By Zohar Bronfman
During the COVID-19 pandemic, over 60 container ships carrying billions of dollars worth of goods were stranded for months at the Los Angeles and Long Beach ports, causing long-term stockouts and significant business disruption. The blockage of the Suez Canal by the Ever Given container ship, which halted an estimated US$9 billion in daily trade, highlighted the fragility of global supply networks.
These incidents are reminders of the vulnerabilities in today’s interconnected world, and they emphasize the urgent need for more resilient supply chains that can predict and manage risks more effectively.
While these events may seem extreme, they are symptomatic of a larger trend in supply chain management. From geopolitical shifts such as trade wars, sanctions, and regulatory changes to fluctuating market demands, supply chains are expected to operate efficiently despite unpredictable disruptions. As we move further into an era of rapid technological shifts, traditional supply chain management approaches are no longer sufficient.
Why Traditional Supply Chains Are Falling Behind
Building resilient supply chains is no easy feat. For decades, supply chain management relied on established forecasting models and manual processes, but these approaches are now struggling to keep up with the pace of change. Traditional methods are often reactive, meaning they respond to disruptions only after the damage has already been done. Moreover, manual processes can slow decision-making, increasing the risk of costly errors. The result? Supply chains are vulnerable to supplier delays and sudden changes in consumer demand.
According to a 2024 survey by the American Productivity & Quality Center (APQC), 62% of organizations missed their supply chain targets in 2023, with 80% falling behind competitors. These challenges are further exacerbated by labor shortages and the slow adoption of new technologies in many regions. Despite this, 55% of organizations plan to increase their supply chain budgets in 2024, focusing on tools, technology, and innovation to overcome these issues.
However, simply increasing investment isn’t enough.
Without the right strategies, businesses risk falling into the same inefficiencies that have plagued them in the past. The key to building more resilient and agile supply chains lies in leveraging advanced technologies to help businesses stay ahead of disruptions rather than constantly playing catch-up.
Enter Machine Learning and Predictive Analytics
This is where machine learning (ML) and predictive analytics come into play. These advanced technologies can potentially transform supply chain management by converting vast amounts of raw data into actionable insights. With the help of machine learning algorithms, businesses can anticipate disruptions before they happen, streamline operations, and optimize every aspect of their supply chain—from procurement to final delivery.
Predictive analytics involves using data mining, statistics, and AI to forecast future outcomes based on historical data. Machine learning, a subset of AI, allows algorithms to learn from the data they process, improving over time. When applied to supply chain management, these technologies can revolutionize processes like demand forecasting, inventory management, and supplier performance monitoring.
For example, machine learning can analyze historical data to identify risk patterns, such as recurring supplier delays or sudden stockouts. By flagging these risks early, businesses can address potential issues proactively before they escalate into costly disruptions. Predictive analytics can also significantly improve demand forecasting, ensuring that companies maintain optimal inventory levels without overproducing or understocking. As a result, companies can reduce forecasting errors by 20-50%, lower warehousing costs by up to 10%, and minimize product shortages.
Real-World Impact: Addressing Global and Regional Challenges
Globally, supply chains have become increasingly interconnected, with businesses often sourcing materials and components from multiple countries. This makes supply chains more vulnerable to disruptions like natural disasters, political unrest, or economic instability. In response, companies are turning to machine learning and predictive analytics to build more agile supply chains that adapt quickly to changing conditions.
Companies in Asia face varying maturity in adopting supply chain technologies. While some have made progress, others still struggle with fragmented systems and a lack of unified data models. This can hinder their ability to gain end-to-end visibility across their supply chains, leaving them vulnerable to disruptions. Yet, as this audience probably knows, the region increasingly prioritizes digital transformation to remain competitive in the global market.
Driving Sustainability with Predictive Analytics
Beyond operational efficiency, predictive analytics and machine learning can also be crucial in advancing sustainability goals—a growing priority for businesses globally. By optimizing transportation routes, machine learning can help companies reduce fuel consumption and lower their carbon emissions. Predictive analytics can also support sustainability efforts by minimizing waste, ensuring that companies produce only what is needed to meet demand.
Sustainability is becoming a critical factor in supply chain management, with regulatory bodies around the world implementing stricter guidelines for emissions and waste management. For businesses, adopting machine learning and predictive analytics not only helps them comply with these regulations but also positions them as leaders in the shift toward greener supply chains.
Preparing for the Future of Supply Chain Management
As supply chains become more complex, advanced solutions like machine learning and predictive analytics are essential for building resilient, future-proof supply chains. Businesses leveraging these technologies to predict and mitigate risks will be better positioned to adapt to disruptions and maintain a competitive edge.
Predictive technologies are becoming a must-have for companies striving for excellence in supply chain management. By integrating machine learning and predictive analytics, businesses can future-proof their operations, navigate uncertainties, and ensure long-term success.
Dr Zohar Bronfman is the CEO and co-founder, Pecan AI
