In the age of Big Data, global supply chains quickly flocked to analytics solutions that offered predictability and agility in a market swayed by geopolitical shifts, regulatory risks and even the weather. Initially, the enterprise struggled to be able to collect the data they needed, and to ensure that the data was of a high enough quality to make use of that information.
As it turns out, however, companies can pretty easily access rich data. In a survey conducted last year by CSCMP’s Supply Chain Quarterly, most professionals said they are at least moderately satisfied with the availability and integrity of their data.
Making use of that data, however, is another story.
“The survey results clearly show that there are cracks … in companies’ ability to bring data together, integrate it, have confidence in it and believe that it is consistent,” said Richard Sharpe, chief executive officer of Competitive Insights, which also took part in the research.
For supply chain professionals, insights and predictability fall by the wayside when data cannot be adequately integrated and analyzed, no matter how good that data is.
Shannon Vaillancourt, founder and president of supply chain data management firm RateLinx, told PYMNTS that this type of Big Data barrier is often the root cause of payment delays and wasted spend in organizations’ supply chains and logistics processes.
“Collecting data is the easy part,” he said. “Integrating it together so you have one picture of everything, that’s the difference. It’s not uncommon to see companies with good, clean data, but it’s just one piece of the story.”
Professionals can be forced to waste valuable time piecing together bits of data and segments of stories if they’re looking for patterns in their supply chains. By the time they’ve done so, said Vaillancourt, the data is already months old.
Here’s one example that he witnesses fairly frequently: A freight forwarder or shipper may update shipping rates, and a corporate client will renegotiate a new contract. An updated agreement could mean that operations teams have new instructions about which primary carriers they have to use in order to obtain that negotiated rate.
Six months down the road, a business may find that they are over their freight budget, and, with the manual data analytics processes they’ve relied on, it takes an additional few months to pinpoint the cause: Negotiated prices and terms were not adequately relayed to all locations in the enterprise, and some teams have been working with the old carriers.
“We see a lot of that,” said Vaillancourt. “And it’s a hard one to diagnose, because you need to know a certain shipment was created at a certain location.”
In another example he offered, retailers often struggle to connect to all of their vendors and notify them that they must use a new carrier with which they have a contract. When suppliers aren’t fulfilling orders with a new carrier, budgets go over, and it can take months for a retailer to figure out why.
A business may be able to aggregate invoice data, purchase order data, shipment data and so on, but in order to aggregate it and pinpoint exactly where business units are over-paying, and why, would take months without an automated solution.
RateLinx recently launched its own product to address this issue: Integrated Data Intelligence, which emphasizes the importance not only of collecting data, but stringing it all together for a bird’s eye view of operational flows.
“You have to examine data across thousands of transactions a day to see patterns of behavior,” explained Vaillancourt. “As humans, we’re not capable.”
The consequences of the inability to quickly pinpoint anomalies, disruptions and root causes of these challenges using internal data are vast, including non-compliance, strained buyer-supplier relationships and, of course, wasted money. Among the largest issues, noted Vaillancourt, is late payments, a massive problem plaguing supply chains today.
Last November, Tungsten Network and the Institute of Finance and Management (IOFM) found that nearly half of businesses surveyed admitted that at least a tenth of their supplier payments are made past invoice due dates. Survey respondents cited slow internal processes, a lack of automation and administrative errors as key reasons behind those delays.
Indeed, Vaillancourt noted that these data bottlenecks are often directly linked to payment delays.
“All of this ends up on the payment team,” he said. “Buyers negotiate contracts, operations personnel operate differently with new rates and this all gets back to paying the bills.”
Accounts payable, accounting and other financial executives are often the first to notice discrepancies, anomalies or over-spend in the financial data, but silos within the enterprise prevent them from quickly connecting those issues to factors linked to purchasing and operations teams.
“They’ll see a large spike in invoices that are out of tolerance, not being billed correctly, but they may not have the new contracts, or they didn’t get the memo from the purchasing folks,” explained Vaillancourt. “That means they get behind in terms of approving and resolving invoices. This causes carriers to be millions of dollars behind in payments in a short period of time.”
These pain points can be experienced by anyone within the supply chain, large and small, and the knock-on effects (particularly as they relate to cash flow down the supply chain) can be profound. It’s why supply chain management has become a particularly attractive target of data analytics innovators.
“Everyone wants to know exactly where all of their shipments are all of the time,” said Vaillancourt. “It’s not different than you and I when we order something off Amazon. Now, companies are expecting that same thing to happen in freight.”