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

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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ABOUT THE AUTHOR

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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