As global markets grow increasingly complex, supply chain management faces unprecedented challenges. The need for agility, efficiency, and risk mitigation has become more vital than ever. In this era of disruption, supply chain professionals are turning to innovative solutions to navigate the complexities of the modern business landscape.

From predictive analytics to autonomous systems, AI and automation have found their way into virtually every industry, transforming various aspects of the supply chain ecosystem. These advanced technologies offer immense potential for mitigating disruptions and driving resilience. In this article, we will delve into the application of AI and automation in supply chain management, using the Panama Canal as a compelling case study.

Understanding supply chain risk

Before we dive into the role of AI in managing disruptions, we first need to understand supply chain risk.

Supply chain management involves a complex web of interconnected processes, stakeholders, and activities that collectively enable the delivery of products or services to end customers. However, this intricate system is not without its risks. To fully comprehend the challenges of managing supply chain disruptions, it is crucial to delve into the intricacies of supply chain risk and its far-reaching impacts.

The supply chain encompasses various entities, including suppliers, manufacturers, distributors, and retailers, each playing a vital role in the smooth flow of goods and services. From sourcing raw materials to delivering finished products, every touchpoint introduces potential vulnerabilities and uncertainties. Any disruption within this delicate ecosystem can have profound consequences.

With so many touches, a single disruption can instantly send shock waves up and down the supply chain. Service suffers, then customer satisfaction, and finally, the bottom line.

Supply Chain Disruption: The Panama Canal

The recent disruption in the Panama Canal due to a severe drought serves as a stark example of supply chain risks and their potential impact on global trade. The backlog of ships waiting to cross the canal has created a bottleneck that appears to only be getting worse. By mid November, the number of ships waiting to cross the canal increased by a staggering 13% in just 24 hours. To make matters even more challenging, ongoing protests in Panama have disrupted operations, leading to irregularities and blockades. Distributors, manufacturers, and retailers are likely to experience far-reaching consequences well into 2024.

The Panama Canal is a critical trade route connecting Asia with other parts of the world, and any disruption in its operations reverberates throughout the entire supply chain. With nearly 40 percent of all U.S. container traffic passing through the canal, the impact is substantial. The backlog not only affects inbound container movement from Asia but also outbound shipments to other regions, leading to inventory shortages and increased pricing pressures.

Inventory management is one of the key areas directly impacted by this backlog, especially during a period when companies should have been ramping up their inventories in anticipation peak season. However, with delays in the Panama Canal, achieving optimal inventory levels becomes a challenge. The disruptions cause a ripple effect, commonly known as the bullwhip effect, magnifying the impact throughout the supply chain, affecting distributors, manufacturers, and retailers alike. As a result, businesses are grappling with inventory shortages, longer delivery times, and the need to adjust pricing strategies to navigate these challenging circumstances.

Risk events & possible outcomes:

This is just one example, but managing supply chain risks requires careful consideration of various factors. External events like natural disasters, geopolitical changes, economic fluctuations, and regulatory shifts can all introduce disruptions and uncertainty. Additionally, internal factors such as capacity constraints, supplier issues, and quality control problems can further complicate the supply chain landscape. Supply chain leaders should be aware of the risk events, and possible outcomes:

1. Delays. Producers do not get the parts and materials they need on time. And this can leave customers waiting for finished goods.
2. Shortages. A lack of parts, materials or components can lead to production stops. Enterprises or entire industries can be forced to shut down.
3. Higher prices. Higher prices for gasoline and fuel squeeze profitability. And price increases for transported goods also drive inflation.
4. Loss in revenue and job cuts. If manufacturers can’t deliver the goods, revenues are reduced. Loss of profitability can lead to layoffs.
5. Contract fines and damage to reputation. Enterprises can face penalties for late delivery or non-fulfillment of contracts. Business partners or consumers can lose trust. 

What is the lesson to be learned from the 2023 Panama Canal bottleneck?

Every link in the supply chain faces its own set of risks and challenges. It’s easy to see how delivering products on time can be an uphill battle for stakeholders, and if you think about it, given the unpredictable, dynamic market we are operating in today, it’s a remarkable feat the industry can manufacture and transport anything.

That’s where the importance of logistics becomes crystal clear. To ensure that our parts and products have the best chance of making it to their destinations on time and in good condition, companies are turning to supply chain predictive analytics. Supply chain leaders crave as much relevant data as they can get their hands on. Gone are the days when checking a few weather reports and choosing the usual mode of transportation were enough. We have stepped up our game by using sophisticated models that revolutionize risk assessments in supply chains, helping us navigate the complexities of the modern business landscape.

AI-Driven Modelling for Risk Assessment

Today’s supply chain predictive analytics use sophisticated technology that improves the accuracy and speed of logistics predictions so leaders can make data-driven decisions with greater confidence. By analyzing vast amounts of historical and real-time data, AI algorithms can generate actionable insights for decision-making. Predictive analytics empower supply chain professionals to anticipate demand fluctuations, optimize inventory levels, and proactively identify potential bottlenecks or disruptions. This data-driven approach enables them to proactively address issues, adjust strategies, and make informed decisions to ensure the smooth flow of goods.

