The use of artificial intelligence (AI) and machine learning (ML) has become widespread in most fields, including e-commerce, logistics, and supply chain. With that being said, applying AI in certain roles such as operations management may not be as straightforward due to the complexity of cognitive work involved in such processes.
In our latest fireside chat, we sat down with Carolin-Carmen Neubauer, the Chief Operating Officer and Co-Founder of FERO.Ai, to understand how AI and ML work in the e-commerce and logistics space.
How Artificial Intelligence and Machine Learning Work in E-Commerce Logistics
Optimizing Delivery Process
One of the ways in which AI and ML are used in e-commerce logistics is to help improve delivery efficiency. These tools can take past user behavior data, learn from them and go on to optimize existing operations. ML can also provide information about the most convenient time to deliver for a particular customer so that carriers can have a faster track turnaround time and avoid potential blockages.
Demand Aggregation for Small Retailers
According to Carolin, AI can offer demand aggregation functionality for various players including logistics companies, distribution partners, or manufacturing businesses.
The implementation of demand aggregation for small retailers in specific geographies — particularly fragmented ones — can be extremely challenging. The conventional method is to use Excel sheets and hire drivers to take orders. However, this manual process can be tedious and inconvenient. With AI solutions such as the one by Fero.Ai, small shop owners can now place orders directly into the company’s ordering system using an interactive assistant. This in turn allows the supplier company to dispatch the orders immediately.
Environmental Benefits of AI in Logistics
In addition, AI and ML can help logistics carriers allocate resources more appropriately. This means they can spend fewer resources for the same amount of businesses. The use of fewer vehicles leads to less fuel usage, which results in a large reduction in CO2 emissions. In 2021 alone, Carolin shared that FERO.Ai was able to save 8,500 tons of CO2, which is equivalent to the amount of CO2 emitted a whole year in a small European city.
Measuring the Return on Investment of AI
As with all other investments, Carolin believes that it is imperative for businesses to weigh the pros and cons before investing heavily in AI. So how exactly can we measure the impact of AI?
Improved Employee Experience
The clear impact of AI on enhancing employee experience is easy to see. By applying AI and ML, companies can take away mundane tasks from their employees which allows employees to work on more complex, intellectually demanding projects. From a logo maker to an email marketing tool, AI is widely used in many elements of business operations.
AI provides synchronization for operation, which cannot be done by an individual. For instance, a person can deal with five to six customer accounts, including turning in quotes, replying to customers, and processing around 60 emails per hour. Meanwhile, AI can handle an unlimited number of emails, responses, and messages with ease, thanks to the ability to implement simultaneously on a large scale.
Improved Customer Satisfaction
AI assists to enhance service quality, thus improving customer satisfaction. To be specific, it can quickly reply to customer questions, leading to an increase in conversion rates. This will then result in a higher proportion of turning to actual contracts that generate revenue for businesses.
Data-Driven Decision Making
Typically, businesses seek comprehensive insights based on data stored by systems of record keeping. Caroline shared how FERO.Ai provides a system of engagement to their customer base. By using Tia, businesses can access real-time data and live engagement, allowing them to make more accurate and timely decisions.
For the full discussion, check out the video below. You wouldn’t want to miss it!