How Dynamic Supply Chain Analytics Smooths Cash Flow

Supply chain management has always presented a challenge for global corporates, with digitization and automation technology aiming to reduce friction and enhance visibility. A surge in supply chain technology available — from freight management portals to IoT sensors — also expands a value-add opportunity for corporate end users: All this digitization generates a flood of potentially valuable data that organizations can use to optimize their supply chains, as well as better meet the needs and expectations of their customers.

The problem? Expectations today are so high that businesses are struggling to make use of that data, even as they progress in their supply chain digitization journeys.

According to Adam Compain, CEO of ClearMetal, this is the “Amazon Effect” on the modern supply chain — in which most businesses believe they are meeting customer expectations, but only a small fraction of customers agree.

It’s not the data that’s the problem, per se, he recently told PYMNTS. Rather, the problem can be linked to the fact that “supply chains are built on static data, static systems and static methodologies,” and businesses are facing some monstrous cash-flow impacts as a result.

Tied-Up Cash Flow

Agility is critical in today’s supply chains, considering the high volume of exceptions that managers must address in the freight management space. These exceptions can be linked to changes in weather, customer demand, and other logistical risks that impact where and when deliveries occur. Yet, planning and decisions are still reliant on static data that fails to adequately address those exceptions.

“As a result, companies are forced to keep extraordinary amounts of excess inventory or buffer stock, spend large amounts on expedited freight, finance inventory over long lead times, lose productivity due to highly manual workarounds and have cash flow tied up due to unnecessary invoice-to-payment cycles,” he said.

Compain offered one example of Global 1000 companies raising concerns about their experiences with high volumes of buffer stock and long lead times, which determine how far in advance a company procures goods or materials to ensure that a delivery will arrive on time, even with an external factor like inclement weather.

He explained that organizations will lengthen their lead times or expand buffer stock as a result of their past experiences. If one company took 50 days to complete deliveries from China to Chicago last year, it may procure 60 days in advance this year. The problem is, one year won’t be identical to the next. As he noted, this reliance on historical, static data means cash remains tied up unnecessarily.

Compain proposed another example of lengthy invoice-to-payment cycles resulting from this reliance on stale information. Static data and a lack of real-time visibility mean that an organization will likely fail to determine when a freight shipment has actually arrived at its destination port, indicating that the company cannot accurately time when it should send an invoice. According to ClearMetal’s own analysis, in 10 percent of cases, corporates are never even notified of a shipment arrival.

Again, cash is locked away, with longer invoicing times inevitably causing payment delays.

Continuous Data

Supply chain digitization presents the ability for professionals to rely on real-time data to make decisions about shipping, inventory, invoicing and more. Unfortunately, that opportunity is rarely unlocked. According to Compain, supply chain managers can misunderstand the ability of their current technologies — and the data they provide — to address these pain points that lead to cash-flow bottlenecks.

“The challenge we see is supply chain executives often thinking that simple access to data … will set them free, and that the quantity of data is what matters,” he said. “In fact, it’s not the quantity of data, but the quality of data that is important.”

For ClearMetal, which recently announced $15 million in new venture capital funding, addressing this challenge means arming the industry with technology that operates on “continuous methodologies,” as Compain explained — meaning the ability to continuously feed real-time data, and provide actionable insights based on that information. Technologies like machine learning can support this demand to continually adjust and re-plan supply chain strategies, connecting managers with accurate real-time visibility into freight locations and insight into the most effective methods of transporting deliveries.

Furthermore, as electronic B2B payments gain greater traction throughout global supply chains, continuous data technologies will have an even larger opportunity to connect managers with actionable insights, as well as dynamic — not static — planning capabilities.

“It’s less about having access to the data — colloquially known as ‘visibility’ — and more about knowing what to do with accurate, intelligent, and trusted information and insights,” said Compain. The result of reduced buffer stock and accelerated invoicing “is where smoothing cash flow really happens.”