Four Key Areas Where AI Can Be Used to Increase Supply Chain Resiliency

Artificial Intelligence (AI) has been gaining significant attention recently due to its potential to transform various industries, and Supply Chain is no exception. Supply Chain professionals are increasingly recognizing the benefits of using AI to enhance their operations and build resilience against disruptions.

To that end let's explore key four areas where AI can be used in Supply Chains to improve resiliency. This is merely a suggested starting point, when an organization decides to start using AI I recommend that it is best to start small and use it as a pilot in a stand-alone area first. When the organizations get fully used to AI and are at the point where they are comfortable with it, only then should it be scaled up and incrementally rolled out across the entire end-to-end Supply Chain network.

That being said, AI can be used in many areas across the entire end-to-end Supply Chain, not necessarily just to increase the organization's Supply Chain resiliency.

Enhanced Risk Management

AI can help identify potential risks within the supply chain and provide early warnings to mitigate them before they become major issues. According to a study by McKinsey, AI-enabled risk management systems can reduce Supply Chain disruptions by up to 50%.

Improved Forecasting Accuracy

AI algorithms can analyze vast amounts of historical and real-time data to provide more accurate demand forecasts, enabling better planning and decision-making. A study by IBM found that AI-powered forecasting can reduce forecast error by up to 50%, leading to improved inventory management and reduced waste.

Streamlined Logistics Operations

AI can optimize logistics operations by identifying the most efficient routes, scheduling deliveries, and tracking shipments in real-time. According to a report by Gartner, AI-enabled logistics can reduce transportation costs by up to 15% and improve delivery times by up to 30%.

Increased Visibility & Transparency

AI can provide end-to-end visibility across the Supply Chain, enabling greater transparency and traceability. A survey by Deloitte found that 79% of companies believe that AI will provide greater Supply Chain visibility, leading to improved compliance and reduced fraud.

Improved Quality Control

AI-powered quality control systems can automatically inspect products and detect defects in real time, reducing the need for manual inspections and improving product quality. According to a report by ResearchAndMarkets, AI-powered quality control can reduce defect rates by up to 90%.

Enhanced Collaboration

AI can facilitate better collaboration between departments and stakeholders by providing a single source of truth and enabling real-time communication. A study by MIT found that AI-enabled collaboration can reduce Supply Chain delays by up to 60%.

Reduced Costs

AI can help reduce costs throughout the supply chain by optimizing resources, streamlining processes, and improving efficiency. According to a report by Accenture, AI-powered Supply Chains can reduce costs by up to 20%.

Competitive Advantage

Organizations that leverage AI in their Supply Chains can gain a competitive advantage by being able to react quicker to changing market conditions and customer preferences. A survey by Harvard Business Review found that 85% of executives believe that AI will enable their companies to obtain a competitive advantage.

Supply Chain predictive analytics and forecasting increase Supply Chain resilience by providing advanced warning of potential disruptions, enabling proactive mitigation strategies, and improving inventory management. These tools allow organizations to anticipate and prepare for changes in demand, Supply Chain disruptions, and other potential risks, thereby reducing the likelihood and impact of disruptions.

With increased visibility and insight into the Supply Chain, businesses can make informed decisions to not only optimize their Supply Chain operations but strengthen their resilience against unexpected events, as well as reduce or negate their potential impact.

Predictive analytics and forecasting are critical components of Supply Chain management. Accurate demand forecasts enable businesses to plan production, inventory management, and logistics more effectively. AI algorithms can analyze historical data, seasonality, and external factors such as weather patterns, economic indicators, and social media trends to generate highly accurate predictions.

By leveraging machine learning techniques, AI systems can continuously learn from new data and adapt to changing market conditions. This enables businesses to respond quickly to fluctuations in demand and minimize stockouts or overstocking situations.

Example # 1 - Walmart

The multinational retail corporation uses an AI-powered forecasting system to predict sales figures for individual products at each store. The system analyzes data from various sources, including point-of-sale transactions, online searches, and weather patterns. By improving forecast accuracy, Walmart has reduced inventory costs and increased on-shelf availability using AI.

Example # 2 - Zara

The Spanish fast-fashion retailer uses AI-powered predictive analytics to forecast demand for its products. The company's AI system analyzes historical sales data, seasonality, and external factors such as weather and cultural events to accurately predict demand for different product categories. Based on the forecast, Zara adjusts its inventory levels and production plans to minimize stockouts and overstocking.

Supply Chain optimization increases Supply Chain resilience by identifying and mitigating potential risks, improving forecasting accuracy, streamlining logistics operations, and enhancing collaboration between stakeholders.

