• Written by Sonali Subhadarsini, Product Manager

A category manager at a large retailer sits in front of a dashboard that nobody opens, filled with charts and tables that are supposed to inform assortment decisions, but in reality, are rarely consulted, and when they are, it's often too late to make a meaningful impact on sales or inventory levels.

Challenges in e-commerce and retail

The fast-moving nature of e-commerce and retail assortments, combined with the direct signal from customers, makes it particularly hard to get assortment decisions right, as the shift supervisor at a warehouse can attest to, having to deal with the aftermath of incorrect forecasts, which often lead to overstocking or understocking of certain products, and subsequently, returns and their root causes become a major issue, as the company struggles to identify the reasons behind the returns, and promotion effectiveness becomes a guessing game, with the marketing team trying to figure out which promotions are actually driving sales, and which ones are just wasting resources.

This is not to say that other industries do not face similar challenges, but the unique combination of fast product cycles, high customer expectations, and thin margins in e-commerce and retail makes it particularly difficult to get things right, and as a result, many companies are looking to AI process automation to help them improve their decision-making and reduce the complexity of their operations, which is why AsscherAi has become such an important tool for many retailers.

Mapping the current process

Before automating any part of the process, it's essential to map out the current workflow, including all the handoffs, decision points, and potential bottlenecks, which can be a time-consuming and tedious task, but it's crucial to understanding where the real pain points are, and where automation can have the most significant impact, as the category manager can attest to, having spent countless hours trying to optimize the assortment planning process, only to realize that the real issue was not the planning itself, but the lack of visibility into the supply chain and the inability to respond quickly to changes in demand.

This mapping exercise can also help identify areas where the process is not as efficient as it could be, and where automation can help streamline things, such as automating the data collection and analysis, or automating the communication between different teams, which can help reduce delays and improve overall productivity, and it's often surprising to see how many manual processes are still in place, even in companies that consider themselves to be at the forefront of technology and innovation.

Handoffs and delays

One of the main reasons why automation efforts often stall is that they focus too much on the individual tasks, rather than the handoffs between them, which can create significant delays, as the shift supervisor at a warehouse can attest to, having to deal with the constant back-and-forth between the warehouse team, the logistics team, and the inventory management team, just to get a simple shipment out the door, and it's not uncommon for a single shipment to require multiple handoffs, each one creating a potential bottleneck, and slowing down the entire process.

Exception handling

Most of the real work in e-commerce and retail is not about automating the routine tasks, but about handling the exceptions, such as a sudden change in demand, a stockout, or a quality issue, which can be difficult to anticipate and prepare for, and often require a high degree of flexibility and adaptability, as the category manager can attest to, having to deal with a sudden surge in demand for a particular product, only to find out that the supplier is unable to meet the demand, and having to scramble to find alternative sources, or adjust the marketing campaign to manage customer expectations.

This is where AI process automation can really shine, by providing the ability to respond quickly to changing circumstances, and to adapt to new information, such as a change in customer behavior, or a shift in the market, and it's often the exception handling that makes or breaks a company's ability to compete in the market, which is why it's so important to get it right, and to have the right tools and processes in place to support it, and if you're interested in learning more about how AsscherAi can help with exception handling, please don't hesitate to reach out.

Limitations of AI process automation

While AI process automation can be a powerful tool for improving efficiency and reducing complexity, it's not a silver bullet, and there are many situations where it's not the right choice, such as when the process is highly creative, or requires a high degree of human judgment, or when the data is incomplete, or inaccurate, and in these cases, it's often better to rely on human intuition, and expertise, rather than trying to automate the process, which can lead to unintended consequences, and a loss of control, and it's essential to understand the limitations of AI process automation, and to use it judiciously, and only when it's truly appropriate.