• Written by Sonali Subhadarsini, Product Manager

A category manager in e-commerce and retail often finds themselves waiting on a report from the procurement team, only to discover that the data is outdated or incomplete, leading to delayed decisions on assortment and supplier management. This is a common scenario, as procurement teams struggle to keep up with the fast-moving nature of e-commerce and retail, where customer demand and preferences can shift rapidly. The shift supervisor on the warehouse floor may also be affected, as they try to manage inventory levels and ensure that products are available to meet customer demand. The category manager, meanwhile, is trying to make sense of the data, to identify trends and patterns that can inform their decisions on which products to stock and which suppliers to work with.

The procurement questions that currently wait on a report

The procurement team is often tasked with answering questions such as which suppliers are providing the best value, which products are selling well and should be restocked, and which categories are experiencing high returns. These questions are critical to the success of the e-commerce and retail business, as they can help to inform decisions on inventory management, supplier selection, and product assortment. However, the answers to these questions are often delayed, as the procurement team waits on reports from various stakeholders, including suppliers, logistics providers, and sales teams. This can lead to a lack of visibility into the procurement process, making it difficult for the category manager to make informed decisions.

Most people get wrong the idea that these questions can be answered through manual analysis of spreadsheets and reports. They are wrong, as this approach is time-consuming and prone to error, and can lead to delayed decisions and lost sales. A more effective approach is to use AI to analyze the data and provide real-time insights, allowing the category manager to make informed decisions quickly and efficiently.

The data behind them: purchase orders, supplier contracts and terms, invoice history, category structure

The data that underlies the procurement process is complex and multifaceted, including purchase orders, supplier contracts and terms, invoice history, and category structure. This data is often scattered across different systems and stakeholders, making it difficult to access and analyze. The procurement team may have to navigate multiple systems, including enterprise resource planning (ERP) systems, supply chain management (SCM) systems, and customer relationship management (CRM) systems, in order to gather the data they need. This can be a time-consuming and laborious process, and can lead to errors and inaccuracies.

The category manager, meanwhile, is trying to make sense of this data, to identify trends and patterns that can inform their decisions on product assortment and supplier selection. They may use tools such as business intelligence software to analyze the data and identify insights, but this can be a slow and manual process.

Sector constraints: assortment decisions and returns and their root causes

The e-commerce and retail sector is characterized by fast-moving assortments and direct customer signal, which can make it difficult for procurement teams to keep up. Assortment decisions, such as which products to stock and which to discontinue, are critical to the success of the business, but can be influenced by a wide range of factors, including customer demand, seasonality, and supplier lead times. Returns, meanwhile, can be a major challenge, as they can eat into profit margins and damage customer relationships. The root causes of returns, such as product quality issues or incorrect sizing, must be identified and addressed in order to minimize their impact.

The shift supervisor on the warehouse floor may be able to provide insights into the root causes of returns, as they are often the first point of contact for customers who are returning products. They may be able to identify patterns and trends in returns, such as a high rate of returns for a particular product or supplier, and provide feedback to the category manager and procurement team.

What improvement looks like: spend under management, price variance to contract, supplier concentration

Improvement in the procurement process can be measured in a variety of ways, including spend under management, price variance to contract, and supplier concentration. Spend under management refers to the percentage of total spend that is managed through formal contracts and agreements, and can help to ensure that the business is getting the best value from its suppliers. Price variance to contract, meanwhile, refers to the difference between the contracted price and the actual price paid, and can help to identify areas where the business can save money. Supplier concentration, finally, refers to the percentage of total spend that is concentrated among a small number of suppliers, and can help to identify areas where the business may be vulnerable to supply chain disruptions.

By using AI to analyze the data and provide real-time insights, the category manager and procurement team can make informed decisions quickly and efficiently, and drive improvement in the procurement process. This can help to reduce costs, improve customer satisfaction, and drive business growth. For more information on how to get started with AI for procurement, please contact us.

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

While AI can be a powerful tool for procurement teams, it is not a silver bullet, and there are certain situations where it may not be the best choice. For example, AI may not be effective in situations where the data is incomplete or inaccurate, or where the procurement process is highly complex and nuanced. In these situations, a more manual approach may be necessary, using tools such as spreadsheets and reports to analyze the data and make decisions.

In other cases, AI may be used in conjunction with other tools and approaches, such as business intelligence software or supply chain management systems, to provide a more comprehensive view of the procurement process. The key is to understand the limitations and potential biases of AI, and to use it in a way that complements and enhances the existing procurement process, rather than replacing it entirely.