The rapid advancement of artificial intelligence (AI) technology has revolutionized the global supply chain industry. AI-powered systems can optimize supply chain operations, minimize costs and enhance efficiency. However, as the use of AI increases, so do related security concerns. AI systems are vulnerable to various cyberthreats such as hacking, data breaches and malware attacks, and the integration of AI into the supply chain ecosystem has introduced new security challenges that demand attention from organizations to ensure a secure supply chain.
It is crucial to understand the significance of securing the AI-enabled supply chain and the importance of vendor management in mitigating cyberrisk. By gaining a comprehensive understanding of the vulnerabilities that exist in the AI-enabled supply chain, organizations are empowered to take measures to address them.
Securing the AI-enabled supply chain will enable organizations to gain a competitive edge by ensuring a reliable supply chain ecosystem
Emerging Trends and Innovations in Supply Chain Security
There are several emerging trends and innovations in supply chain security of which organizations should be aware:
- Blockchain—Blockchain technology has the potential to revolutionize supply chain security by providing a tamper-proof and transparent ledger of all transactions. For example, Walmart Canada partnered with DLT Labs to create a blockchain solution for managing invoices and payments to its 70 third-party freight carriers.1 The solution reduced invoice discrepancies from 70 percent to less than one percent and flagged and resolved any issues quickly, leading to timely payments for carriers. The project’s return on investment (ROI) was achieved in only three months.
- ML—Machine learning (ML) can be used to analyze vast amounts of data in real time allowing organizations to quickly identify and respond to potential threats. For example, a major retail enterprise implemented ML operations (MLOps) to enhance its supply chain management.2 The enterprise employed ML algorithms to anticipate product demand and optimize resource distribution within its warehouses. Consequently, this led to an enhanced precision in demand prediction, decreased waste and increased supply chain efficiency.
- IoT—The Internet of Things (IoT) can be used to monitor the supply chain in real time providing organizations with greater visibility and control over their operations. For example, a shipping enterprise installed IoT sensors on its cargo containers to monitor their location, temperature and humidity in real time.3 This enabled the enterprise to track cargo throughout the entire supply chain, ensuring it was stored and transported under optimal conditions. With this information, the enterprise was able to identify potential issues early on and take corrective actions to prevent any damage or spoilage, ultimately leading to improved customer satisfaction and reduced losses.
- Cloud-based security solutions—Cloud-based security solutions can provide organizations with greater flexibility and scalability, allowing them to easily adapt to changing threats and requirements. For example, a major ecommerce enterprise can use cloud-based security solutions to secure its supply chain management system. The cloud- based security solution allows the enterprise to easily scale up or down its security measures depending on the changing threat landscape. The solution also provides real-time monitoring and threat detection capabilities, enabling the enterprise to quickly respond to any security incidents. This improves the overall security posture of the supply chain and helps protect against potential cyberthreats.
AI technology is transforming the supply chain management landscape by improving efficiency, optimizing inventory management, enhancing forecasting accuracy and reducing costs.
AI
AI technology is transforming the supply chain management landscape by improving efficiency, optimizing inventory management, enhancing forecasting accuracy and reducing costs. There is value to be gained by considering some examples of how AI is being used in supply chain management and analyzing the associated benefits and risk.
For example, Oracle’s AI and automation capabilities, introduced in April 2023,4 aim to help customers improve supply chain planning, operational efficiency and financial accuracy. The features of Oracle Fusion Cloud Supply Chain and Manufacturing (SCM) include AI-powered lead-time estimates designed to identify lead-time trends and anomalies and minimize their potential impact on customers through resolution suggestions and prioritized actions. Oracle has also enhanced its quote-to-cash process in Oracle Fusion Applications, centralizing use ratings and recognizing revenue accordingly. Oracle is also launching new rebate management capabilities in Oracle Channel Revenue Management, automating the entire rebate management process, improving payment accuracy, reducing administration costs and enabling customers to quickly settle their rebate claims.
