How to solve the problem of imperfect procurement data in automotive
If you work in indirect procurement, you’re definitely familiar with the problem of imperfect procurement data. Data that’s “imperfect” is unstructured, incomplete, or inaccurate, and is a result of any number of factors – including multiple ERP systems that don’t communicate with each other, widespread locations that operate independently, and people not following the correct process. When you have imperfect data, it’s impossible to accurately understand how your organization is doing or discover ways you could be saving.
To put this into a real-life example, let’s take a look into the life of Frank the factory worker.
Frank works at a Tier 1 automotive component supplier, where he operates the metal stamping press. To protect him while he’s working, Frank wears special equipment such as gloves, a hard hat, and safety glasses. One day, when Frank’s removing his safety gear at the end of his shift, one of his gloves rips. He looks for Patrick, the purchasing manager, to let him know he needs a new pair, but can’t find him. Frank is in a rush to get to his bowling league, so he decides it will be quicker to just order them himself. He calls the supplier directly and reads them the item number off the gloves, which is enough for them to fulfill his order. Satisfied that it’s taken care of, Frank punches out and leaves for the night.
A couple of weeks later, Frank overhears Patrick and Alice from accounts payable arguing in the hallway. Alice is asking Patrick about an invoice that just came in from the supplier Frank ordered the gloves from. She’s frustrated because the invoice only has the item number on it, but no details about what the item is, and she can’t find any record of the request ever being approved.
Watch this clip from our webinar on the biggest problems in automotive procurement to learn more about why imperfect data is a challenge.
The harsh reality is this: since Frank (and others like him) don’t regularly purchase supplies, it’s not really important to them to make sure they’re doing it the right way. However, not following the complete or correct process means that the data being captured also isn’t complete or correct (a.k.a., imperfect).
To be frank…don’t be like Frank. You might think that just one person not following the process isn’t that big of a deal. But Frank isn’t the only one in his organization doing this. Every organization has multiple people just like Frank. And when you take the single incomplete line item of data that results from Frank’s order and multiply it by everyone who does this across an organization, you can see how this becomes a huge problem.
With imperfect data, you don’t have enough information to be able to see trends in your organization’s spend. Imperfect data makes it virtually impossible to truly understand your organization’s performance, shape goals, and discover opportunities to save.
Upgrade your process with AI
You can do your part by making sure you follow the right process, but that doesn’t mean everyone else will. While having a good process that everyone follows definitely helps, it’s only going to provide incremental improvements. Supplementing an already good process with AI, though, can enrich the quality of the data you have – leading to increased visibility and better insights.
With Xeeva’s powerful and patented AI, imperfect data can be a thing of the past. We take your imperfect historical spend data and quickly cleanse, classify, and categorize it down to the line level to give you a clearer picture into your spend. The benefit? The ability to make better decisions and improve your bottom line.
Now that you know how AI-powered tech can take your imperfect data and turn it into real results, find out all the amazing things it can do to solve the other biggest indirect procurement problems in automotive.