There is no Excuse!

Ever hear this old logistics joke? There are 2 definitions for ASN: advance ship notice and already shipped notice.

OK, this is 2013 and too many of us are still haunted by poor data quality. It gets in the way of supply chains running efficiently and effectively and stifles multi-party collaboration – the next wave of supply chain competitiveness. The sad thing is that there is just no excuse for it 30+ years after barcoding inventory and EDI became commonly accepted practices. So what are you going to do about it?

Here are some points to consider.

Understand what makes for poor data quality. It has four elements: accuracy, completeness, syntax and timing. Accuracy – is it correct and complete – is all the information there, are relatively well understood; syntax and timing, less so. Syntax has to do with the structure of the data – it may all be there, but not in the right order and place. This is important in multi-party business processes where there is an expectation of how the parties are going to communicate. Timeliness is more critical as enterprises try to run their supply chains in real time. For example, if the product beats the related information to the dock door, then it is not actionable and practically useless from an operational perspective.

You can’t manage what you don’t measure. Just as you scorecard suppliers or carriers on the timeliness and completeness of orders delivered, for example, you need to do the same thing with data timelines and completeness. One of the first things we do when we implement a visibility system, for example, is to scorecard trading partner and carrier data quality. You simply cannot take anybody’s word that they are good at data quality until you actually see their performance.  I also recommend that your trading partner and carrier contracts include data quality performance. It’s the best way to set the right data quality expectations.

Applications don’t correct data quality problems; they just expose how bad they are. Over the years, I have heard many misguided CIOs say that by implementing enterprise applications, their data will get cleaned up because they will have “one version of the truth”. How about one BAD version of the truth? Supply chain visibility, which was such a hot item a decade ago, quickly faded when companies realized that they didn’t have the data quality they needed to use these valuable solutions. The problem is worse today because we are relying on more outside parties than ever to provide us with the data we need to run our supply chains. To fix the problem, you need to have your own data police – whether you decide to do it in-house or use a third party. This is a critical issue for your enterprise that needs constant attention, not just during initial implementation.

There are the “80/20” and the “20/80” rules in supply chain data. The typical or “80/20” approach is to focus on the bigger trading partners and carriers that have the large shipment volumes. However, shipment volume doesn’t directly correlate to information volume and management attention. That’s the “20/80” rule comes into play with smaller trading partner and carriers. Larger trading partners and carriers generally do a better job because they have to do this for others. For smaller organizations, there seems to be an implicit lower set of expectations, because they lack the technical sophistication. Most companies have avoided the “long tail” of smaller supply chain partners and struggle to manage their effectiveness. However there are web-based solutions available now that can allow the smaller trading partners and carriers to provide the data you need and in a timely fashion just as if they were a larger, more sophisticated supply chain partner.

The foundation of world class supply chain performance is world class data quality. It sounds so mundane, but if you look at the impact it has on inventory reduction and supply chain responsiveness, the story gets much more interesting. To get there you need to view data quality as a never ending journey, not a project. That takes putting measurement systems in place, setting expectations with large and small trading partners and carriers and doggedly addressing it every day. It’s not rocket science, there are systems and partners available to help and no excuse for not fixing it.

What is your organization doing to improve its data quality? Let me know.