In addition, Inspectorio, a software enterprise based in Seattle, Washington, USA, has introduced the world’s first generative AI-driven supply chain management Software-as-a-Service (SaaS) product.5 The product provides end-to-end supply chain visibility, from product development and manufacturing to transportation and distribution, and uses machine learning (ML) algorithms to identify and predict quality issues before they occur. This helps manufacturers and retailers identify and address quality issues early, resulting in reduced waste, minimized inventory costs and improved customer satisfaction.
Vendor Management Risk
There are several types of vendors involved in the AI-enabled supply chain including manufacturers, suppliers, distributors and logistics providers. These vendors may offer a range of products and services such as raw materials, finished goods, transportation and warehousing. Each vendor has a unique role to play in the supply chain, and any disruption in its operations can impact the entire supply chain.
Identifying potential vendor management risk is crucial to ensuring the smooth functioning of the supply chain. One of the most significant risk factors is vendor dependency, wherein the failure of a critical vendor can result in a disruption of the supply chain. Another potential risk is vendor fraud, wherein vendors may provide false information or overcharge for their products or services. Vendor noncompliance with regulations and standards can also pose significant risk, because noncompliant vendors can lead to fines, legal action and reputational damage.
Best practices to mitigate vendor management risk include:
- Establish clear vendor selection criteria—Vendor selection criteria should include evaluating a vendor’s financial stability, reputation and compliance with regulations and standards.
- Conduct due diligence when reviewing vendors—Due diligence requires checking vendors’ references, reviewing their financial statements and conducting site visits to ensure that vendors meet the required standards.
- Regularly monitor vendor performance—Organizations should monitor vendor performance regularly to identify any potential issues as soon as possible. Performance monitoring tasks include tracking vendor compliance with contracts and service-level agreements (SLAs), evaluating their responsiveness and communication, and conducting periodic audits and assessments.
Another best practice for mitigating vendor management risk is to establish strong contractual relationships with vendors. Contracts should clearly outline the scope of work, delivery timelines and quality standards, and the consequences of noncompliance or a breach of contract. Contracts should also include clear dispute resolution mechanisms and termination clauses.
As supply chains become increasingly dependent on AI, cybersecurity and vendor management become more critical than ever.
Organizations can also leverage technology solutions to mitigate vendor management risk in the AI-enabled supply chain. For example, vendor management software can automate vendor selection, due diligence and performance monitoring, providing real-time insights into vendor compliance and performance. Contract management software can also streamline the contract management process, enabling organizations to manage contract terms and obligations effectively.
Cybersecurity Risk
The integration of AI into the supply chain has brought with it a host of cybersecurity risk factors. These sources of risk can range from data breaches to financial loss to reputational damage. It is important to identify and understand these risk factors to be able to mitigate them effectively.
The increased amount of data that is collected and analyzed by AI algorithms leads to new attack vectors of cybercriminals. For example, there is a risk of malware being introduced into the AI system, which can lead to manipulation of data and cause business disruption or financial loss. Other threats include social engineering attacks (e.g., phishing, spear-phishing), ransomware and other types of malware. Organizations must also be aware of insider threats, such as employees or vendors with access to sensitive data who may be tempted to steal or misuse the data.
To secure the AI-enabled supply chain, it is crucial to implement a comprehensive cybersecurity strategy that includes a combination of preventive and detective measures, including ensuring that all AI systems and components are updated with the latest security patches and updates, monitoring the supply chain ecosystem for potential cybersecurity risk, and managing it accordingly.
Addressing Cybersecurity and Vendor Management Risk
AI technology is revolutionizing the supply chain management landscape, and with it, the associated risk and challenges. As supply chains become increasingly dependent on AI, cybersecurity and vendor management become more critical than ever. Organizations must ensure that they have robust cybersecurity protocols in place to protect against cyberthreats and implement effective vendor management strategies to manage and mitigate risk associated with third-party vendors. In addition, enterprises must ensure that AI systems are transparent, accountable and ethical, and that they do not perpetuate or amplify biases in decision-making. By addressing these challenges proactively, organizations can maximize the benefits of AI technology in supply chain management while minimizing the associated risk.
