How to Use AI and Data for Supply Chain Risk

Are Procurement & Supply Chain making the most of AI’s potential, or simply still caught up in the ‘complexity’ of it all?

Once a concept of sci-fi movies, artificial intelligence (AI) has now become mainstream. Applications of this technology are ever-growing and affecting businesses in different and unexpected ways.

When it comes to Procurement & Supply Chain, for example, organizations are still struggling in embracing the full potential of AI.

How can this technology be used successfully then, without businesses being simply caught up in the ‘complexity’ of it all?

As we see from Deloitte’s CPO Survey in 2018, AI is only fully deployed in 2% of procurement organizations, and is far from making any real impact at scale within the digital ecosystems procurement teams are so eagerly trying to build. To top it off, there’s only 27% percent of procurement leaders considering AI/Cognitive technology, and 55% who haven’t considered it at all. 

Cut to 2019, Deloitte published its newest CPO Survey where they found that 81% of chief procurement officers with fully implemented solutions in the space of Supply Chain Risk & Compliance Management aren’t satisfied with their solutions. 

Eighty-one percent.

That’s a whole 25% higher than Donald Trump’s disapproval rate when it reached its peak.

A Complex Reality

Procurement teams are combating complexity in multiple dimensions, one of which is inherently digital. In other words, they are trying to figure out how in the world they’re going to leverage data, digital platforms, and emerging technologies to become more intelligent – more data-driven – in their strategies and operations. 

Expectations are that Artificial Intelligence and Machine Learning should be able to be applied in order to create more cognitive procurement organizations, but ask yourself: has my procurement team yet understood what AI is, or where it’s best applied?

Artificial Intelligence is intelligence displayed by machines, in which learning and action-based capabilities mimic autonomy rather than process-oriented intelligence. The simplest way to understand the potential application of AI is to clearly define its potential value-added.

Introduced by Gartner Analyst, Noha Tohamy, at Gartner’s Supply Chain Executive Conference, AI was broken down into two categories.

  • Augmentation: AI, which assists humans with their day-to-day tasks, personally or commercially without having complete control of the output. Such Artificial Intelligence is used in Virtual Assistant, Data analysis, software solutions; where they are mainly used to reduce errors due to human bias.
  • Automation: AI, which works completely autonomously in any field without the need for any human intervention. For example, robots performing key process steps in manufacturing plants”.

Artificial Intelligence is a rather cutting edge technology & is being used consistently within applications from some of the top-tier technology providers that we’re exposed to on a daily basis in our personal lives; Netflix, Google, Amazon, Facebook, among others. Things have developed incredibly fast in the last few years of technological development, and they’re only going to speed up.

Futurist Gerd Leonhard once predicted in a Forbes Magazine article that “Humanity will change more in the next 20 years than the previous 300 years. It’s a big call, and I will happily admit that I’ve had some people snicker at this statement. ‘You’re grandstanding!’ they say, ‘That’s unbelievable!’

“And whilst it may feel like a dramatic statement, I stand by it,” Leonhard adds. “If anything, I consider it to be an understatement when you assess the reality of exponential and combinatorial technological change. When AI (artificial intelligence) meets HI (human intelligence), business, as usual, is dead.

In reality, machine-kind is becoming smarter than mankind in certain instances, but not without Human Intelligence (HI) being applied in parallel, and certainly not without access to robust data sets. 

So, how can we take the stigma away from these two feared letters, and begin to find practical applications of artificial intelligence that can add real business value fast? Moreover, how can procurement teams do this without breaking the bank, or their brains from thinking too hard?

AI + Data = Supply Chain Risk Management 

Data is plentiful, and therefore content & information is plentiful. 

Forbes published an article back in 2018 that claimed that 90% of the world’s data had been created during the two years previous. 

This article itself will too create its own unique impression on the internet, once published. It will have specific attributes that render it searchable for a reader interested in learning more about the topic of focus; in this case AI, Data, and Supply Chain Risk Management.

Bots powered by search engine providers will scroll the internet, find this article, process the language in the article, recognize a thread of relevance, typify the content, fold it up and pop it into its respective filing cabinet in some corner of the internet’s vast warehouse. 

Information is more robust than ever before. As the amount of information and the accessibility to information increase exponentially, it is becoming equally simple and difficult to find things all at once. 

While this angry sea of content, data & insights may seem vast enough to drown one in its swells, artificial intelligence might just be able to throw us a life vest, so we can bear the storm and make it safely to shore. I promise, you’re not seeing a mirage, AI + Data can offer a real solution for Supply Chain Risk Management, fast. 

AI, when applied properly can quickly, and concisely, make sense of large data sets, organizing the bits and pieces that are most important to a procurement, sourcing or supply chain professional to manage supply chain risk. 

As we’ve pointed out, information is abundant, but it’s not always in our language, or easy to find, or necessarily searchable in a ‘normal database’. However, there are solution providers that are applying AI technology to social, news and search databases in order to put relevant supply chain signals in front of professionals that can utilize that data/information to manage risk.

AI-driven insights leverage already existing data and keep relevant information in one’s peripheral that can provide strong indications of brand health, customer perceptions, financial stability, and more. 

Receiving a quick-view of these kinds of risk-related insights, in real-time, is invaluable to make data-driven analysis & decisions based upon supply chain risks. In a world where 56% of consumers are willing to be lifelong loyal customers of ‘transparent brands’, supply chain risk management is becoming central to enhance market value, safeguard brand and increase competitive edge. 

AI…. not so scary after all, huh?

If you’re interested in learning more about how AI-driven insights can help your organization to manage Supply Chain Risk, contact the team, or the Kodiak Rating team.

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