Generative AI in Action: 5 Practical Use Cases for the Modern Demand Planner

Blog / Generative AI in Action: 5 Practical Use Cases for the Modern Demand Planner

Imagine sitting at the heart of a global retail operation in Chicago, orchestrating your next move. As you analyze patterns in historical data and seasonal trends, a news alert pops up: a sudden port strike in Baltimore is about to throw your meticulously planned supply chain into disarray.

On the East Coast, an unexpected Lakers’ playoff win prompts a surge in demand for fan gear— an outlier your forecast models didn’t see coming. Down south, the imminent arrival of a cyclone in Florida threatens to disrupt the delicate balance of your inventory. Meanwhile, a price-slashing campaign from a competitor blindsides you.

These real-world factors—often unpredictable, always impactful—continually redraw the demand landscape. Traditional forecasting methods, reliant primarily on internal historical data, grapple to stay afloat in this sea of complexity. The ever-increasing number of variables at play and the lack of tools capable of unifying these disparate data sources make this an uphill battle.

To stay afloat in a sea of uncertainties, demand planners must anchor their questions around the following:

• How do I account for real-time events that disrupt my supply chain?
• What’s the impact of a sudden social media trend on product demand?
• How can I incorporate multiple external signals simultaneously into my forecast models?
• How do I mitigate the risks posed by unforeseen global events?

Is AI the Answer? 

AI is not a silver bullet that replaces the demand planner’s intuition or industry know-how. It’s an enabler, augmenting human capabilities with powerful insights gleaned from vast data reservoirs. It uncovers patterns, relationships, and trends humans might overlook, empowering planners to make more informed decisions.

What demand planners need is AI embedded in their decision-making process. But this integration isn’t possible with a generic SaaS product that comes with pre-packaged solutions. What they need is a custom-built application, one that understands the specific context of their business, the nuances of their datasets, and responds accordingly. This is exactly what Demand ML offers.

Demand ML – Your AI Assistant in Demand Forecasting 

Demand ML, powered by Google Cloud and running on SAP BTP, is a demand forecasting solution, bringing in external datasets and Generative AI capabilities to your planning landscape. Embedded in Pluto7’s comprehensive Planning in a Box platform, it can help you connect the dots between various business processes—from sales and marketing to supply chain and finance.

Key benefits and features of Demand ML:

• Custom-Built: Personalized solution addressing your unique challenges.
• Conversational AI: Ask a question and get the answer—no complex coding required.
• Holistic Planning: Connects the dots between sales, marketing, supply chain, and finance.
• Robust Infrastructure: Powered by Google Cloud and runs on SAP BTP.
• Unifying Data Sources: Integrates multiple data sources into a unified platform.

Experience the Future of Demand Forecasting: Start Free Trial

Now, let’s explore some practical scenarios where AI proves indispensable for demand planners. These use cases are derived directly from insights shared by our customers. For a deeper dive into these actual case studies, please visit Pluto7’s case studies section. 

Use Case #1: Improving Demand Forecasting in Unpredictable Weather Conditions

Scenario: 

Let’s consider the case of a national grocery chain in the United States. They’ve noticed that weather fluctuations significantly impact the sales of certain products. For instance, during a heatwave, sales of ice cream, cold beverages, and barbecue-related products tend to spike. During colder days, sales of hot beverages and comfort foods increase. However, their current forecasting model does not consider weather as a variable, causing mismatches in forecasted and actual demand.

Current Challenge:

• Integrating real-time weather data into demand forecasting models is beyond the capabilities of their existing tools.
• Fragmented sales data across different systems impedes the extraction of meaningful insights.

Generative AI Solution:

With Demand ML, the grocery chain:

• Leverages AI algorithms to uncover complex patterns in this enriched dataset, identifying correlations like temperature’s influence on sales.
• Auto-adjusts inventory levels based on these insights, improving inventory management efficiency.

Questions a Demand Planner might ask their data:

• How can our demand forecasting model be adjusted to consider weather variations?
• What is the potential impact of an upcoming heatwave on the demand for specific products?
• How can we manage our inventory efficiently to meet the weather-induced demand fluctuations?

End Results:

• Successfully integrated historical sales data with local weather forecasts, breaking down the data silo between the meteorological department and sales analytics team.
• Achieved granular visibility of how weather fluctuations impact the demand for cold beverages in specific regions, empowering swift inventory adjustments.
• Uncovered unique consumer behavioral patterns – spikes in demand for certain beverage variants during warmer days, providing rich insights for tailored marketing campaigns.

Read Case Study: SoCal’s Gourmet Food Market improved forecast accuracy and optimized their inventory and assortment.

