Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing the field of supply chain management, offering new and innovative ways to optimize processes, reduce waste, and improve efficiency.
These cutting-edge technologies use advanced algorithms to analyze large amounts of data, identify patterns, and make predictions about future trends, helping companies make informed decisions and stay ahead of the competition.
Whether it’s predicting consumer demand, managing inventory, or tracking shipments, AI and ML are providing businesses with the tools they need to succeed in a rapidly changing marketplace.
As these technologies continue to evolve, they have the potential to transform supply chain management, making it more efficient, accurate, and sustainable than ever before.
In this article, we’ll explore how AI and ML are changing the face of supply chain management, the benefits they offer, and the key inputs required to get the most out of these technologies.
AI Vs. ML
AI refers to the development of computer systems that can perform tasks that would normally require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. ML, on the other hand, refers to the ability of AI systems to learn from data and improve their performance over time without being explicitly programmed.
Putting AI into Practice
In the supply chain, AI and ML are being used to automate and optimize a range of processes, from demand forecasting and inventory management to transportation and logistics. For example, AI-powered systems can analyze large amounts of data in real time, providing insights into consumer demand patterns, supplier performance, and potential supply chain disruptions. This information can then be used to make informed decisions, reduce waste, and improve overall efficiency.
ML algorithms can also be used to predict potential problems in the supply chain, such as late deliveries or stockouts, allowing companies to proactively address these issues before they occur. Additionally, AI and ML can be used to improve supply chain visibility, track products from origin to delivery, and ensure that all stakeholders have access to real-time information.
Getting Started with AI & ML
Innovation and cutting-edge technology are always an incredibly attractive concepts and many companies are starting to get excited about the potential opportunities this brings. However, these new AI and ML platforms rely on a significant amount of data input in the first instance to be able to be used effectively. To get started with Artificial Intelligence (AI) in the supply chain, a variety of inputs are required, including:
Data
AI systems require large amounts of data to be trained and produce accurate results. This data can come from a variety of sources, such as sales data, supplier performance data, shipment data, and customer data. It’s important to ensure that the data is accurate, up-to-date, and relevant to the specific use case.
Algorithms
AI algorithms are used to process the data and provide insights. A wide range of algorithms are available, including supervised learning algorithms, unsupervised learning algorithms, and reinforcement learning algorithms. The choice of algorithm will depend on the specific use case and the type of data being analyzed.
Infrastructure
To use AI in the supply chain, a robust computing infrastructure is required, including powerful servers and high-speed networks. This infrastructure must be able to handle the large amounts of data that AI systems generate and provide the processing power required to run complex algorithms.
Human Expertise
While AI systems can automate many tasks, they still require human expertise to set up, maintain, and interpret the results. This includes expertise in data science, machine learning, and supply chain management. It’s important to have a team of experts in place who can work together to achieve the desired outcomes.
Integration with existing systems
AI systems must be integrated with existing supply chain management systems, such as enterprise resource planning (ERP) systems and transportation management systems (TMS). This integration ensures that the data generated by AI systems is actionable and can be used to make informed decisions.
These are some of the key inputs that are required for the effective use of AI in the supply chain. By carefully considering these inputs, forward-thinking companies can ensure that their AI systems deliver value and provide a competitive edge.
Mainstream Awareness, Minimal Adoption
In 2023, Artificial Intelligence (AI) hit the consciousness as people become more familiar with its applications in various fields, industries and disciplines, including supply chain management. The exponential growth in data and computing power has enabled AI systems to become more sophisticated and accurate, providing businesses with the tools they need to stay ahead of the competition.
The integration of AI in chat applications, specifically through language models like GPT-3, has made it more accessible to people and allowed them to experience its capabilities firsthand. This has helped to bring AI into the mainstream consciousness as more people have become aware of its potential and how it can be used in their daily lives.
Additionally, the media has also played a role in raising awareness about AI and its potential, as well as its limitations. The coverage of AI in popular news outlets and technology websites has helped to educate the public and increase their understanding of the technology.
The increased availability and accessibility of AI technology, combined with greater public awareness and understanding, has helped to make AI a mainstream topic and a staple in modern discussions about technology and its role in our lives.
AI-powered solutions are now becoming more commonly used for a range of tasks in the supply chain, from demand forecasting and inventory management to transportation and logistics. Companies are seeing real benefits from using AI, including increased efficiency, reduced waste, and improved accuracy, however, adoption is still in its infancy as companies struggle to realise the potential of AI and ML due to struggling with supplier data in the first instance with which to train these systems.
Investment in AI technology is increasing, and it is becoming a crucial component of any successful supply chain strategy. With the continued evolution of AI technology, its use will likely become even more widespread in the years to come.
If you’re looking to embark on the voyage to the future of supply chain management, speak to one of our supply chain experts today. We’d love to discuss how SourceDogg’s supplier data platform, combined with our ability to connect data sources and tie in supplier relationship and performance management can be enhanced through the use of AI and ML platforms.
