Gaining Visibility Through Data
Q: Data impacts the way many organizations make decisions. Can a freight audit and payment system provide added visibility?
A: If shippers don’t have valid insight, they can’t make valid decisions. Data analysis and benchmarking can provide this visibility, allowing shippers to identify outliers in performance and cost. Yet most companies don’t have the technology and expertise in house to effectively collect, cleanse and analyze data. That’s one key reason they turn to outside vendors.
By using business intelligence tools, like those available through U.S. Bank Freight Payment, companies can benefit from a really detailed view of what freight costs they’re paying for such as line haul, fuel, demurrage and toll charges. Companies can then take these insights in order to comprehensively evaluate their processes and procedures, and take the action required to prevent any issues from becoming trends. For example, are there operational changes they could make to cut demurrage charges? Is every carrier billing equally for demurrage, or is one carrier in particular ending up with wait times?
Q: Once data is collected, it’s important to have a basis for comparing the information. What can companies do to establish a benchmark?
A: Benchmarking freight costs against similar shippers can be an interesting way to ensure that your costs are in line. The process for doing so doesn’t always have to be formal research. It can include conversations with peers, networking and even feedback from carriers. When shippers make benchmarking—both formal and informal—part of their everyday processes, they can identify cost reduction opportunities and also determine if they’re leveraging their systems appropriately.
Q: Organizations today are acquiring extraordinary volumes of data. How can they be assured the data they are assessing is “good?”
A: While any company that processes payments electronically should have extensive data, if that data hasn’t been cleansed it won’t lead to trustworthy, actionable results. Simply put, imagine how much a cost-per-mile metric would be skewed if mileage details were missing from a group of shipments. An effective data cleansing process takes care of those anomalies, looking for outliers and duplicates, filling in incomplete data when possible and removing inaccurate data.
Shippers are increasingly learning that to uncover efficiencies and forecast effectively they need visibility into their supply chains—including their payment process. And getting that visibility requires both the right data and the ability to analyze it. It’s not an overnight solution, but once you’re analyzing the data correctly, you can become almost systematic about how you approach controlling costs.