A Closer Look at the Future of AI in Supply Chains

Modern freight and supply is a high-wire act, battling rising customer demands, fierce competition, and geopolitical disruptions. To make matters worse, the sector’s manual data management systems are time-consuming, error-prone, and lack real-time insights. At the same time, many companies operate in data silos, hindering the collaboration needed to make significant strides.?

These challenges make the industry ripe for the transformative power of GenAI.

However, Integrating AI into supply chain operations isn't a simple plug-and-play process. What’s the key to unlocking the benefits of GenAI? Structured data. That’s because AI algorithms rely on high-quality, organized data for accurate predictions, but supply chain data often exists in unstructured formats and silos.

To bridge this gap, companies must focus on breaking down data silos, establishing centralized data repositories, and ensuring data consistency and accessibility, which will lay the foundation for successful AI implementation across various aspects of supply chain management, such as risk mitigation, logistics optimization, and demand forecasting.

The current state of AI in supply chain planning and management

The future of supply chain management is data-driven and powered by GenAI. We can expect even more sophisticated applications like real-time supply chain visibility, dynamic routing optimization, and predictive maintenance as AI advances.?

AI is powering risk mitigation and resilience planning?

The conflict between Russia and Ukraine in the Black Sea region has disrupted global supply chains for key commodities such as grain, oil, and fertilizers. Companies with AI-powered risk mitigation systems have been better positioned to identify potential risks and develop contingency plans to minimize the impact of these disruptions.?

By continuously monitoring data from various sources, such as geopolitical events, trade sanctions, and commodity prices, these systems provide early warning signals of potential supply chain disruptions and recommend proactive measures to mitigate risks.?

For example, a food manufacturer using AI-powered risk mitigation could have identified the potential impact of the Black Sea conflict on its wheat supply chain and developed contingency plans to source alternative suppliers or adjust its production processes to use alternative ingredients.

AI is driving logistics and transportation optimization

The ongoing conflict in Yemen has led to increased risks and disruptions in the Red Sea, and AI-powered logistics optimization systems are playing a crucial role in helping companies navigate these challenges by identifying the most efficient and secure shipping routes.?

By analyzing real-time data on security threats, weather conditions, and port congestion levels, these systems recommend optimal routing strategies to minimize the risk of disruptions. For instance, a shipping company using AI-powered optimization could have quickly adjusted its routes to avoid high-risk areas in the Red Sea while identifying opportunities to optimize fuel consumption and reduce overall transportation costs.

By leveraging AI-powered optimization, companies can not only navigate regional disruptions but also significantly improve their day-to-day operations. Apart from helping with regional conflicts, a leading US logistics company uses a proprietary AI platform to optimize picking routes, resulting in a 30% increase in workforce productivity and cost savings from improved space and materials handling efficiency.

AI is pushing the boundaries of demand forecasting in supply chain management

AI technologies excel in forecasting demand, enabling accurate predictions about shortages and demand fluctuations. Gartner's survey reveals that forecasting is the leading machine learning (ML) application in supply chain planning – almost half of all supply companies have invested in it, and 43% plan more to adopt AI-driven demand forecasting within the next two years.

Why is that? According to McKinsey , AI-powered forecasting can reduce errors by 30 to 50% in supply chain networks, leading to a 65% reduction in lost sales due to inventory stockout situations, while warehousing costs decrease up to 50%.?

Here’s an equally impressive statistic from ZF Friedrichshafen, a global leader in automotive technology. With AI integrated into their SAP Integrated Business Planning, the company cut forecast turnaround time by 92% , freeing planners from repetitive tasks and gaining flexible control over supply chain elements like location, product, channel, and customer.

Common pitfalls

As AI advances, we can expect even more sophisticated applications in supply chain optimization. The possibilities are endless, from self-healing supply chains that autonomously detect and resolve issues to AI-powered demand forecasting that adapts to changing market conditions.

But while AI technologies offer undeniable benefits, effectiveness hinges on the quality and completeness of the data it's trained on.

