Companies are gradually focusing on meeting the needs of their customers more effectively. This includes detecting products that have been sold out and preferably re-stocking beforehand or knowing exactly what they do not have in stock to avoid disappointing customers.
Machine learning is being used to track the accuracy of different suppliers in reporting the products stocked in their inventory to improve customer satisfaction. Car dealerships are also using AI to forecast customer behavior patterns before stocking and advertising cars, as unmoved inventory can lead to losses. AI can also accurately predict whether a certain damaged product, such as a cracked windshield, needs to be repaired or replaced.
AI has also proved useful in agriculture and the food processing industry—weather stations and pesticide monitors can keep track of the ripening of crops and when they need treatment. The IoT precision agriculture system being developed by Microsoft for small farmers utilizes machine learning to monitor the pH of the soil and timely watering of crops. Grain sorting systems use image recognition to separate the contaminated grain from the good ones.
What these varied scenarios prove is that while inventory and supply chains have traditionally been seen as cost centers, their engines ensure that the work is done automatically. The use of AI to make supply chains nimble transforms it from a mere cost center to an enabling function in today’s market. The use of AI increases responsiveness to volatility and enables the handling of several variations of products in different channels, and its use in inventory management can only increase over time. It can also monitor the inventory aging for products with short shelf life, like fresh produce.
Using AI to predict the change in demand can be a risky move, as consumer patterns can change unexpectedly, while machine learning takes time to cope with the same. This is because machine learning is highly efficient at replicating consistent human behavior but reacts badly to new situations. In supply chain predictions, machine learning aids in making logical decisions.
Businesses seeking to use AI to their advantage should try to optimize existing systems before introducing new technologies gradually. The mechanisms needed for increased accuracy in predictions have become progressively complex, and the more revolutionary solutions might require the knowledge of data science and increasing hardware investment.
As the expertise of businesses increase and they start creating custom monitoring and prediction systems, they become part of a large-scale intelligence system that has multiple factors as well as data pipelines connecting the entire system that need to be considered. All in all, AI-powered inventory management systems result in an optimized system, reduced operational costs, increased customer satisfaction, and increased ROI.