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Handling Holiday Sales Surges with AI Agents: A 4th of July Postmortem

July 5, 2024 | Tarun Kumar

Blog / Handling Holiday Sales Surges with AI Agents: A 4th of July Postmortem

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The 4th of July is an exciting time for shoppers, but it can be a challenging period for businesses. With the surge in holiday demand, companies often find themselves scrambling to keep up. Predicting customer behavior becomes a guessing game, inventory levels are hard to manage, and ensuring product quality under pressure is a constant struggle.

During this hectic time, traditional methods often fall short. You might find yourself overstocked with items that don’t sell or, worse, out of stock on bestsellers. The rush to meet demand can also lead to quality control issues, resulting in defective products reaching customers and hurting your brand reputation.

These challenges extend beyond the 4th of July to other major retail events such as Back-to-School season, Halloween, Black Friday, Cyber Monday, Christmas, and New Year’s sales, each requiring robust strategies for inventory management and quality control.

In many of these events, some of the most common scenarios companies face include:

Disparity Between Demand Plans and Actual Sales

You plan meticulously for the holiday rush, yet actual sales often surprise you. The gap between forecasted and real demand can lead to overstocking or understocking, each causing operational headaches and financial losses.

Out-of-Stock Situations

Nothing frustrates customers more than finding their desired items out of stock. Despite your best efforts, sudden surges in demand can leave shelves empty, driving customers to competitors and damaging your brand loyalty.

Defective Products

The rush to meet increased demand can strain your quality control processes, leading to defective products reaching customers. This results in returns, refunds, and a tarnished reputation, making it harder to retain customer trust.

You Need AI, and That’s a No-Brainer

Demand planning is an intricate process riddled with complexities. You’re not just dealing with a single data point but a multitude of variables that influence demand. These include historical sales data, market trends, promotional impacts, weather patterns, and economic conditions. Each of these variables can significantly affect consumer behavior.

The Challenge of Integration

The challenge lies in integrating these diverse data signals into a cohesive demand forecast. Sales data from your ERP system, market trends from Google Trends, promotional activities, and external factors all need to be considered. This is where AI becomes indispensable. AI can handle vast amounts of data and identify patterns and correlations that are impossible to detect manually.

Enterprise-Grade AI That You Can Trust

While the need for AI is apparent, the reluctance is equally palpable. The success of AI rests on multiple parameters:

  • How am I going to manage the data? (master data management)
  • How can I transform the data? (data analytics with AI)
  • How can I make the data accessible to all? (turning complex data into simple insights)
  • How am I going to ensure my teams use the new technology? (change management)

In the enterprise context, companies are exploring three options for deploying AI:

  • Multiple Vendors: This approach creates complexity, with many loose ends and long implementation times.
  • Building Their Own Platform: Large enterprises may do this, but it requires extensive resources, including data engineers, scientists, domain experts, and analysts, making it difficult to scale.
  • SaaS Products: These often don’t understand unique enterprise contexts, lack customization, and therefore, planners may not fully trust them.

Why You Need a Decision Intelligence Platform Like Planning in a Box

Decision Intelligence Platforms act as an intelligence layer on top of your ERP. This means if a planner uses SAP IBP, they can leverage these platforms for advanced analytics and integrate the insights back into their demand plans. Planning in a Box, Pluto7’s platform stands out for several reasons:

  • Data Foundation: It creates a central hub, pulling data from ERP, CRM, web analytics, and unstructured sources.
  • Flexible Architecture: Capable of solving complex problems for companies of any size.
  • Comprehensive Design: Built to address planning challenges across sales, marketing, supply chain, and manufacturing.
  • Integrated AI: Pi Agent, an integral component of Planning in a Box, uses Google Cloud’s Gemini to solve complex supply chain problems with remarkable speed.

Making Sense of Seasonal Demand with Pi Agent

While Planning in a Box creates a strong data foundation, Pi Agent translates complex data into everyday conversations. This ability to create dashboards, generate demand plans, and identify aging SKUs with just a prompt becomes invaluable when time is limited, and accuracy is essential. Let’s explore a few scenarios to see how Pi Agent can simplify supply chain planning.

“Hey Pi, Which Products Will See a Demand Surge?”

Forecasting demand for peak sales periods typically requires accessing data from multiple sources. Manually consolidating this data and generating insights can take up to 10 hours. With Pi Agent, planners can ask, “Hey Pi, what products will be in high demand?” Pi does an enterprise data search, analyzes the data in BigQuery, and returns insights in under 10 minutes. 

“Hey Pi, How Should We Adjust Inventory?”

Demand planning is one piece of the puzzle. The other is inventory optimization. Typically, a supply chain analyst spends 8 hours checking stock levels, projecting sales, and placing orders. With Pi Agent, they can ask, “Hey Pi, how should we adjust our inventory?” Pi monitors real-time inventory data and suggests reordering fast-selling items in less than 5 minutes. 

“Hey, Pi, What’s the Defect Rate for the Current Batch?”

In high-speed production, you can’t afford a 10-20% defect rate. This not only wastes resources but also risks delays or faulty products reaching customers, who may then switch to competitors. Typically, you would need up to 12 hours for manual inspections and quality checks to solve this. With Pi Agent, a quality control manager can ask, “Hey Pi, what’s the defect rate for the current batch?” Pi integrates with manufacturing data, identifies anomalies early, and provides actionable insights in real-time. 

Error-Prone to Error-Free in 48 Hours

It’s a misconception to think such AI capabilities will take years to implement. With the right approach, you can see tangible outcomes in less than 4 weeks. Many supply chain leaders have told me, “My data is all over the place. I’m not ready for AI.” To them, we have shown that Planning in a Box addresses data silos, ensures data security, master data management, and governance. It ticks all the boxes in data preparation, and that’s why it is able to provide high-quality output.

While you review what went wrong in your next planning review meeting, consider this: AI can be your biggest competitive advantage, and we can help you achieve that. Reach out to us below for a workshop and see the transformation we can help you achieve in 48 hours. 

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Tarun Kumar, VP of Global Sales at Pluto7, is an MIT-endorsed Senior Data Architect with deep expertise in Google Cloud solutions. He has spearheaded data platform adoptions for diverse organizations, championing supply chain transformations with Gen AI. As an Agile Scrum Master and TOGAF® 9 Professional, Tarun seamlessly bridges tech innovation with tangible business value.

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