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How to Use Technology to Reduce Supply Chain Risk

Today’s leaders are increasingly interested in the most actionable and appropriate ways to reduce supply chain risk. Succeeding in this aim could support profits and help companies enjoy better reputations and improved resilience despite challenging circumstances. Many decision-makers quickly recognize the many benefits of deploying technology as a risk-management strategy. Here are some specific ways they can do that.

Choose Data Analytics as a Supply Chain Risk Management Technology

Data analytics platforms can process gigantic amounts of data much faster than humans could without help. Thus, they can often discover risk factors that would have otherwise remained hidden. It then becomes possible and more straightforward for supply chain decision-makers to adapt before catastrophes occur.

In one example, researchers from MIT and the Ford Motor Company’s Research and Innovation Center assessed the supply chain risks affecting hybrid vehicles versus gas-powered ones. They gathered information about the compounds associated with 350,000 car parts used by one vehicle manufacturer. Moreover, the team focused on gas-powered cars, plug-ins and self-charging hybrids. The results showed the hybrids were twice as vulnerable to supply chain issues than those that used gas.

The researchers also calculated the amounts of 76 chemical elements present in each car. The analysis included determining each element’s weight and average price volatility between 1998 and 2015. Both categories of hybrids examined for this study had twice the raw materials risks compared to gas-powered vehicles. That was the equivalent of $1 billion in additional costs for 1 million sedans and SUVs. Additionally, battery-related elements were among the most significant cost-related risks.

Data analysis can also pinpoint likely future threats. Research from the University of Sydney used modelling to see the wide-ranging and far-reaching impacts of climate change and extreme weather on Australia’s food supply chain. The results showed adverse events could restrict food availability and employment opportunities. Moreover, the supply chain’s interconnectedness may mean regions far from the worst-hit areas experience adverse effects.

Studies like this enable supply chain leaders to quantify the most pressing risks they face accurately. However, due to the massive amounts of information required to generate such results, it’s not feasible to try and reduce supply chain risks this way without data analysis platforms.

Build Digital Twins to Aid Supply Chain Planning

People cannot predict the future with certainty. However, they can prepare for numerous scenarios by creating digital twins. Those highly realistic virtual models can help answer various questions about packaging suitability, weather patterns and other variables that may affect the supply chain. A digital twin can also highlight where companies may have flexibility in changing materials, designs or other aspects to cope with supply chain shortages as needed.

Digital twins offer a technology-driven way to understand how specific scenarios would affect the business. Even if many of them don’t occur in real life, it’s still advantageous to think about the best ways to handle them and plan accordingly. The ability to rapidly run simulations on a digital twin makes people interested in its use as a supply chain risk management technology.

Whether your top concern is weather or a raw materials shortage, a digital twin helps you see where weaknesses exist. It’s then easier to target those shortcomings and develop practical strategies for addressing them.

However, a digital twin can do more than alert people to problems. It could show opportunities to build resilience or expand a supply chain, both of which can indirectly reduce supply chain risk. Consider the case of a procurement leader at a manufacturer with a presence in Latin America and Asia. The company created products bound for the United States and South American markets.

It received an offer from an Asian supplier that provided an attractive discount and wanted to improve an existing business relationship.  The company that received the supplier’s proposal used a digital twin to study how it stacked up to other options. Price was only one factor of many the company’s leaders weighed before deciding to proceed. The digital twin brought clarity to guide the decision.

Reduce Supply Chain Risk With Artificial Intelligence

Artificial intelligence (AI) is becoming more widely used in industries ranging from marketing to health care. Some company leaders also use it as a supply chain risk management technology. Well-trained AI algorithms can assess information to give predictions, highlight previously unknown shortcomings and more.

In one example, researchers from the University of Tehran and Norway’s Nord University Business School created a deep learning algorithm that could reduce supply chain risk associated with meat.

It helped to detect spoilage, which made the supply chain safer. However, knowing the right time to discard food rather than put it on the market is essential to boosting sustainability by minimizing unnecessary waste.  The fully trained algorithm was 100% accurate in detecting spoiled versus fresh meat. The researchers taught with a set of 1,896 images of meat adapted from previous work. They clarified that the algorithm could help companies move away from manual monitoring efforts, thereby minimizing problems associated with human error.

Elsewhere, evidence from McKinsey & Company about consumer packaged goods (CPG) suggested AI and machine learning could reduce supply chain risk factors by cutting costs and reducing unnecessary inventory. However, McKinsey representatives found there is a long way to go before most leaders implement those technologies. Interviews with CPG senior leaders in Asia revealed that about 80% still follow traditional planning processes. Thus, many still need more real-time decision-making and automation capabilities that AI could otherwise bring.

Some people in the study indicated they used data to make decisions at the local level. That’s a step in the right direction. However, letting AI track global and industry-wide trends is a great way to see its full potential and reduce supply chain risk by having the data to be more responsive to problems.

How Will You Reduce Supply Chain Risk With Technology?

These are some of the top ways today’s supply chain leaders deploy technology to reduce supply chain risk and become stronger against potential threats. These examples can give you inspiration if you’re thinking about following their lead and using supply chain technologies in your organization.

Emily Newton is the Editor-in-Chief of Revolutionized Magazine. She has over six years experience writing articles for the tech and industrial sectors. Subscribe to the Revolutionized newsletter for more content from Emily at
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