Global organizations want more automation within their supply chain to tackle issues like cost escalation and demand volatility. Generative AI has emerged as a transformative force with the potential to revolutionize the global supply chain, including the power to serve as a co-pilot to help operations and customer service organizations do their job better, faster and reducing the impact of turnover.
Let’s explore a few use cases for generative AI’s impact on the supply chain.
Use Case #1: Autogenerating Customs Documents and Other Logistics Documents
Generative AI can be used to autogenerate customs documents and other logistics documents through a process known as natural language generation (NLG). NLG is a subfield of AI that focuses on generating human-like text based on given input or data. The AI system needs to be trained on a large dataset of existing customs documents, including different types of forms, declarations, and regulations. This training data helps the AI model learn the patterns, structures, and specific language used in these documents. All documents need to be within compliance, and it is important they are exact when generated. Generative AI ensures compliance across all documents it generates.
Use Case #2: Copilot for Control Tower Users to Optimize Disruption Outcomes
By analyzing data, simulating scenarios, and providing recommendations, generative AI can help mitigate the impact of disruptions. They can process large volumes of data from various sources such as historical supply chain data, real-time sensor data, market trends, weather conditions to identify patterns, correlations, and potential causes of disruptions.
Predictive models can forecast potential disruptions based on historical data patterns and other factors. For example, the system might identify that certain weather conditions often lead to delays in shipments or that certain suppliers are more prone to delivery issues. It can also simulate different scenarios by considering various factors and parameters, such as a supplier delay, a transportation breakdown, or increase in demand. Copilot also has the ability to learn from human input, as well, which is key in its predictive modeling.
It can also suggest optimized solutions and recommendations to mitigate disruptions, such as alternative sourcing options, identify backup suppliers, propose alternative transportation routes, suggest inventory reallocation strategies, or adjust production schedules. The AI system can optimize these recommendations by considering cost, time, capacity constraints, and other relevant factors. However, that is only beneficial if there is sufficient visibility and monitoring. Generative AI can provide timely alerts and suggest adaptive measures to minimize disruptions.
Use Case #3: Autogenerate Standards
Standards allow for the fast movement of items through supply chains and organizations’ efficient inventory and transaction tracking. Generative AI can autogenerate these standards for increased efficiency.
Using the identified best practices and insights, the system can generate standards for various aspects of the supply chain. It can create guidelines, protocols, and procedures that define how different operations should be conducted to achieve desired outcomes. These standards can cover areas such as procurement, inventory management, production, logistics, quality control, and sustainability, among others.
Challenges and Ethical Considerations
Privacy and data security must be prioritized as generative AI relies heavily on large datasets. Organizations must ensure compliance with regulations and establish robust data protection measures to safeguard sensitive information. This will also help limit hallucinations produced by generative AI.
There is a need for transparency and accountability when using generative AI algorithms. Human oversight is crucial to ensure that AI-generated designs and solutions align with ethical, legal, and societal standards.
Takeaways
It is important that human decision-makers and supply chain experts play a crucial role in evaluating and implementing the suggested actions of generative AI. They bring their expertise, contextual knowledge, and judgment to make informed decisions based on the AI-generated insights and recommendations.
Pervinder Johar is CEO at Blume Global.
Generative AI’s Impact On The Supply Chain (3 Use Cases)
Global organizations want more automation within their supply chain to tackle issues like cost escalation and demand volatility. Generative AI has emerged as a transformative force with the potential to revolutionize the global supply chain, including the power to serve as a co-pilot to help operations and customer service organizations do their job better, faster and reducing the impact of turnover.
Let’s explore a few use cases for generative AI’s impact on the supply chain.
Use Case #1: Autogenerating Customs Documents and Other Logistics Documents
Generative AI can be used to autogenerate customs documents and other logistics documents through a process known as natural language generation (NLG). NLG is a subfield of AI that focuses on generating human-like text based on given input or data. The AI system needs to be trained on a large dataset of existing customs documents, including different types of forms, declarations, and regulations. This training data helps the AI model learn the patterns, structures, and specific language used in these documents. All documents need to be within compliance, and it is important they are exact when generated. Generative AI ensures compliance across all documents it generates.
Use Case #2: Copilot for Control Tower Users to Optimize Disruption Outcomes
By analyzing data, simulating scenarios, and providing recommendations, generative AI can help mitigate the impact of disruptions. They can process large volumes of data from various sources such as historical supply chain data, real-time sensor data, market trends, weather conditions to identify patterns, correlations, and potential causes of disruptions.
Predictive models can forecast potential disruptions based on historical data patterns and other factors. For example, the system might identify that certain weather conditions often lead to delays in shipments or that certain suppliers are more prone to delivery issues. It can also simulate different scenarios by considering various factors and parameters, such as a supplier delay, a transportation breakdown, or increase in demand. Copilot also has the ability to learn from human input, as well, which is key in its predictive modeling.
It can also suggest optimized solutions and recommendations to mitigate disruptions, such as alternative sourcing options, identify backup suppliers, propose alternative transportation routes, suggest inventory reallocation strategies, or adjust production schedules. The AI system can optimize these recommendations by considering cost, time, capacity constraints, and other relevant factors. However, that is only beneficial if there is sufficient visibility and monitoring. Generative AI can provide timely alerts and suggest adaptive measures to minimize disruptions.
Use Case #3: Autogenerate Standards
Standards allow for the fast movement of items through supply chains and organizations’ efficient inventory and transaction tracking. Generative AI can autogenerate these standards for increased efficiency.
Using the identified best practices and insights, the system can generate standards for various aspects of the supply chain. It can create guidelines, protocols, and procedures that define how different operations should be conducted to achieve desired outcomes. These standards can cover areas such as procurement, inventory management, production, logistics, quality control, and sustainability, among others.
Challenges and Ethical Considerations
Privacy and data security must be prioritized as generative AI relies heavily on large datasets. Organizations must ensure compliance with regulations and establish robust data protection measures to safeguard sensitive information. This will also help limit hallucinations produced by generative AI.
There is a need for transparency and accountability when using generative AI algorithms. Human oversight is crucial to ensure that AI-generated designs and solutions align with ethical, legal, and societal standards.
Takeaways
It is important that human decision-makers and supply chain experts play a crucial role in evaluating and implementing the suggested actions of generative AI. They bring their expertise, contextual knowledge, and judgment to make informed decisions based on the AI-generated insights and recommendations.
Pervinder Johar is CEO at Blume Global.
TAGS
Subscribe to Our YouTube Channel
TOPICS
Categories
Subscribe to Our Podcast
TRENDING POSTS
Sponsors