Talking about artificial intelligence (AI) in supply chain management might seem, at first glance, to be a “futurist” topic — a prediction of what is to come. In actuality, AI in supply chain forecasting, planning and management is extensively used today, and has been for some time.
How so? When we talk about artificial intelligence, we’re talking about any system that responds in different ways based on feedback received during a process. Supply chain AI is seen in areas such as automated pickers, data analysis, even customer service modules. It’s important to note that these areas are only the beginning, and artificial intelligence in supply chain planning and management is just beginning to show its full potential.
AI and Machine Learning in Supply Chain
In addition to AI, we’ll be discussing machine learning in supply chain, a term that is sometimes used interchangeably with AI. Machine learning (ML) and AI should be viewed as two distinct concepts, though they share some overlap.
AI uses technology and feedback to increase the chances of success while performing a task. Machine learning is a form of AI which, in a nutshell, can draw its own conclusions and implement improvements to its programming based on feedback and data.
How Are AI and ML Currently Used in Supply Chain?
As mentioned above, AI and machine learning are already in use in supply chain processes. We’ll look at some of the ways in which they are currently implemented, as well as the potential of these processes.
- Coordinating deliveries: AI can drive communications between manufacturers and suppliers, coordinating delivery dates, times and quantities. Using historical data as well as warehouse and point-of-purchase information, these systems are able to facilitate efficient inventory management with much less human effort.
- Automated picking: Already in use in numerous warehouses, autonomous robots are able to locate, pick and deliver inventory to a fulfillment location on demand. RFID drives much of this technology, with more sophisticated location mapping increasing in use as well.
- Demand forecasting: Aside from delivery coordinating, AI also drives longer-term demand forecasting. Historical data combines with global trends and other factors to facilitate these critical decision-making processes.
- Customer relations: If you’ve ever encountered an automated “chatbot” upon visiting a website, you have worked with AI. These systems are able to assist customers with simple requests quickly and efficiently, allowing personnel to handle higher-priority problems.
The Future of AI and ML in Supply Chain
In addition to these current uses, the potential of AI and ML in supply chain is continually expanding. Applications in the future include:
- Even more accurate forecasting: As more data becomes available via connected devices, demand forecasting can be automated with more accuracy, over a greater span of time. Currently, demand forecasting generally focuses on big-picture mid- to long-term planning (typically monthly and annually). ML will enable more accurate forecasting, even down to the day-to-day level, making for unprecedented efficiencies in inventory management. At the same time, it will also be able to extend forecasting even further, potentially to five years and beyond.
- Autonomous delivery: The potential for autonomous vehicles in delivery and fulfillment is massive. With no rest time and near-perfect accuracy, the impact of these AI and ML-driven devices on supply chain planning and management will be vast, allowing for major improvements in fulfillment time.
- Supplier selection: Working with the wrong supplier can be risky to your supply chain and reputation. Late or missed deliveries can lead to production delays, and poor-quality materials will lower the value of your end product. AI and ML offer great potential for informed supplier selection: reviewing past performance, credit histories, even positive and negative coverage in the news, helping you to quickly make a more informed decision.
Pros and Cons of AI in Supply Chain
While AI and ML in the supply chain offer numerous benefits — many described above — they must be used the right way in order to be effective. Below, we’ll summarize those benefits and offer the necessary caveats. While these aren’t necessarily “cons” of AI, they are factors to consider when implementing a system.
- Increases inventory management efficiency
- More accurate forecasting
- Reduces human effort for delivery coordination
- Can handle basic customer service queries, freeing resources for higher-priority needs
- Reduces manual efforts such as picking and packing
- More informed supplier decisions
- Not a “solve-all” — implementations should be focused on specific issues and solutions
- Requires skilled personnel to manage inputs, feedback loops and outputs
- More advanced implementations require vast amounts of data from multiple streams
- If issues occur, the root can be opaque if a system’s process isn’t well-documented or understood
At Vigilant, we offer managed supply chain solutions that can help you gain the efficiencies of the best supply chain management — while taking the manual, time- and resource-intensive parts of it off your plate. For more information, contact us.