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How Retailers Can Avoid the Costly Trap of Excess Inventory During Christmas

December 11, 2023 | Manju Devadas

Blog / How Retailers Can Avoid the Costly Trap of Excess Inventory During Christmas

Here we go again: The Christmas rush. But behind the festive cheer, retailers are wrestling with a familiar foe — excess inventory. Picture this: shelves brimming with goods, but your real-time visibility across stores?  Quite limited. You’re flying half blind, relying on older data, hoping it still holds true. But wait, consumer behavior could change 180 degrees at a product level overnight, thanks to a viral TikTok and other social media trends that you did not see coming.

And there’s the challenge: You’re stuck with piles of ‘hot-sellers’ from yesteryear while scrambling for this season’s unexpected hits. All because the last year’s less-than-accurate forecast has become this season’s misstep. It’s a juggling act between too much and not enough, and frankly, it’s a guessing game that’s costing you more than just dollars.

According to a recent Reuters analysis, major U.S. retailers like Dollar General, Walmart, and Macy’s are bracing for potentially excessive stock for the second consecutive year. With 37% of retailers having too much cash tied up in inventory and 45% struggling with manual demand forecasting, the need for a more robust solution is clear. As many as 41% of retailers find it challenging to buy the right amount of stock, and 45% need to cut down on inventory carrying costs.

The Challenge: Diverse Preferences and Supply Chain Intricacies

The Christmas season amplifies the complexity of predicting consumer preferences. Regional differences become more pronounced – what’s trending in New York might not align with preferences in Texas. Supply chain challenges, from sourcing to logistics, add another layer of complexity.

Why Retailers Miss the Mark: Data and Decision Lags

Retailers have access to vast data – from in-store sales to online traffic and social media. Yet, data silos, real-time analysis lags, volume versus value of data, and inter-departmental gaps often hinder effective decision-making. This is where Generative AI and decision intelligence platforms like Planning in a Box become invaluable.

Planning in a Box: A Decision Intelligence Platform Powered by Gen AI

Built on Google Cloud, Planning in a Box offers real-time insights and recommendations. It centralizes data from various sources like SAP ERP, Oracle EBS, Salesforce, and Adtech, enriched with external demand signals from Google and other sources. This integration allows retailers to adapt swiftly to market demands and ensure optimal inventory levels.

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Example Scenario: Meeting Emily’s Christmas Wish with Planning in a Box

Imagine Emily, a customer from Denver, Colorado. She’s searching for the perfect Christmas gift, a special edition ‘Starlight Snow Globe,’ which she spots on ACME Retail’s website. Captivated by its design, Emily adds it to her cart but hesitates to complete the purchase, deciding to think it over.

From ACME Retail’s perspective, Emily’s action raises several challenges:

  • Recognizing Customer Intent: How can ACME discern whether Emily will complete her purchase or if she’s browsing elsewhere for better deals? Understanding her buying intent is crucial to managing their inventory effectively.
  • Inventory Visibility: ACME has a limited number of ‘Starlight Snow Globes’ in stock, spread across various warehouses. How can they ensure that the inventory levels align with real-time demand, especially from potential buyers like Emily?
  • Last Mile Delivery: In case Emily decides to buy the snow globe, ACME needs to ensure prompt delivery. However, with her located in Denver and the nearest stock in a Dallas warehouse, how can they streamline the logistics for timely deliver
  • Demand Forecasting: As Christmas nears, can ACME accurately predict a surge in demand for unique items like the ‘Starlight Snow Globe’? Proper forecasting is vital to adjust their production and inventory to meet potential increases in demand.

“We would need an army of data scientists to make faster decisions on pricing and inventory levels. With Google Cloud Platform machine learning and artificial intelligence, we don’t need that. We can make much faster pricing decisions to optimize profitability and move inventory.” – Deepak Mehrotra, Co-founder & Chief Adventurer, CDD

Read the full case study.

How Planning in a Box Optimizes ACME Retail’s Inventory in Real-time

  • Unified Customer Profile: By merging data from ACME’s ERP and Salesforce systems with external consumer trends, Planning in a Box creates detailed customer profiles. This integration aids ACME in making data-driven inventory decisions, ensuring they stock products aligned with current consumer preferences.
  • Real-Time Inventory Management: The platform provides instantaneous inventory updates, alerting ACME to crucial stock changes. It empowers them to respond rapidly to fluctuating demand, preventing both overstock and stockouts.
    • Utilizes advanced analytics to assess demand shifts.
    • Identifies regional variations, predicting where products like the ‘Starlight Snow Globe’ might sell quickly, like in New York, and where they might linger on shelves.Localized Demand Forecasting:
    • This insight allows ACME to strategically distribute their inventory, aligning stock levels with regional sales patterns.
  • Event-Driven Automation: Planning in a Box’s ability to analyze trends and sales data in real-time, including location-based demand, leads to proactive inventory adjustments. It enables ACME to replenish orders almost instantly, adapting to market shifts as they happen, ensuring optimal stock levels across locations.
  • Generative AI-powered Data Analytics:
    • Incorporates Generative AI for in-depth, quick data analysis.
    • Multilingual voice command capabilities enable ACME’s team to query data efficiently. For instance, a manager can ask in Spanish ‘What are the recent sales of ‘Starlight Snow Globe’ in our New York stores?’. The system promptly analyzes and responds in Spanish, facilitating effective collaboration with the sales team in that location.

Reduce Inventory Costs by 20% with Planning in a Box

Confront your retail’s biggest challenge – excess inventory – head-on. Planning in a Box revolutionizes your approach in just four weeks, pivoting from outdated methods to a data-driven, AI-powered strategy. Planning in a Box has already transformed the operations of leading brands like Tacori, Levi’s, AB InBev, and Ulta Beauty, turning complex data into a strategic advantage.

Embrace the potential to reduce inventory costs by 20% within a year. By integrating ERP and CRM systems with advanced AI analytics, Planning in a Box empowers you to anticipate and meet customer demands proactively. It’s about shifting from reactive to proactive, ensuring every decision is underpinned by robust, real-time data insights. Make the leap with Planning in a Box – your pathway to turning inventory challenges into opportunities for growth and savings.

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Manju Devadas is the Founder and CEO of Pluto7, bringing 20+ years of experience in predictive analytics for Supply Chain, Retail and Manufacturing. With expertise in AI, Deep Learning, and Machine Learning, he has been instrumental in improving efficiency and strategic growth across industries.

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