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Curing the supply chain virus with AI

Coronavirus, for all its devastating toll on human life and global economic activity, may also very well be branded and remembered as the ‘supply chain virus’.

Appropriately responding to unprecedented supply chain disruption today requires to re-think forecasting and its role in driving daily decision-making: “only when the tide goes out – as Warren Buffett famous quote goes – do you discover who’s been swimming naked”. Not everyone will be up to the challenge.

Artificial Intelligence to the rescue: using wider data sets and faster adapting algorithms can accelerate the pace of learning, with immediate impact.

The supply chain challenge

Most current supply chain systems still rely on historical data from last year, month, or week as their baseline.

This works, until it doesn’t: the virus all but killed the historical data set.

Entire supply chains around the world are being disrupted: nearly impossible for executives to know when, and how, things will ‘return to normal’; or even, what this ‘new normal’ will really look like.

End-to-end disruption:

  • Sales of nearly any category essentially zeroed out except grocery and health
  • Stockouts of in-demand products even while overall inventory levels build up
  • Closed factories, sourcing challenges and inefficient global logistics.

Main outcome? Unpredictable, highly erratic demand patterns. And rapid loss of relevance for historical-data-driven supply chain systems.

As the future is hopefully going to look different from the past – surely from the recent past – traditional methods will fail to respond to critical market disruptions, leading to inaccurate advice and significant risks.

Weathering the storm: the ‘AI cure’

Artificial Intelligence blends together real-time data from multiple sources, and matches it with the trajectory of historical business sales data using automated attribute-tagging of products, location and customers.

Therefore, rather than just projecting the future as ‘more of the same’, this new approach relies on granular multi-dimensional de-composition of sales into hyper-local constituents, enabling exponential responsiveness and capability growth.

Sounds complex? In reality, the foundation is a simple idea, scaled up exponentially: vast amounts of data produced every day already allow to automate the answer to many questions with much greater relevance.

Imagine having a dedicated team of managers, to look at every single data source, for every single product for each geography and customer segment? Now imagine giving this person access to sales and inventory data, social media trends, search engine information, weather data, and any other relevant set of data – with plenty of time to digest it? Start to get the idea?

If only one could systematically unpick the right data at the right level for the right purpose – and have many dedicated people committed to the task, would it not be simple to respond in a smarter, faster and more effective way to new information?

This, in a nutshell, is what AI does.

The ‘human-machine alliance’: the AI blind spots

AI can digest any relevant information, map micro-patterns and micro-trends, learn from its own mistakes, and constantly adjust, all automatically. Just like a human would do, but more efficiently – and also more constrained by the data.

Due to its over-reliance on data, AI can very hardly innovate on its own; but once armed with new data and even limited observations, it does learn and respond very well, much better than any economically viable team of human managers armed with Excel and their gut feel alone ever could.

Therefore, AI and human managers must learn how to play different, complementary roles. We set strategy, objectives and rules; AI implements them for us, fast and effectively, even when faced with new challenges like coronavirus.

AI-powered supply chain: remaining relevant

At Evo, we are all ‘smart working’ to invest on the future: preparing our clients to return to normal faster and stronger than before.

We run our proprietary Monte Carlo simulation engine to determine likely scenarios; suggesting the median performance gain thanks to AI as in the image:

Within the first 7 days when lock-downs end, AI will have already learned for each product, customer segment and local geography what the societal and economic impact of COVID-19 will have been, and therefore how to respond every day.

In the first quarters as economic activity picks back up, AI will quickly grasp the emerging reality of the new supply chain, e.g.: ‘whiplash’ from surging customer demand, intense competition chasing a temporarily reduced economic footprint, and likely new discount-driven strategies that might lead to further value destruction.

Longer-term as impact from the initial disruption fades, AI will continue to learn and grow its own data base, thus enabling further virtuous circle of impact and sustained competitive advantage, including being better prepared to whatever challenges the future will throw at us.

Even in today’s challenging context, Artificial Intelligence can help the Supply Chain ‘return to normal’ by:

  • Adjusting real-time availability levels, purchasing and allocation
  • Responding to competitor reactions as relevant to maintain advantage
  • Linking pricing and discounting decisions for integrated margin recovery.

About the author

Kaitlin Goodrich is Evo’s main storyteller who helps communicate Evo’s message to the world.
Kaitlin received her BS in International Affairs and Modern Languages at Georgia Tech and then an LLM in International Trade Law from the University of Turin. She worked in Latin America doing education outreach for U.S. binational centers and has since worked as a content writer for international clients.
In her free time, she likes to travel or curl up with a good book.

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