Asking ChatGPT about the role of ChatGPT with supply chains
By Michael Lev for MxD
The artificial-intelligence breakthrough everyone’s talking about is ChatGPT, the intuitive, conversational, all-knowing chatbot. What are the implications for AI chatbots on supply chain management? I decided to interview an expert—and who better than ChatGPT?
It was a good conversation (I guess you’d call it a conversation). The chatbot answered my questions in detail nearly instantaneously. Spoiler alert: Despite fears, AI chatbots will not render humanity, including supply chain executives, obsolete. At least not immediately.
As a writer, I found ChatGPT to be a better, faster search engine than Google, but I still need to do my own reporting, which includes interviewing humans. Here are edited excerpts:
Michael: How can AI chat help supply chain management?
ChatGPT: AI chat can help supply chain management in several ways:
1. Customer service: AI chatbots can provide 24/7 customer service to answer any inquiries related to the supply chain. This can help reduce the workload on human customer service representatives and increase customer satisfaction.
2. Inventory management: AI chatbots can monitor inventory levels and automatically reorder products. This can help reduce stockouts and improve supply chain efficiency.
3. Predictive maintenance: AI chatbots can monitor equipment and predict when maintenance is needed.
4. Supplier management: AI chatbots can help manage supplier relationships by monitoring supplier performance and suggesting alternative suppliers when necessary.
5. Demand forecasting: AI chatbots can analyze historical sales data and other factors to forecast demand, helping to optimize inventory levels and reduce waste. Overall, AI chat can help supply chain management by providing real-time insights and automated processes that improve efficiency, reduce costs, and enhance customer satisfaction.
Michael: How does the chatbot analyze data?
ChatGPT: AI chatbots can analyze data on potential suppliers based on a set of criteria such as cost, quality and delivery times. The chatbot can evaluate the data using machine-learning algorithms and natural-language processing to identify patterns and trends.
For example, the chatbot can analyze data on the cost of products or services offered by each supplier, including any discounts or incentives they may offer. It can also analyze data on quality metrics such as defect rates or customer satisfaction ratings. Delivery times and lead times can also be evaluated.
The chatbot can then provide recommendations based on its analysis, such as which supplier offers the best combination of cost, quality and delivery times. It can also provide a detailed explanation of why one supplier is recommended over another.
Michael: Can you give a real-life example of this type of recommendation?
ChatGPT: Sure, here’s a real-life example of how an AI chatbot could provide supplier recommendations based on a set of criteria:
Suppose a supply chain manager for a company that manufactures electronic devices needs to choose a supplier for a specific component. The manager has a list of potential suppliers and wants to choose the one that offers the best combination of cost, quality and delivery times.
The AI chatbot can analyze data on each supplier’s cost, quality, and delivery times by scraping their websites, checking their ratings on review sites, and looking at their historical performance records.
Based on this analysis, the chatbot might recommend Supplier A because they offer the lowest cost, high quality, and quick delivery times. The chatbot can also provide a detailed explanation of why Supplier A is recommended over other potential suppliers, highlighting specific factors such as their cost competitiveness, quality track record, and speedy delivery times.
Alternatively, the chatbot might recommend Supplier B because they offer the highest quality components, even though they are slightly more expensive and have longer delivery times.
Michael: Should I really trust all this secondhand data? Shouldn’t the chatbot recommend that I follow up by interviewing a representative from each company?
ChatGPT: While the AI chatbot can provide valuable insights and recommendations based on data analysis, it’s always a good idea to verify the information and follow up with representatives from the potential suppliers.
The chatbot’s recommendations are based on publicly available data such as supplier websites, ratings on review sites, and historical-performance records. This information can provide a useful starting point for supplier evaluation, but it may not provide a complete picture of each supplier’s capabilities, offerings, or business practices.
By following up with representatives from the potential suppliers, the supply chain manager can get a more complete understanding of each supplier’s strengths and weaknesses, as well as any specific requirements or preferences they may have. This can help the supply chain manager make a more informed decision about which supplier to choose.
The AI chatbot can still provide value in this process by assisting with supplier communication and helping to identify any potential issues or concerns that may arise during the follow-up process. The chatbot can also provide insights into supplier performance and track any changes or updates to the data it has analyzed.
Overall, while the AI chatbot can provide valuable recommendations based on data analysis, it’s important to follow up with representatives from the potential suppliers to ensure that all relevant information is taken into account before making a final decision.
This first appeared in Issue 33 of ChainMail here.
