Before the COVID-19 pandemic, many supply chains strove for efficiency at all costs. While this approach lowered costs and shortened lead times, it led to significant disruption at the pandemic’s onset. Now, organizations favor adaptability and supply chain AI could be the key to achieving it.
Transitioning from a lean business model to more flexible alternatives is a substantial and challenging shift. While it can seem intimidating, new technology — namely, artificial intelligence — provides a path forward. The key lies in using it effectively.
A Global Supply Chain Problem
Supply chain AI’s value is clearer when organizations understand the scope of the problems it solves. Today’s supply chains are in dire need of change. The pandemic has revealed how these networks have too many single dependencies, are too risk-prone, and have too few fallbacks and safeties to mitigate disruption.
A staggering 83% of supply chain organizations have experienced raw material shortages within the past year. These shortages are often disruptive, too, as 45% of companies have no visibility past their first-tier suppliers. Many others rely on just one or two sources for mission-critical resources. If something happens at a single upstream supplier, companies often won’t see it coming and are stuck until the supplier can recover. In light of how frequent disruptions are today from severe weather, geopolitical conflict and worker shortages, something needs to change. Supply chains must become adaptive so they can respond quickly to prevent delays and shortages.
The Promise of AI
Businesses can arrive at adaptive supply chains through different paths. Regardless of the specifics, though, AI makes the journey easier.
Tackling Inventory Management
One of supply chain AI’s biggest areas of impact is inventory management. Adaptive supply chains must be able to keep sufficient safety stocks, and adjust stock levels to meet shifting supply and demand. That’s difficult with conventional approaches, but AI offers more insight.
Businesses can use AI to analyze their sales and shipment history to categorize items based on volatility, criticality, and value. It’ll then be easier to understand which are most important to keep larger safety stocks of.
Similarly, AI can predict demand shifts so supply chains can adjust their inventories to meet these changes without surpluses or shortages. Coca-Cola does this to deliver 500 million beverages at optimal times across Japan. By adapting shipping practices to AI’s predictions, they can prevent waste and stock-outs simultaneously.
Highlighting Weaknesses
Inventory management may be an easy area of improvement to spot, but other issues can be harder to identify. Brands may not know where their weaknesses lie, making it difficult to optimize effectively. AI can clear this up, too.
It starts with creating a digital twin of the supply chain — a virtual model of the network based on real-world data. AI can then analyze the digital twin to find improvement areas. That could be a single dependency for a raw material supply, a warehouse with limited visibility or anything else that hinders flexibility.
Human analysts could theoretically do the same thing, but they’d take longer and be less accurate. AI can often spot subtle connections humans miss, so it’s the ideal tool to identify these supply chain weaknesses. Once companies know where they fall short, they can address those areas to boost adaptability.
Predicting Disruption
Of course, some disruption is inevitable. Even if enterprises optimize their inventories for adaptability and create a stronger supplier network, unexpected problems can still happen. However, these disruptions aren’t so disruptive if organizations see them coming and supply chain AI enables that foresight.
Just as AI models can predict demand shifts based on past trends, they can tell when a disruption is likely. By picking up on early warning signs, these models alert businesses of possible incoming problems. Companies can then react ahead of time to minimize the damage.
If AI predicts a shortage of a certain material, warehouses can increase their safety stocks ahead of time. If it suggests storms will slow transportation, logistics brands can inform downstream partners and schedule more time for deliveries. AI could even predict price shifts, letting enterprises know to save elsewhere to account for higher tax or fee spending.
Refining Documentation and Reporting
While pure efficiency over everything is no longer supply chains’ goal, efficiency is still important. Businesses must be able to act quickly if they hope to adapt to incoming disruptions or shifting trends. Once again, supply chain AI provides the solution.
AI can eliminate error-prone manual processes like data entry, billing and other documentation or reporting tasks. This kind of work doesn’t add much value to supply chains but takes a lot of time. Consequently, when companies manage it manually, they lose valuable time and effort they could otherwise spend on more nuanced, critical work like planning to adapt to incoming changes.
By automating back-office administrative tasks, supply chain organizations become more agile. They’ll be able to divert more resources to adaptation and get more done at once. This automation also mitigates labor shortages, enabling even more efficiency, which, in turn, enables adaptability.
The Road Ahead
The benefits of supply chain AI are hard to ignore. However, any seasoned business leader can attest things are often more complicated in real life than they are on paper. If companies want to capitalize on this potential, they must approach AI carefully.
As beneficial as this technology can be, it’s hard to get right. Between 60% and 80% of AI projects fail, mostly because of issues in the application, not the tech itself. Supply chains must understand AI’s weaknesses along with its strengths to use it effectively.
One key weakness to be aware of is the need for sufficient, accurate data. Brands must collect real-time and historical data across their supply chain to give AI enough information to draw correct conclusions. That means implementing data-gathering technologies like the Internet of Things and cleansing data before feeding it to AI models.
Organizations must also recognize this technological transformation is expensive and disruptive. Consequently, going all in on it at once will result in astronomical costs and poor results. Instead, businesses should identify where AI is most useful — typically, data-centric, analytical tasks where manual alternatives are least effective — and start there.
Applying AI to a single use case helps companies spread out the costs. They can then learn what works and what doesn’t to use it more effectively when they expand their AI in the future.
Supply Chain AI Could Be the Key to Adaptability
Supply chains must become more adaptive to meet tomorrow’s demands. To do that, they’ll have to embrace supply chain AI.
Implementing AI can be challenging, but if organizations do it well, it can push them ahead of the competition. They’ll then have the insights and efficiency necessary to adapt to a quickly shifting world.
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