Transforming Supplier Risk Management: The Role of AI and Predictive Analytics in Complex Supply Chains
Supply chains globally are growing at a massive scale. This interwoven web has become so complex, that it has become an insurmountable challenge for companies. With this growth, the supplier’s risks have increased exponentially.
According to a report by KPMG, 50% of organizations have a very limited knowledge about their risk exposure and the compliance issues that might result from that. Almost 29% of companies didn’t have the necessary structure to aggregate these overall exposures. In addition, 13% of the world’s biggest companies didn’t have visibility of the end-to-end supply chain.
So the risks are increasing at an unprecedented level, and the companies’ blindness to these issues are not helping either. So, how can this be solved? Fortunately, with the added complexity, the world has advanced in the technological realm as well, with artificial intelligence (AI) and machine learning (ML). These tools are now being used to combat the problems that supply chains globally are facing.
In this article we will look at how AI and ML are revolutionizing supplier risk management, with a deep dive into predictive analytics as a tool. By examining the capabilities of AI and ML in processing large datasets, predictive analytics, automation, and real-time monitoring, we will understand how these technologies can proactively manage risks and enhance decision-making for suppliers.
The Imperative for Resilient Supply Chains
The whole global economy is a cluster of supply chains so entangled with each other that it is nearly impossible to pick them apart. Serving as the backbone of international trade and commerce, these supply chains are essential for the current global economic order, making resilience and the ability to recover quickly paramount.
The COVID-19 pandemic starkly highlighted the fragility of traditional supply chains, emphasizing the need for more robust systems. Geopolitical tensions, such as the ongoing trade war between the United States and China, have significantly disrupted the flow of goods between the world’s two largest economies.
A recent example of supply chain vulnerability is the Ever Given incident in the Suez Canal in March 2021, where the grounding of a massive container ship blocked one of the world’s busiest trade routes for six days, delaying billions of dollars worth of goods and causing a ripple effect across various industries. These events underscore the urgent need for resilient supply chains that can anticipate, adapt to, and recover from disruptions.
Deep Dive: Predictive Analytics in Supplier Risk Management
While AI and ML are basic technologies underlying thousands of solutions geared towards creating resilient supply chains, predictive analytics stands out as a pivotal tool in supplier risk management.
Predictive analytics is a game-changer in managing supplier risks, offering a proactive approach to identify and mitigate potential issues before they escalate. By leveraging vast amounts of data from various sources, predictive analytics can provide valuable insights into supplier performance, potential disruptions, and market trends.
Identifying Potential Risks:
Predictive analytics uses historical data, market trends, and external factors to identify patterns and correlations that might indicate potential risks. For instance, a sudden drop in a supplier’s performance metrics or frequent delays in delivery could signal underlying issues that need immediate attention. By flagging these risks early, companies can take preventive measures to address them.
Enhancing Decision-Making:
With predictive analytics, companies can make informed decisions based on data-driven insights. This includes selecting the most reliable suppliers, optimizing inventory levels, and planning for potential disruptions. For example, if predictive analytics indicates a high likelihood of a natural disaster affecting a supplier’s region, companies can proactively adjust their supply chain strategy to minimize impact.
Improving Supplier Performance:
Predictive analytics can also be used to monitor and improve supplier performance. By analyzing data on delivery times, quality of goods, and compliance with contractual terms, companies can identify areas where suppliers need to improve and work collaboratively to enhance performance. This not only mitigates risks but also strengthens supplier relationships.
Real-Time Monitoring and Automation:
The integration of AI and ML with predictive analytics allows for real-time monitoring of supply chains. Automated systems can continuously track supplier performance, market conditions, and external factors, providing real-time alerts for potential risks. This enables supply chain managers to respond swiftly to any issues, ensuring minimal disruption to operations.
Case Studies
Western Digital:
During the COVID-19 pandemic, Western Digital used a predictive risk engine to protect its supply chain. By anticipating disruptions and taking proactive measures, the company saved millions of dollars and maintained smooth operations.
Walmart:
Walmart has been using predictive analytics to manage their almost 11,000 stores in over 19 countries.They are using the data from millions of transactions from their stores, optimizing their inventory based on buying patterns, and reducing supply chain risks, while managing their more than 10,000 vendors.
UPS:
UPS leverages predictive analytics to gain insights into its logistics network. Delivering more than 21 million packages daily, the company leverages its data to predict demand and optimize routes, saving on fuel costs and increasing efficiency.
Maersk:
The logistics giant is using predictive analytics to optimize their shipping patterns, specially for fresh foods and produce, helping eliminate waste and unnecessary spoilage during shipping.
Challenges and Considerations
Implementing predictive analytics in supply chain management presents several challenges.
Data quality and availability are crucial; predictive models rely on accurate, complete, and error-free data. Integrating data from various sources can be complex, and inconsistencies can hinder effectiveness.
Data privacy and security are also significant concerns. Ensuring compliance with governance policies and safeguarding sensitive data through encryption is essential to prevent unauthorized access.
Organizational resistance and change management pose additional hurdles. Gaining stakeholder buy-in and overcoming reluctance to adopt new technologies require effective communication, training, and pilot projects to demonstrate value.
Continuous improvement and optimization are vital. Predictive models must be regularly assessed and updated based on new data. Establishing feedback loops and leveraging machine learning algorithms can enhance model accuracy.
Human factors are another challenge, as implementing predictive analytics requires domain knowledge, analytical skills, and technical expertise. Finding and nurturing the right talent is critical.
Lastly, the return on investment (ROI) can be questionable due to the substantial initial costs in infrastructure, technology, and personnel. Organizations must evaluate long-term benefits and establish clear metrics to measure the impact of predictive analytics initiatives.
Conclusion
Predictive analytics, powered by AI and ML, is transforming supplier risk management by providing valuable insights, enhancing decision-making, and enabling proactive risk mitigation. As supply chains become increasingly complex, the ability to anticipate and respond to disruptions is crucial. Companies that embrace predictive analytics will be better equipped to navigate the challenges of today’s dynamic business landscape, ensuring resilient and efficient supply chains.
The views expressed in this column are solely the author’s and do not represent the views of Amazon.
Hardik Chawla is a product lead at Amazon responsible for building the company’s supply chain connectivity platform, ensuring operations for 100K+ trading partners worldwide.
Prior to Amazon, Chawla worked as a data scientist at ZS Associates, where he scaled several teams to develop AI solutions that transformed clinical trial operations, optimized drug marketing strategies, and improved healthcare outcomes.
Chawla is also active in the VC space with experience in evaluating early-stage startups.