To address cybersecurity and vendor management risk in AI-enabled supply chains, there are several strategies organizations should consider:
- Conduct thorough risk assessments—Thorough risk assessments help identify potential cybersecurity and vendor management risk associated with their AI-enabled supply chains. This includes identifying all vendors involved in the supply chain and any potential vulnerabilities in the AI systems being used.
- Enforce strong vendor management practices—These include conducting due diligence on vendors and monitoring their performance on an ongoing basis.
- Implement strong cybersecurity measures—These include using strong encryption, implementing access controls and regularly testing for vulnerabilities.
- Train employees in cybersecurity best practices—This ensures that everyone in the organization is aware of cyberrisk and how to mitigate it.
- Monitor the supply chain for potential risk—Potential risk includes unusual activity or suspicious behavior from vendors.
Conclusion
The integration of AI technology into the supply chain industry has enhanced efficiency and minimized costs, but new threats and challenges have also emerged. Vendor dependency, fraud and noncompliance are significant risk factors related to vendor management. However, with the right strategies and best practices in place, organizations can effectively manage risk and secure their AI- enabled supply chains, including documenting clear vendor selection criteria, conducting due diligence, monitoring vendor performance and establishing strong contractual relationships. It is important to implement a comprehensive cybersecurity strategy that includes enforcing preventive and detective measures, keeping AI systems updated and monitoring the supply chain ecosystem.
In the future, new innovations and technologies may also emerge that will further enhance supply chain security, such as advanced ML algorithms and new blockchain-based solutions.
Overall, securing AI-enabled supply chains requires a proactive and strategic approach, with a focus on strong cybersecurity and vendor management practices. By staying up to date on emerging trends and innovations in supply chain security, organizations can effectively manage risk and ensure the integrity and reliability of their supply chains.
Author’s Note
Please note that the views expressed in this article are personal and do not reflect the opinions or affiliations of any organizations associated with the author.
Endnotes
1 Hawkins, L.; C. Versace; M. Abssy; “Blockchain Technology for Supply Chains: Who Is Using It and How?” Nasdaq, 17 March 2022, http://www.nasdaq.com/articles/blockchain-technology-for-supply-chains%3A-who-is-using-it-and-how
2 Oladele, S.; “How These 8 Companies Implement MLOps: In-Depth Guide,” Neptune Labs, 17 August 2023, http://neptune.ai/blog/how-these-8-companies-implement-mlops
3 Telenor IoT, “PEOPLE: Delivering Sustainable Innovation to the Global Freight Industry,” http://iot.telenor.com/iot-case/people-container2/
4 SupplyChainBrain, “Oracle Introduces New SCM AI and Automation Capabilities,” 19 April 2023, http://www.supplychainbrain.com/articles/37048-oracle-introduces-new-ai-and-automation-capabilities-for-supply-chain-management
5 Inspectorio, “Inspectorio Introduces World’s First Generative AI-Driven Supply Chain Management SaaS Product,” Chinook Observer, 20 April 2023, http://www.prnewswire.com/news-releases/inspectorio-introduces-worlds-first-generative-ai-driven-supply-chain-management-saas-product-301803004.html
ANUJ CHOUDHARY | CISA, CA, CFE
Is a corporate compliance manager at Dr. Reddy Laboratories. He has led several high-profile investigations and complex projects for clients in multiple industries and participated in several large data analytics and software development projects. Previously, Choudhary was a manager of forensics and integrity services for Ernst and Young LLP (India) and an assistant manager in business advisory services for BDO India LLP. He writes blogs on professional topics, which he publishes on his personal website, heyanuj.com. He is a member of ISACA® and an associate member of the Institute of Chartered Accountants of India and the US Association of Certified Fraud Examiners. He is also a member of the Young Singapore International Arbitration Centre and the Young Mumbai Centre for International Arbitration.