Easy-to-understand processes are the cornerstone for having clean spare parts data. But not all data is created equal, and the journey to clean spare parts data isn't always smooth sailing.
There is a lot to keep track of with the variety of information needed for different types of parts. There’s part numbers, descriptions, norms and standards, technical attributes, classifications—you name it. When duplicates, inconsistencies, and incomplete data invariably surface, things get complicated fast. It stands to reason, then, that the cleaner your spare parts are, the better.
When we talk about spare parts data cleaning, we should distinguish “cleaning,” or transforming bad data into clean data, and “maintaining,” or the practice of creating new data correctly or correctly updating existing data. Both are important, and both require an accurate and organized approach. This article will help you not only understand what that means, but also take action on it. Read on for a straightforward, practical roadmap for spare parts data cleaning.
Why you need to clean your data for efficient spare parts management
Incorrect or missing data doesn't just lead to minor glitches—it translates to friction and delays in parts search for maintenance teams and longer ordering cycles, all of which can affect your operational bottom line. The silver lining? You don’t need perfect data to start your cleaning journey. Every journey begins somewhere, and the end goal is always the same: clean spare parts data.
A Simple Framework for Spare Parts Data Cleaning
Embarking on a data cleaning journey can seem overwhelming, especially given the intricacies of spare parts data. However, breaking it down into manageable steps can make the process manageable. Here’s a systematic approach to ensure that your data achieves the accuracy and consistency you need to manage your spare parts effectively.
Our best practices for spare parts data cleaning
Step 1: Audit your data
Data auditing isn't just an initial step; it's the foundation for your entire data cleaning journey. It allows you to get an overarching view of your existing data landscape. Audit steps range from verifying each part has a manufacturer part number to more advanced requirements like classifying each part according to your system of choice
When you're auditing, it's important to have clear goals. The main goal is to make sure each part has its own unique ID and is described clearly. Having unique IDs means you won't have duplicates, and ordering becomes easier. Clear descriptions mean your maintenance teams can quickly find what they need. To check for unique IDs, first look at fields like "manufacturer name" and "manufacturer part number." If those aren't filled out, is there supplier information for each part? For standard parts, it's also good to see if they're correctly labeled with standards like DIN964 and that all their technical details are filled out right.
Checking if descriptions are clear can be a bit harder. It depends on the type of part and what your company's guidelines are. But, in simple terms, a good description should help a new worker figure out what the part is without having to actually see it.
If you have a lot of parts to check (like some Sparrow users who have more than 500k parts), this can be a big task. Fortunately, Sparrow can help. A good way to start on your own is to just pick a few hundred parts at random and check those.
Step 2: Identify issues
Identifying inconsistencies, duplicates, and missing information is a pivotal next step. Begin with a focused approach. If your data is organized, hone in on a specific part category like sensors, or focus on a particular manufacturer such as Festo. Examine the consistency in manufacturer names: is it "Festo" or "Festo AG"? Check the uniformity in identifiers: is it "02321" or "2321"? Also, ensure configurations are consistently noted, like "DNC-125-100-PPV".
When it comes to descriptions, verify if similar parts adhere to a common naming structure: is it "Cylinder pneumatic" or "Pneumatic cylinder"? Are attributes, such as length or material, used across descriptions consistently? By methodically reviewing groups of parts this way, you'll be better equipped to streamline your data cleaning tasks.
Step 3: Standardize your data
Standardization boils down to achieving clarity. There are two key elements to this step. The first is putting data in the right place. If you’re entering the manufacturer name, for example, it should go in the field that’s been specifically designated for that purpose. The second is being consistent in how you input that data within the field. When data is standardized, it’s much easier to weed out duplicates and efficiently locate and order parts.
By ensuring a consistent format, you mitigate potential confusions and ambiguities down the line. Whether it's naming protocols or data entry formats, consistency is key. Sparrow simplifies this by maintaining a data model that helps ensure data is placed in the right place as well as according to industry standards (e.g. for short descriptions).
Step 4: De-duplicate
Take note: only after your data has been standardized can you start removing duplicates. The first step is to check whether parts with the same identifying attributes appear more than once in the data set. This could be two parts from the same company with matching part numbers or two standard parts with identical features. You need to have some knowledge of the different manufacturers, because a number alone isn't always enough to enable you to identify a unique part. And yes, when you're dealing with data from thousands of manufacturers, this can feel a bit overwhelming.
The value of de-duplication goes beyond conserving space. It’s essential for clear, actionable data, since every duplicate is a potential source of error in your decision-making or operations.
Step 5: Validate and verify
Now that you’ve cleaned your data, it’s time to ensure that it’s accurate. The best way to do this is to show the newly organized data to those who interact with it daily and seek their feedback.
Start by sharing a representation or sample of your cleansed database with key team members and asking them to check if the parts they use or order regularly are correctly described and classified and if any critical details are missing.
Having a system where users can flag discrepancies, provide corrections, or make suggestions is essential to making this process efficient. You can use a spreadsheet for this, but for the best and most painless results, use the dedicated feature within your spare parts management software.
Snapshot: A real-life spare parts data cleaning story
Let’s look at an example. A European automotive OEM suspected that they had issues with harmonizing their spare parts catalog across sites and BUs.
A quick scan with the Sparrow app revealed up to 30% duplicate materials between sites and BUs, among other issues. Based on this scan, the OEM subscribed to the Sparrow app to clean, standardize, and de-duplicate spare parts across Europe. Within 6 months, stock levels were reduced by over €1 million by eliminating duplicates. The benefits extended to maintenance teams, who reported shorter spare parts search times, and reliability engineers, who now knew that over 12% of their parts were obsolete.