Wring The Risk Out Of Your Supply Chain With Real-Time Analytics


Like adding fuel to a fire, the arrival of Big Data promises an explosive new mix of complexities for what is arguably the most complex of enterprise ecosystems, the supply chain.

That supply chains are highly complex organisms shouldn’t come as news to manufacturers. Rising customer expectations, volatility in commodity markets, added parts-counts in today’s technology-oriented products – these and other factors add daily to the rising complexity of the supply chain.

But Big Data and the Internet of Things portend not just more to come, but exponentially more to come.

And with complexity comes risk.

Today’s manufacturers are working hard to incorporate the benefits of Big Data into their central operations. Sensor-equipped machines and production lines are able to self-diagnose failures and breakdowns before they happen. Analytics-equipped manufacturers can tie production information in with operational systems, too, to ascertain what other systems, from inventory to customer service, may be affected by factory slowdowns.

But for many manufacturers the supply chain is different. There are more moving parts, and those moving parts are not under the same roof or, for many, even within the same continent. When things go wrong, they can effect many parts of the business ecosystem, and do long-lasting damage not just to profits but to brand reputation as well.

To appreciate how real-time analytics can help, start by looking at the unique vulnerabilities of supply chains. Some come from nature itself, with weather and climate anomalies wreaking havoc on shipping timetables. Others are man-made or machine-induced. A spike in unanticipated demand may wreak havoc on supply schedules or supplier relationships. Problems with supplier production capabilities or quality issues can create bottlenecks that slow deliveries or even stop production assembly lines.

And not to be overlooked are fraud-related risks. Results of a Deloitte survey published in the Wall Street Journal showed that about 30 percent of the professionals polled said their companies had experienced supply-chain fraud, waste or abuse.

Says Deloitte advisory principal Mark Pearson, “Many organizations are trapped in a pay-and-chase model for fighting supply chain fraud. Invoices are paid first, then retribution is sought much later when fraud is found, if it’s found at all.”

Turning Risk To Reward With Real-Time Analytics

The complexities inherent in the supply chain bring risks, but they can also work in favor of the business. There are many opportunities to wring out costs and improve productivity, and real-time analytics can be effective in finding and exploiting those opportunities.

At a high level, real-time analytics, combined with the information coming from the IoT, can help the manufacturer look to the supply chain’s future, rather than just its history.

Thanks to EDI documentation, supply partnerships produce plenty of detail on purchase orders, invoices, shipping notices and other activities. Manufacturers can apply analysis to this information to help determine where problems or inefficiencies may exist. But the analysis is generally backward-looking, at past shipments, in an effort to find solutions to current problems.

By contrast, real-time analytics take advantage of the new streams of data coming from IoT sensors embedded throughout the supply chain. These sensors, and this data, create a vivid pulse of supply chain operations. They help reduce risk – by real-time monitoring of inventory movement or fleet management, for instance. And they can point the way to improvements in related operations.

“Tracking numbers and bar codes used to be the standard method for managing goods throughout the supply chain,” says Futurum analyst Daniel Goodman in Forbes.

“But with the IoT, those methods are no longer the most expedient. New RFID and GPS sensors can track products ‘from floor to store’ – and I’d venture, even beyond. At any point in time, manufacturers can use these sensors to gain granular data like the temperature at which an item was stored, how long it spent in cargo, and even how long it took to fly off the shelf.”

That last part – “how long it took to fly off the self” – is key to the other major advantage of real-time, IoT analytics: end-to-end visibility across the manufacturer’s entire value chain.

An end-to-end real time analytics infrastructure can create a visibility plane across the entire enterprise, from supply chain to customer sales and service.

A spike in consumer activity can be communicated to production, purchasing and other operations in time to make instant adjustments. For competitive reasons, this real-time connection becomes more important every day for all manufacturers, but especially those who want to offer build-to-offer – BTO – features to their customers.

A Growing Advantage

With a real-time analytics layer in place, the manufacturer can add more-advanced analytics functions to the visibility plane. These functions add future-looking qualities, using machine-learning models to compare real-time sensor inputs with historical and contextual information coming from suppliers, logistics, production, business operations and customer services.

Such advanced analytics can further wring the risk out of the supply chain by predicting events before they happen, and even, in some cases, by automating proactive remedial responses.

Perhaps best of all, a real-time analytics infrastructure creates coherency out of the supply chain’s inevitable complexity. It then shows not just how to lessen the risks, but how also to turn those risks into real competitive advantage.

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