ChatGPT has a lot to say about merchandise planning.

By Lennon Barrow, Business Analyst Intern at Enhanced Retail Solutions

Merchandise planning is of paramount importance in the retail industry and plays a crucial role in the success of a business. To put it simply, merchandise planners on both the wholesale and retail side seek to satisfy consumer demand by putting the right product in the right place, at the right time, and for the right price. In today’s dynamic retail environment, merchandisers need a data-driven and adaptive approach to effectively perform this role.

So, what better way to explore the use of AI in merchandise planning than by asking ChatGPT? Here is what we learned:

How can merchandise planners use artificial intelligence to their advantage?

Merchandise planners can leverage artificial intelligence in various ways to optimize their inventory management, sales forecasting, pricing strategies, and overall decision-making processes. Here are some key ways merchandise planners can use AI:

Demand Forecasting:

AI can analyze historical sales data, seasonality patterns, market trends, and external factors (e.g., weather, holidays) to generate accurate demand forecasts. This helps merchandise planners stock the right quantity of products to meet customer demand without overstocking or understocking.

Inventory Optimization:

AI-driven algorithms can determine the optimal inventory levels for each product, taking into account factors like lead times, carrying costs, and sales velocity. This ensures efficient use of capital and reduces the risk of stockouts or excess inventory.

Assortment Planning:

AI can assist in selecting the right mix of products for a store or website. By analyzing customer preferences and market trends, AI can suggest which products should be included or excluded from the assortment to maximize sales and profitability.

Price Optimization:

AI-powered pricing algorithms can continuously monitor market dynamics, competitor pricing, and customer behavior to recommend optimal pricing strategies. This helps merchandise planners set prices that maximize revenue and margins.

Markdown Optimization:

AI can assist in determining when and by how much to mark down products that are not selling well. It can analyze factors like remaining shelf life, historical sales data, and seasonal trends to recommend effective markdown strategies.

Allocation and Replenishment:

AI can automate the allocation of products to different store locations based on demand patterns, ensuring that each location has the right mix of products. It can also recommend when and how much to reorder based on real-time sales data.

Customer Segmentation:

AI can help merchandise planners segment customers based on their preferences and behaviors. This allows for personalized marketing strategies and product recommendations, increasing customer engagement and loyalty.

Vendor Collaboration:

AI can facilitate communication with suppliers by providing insights into demand forecasts and inventory levels. This can help improve the supply chain’s efficiency and reduce lead times.

Predictive Analytics:

AI can identify emerging trends and customer preferences by analyzing social media data, online reviews, and other unstructured data sources. This information can inform merchandise planning decisions.

Real-time Insights:

AI can provide real-time data and insights into sales, inventory, and customer behavior, allowing merchandise planners to make agile decisions and respond quickly to market changes.

What are the challenges merchandise planners should consider when utilizing artificial intelligence?

While AI offers significant benefits to merchandise planners, there are several challenges and considerations they should be aware of when implementing AI solutions:

Data Quality and Availability

AI models heavily rely on data. Inaccurate, incomplete, or biased data can lead to flawed insights and predictions. Ensuring data quality and availability is a crucial challenge, as merchandise planners may need to clean, normalize, and augment data before it can be used effectively.

Integration with Existing Systems:

Integrating AI solutions with existing merchandise planning systems and processes can be complex. Ensuring seamless integration without disrupting day-to-day operations is a challenge.

Model Interpretability:

AI models, especially deep learning models, can be complex and challenging to interpret. Understanding why a model makes a particular recommendation is important for merchandise planners to trust and act upon those recommendations.


AI models can overfit to historical data, which means they may perform well on past data but poorly on future data. Ensuring that models generalize well to new scenarios is a critical challenge.


As businesses grow, AI systems must scale to handle increasing data volumes and complexity. Scalability can be challenging without careful architectural planning.

Vendor Selection:

Choosing the right AI solution provider or technology partner is crucial. Merchandise planners should evaluate vendors based on their expertise, track record, and ability to address specific business needs.

To overcome these challenges, merchandise planners should start with a clear understanding of their objectives, conduct thorough feasibility assessments, and collaborate closely with data experts and stakeholders. Regular monitoring and adjustment of AI systems are also essential to ensure they continue to deliver value over time.

The Best Solution

ERS is the perfect solution because it offers both a hands-on approach and the latest technology. They’ve already won awards for their use with AI, but it is their curation of the information that makes it actionable for wholesalers and retailers.

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