniform set of conventions for your spare parts data, such as names and attributes, as well as other information such as units of measure or research sources. (You can find out more about standardization with this blog post!)
Step 4: Cleansing commences – Possibly the lengthiest step in the project, cleansing comes after you’ve established your new dictionary and standards. This involves carefully combing through legacy spare parts data and enhancing and enriching it in multiple ways, like resolving missing or ambiguous descriptions or attributes, linking spare parts to their parent equipment, and deciding what plant-specific information should be kept and what should be discarded. (You can learn more about cleaning with this blog post!)
Step 5: Review – There’s no use in carrying out a cleanse if some of your bad data remains unchanged after the project! An internal team or external consultants should conduct a quality assurance check to verify that the spare parts data now meets an acceptable level of accuracy and completeness before it can be reloaded into the EAM system.
Step 6: Reload clean data back into EAM system – Finally, once you’ve verified your materials data is cleansed and ready, begin the process of transitioning it back into your asset-management system.
And then keep it clean!
The tasks above are a general set of steps and do not encompass the ongoing sustainment involved once your materials data is cleansed. Keeping your spare parts data clean deserves a miniature guide in itself, and even the list above does not capture some of the additional and more specific ways your organization may need to approach cleansing spare parts data. If you’d like to learn more about how you can clean your spare parts data, check out one of our resources below, or book a demo with us to get started on your next cleansing project!
What Do We Mean When We Say Clean?
More Ways to Save Money with a Materials Data Cleanse
Building BoMs with Bad Spare Parts Data
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