This enables organizations to respond quickly to disruptions, maintain product flow, and minimize the impact of unexpected events. Also, optimized Supply Chains are better equipped to adapt to changing market conditions and customer preferences, allowing businesses to stay competitive and agile in the face of uncertainty.

Supply Chain optimization involves identifying the most efficient way to source, produce, and deliver products while minimizing costs and maximizing service levels. AI algorithms can help optimize various aspects of how Supply Chains are managed, such as route planning, scheduling, and network design.

For instance, AI can determine the best transportation routes based on real-time traffic updates, vehicle capacities, and delivery time windows. AI can be used to assist with strategic decision-making like selecting the optimal location for warehouses, fulfillment and distribution centers.

Example # 1 - Amazon

The e-commerce giant utilizes AI to optimize its vast logistics network. The company's algorithm takes into account factors like package dimensions, weight, delivery speed, and route density to choose the most suitable delivery method for each shipment. By optimizing its logistics operations, Amazon has reduced shipping times and costs, leading to higher customer satisfaction and loyalty.

Example # 2 - Nike

The US-based sportswear manufacturer uses AI to optimize its global Supply Chain. The company's AI system analyzes data from various sources, including supplier performance, transportation costs, and customs clearance times, to identify bottlenecks and inefficiencies. Based on the insights generated, Nike adjusts its Supply Chain configuration and, if needed selects alternative suppliers to meet predetermined schedules. They also use AI to identify and implement cost-saving measures, reduce lead times, and lower logistics costs.

Supply Chain cybersecurity measures can increase supply chain resilience by protecting against cyber-attacks that could disrupt or compromise the integrity of the supply chain. These measures include implementing secure protocols for data exchange, authenticating transactions, and monitoring for suspicious activity.

By preventing unauthorized access and manipulation of Supply Chain data, organizations can ensure the accuracy and reliability of their Supply Chain operations, reducing the risk of disruptions and improving overall resilience.

As Supply Chains become increasingly digitalized, cybersecurity is becoming a growing concern. Cyber-attacks can lead to significant disruptions, data breaches, and financial losses. AI-powered tools can help detect and prevent cyber threats by analyzing network traffic, identifying anomalies, and responding to incidents promptly.

AI-driven systems can also monitor for vulnerabilities in software and hardware, enabling organizations to patch them before they can be exploited.

Example # 1 - Maersk

The global container shipping company, fell victim to the NotPetya cyber-attack in 2017, which caused extensive damage to its IT systems. To strengthen its cybersecurity posture, Maersk implemented an AI-based solution that monitors its networks for suspicious activity and provides real-time threat intelligence. The system has helped Maersk detect and mitigate several potential cyber threats, reducing the risk of future attacks.

Example # 2 - Procter & Gamble

The US-based consumer goods company deploys AI-powered cyber security solutions to protect its supply chain from cyber threats. The company's AI system monitors network traffic, identifies unusual patterns, and detects potential security breaches in real-time. When a potential threat is detected, the AI system alerts the security team, who can then take immediate action to contain the threat and prevent any further damage.

The use of AI-powered natural language processing (NLP) can improve a Supply Chain's resilience by enabling organizations to extract valuable insights from unstructured data, such as emails, texts, and social media posts, to identify potential disruptions and take proactive measures to mitigate risks. NLP can also help organizations detect sentiment and identify early warnings of Supply Chain disruptions, allowing them to respond quickly and effectively to minimize the impact of disruptions.

NLP can assist in automating routine tasks and providing real-time updates. This enables Supply Chain professionals to focus on high-priority tasks and make data-driven decisions to ensure the smoother operation of the Supply Chain, especially during particularly volatile or uncertain market conditions.

Using AI-driven NLP can serve as a type of early warning system and improve preparedness, which is especially useful during particularly volatile regional geo-political unfolding events, that could quickly turn into a major Supply Chain disruption.

Example # 1 - Coca-Cola

The multinational beverage corporation uses NLP to analyze customer feedback and identify potential issues in their supply chain. By analyzing text data from customer complaints and feedback, Coca-Cola's NLP algorithms can identify patterns and trends that indicate problems with their products or packaging. This allows Coca-Cola to take proactive measures to address these issues and improve the quality of their products.

A good example is, Coca-Cola's NLP algorithms detected a pattern in customer feedback indicating that some customers were experiencing issues with the sealing of their plastic bottles. Based on this analysis, Coca-Cola identified the root cause of the issue and implemented changes to their manufacturing process to improve the sealing of their bottles.

Example # 2 - Intel

Intel, a technology company, uses NLP to analyze sensor data and identify potential issues in their Supply Chain. By analyzing text data from sensors embedded in their products, Intel's NLP algorithms can identify patterns and anomalies that indicate potential issues with their products or manufacturing process. This allows Intel to take proactive measures to address these issues and improve the quality of its products.

