If Your Data Isn’t Up to Snuff, It’s Garbage In, Garbage Out
What Data-Oriented Supply Chain Departments Need to Know
Supply chain departments are increasingly relying on data to drive their operations, processes, and efficiencies. More than ever, they need detailed, specific facts and figures to guide them in making decisions that will impact not only their department, but their entire business. If those facts and figures—and the data they represent—are inaccurate or at all lacking, the bottom line could suffer.
Recently, I was pleasantly surprised by a statistic I read from RSR Research. In it, a graph demonstrated the emphasis the departments within various companies place on data. Respondents were asked to choose between three options:
- Primarily Data-Oriented (45%)
- Data + Experience/Intution (45%)
- Primarily Experience/Intuition (9%)
What surprised me was how many of the supply chain respondents answered they were “primarily data-oriented” (45%). Perhaps I was relying on my own “experience and intuition” too much after years of working with different logistics-related customers, but I expected that number to be smaller.
The fact that it’s not, however, is a good sign. It means that more and more supply chain departments are making more and more decisions based on quantitative rather than qualitative measures.
Shifting the focus so heavily towards data raises other concerns, though. Specifically, it demands more from a company’s data—not only in terms of quality, but also in frequency and consistency. In other words, companies must ensure the data on which they are basing their more important supply chain decisions is high quality and continuously monitored and updated to account for the latest changes; otherwise, it’s garbage in, garbage out.
The other thing that supply chain departments must consider as they rely more heavily on data is how they analyze that data. For example, a recent article discusses how a parcel shipper actually lost money when renegotiating their carrier contract based on certain data sets they thought would favorably impact their parcel spend. In the end, the renegotiation negatively impacted their rates some 3-4 months later, when it became clear that other “under-the-surface” issues were the true cause for their rising costs. (For more on the importance of proper data analysis, read the full article here.)
It’s a positive step forward that more supply chain departments are relying on data to make their decisions, but unless the data is good, current, and properly analyzed, the solutions created based on that data will likely be just as bad—if not worse—than the problems they were intended to address.
And for the 9% of companies or shippers in the minority, relying on experience and intuition to guide their business decisions, make sure not to get left behind. If your competitors are using data, you should be, too.