Procurement Supplier Data: Stop the Garbage!
In the Australian city of Kwinana, nets at several drainage pipes in the area have been installed. You see, Kwinana has a major issue: garbage carried via storm runoff into waterways and ultimately polluting the environment.
By installing the nets, Kwinana has vastly cut down on the amount of garbage deposited in the rivers and lakes. In a mere six months, Kwinana has stopped 815 pounds (370 kilograms) of waste from entering the waterways. Not only is this better for the environment, it saves money - the cost to clean up garbage goes up 4x once it is in the waterways as opposed to catching it in these nets.
So why are you reading about garbage nets on a blog about procurement supplier data? Because the message is clear. Catching garbage up front saves money and time in the long run and protects your ERP environment. If you catch the garbage now, you won’t have to deal with it later. This is true of actual garbage, but it is also true of your supplier data. There are two methods to deal with the garbage supplier data, manually or automatically with Nitor DATA ASSURE. Let’s first look at the manual process.
Types of Supplier Data Errors
There are several types of supplier data errors, but there are two types of data we need to be worried about in a procurement solution:
- Master Data (accounting strings, user data, commodity codes, etc.)
- Transactional data (PO data, Invoice data, etc.)
Source to Pay systems like SAP Ariba are very good at catching errors in transactional data. You will set up all of your checks, exceptions, tolerances, and approvals up front to make sure that when an invoice is approved and is sent to your ERP, it is correct, accurate, and ready to go.
So, we’ll focus on master data. Let’s divide the data into two categories.
- Legacy master data: The master data already contained in your ERP or other supporting systems
- Novel master data: New data, like when a supplier fills out a registration form
The most common issues when it comes to legacy master data are usually supplier record based. Specifically, duplicate suppliers. Nearly every implementation requires some level of supplier de-duping.
Novel master data is more difficult to handle. The most common issues we see are: supplier addresses are incorrect, the supplier TIN is not properly validated, and surprisingly, bank routing numbers were entered incorrectly. But none of this should be tremendously surprising. These are the three areas which are most easily fat-fingered. Who hasn’t missed a digit when entering a street address, or double-entered a digit in a 9-character long string of numbers?
All of these errors can be disastrous down the line. Duplicate suppliers become a major obstacle when attempting to analyze spend, enabling suppliers on the SAP Ariba Network, or even when placing a simple order.
But more insidious are the mistakes that can occur when a supplier makes mistakes entering their own data.
With the wrong address, tax jurisdiction codes have a high probability of being wrong. Then later down the line, you may need to correct tax payments to various governmental entities. Not to mention the potential legal ramifications of consistently paying taxes incorrectly.
If routing numbers are incorrect, you may be paying the wrong account for a month or two before realizing the mistake. The funds could have gone to another account, your supplier is unhappy because they still have not been paid, and you have to figure out how to retrieve the funds which were sent to a small business located somewhere in the high desert of Eastern Oregon who didn’t even realize they were being paid.
Then there are TIN’s. Get those wrong, and there could be major issues come tax time. It’s unlikely that the mistake would last more than one year, as the mistake and its costly errors should be found come tax time, but the mistake shouldn’t have lasted a single day.
If we don’t catch the garbage up front, we WILL have to deal with it eventually.
Solutions to Clean your Dirty Supplier Data
Believe it or not, the point of this article is not to strike fear in the hearts of procurement professionals. It’s to inform and prepare you so that you can avoid these costly mistakes when implementing your SAP Ariba system and developing processes around data management.
Let’s start with the de-duping of supplier data. This can be tricky, but there are a few key fields you should look for:
Name: This seems obvious, but you would be surprised how often the same or similar name is listed as a supplier two, three, or even more times.
Tax ID: Tax ID is a fairly unique identifier. Searching on Tax ID will help you catch those instances where a name may be different, but the Tax ID doesn’t lie.
VAT ID: Where applicable, VAT ID will function very similar to the Tax ID for de-duping purposes, but it gives you another data point on which you can identify duplicates.
Deduping can be done in Microsoft Excel for a one-time pass, but it is better to build a query directly in your database. By building out the query, you can perform a check once or twice a year just to ensure no duplicates have slipped through. This is particularly helpful if your organization has a decentralized supplier entry policy.
Catching Supplier Mistakes
On to the novel master data. What processes need to be in place to ensure supplier data is cleaned as it enters your system?
Supplier Addresses: There’s no way around this one. You’re going to need to validate each address manually. If you’re a fan of listening to music while you work, this is the time to turn on some tunes. There are several address validation services. You’ll need to copy and paste each address into one of these address validation services to ensure the address is accurate. Set aside time each week to validate new supplier addresses. This can be a lengthy process, and you’ll want to ensure you have time blocked.
Taxpayer Identification Numbers (TIN): This one is a bit simpler. The IRS maintains an online portal where TINs may be validated. It can take quite some time to be approved to utilize the portal, and validations can take up to 24 hours, but the IRS makes this available to payers.
You can access the tool at: https://www.irs.gov/tax-professionals/taxpayer-identification-number-tin-matching
When you set up your account, ensure you do so with a generic e-mail address ([email protected]) instead of a personalized e-mail address ([email protected]). That way anyone in your Vendor Management organization can access the account to perform TIN matching.
Bank Routing Numbers: This one is probably the easiest to validate. There are several services (many of them free) which will validate bank routing numbers. Simply copy and paste the routing number into the tool and validate that the bank listed is correct. The key is consistency. Ensure you perform this check each time you set up a new supplier for invoicing. You don’t want your payments failing, and you especially do not want your payments going to someone at a different bank entirely.
These processes should be part of your supplier onboarding process. While true that some of them are time consuming, they consume much less time than fixing data errors in the future once they have already begun to wreak havoc.
A Better Alternative: Use Nitor DATA ASSURE to automatically validate your Supplier Data, without human interaction
Another option would be to utilize a service that can validate your data for you automatically. While data validation can certainly be performed manually, even the best procedures are prone to mistakes, and in the best case requires staff time and resources.
A tool like Nitor DATA ASSURE inserts itself into your supplier approval flow and performs data validations automatically. If there is a mistake to be found, it will simply reject the questionnaire back to the supplier for them to make the correction with no intervention required by your team. Alternatively, DATA ASSURE can notify your users of the mistake so they can take corrective action before rejecting to the supplier; Data Assure is configurable to meet your needs. Clean data without the time and manual resources typically required.
Nitor DATA ASSURE works with all SAP Ariba SLP related forms: request forms, registration forms, and qualification forms. No matter how you gather data from your suppliers and potential suppliers, you can be assured that the data is accurate. And you can validate more than just addresses.
Nitor DATA ASSURE validates:
- Legal Name
- Address (All types of SAP Ariba SLP address fields)
- IRS Tax Number
- Office of Foreign Assets Controls (OFAC) checks
- Excluded Parties List System (EPLS)
- Excluded Individuals and Entities (LEIE)
- Custom Validations are also available
- Bank Routing Number (Coming Q4 2020)
Setting up Nitor DATA ASSURE is simple, and within a week you can start catching data errors before they cause problems. Just like the people of Kwinana, we are putting in the time to catch the garbage up front, so that we don’t have to worry about the garbage polluting our supplier data environment.