How AI Models Work

Utilizing machine learning algorithms, AI models employ simulations to predict the likelihood and impact of various risks. This is also known as Digital twins.

A digital supply chain twin, as defined by Gartner, is a “digital representation of the physical supply chain that can be used to create plans and make decisions.”

Just like the examples provided earlier, embracing the concept of automation and AI influences how supply chain leaders manage their resources and make decisions that align perfectly with corporate objectives. Moving forward, we’ll refer to this technology as digital twins, as it brings together the power of AI and automation within the context of supply chain decision-making. By assigning probability distributions and running digital twins, businesses gain a probability-weighted view of financial and service level impacts, allowing them to effectively manage risk exposure and make informed decisions.

Weather Forecasting and Risk Mitigation

In the case of the Panama Canal disruptions, ships had to redirect to unload, in some cases, as much as 45% of their cargo to wait to traverse the canal. That has a huge delivery time and potential total cost implication.

Panama’s climate-related issues began months before ships started backing up at the entrances to the canal. In April this year, the Panama Canal Authority imposed draft restrictions on the largest ships that use the canal; the restrictions were necessitated by falling water levels in nearby lakes.

Information like this should put supply chain leaders on alert. digital twins take this information, alongside weather data, shipping routes and identify potential weather-related disruptions. By considering historical weather patterns, current conditions, and predicted future trends, businesses can proactively adjust shipping schedules, reroute shipments, or consider alternative transportation modes to minimize the impact of delays caused by adverse weather conditions. Real-time monitoring and tracking systems can further enhance visibility, allowing companies to identify potential bottlenecks and take preventive measures promptly.

Capacity Planning and Optimization

Another level that supply chain managers need to consider is how, when, and where they deploy inventory. Managers should pay attention to shifting demand patterns to ensure they have the appropriate level of inventory to match customer demand. Digital twins can also optimize capacity planning in the supply chain. By monitoring historical data on traffic volume, vessel sizes, transit times, and other relevant factors, companies can forecast future demand and allocate resources effectively. This proactive approach enables businesses to plan for peak periods, adjust shipping schedules accordingly, and reduce congestion at critical transportation hubs like the Panama Canal. By optimizing capacity utilization, identifying potential choke points in their supply chain, companies can minimize delays and ensure timely delivery of goods.

Supplier Risk Management

Another component to consider is supplier risk management. Automation and predictive analytics streamline supplier evaluation and monitoring by integrating external data sources. This enables companies to analyze suppliers’ financial stability, performance history, and geographic risks efficiently. With predictive analytics, high-risk suppliers can be identified promptly, allowing businesses to take proactive measures such as diversifying the supplier base, implementing contingency plans, and conducting regular risk assessments. This not only ensures a resilient supply chain but also mitigates potential disruptions.

Scenario Modeling and Simulation

Digital twins allow for scenario modeling and simulation in the supply chain. By simulating different scenarios and assessing their potential impact, companies can evaluate the feasibility and risks associated with alternative routes, transportation modes, or sourcing strategies. For example, in the case of the Panama Canal delays, companies could have used scenario modeling to assess the impact of rerouting shipments through alternative ports, exploring the feasibility of air freight, or prioritizing certain products. This proactive approach allows businesses to make informed decisions and develop contingency plans to mitigate risks and reduce disruptions.

Real-Time Monitoring and Adaptability

Information is indeed a powerful tool, and successful businesses understand the significance of staying informed with current, precise, and thorough data. By harnessing the potential of Internet of Things (IoT) devices, sensors, and data streams, integrated systems gain access to a wealth of real-time data concerning different aspects of the supply chain. From monitoring transportation routes to tracking inventory levels and evaluating supplier performance, real-time monitoring ensure continuous visibility and prompt alerts when deviations from planned parameters occur.

How can you leverage this technology to improve your decision making?

To enhance the capabilities of digital twins and optimize supply chain decision-making, Microsoft offers a range of powerful solutions that integrate seamlessly with the discussed concepts.

• Microsoft Power Platform, equipped with AI-powered low-code tools, provides a valuable resource for retrieving information, automating notifications, and visualizing data through the intuitive Power BI platform. This technology allows anyone on your team to expand on and interact with digital twin data.
• Microsoft Fabric, recognized for its robust data analytics capabilities, serves as a comprehensive platform for hosting and managing this data. It can help you build predictive analytics models in a single analytics platform.

Learn more here:

Microsoft Tools for AI Modeling in Supply Chain Management

Don’t Wait for the Next Risk Event

Among today’s most nimble, competitive businesses are those that have mastered the management of supply chain risk. The challenges faced by suppliers that rely on the Panama Canal will soon be replaced by problems of a different nature. Whatever they may be, LIDD can help you make sure your supply chain solution is ready for them.

Whether it’s utilizing Power Platform’s low-code tools and Power BI for data visualization or leveraging Microsoft Fabric’s data analytics capabilities for building predictive models, we have the expertise to guide you in implementing the right solution to safeguard your operations and outperform your competitors.

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