10 advantages, disadvantages of using AI in supply chain

5 advantages of using AI in supply chain

AI can help improve aspects of operations throughout the supply chain, from giving more insight into supply chain operations to speaking with customers. Learn more about how it can optimize tasks.

1 . Optimized operations

AI can help identify areas for improvement across the supply chain, which can help reduce inefficiencies.

AI can analyze supply chain processes and identify areas for improvement, which can reduce costs and improve the completion time for certain processes. For example, AI may be a better fit than a human employee for carrying out tasks like analyzing large amounts of data.

2. Increased visibility into operations

AI can provide insight into supply chain processes because of its availability to analyze data.

For example, AI can compile data from distributors, manufacturers and inventory warehouses as users upload the data and provide supply chain leaders with information such as available stock, prices of sourced materials and who is available for transporting goods.

This increased visibility can lead to a variety of improvements, including better delivery tracking, faster identification of bottlenecks and mitigation of supply chain disruptions.

AI's capabilities can also help improve supplier relationships, as everyone in the supply chain will receive information more quickly and the information will be more accurate, which will facilitate supplier communication.

3. More accurate forecasting

AI algorithms can compare data sets, identify trends and predict those trends' behavior in the future. This capability can help users better understand customer behavior and the future of their market.

For example, predictive analytics can sort through customer buying trends and identify the likely timing of a demand spike so supply chain leaders can plan accordingly.

4. Improved warehouse efficiency

AI can optimize warehouse efficiency in a couple of different ways.

AI can optimize warehouse aisles and floor layouts by analyzing shelf space and worker foot traffic, then suggesting improvements. It can also suggest the best inventory storage locations for avoiding traffic bottlenecks, which will help workers fulfill orders faster.

AI algorithms can also compare stock data and demand data to determine the amount of stock that a company must store. This analysis can help reduce under- and overstocking, helping improve company expenses and overall operations.

5. Improved customer experience

Benefits from AI, such as increased delivery visibility and increased stock availability, will help improve customer experience, and natural language processing's capabilities can potentially help improve customer service as well.

AI-enabled chatbots with NLP capabilities can answer customers' questions after business hours, when a customer support line may be closed, and chatbots can recommend products based on past customer purchases, which could be helpful for consumers.

5 disadvantages of using AI in supply chain

However, AI comes with some pitfalls as well. Here are several potential disadvantages of using AI for supply chain operations and how supply chain leaders can potentially mitigate some of them.

1. Upfront investment costs

Implementing AI is a major investment of time and money. Supply chain ecosystems are often complex, and partners across the supply chain may require specialized hardware to use AI.

AI can also require a significant amount of upkeep in the beginning as IT integrates applications and data sources and trains AI models, among other required tasks.

2. Employee training costs

Employees must possess the proper expertise to use AI or problems may result. Flawed inputs and data interpretations can cause inaccuracies across the supply chain, among other issues.

Because AI is evolving so rapidly, hiring employees who possess the required expertise in AI may be difficult. Training current employees to use AI is one alternative, but doing so will take time and money.

In addition, employees may also react negatively to the training because they are worried about AI replacing their jobs.

3. Difficult integrations with legacy systems

Legacy infrastructure may not be well-suited for an AI integration, resulting in technical issues.

To avoid these potential problems, supply chain leaders should work with other leaders at their company to decide how best to apply AI, how to pull in clean data from all relevant sources and whether a complete system overhaul is needed. The focus should be on improving data availability and quality while minimizing disruptions and considering how using AI will affect everyone involved in the supply chain, from company employees to suppliers to customers.

4. Privacy and security risks

AI's potential data security and privacy issues may put company data at risk in a variety of ways if organizational leaders do not consider potential problems before implementing the technology.

For example, customers may consent to provide their personal data to an organization for a specific purpose. Feeding that data to an AI application that uses the data in a way that customers did not consent to is a breach of customers' privacy. In addition, bad actors may target AI applications to try to obtain sensitive data.

Supply chain leaders must work with other company leaders to consider the best strategies for safeguarding against these problems. Potential steps include working with a data protection officer and setting rules around data usage and privacy rights.

5. Overreliance on AI

AI may negatively affect both employees' skill sets and customer experience.

A skills gap may result if employees use AI to carry out certain tasks and don't know how to do the tasks themselves. For example, if the AI technology becomes temporarily unavailable for some reason, employees may require training to complete certain aspects of their job.

In addition, some customers may become frustrated when interacting with a customer chatbot if the chatbot is unable to answer their questions or isn't functioning properly. The displeased customer may decide to purchase their item from another company because of their negative experience.

Jacob Roundy is a freelance writer and editor with more than a decade of experience with specializing in a variety of technology topics, such as data centers, business intelligence, AI/ML, climate change and sustainability. His writing focuses on demystifying tech, tracking trends in the industry, and providing practical guidance to IT leaders and administrators.