• Written by Sonali Subhadarsini, Product Manager

A shift supervisor in an e-commerce warehouse is often left waiting for a report to answer a simple question: which orders are at risk of missing their promised delivery date. This is not an unusual situation, as many operations teams in e-commerce and retail rely on periodic reports to inform their decisions, rather than having access to real-time data. The wait for these reports can lead to delays and inefficiencies, as teams are forced to react to problems rather than anticipating and preventing them. For instance, a category manager may need to compare the performance of different suppliers, but the data to do so may only be available on a weekly or monthly basis.

The operations questions that currently wait on a report

Many operations questions are currently dependent on reports that are generated on a periodic basis. These reports may provide insights into sales trends, inventory levels, and supplier performance, but they are often not available in real-time, and may not provide the level of detail that operations teams need to make informed decisions. For example, a logistics manager may need to know which orders are currently being held up in the warehouse, and why, in order to prioritize and resolve the issues. However, this information may only be available in a weekly report, by which time the issues may have already caused delays and disruptions.

The reliance on periodic reports can lead to a reactive approach to operations management, where teams are constantly playing catch-up, rather than being proactive and preventative. This can result in missed delivery dates, stockouts, and other issues that can have a significant impact on customer satisfaction and loyalty. By having access to real-time data, operations teams can ask more nuanced questions, such as which products are most frequently returned, and what are the root causes of these returns.

The data behind them: process and exception records, project status, budget consumption, supplier performance

The data that underlies operations management in e-commerce and retail is complex and multifaceted. It includes process and exception records, which provide insights into the flow of goods and services through the supply chain. It also includes project status updates, which track the progress of initiatives and investments, and budget consumption data, which monitors the financial performance of different projects and activities. Additionally, supplier performance data is critical, as it provides insights into the reliability and quality of different suppliers, and informs decisions about which suppliers to use, and how to manage them.

This data is often scattered across different systems and departments, making it difficult to access and analyze. However, by bringing this data together, and making it available in real-time, operations teams can gain a more complete and accurate understanding of their operations, and make more informed decisions. For instance, a procurement manager can use this data to compare the performance of different suppliers, and make decisions about which ones to use for future purchases.

Sector constraints: assortment decisions and returns and their root causes

E-commerce and retail operations teams face a number of unique constraints and challenges. One of the most significant is the need to make rapid assortment decisions, in response to changing customer demand and preferences. This requires a deep understanding of customer behavior and preferences, as well as the ability to analyze and respond to sales trends and data. Another key challenge is managing returns, which can be a significant cost and logistical burden. By understanding the root causes of returns, operations teams can take steps to reduce them, and improve the overall customer experience.

For example, a product manager may use data on returns to identify which products are most frequently returned, and why. This information can be used to inform product development and design, and to improve the overall quality and reliability of products. By reducing returns, and improving the customer experience, operations teams can help to drive sales and revenue growth, and improve customer satisfaction and loyalty. To learn more about how AI can support operations teams, visit our operations page.

What improvement looks like: on-time delivery, exception rate, expediting cost

So what does improvement look like, in terms of operations management in e-commerce and retail? One key metric is on-time delivery, which measures the percentage of orders that are delivered on or before the promised delivery date. Another important metric is exception rate, which measures the percentage of orders that are delayed or disrupted in some way. By reducing the exception rate, and improving on-time delivery, operations teams can help to drive customer satisfaction and loyalty, and improve the overall efficiency and effectiveness of their operations.

Expediting cost is another key metric, which measures the cost of expedited shipping and other emergency measures that are taken to recover from delays and disruptions. By reducing expediting cost, operations teams can help to improve the overall profitability and sustainability of their operations, and reduce the risk of delays and disruptions. For instance, a supply chain manager can use data on expediting cost to identify which products and routes are most frequently subject to expedited shipping, and take steps to optimize the supply chain and reduce the need for expedited shipping.

What this does not do, or when it is the wrong choice

While AI for operations in e-commerce and retail can be a powerful tool, it is not a panacea, and there are certain situations where it may not be the best choice. For example, where the data is incomplete or inaccurate, AI may not be able to provide reliable insights or recommendations. Additionally, where the operations are highly complex or nuanced, AI may not be able to fully capture the subtleties and complexities of the situation. In these cases, human judgment and expertise may be necessary to interpret the data and make informed decisions. If you have questions about how AI can support your operations team, or would like to learn more about our solutions, please contact us.