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Supply Chain Risk Managers Must Be Fuzzy, Or Fail

I’ve been eager for the chance to inject the fashionable word “fraught” into one of my blogs for a while, and a recent conversation about supply chain risk presented the ideal opportunity.

During an exclusive roundtable at the SAP Ariba Live 2017 event in Las Vegas entitled “Managing Risk in Your Supplier Engagements,” three experts talked about how companies can prevent the worst from happening in a world fraught with stuff that can go wrong.

Their message was that companies can use advanced technologies like machine learning and predictive analytics to neutralize the impact of natural disasters, global currency fluctuations, and labor strikes, more easily ensure compliance with increasing regulations, and even address evils like forced and slave labor in their supply chain ─ but only if all that tech is backed by a corporate commitment to do good.

Cognitive computing changes the game for risk managers

Investigators and risk managers require both data transparency and context, something Padmini Ranganathan, vice president, products & innovation at SAP Ariba, said is foundational to how the SAP Ariba network of buyers and sellers operates. Dan Adamson, CEO of OutsideIQ, an SAP Ariba partner, discussed his company’s cognitive computing platform, which, together with SAP Ariba, changes the game.

Ranganathan noted that advanced technologies can help companies make sure they have the right data at the right time in the right place, and with the right person able to act. “When your supplier is tripping up somewhere, you need to be there to catch it,” he advised. “Technology is a very powerful tool with the ability to machine learn and pattern match to find out what’s going on.”

“Until now, machines have been great at combing through vast amounts of data but not providing context,” he added. “We bring in the right data and apply the first layer of context to make sure it’s a risk you would care about. How you deal with it is another level of context. We’ll see an evolution because some of your suppliers, depending on your industry, might have a heavy regulatory slant, and you need to treat them differently. Our layers of cognitive computing help filter out the noise and bring the relevant events to bear.”

Outside IQ conducts research far beyond simple watch list monitoring. “We go deeper with our cognitive process, replicating what a researcher would do, looking for patterns and links,” Ranganathan continued. “What might be clean today may have a news report tomorrow. Companies need to know before something becomes an explosive issue. The power SAP Ariba brings in is the whole layer of scoring indicators with relationship insights.”

Purpose-driven supply chain

James Edward Johnson, director of supply risk and analytics at Nielsen, said companies have a shared responsibility in managing supply chains for the greater good. The SAP Ariba network helps Nielsen conduct due diligence at scale faster and more cost-efficiently.

“World development has made some people richer and left a lot of people behind,” Johnson noted. “Because we’re so active in the supply chain, we actually touch millions of lives. How do you make sure that’s a force for good, that when you negotiate deals your push for price isn’t merely favoring companies that will cut corners, abuse their workers, enslave people, or rip up the environment by dumping chemicals into lakes?

“SAP Ariba is a great platform because it’s to a degree, data-neutral. A group like Outside IQ will find and read documents from everywhere in the world. If we can find and solve problems in our supply chain, we can make a difference in the world.”

Forget focus, follow the arc to uncover bad behavior

Responding to an audience member question, Johnson cautioned against zeroing in on risks.

“The moment you start focusing, you’re going to fail to capture risk, which is about seeing the unseen,” he said. “Sometimes your peripheral vision is more effective than your central vision. This is the arc of whatever risk you’re looking at. For example, I can guarantee financial indicators are a good leading indicator. The moment a company starts to fail at meeting their numbers, they’ll start taking risks. The question is where those risks materialize. You have look at other things that might provoke bad behavior.”

Every risk manager should be willing to say, “The answer I just gave you is wrong.”

Make data actionable, but accept fuzziness

These experts agreed that people need to factor risk indicators into contract negotiations while recognizing the level of uncertainty inherent to all kinds of data.

“Everyone in risk management should be willing to say ‘the answer I just gave you is wrong’ – the question is by how much and in what direction,” said Johnson. “Too often people are called on to give specific answers they can hang their hat on. That might teach people to manipulate the data or give people who are politically capable an advantage over people who are technically capable, so you might end up promoting people who are better at talking.”

Machine learning promises to strip out biases like recency and sample selection to give decision makers greater objectivity in understanding actual and potential risks and how to address them. “We should have science-based answers, we should have the data, and we should be able to know how well we know what we say we know,” said Johnson.

For more supply chain risk management strategies, see Managing Third-Party Risk Through Verified Trust.

Follow me: @smgaler


Source: Digitalistmag By SAP, Big Data, Analytics, Blog Posts – http://www.digitalistmag.com/technologies/big-data

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