Use Case #2: Launch of a New Fashion Line in the e-Commerce Sector

Scenario: 

An e-commerce giant is launching a new fashion line and is uncertain about the demand due to a lack of historical data.

Current Challenge:

• Lack of historical sales data for the new fashion line leads to uncertainty in demand forecasting.
• Difficulty in integrating external datasets such as fashion trends, influencer marketing analytics, similar product sales from competitor sites, and broader economic indicators.

Generative AI Solution:

With Demand ML, the e-commerce giant:

• Incorporates external datasets such as fashion trends from social media, influencer marketing analytics, competitor sales, and broader economic indicators into their demand forecasting model.
• Uses AI to input additional parameters such as target demographic, product price range, and marketing budget, creating a more comprehensive demand forecast.

Questions a Demand Planner might ask their data:

• Which product types are likely to be popular amongst our target demographic?
• How might marketing campaign data from similar product launches affect demand for our new fashion line?
• What are the key economic indicators we should be paying attention to?

End Results:

• Bridged the data silo between marketing, sales, and economic data to develop a comprehensive forecast model.
• Achieved visibility into how external factors like fashion trends and economic indicators influence demand for the new product line.
• Unearthed key insights into demographic preferences, aiding the refinement of the product assortment and optimization of marketing strategies.

Read Case Study: Tacori, a leading jewellery design house improved their demand forecasting and marketing ROI.

Use Case #3: Harnessing Viral Trends for Demand Forecasting

Scenario:

A global toy retailer is grappling with unexpected demand fluctuations due to a viral trend—the “Barbenheimer” wave. The demand planner is unsure about the trend’s lifespan and its impact on the demand for related products.

Current Challenge:

• Difficulty in assessing the impact and duration of a viral social media trend on product demand.
• Challenges in integrating social media trends, Google search data, and movie ratings alongside the company’s internal data.

Generative AI Solution:

With Demand ML, the toy retailer:

Incorporates social media trends, Google search data, and movie ratings with the company’s internal sales and inventory data.

Uses this enriched dataset to understand the impact of the viral trend on demand and forecast accordingly.

Questions a Demand Planner might ask their data:

• How is the ‘Barbenheimer’ trend affecting the demand for Barbie products?
• How can we determine when this trend might peak or taper off?
• How should we adjust our inventory and distribution strategies based on this trend?

End Results:

• Allowed for the integration of internal and external data, breaking down data silos.
• Provided visibility into the impact of a particular trend on demand.
• Gave insights into the trend’s lifecycle, enabling proactive inventory and distribution adjustments.

Read: Leveraging Viral trends to forecast demand and align inventory outpacing competitors

Use Case #4: Mitigating Regional Supply Chain Disruptions

Scenario:

A large multinational fashion retailer is facing a regional supply chain disruption due to a sudden labor strike in a key manufacturing hub.

Current Challenge:

• The sudden disruption has a cascading effect on their global supply chain and inventory levels.
• Difficulty in integrating real-time external data with internal supply chain information.

Generative AI Solution:

With Demand ML, the fashion retailer:

• Integrates real-time data on labor strikes and socio-political developments with their internal supply chain data.
• Uses this information to generate proactive forecasts, helping them re-balance their inventory across unaffected regions.

Questions a Demand Planner might ask their data:

• How can we re-balance our inventory across unaffected regions?
• What is the impact on our sales forecasts for European and North American markets?
• How long will it take to normalize the supply chain post-disruption?

End Results:

• Integrated external real-time data with internal supply chain information.
• Enhanced visibility into the impact of regional disruptions on global supply chains.
• Enabled the retailer to proactively re-balance inventory across regions.

Request a Demo: Experience game-changing potential of Demand ML

Use Case #5: Predicting Demand for New Products

Scenario:

A leading technology company is launching a new product and wants to accurately forecast the demand to optimize production and distribution.

Current Challenge:

• The absence of historical sales data for the new product.
• Challenges in leveraging competitor product launch data and consumer sentiment to forecast demand.

Generative AI Solution:

With Demand ML, the tech company:

• Leverages historical sales data, competitor product launch data, and consumer sentiment from social media and online reviews.
• Uses this data to generate robust demand forecasts guiding the optimization of their production and distribution strategies.

Questions a Demand Planner might ask:

• How can we predict demand for our new product, given the absence of historical sales data for the same?
• How can we leverage competitor product launch data to forecast demand for our new product?
• How can we use social media and online review data to gauge consumer sentiment towards our new product?

End Results:

• Integrated internal sales data with external competitor product launch data and social media sentiment, breaking data silos.
• Achieved detailed visibility into potential demand for the new product.
• Enabled the tech company to optimize their production and distribution strategies, maximizing sales and minimizing stockouts or overstocks.