The applications for AI-based automation and analytics continue to increase, and supply chain risk mitigation has become a primary area of potential improvement. As artificial intelligence applications expand in supply chain management, the market is expected to reach $1.3 billion by 2024.
PwC reports that 72% of executives believe AI will be critical for their success, saying they anticipate it will give them a business advantage. According to McKinsey, 61% of companies who have implemented AI into their supply chains report a decrease in costs, and 53% report increased revenues resulting from their AI investment.
As modern supply chains become increasingly complex, internal, external, environmental, and even political risks have become inevitable. By leveraging a system that uses artificial intelligence, such as Vanguard Predictive Planning™, organizations can instantly analyze more data sources with automation to notify planners whenever a potential risk area increases or impacts supply chain performance. AI enables organizations to avoid supply disruptions, product outages, and unexpected sourcing surprises. More importantly, it helps organizations gain a competitive advantage by adapting quickly to market changes, securing the best-available resources, and maintaining sufficient sourcing options.
Readwrite says AI in supply chain management “helps improve different areas of [the] supply chain like supply chain transparency and route optimization. AI provides the supply chain with contextual intelligence, which reduces operating costs and helps to manage inventory.”
Using Predictive Analytics in AI
Optimize supply chain outcomes by using AI to weigh scenarios and predict the impact of any action. Predictive analytics advances supply chain planning beyond risk identification and into risk mitigation. According to an MIH survey, predictive analytics is expected to reach an adoption rate of 87% over the next five years.
Automation is critical to identifying supply chain risk quickly. Automating processes, including the decision-making process, is where artificial intelligence shines. Actionable, predictive analytics uses machine learning techniques to make risks more predictable, even risks as seemingly unpredictable as environmental conditions and natural disasters, such as floods, extreme temperatures, and severe storms. This intelligence helps supply chain planners decide the best possible plan to mitigate these potential risks.
In addition to temporal risk, incorporating AI in supply chain management enables planners to identify longer–term risk areas, such as seasonality. AI in supply chain management provides historical context so that supply chain planners can identify trends, make informed decisions, and hone their processes to learn and improve. The better and faster they become at identifying, assessing, and mitigating risks, the more agile their supply chain becomes as a whole.
Predictive analytics shines a light on potential events and recommends actions. With a past, present, and future perspective, supply chain planners are more aware of their entire ecosystem and can make decisions with greater confidence by understanding context, dependencies, and scenarios. Predictive insights justify planning decisions within the organization and externally with vendors and customers, setting appropriate delivery expectations and keeping communication lines open.
Bringing AI into Your Supply Chain
If you are considering an investment in artificial intelligence for your supply chain, you can either buy an AI application from a vendor or build it in-house. Supply Chain Dive says, “Bringing the required talent on board can be a struggle. 56% of respondents considered hiring a top challenge in the current environment.”
Instead, focus your budget on a platform purposely built to make risk more predictable, actionable, and manageable, such as Vanguard Predictive Planning™. With comprehensive risk analysis, unlimited scenario planning, and automated decision–making, planners have greater confidence in their supply chain processes.