• Failure to consider external factors such as economic conditions, competitor actions, and marketing data can lead to inaccurate predictions, especially for new products without historical data.
• Overreliance on historical data can result in forecasting errors, particularly in dynamic markets where consumer preferences change rapidly.
• Lack of department collaboration can result in suboptimal forecasts and inefficient inventory management.

It’s also important to note that the transition to a fully autonomous supply chain will be gradual and require close collaboration between humans and machines. While AI can automate many tasks and make intelligent decisions, human oversight and strategic guidance will remain crucial to ensure supply chain operations align with business objectives and ethical considerations.

How will AI capabilities evolve to improve supply chain management further?

Once slow-moving and burdened by siloed data and reactive processes, the global supply chain stands at an inflection point with the promise of massive improvements at every point of the supply chain.??

Let’s unpack this not-so-distant future.

1. Autonomous agility:? AI will revolutionize decision-making by shifting from reactive to autonomous, using real-time data and advanced algorithms to manage everything from demand forecasting to logistics, adapting instantly to market shifts and disruptions.

2.? Even more improvement opportunities: Imagine a supply chain where all stakeholders can access real-time data —from raw materials to last-mile delivery – on one dashboard. AI makes this possible by integrating data from sensors, blockchains, and social media, providing complete visibility across the supply chain as more manual processes are automated.?

3. Predicting the unpredictable:? Few of us could have predicted the Red Sea disruptions at the turn of the new year. But AI can help all of us better prepare for future disruptions. By analyzing vast datasets, AI can predict potential risks like natural disasters or supplier failures, allowing companies to take proactive measures and build resilience.

4. Sustainability on autopilot: AI is helping companies reduce waste and emissions by analyzing environmental impact data and ensuring ethical labor practices throughout the supply chain. For example, In 2023, Unilever implemented an AI-powered sustainability management system that analyzes environmental data from suppliers, factories, and logistics partners, helping the company reduce its carbon footprint by 20% .

Why is structured data the key to unlocking GenAI in supply chains?

While GenAI promises a future of autonomous, cognitive supply chains for companies, integrating the technology into their IT systems isn’t exactly a plug-and-play scenario. The good news? It isn’t that complicated to integrate, either.?

If your company is ready to embrace GenAI but you're unsure where to begin, focus on your structured data. It forms the foundation for AI algorithms to learn, analyze, and make accurate predictions. For example, demand forecasting requires historical sales data, customer information, and market trends in a structured format. Inventory optimization needs data on supplier lead times, production schedules, and stock levels to be consistently formatted and updated.?

The problem is that most supply chain data is unstructured (free text, scans, spreadsheets) and semi-structured (error-prone data from APIs and EDIs). GenAI platforms like Stardox convert this low-quality data into a structured format through techniques like natural language processing (NLP) and image recognition.?

GenAI platforms extract relevant information from unstructured sources, enriching the structured data used by Business Intelligence tools for analysis and decision-making. Advanced analytics, like predictive modeling and simulation, leverage structured data to anticipate and mitigate supply chain risks. Meanwhile, AI-powered recommendation systems utilize structured historical data and real-time conditions to suggest optimal suppliers, transportation routes, and inventory levels.

One thing is certain: the more sophisticated AI becomes, the greater its reliance on structured data will be. It’s the cornerstone for building intelligent, agile, resilient supply chains.

Take the first step towards digitizing your supply chain.?

As geopolitical fault lines and regional conflicts persist, the industry faces unprecedented challenges and rapidly shifting consumer demands; the ability to leverage structured, actionable data for enhanced visibility, agility, and resilience is crucial.

Stardox, our GenAI platform Stardox that speaks “freight,” is at the forefront of this data-driven transformation. Able to be implemented in just four weeks and deliver ROI within 12 weeks, Stardox integrates with supply chain systems to transform semi-structured data from APIs and EDIs and unstructured data from email inboxes into structured data, generating 20% more annual revenue.

?Learn more at www.stargo.co .