Another example is where Intel's NLP algorithms detected a pattern in sensor data indicating that some of their products were experiencing unusual temperature fluctuations during transportation. Based on this analysis, Intel identified the root cause of the issue and implemented changes to its transportation process to reduce the risk of temperature fluctuations.

When it comes to AI implementation, the steps are fairly common for all, here’s an abbreviated run-down of them;

Change Management

o??? Communicate the benefits and limitations of AI adoption to stakeholders.

o??? Prepare a change management plan to address potential resistance or disruption to existing processes and roles.

o??? Train employees on the use and implications of AI in managing AI-driven Supply Chains.

Ethical Considerations

o??? Ensure that the AI system is fair, transparent, and unbiased.

o??? Address privacy concerns related to data collection and usage.

o??? Comply with relevant regulations and laws regarding AI adoption.

Data Collection

Collect large amounts of data related to your Supply Chain operations, including but not limited to;

o??? Historical sales data

o??? Seasonality data

o??? Product information

o??? Supplier data

o??? Transportation data

o??? Inventory data

o??? Customer data

Ensure that the data is clean, accurate, and consistent. (For further reading you can read my article; “DATA LAKES: From a Supply Chain Perspective”, here.)

Data Preparation

o??? Preprocess the data to make it suitable for AI model training.

o??? Normalize the data and transform it into a format that can be easily consumed by AI algorithms.

o??? Remove any irrelevant or redundant data.

Model Selection and Training

o??? Choose an appropriate AI algorithm for your specific use case, such as machine learning, deep learning, natural language processing, or computer vision.

o??? Train the selected AI model using the prepared data.

o??? Validate the performance of the trained model using various metrics and testing methods.

Performance Evaluation

o??? Establish key performance indicators (KPIs) to measure the success of AI adoption.

o??? Regularly evaluate the effectiveness and efficiency of AI-driven Supply Chains.

o??? Adjust AI models and strategies based on performance feedback and evolving business needs.

Model Deployment

o??? Deploy the trained AI model in a production environment.

o??? Integrate the AI model with existing Supply Chain systems and processes.

o??? Monitor the performance of the AI model in real-world scenarios.

Model Maintenance and Updates

o??? Continuously monitor the performance of the AI model and update it with new data to ensure that it remains accurate and effective.

o??? Address any issues or errors that arise during deployment.

o??? Iterate and refine the AI model over time to improve its performance and applicability.

Continuous Improvement

o??? Encourage a culture of innovation and experimentation.

o??? Foster partnerships with AI research institutions and startups.

o??? Stay updated on emerging technologies and trends in AI and Supply Chain Management.

o??? Continuously assess and improve AI adoption to achieve long-term success.

Artificial intelligence (AI) will over time revolutionize Supply Chains and Supply Chain Management it’s hard to imagine where it couldn’t be used in Supply Chains. AI can not only enhance Supply Chain resilience, but also efficiency, and customer satisfaction.

By utilizing AI-powered solutions, businesses can improve predictive analytics and forecasting, optimize supply chain processes, reinforce supply chain cybersecurity, and use NLP to warn organizations of potential disruptions. If all these are used in concert it provides many, many future-proofed competitive advantages.

However, the adoption of AI technology comes with its unique set of obstacles, including concerns regarding data privacy and security, the necessity for specialized talent, and substantial investments in infrastructure and training. Also, AI models can perpetuate biases present in the training data, resulting in unfair outcomes. To overcome these challenges and fully capitalize on the potential of AI in Supply Chains, organizations must develop a comprehensive strategy that aligns with their business objectives and values.

This needs to involve investing in data infrastructure, assembling diverse teams with the required skills, up-skilling and re-skilling existing Supply Chain key stakeholders and employees, and of course, promoting collaboration between humans and machines. Continuous monitoring and evaluation of AI performance are also indispensable to identify areas for improvement and uphold ethical standards.

There is no doubt that the integration of AI in Supply Chains can create resiliency, as well as more agile, adaptable, and responsible Supply Chains.

By embracing AI, businesses can construct resilient and sustainable Supply Chains that can efficiently respond to disruptions and cater to the demands of an ever-changing world. Along with resiliency, the judicious deployment of AI can lead to increased efficiency, decreased costs, and enhanced customer satisfaction, making it a vital tool for businesses seeking to thrive in today’s increasingly dynamic and competitive marketplace.

[And, if you need a remote Supply Chain specialist, Subject Matter Expert, Advisor, Consultant, Project Manager or know someone that does, please feel free to connect & message me directly on LinkedIn.]