A Data-Driven Journey to Better Inventory Management

Introduction

Ever wondered how your local tire shop always seems to have exactly what you need? It’s not magic—though sometimes it feels like it! Let me take you behind the scenes of our latest project: a sophisticated tire sales prediction system that ensures we keep the right tires in stock at the right time.

I posted it on Github for all coding lovers:

https://github.com/licai918/PROPHETFORECAST3.git

The Challenge

Picture this: It’s the first snowfall of the season, and suddenly, everyone remembers they need winter tires. Without proper planning, this could lead to what we in the industry call the "Winter Tire Panic"—empty racks and frustrated customers. Not ideal, right?

Our Solution: The Prophet of Tires

We’ve developed a comprehensive prediction system (let’s call it the "Prophet of Tires"—yes, we’re that nerdy!) that uses advanced machine learning techniques to forecast tire demand. Here’s how it works:

The Tech Stack

• Python – Our trusty programming language
• Flask – For building a sleek web interface
• Pandas & NumPy – For data crunching
• Plotly – For beautiful, interactive visualizations
• Bootstrap – Making everything look polished and professional

The Secret Sauce: Our Prediction Model

1. Historical Data Analysis

   • Sales patterns over multiple years
   • Seasonal trends
   • Growth rates
   • Customer preferences by tire type and size
2. 

   Smart Seasonality Handling

   seasonality_adjustments = {
       'WINTER': 2.5,      # Winter tires peak in fall
       'ALL_WEATHER': 1.2, # Steady demand with slight seasonal variation
       'PASSENGER': 1.0,   # Base reference
       'LIGHT_TRUCK': 1.3, # Higher summer demand
       'COMMERCIAL': 1.1   # Business-driven demand
   }
   

3. Winter Tire Pre-order System

   • Automatically calculates optimal pre-order quantities
   • Considers storage capacity and lead times
   • Plans deliveries for September (before the rush!)

The Magic Behind the Numbers

Our system doesn’t just throw random numbers at the wall, hoping something sticks. It considers:

• Lead Time – How long it takes to receive tires from manufacturers
• Storage Capacity – Because we can’t store tires in the coffee room (we tried, HR wasn’t happy)
• Safety Stock – Because running out of tires is like a pizzeria running out of cheese
• Growth Trends – Because what’s popular today might be even more popular tomorrow

Real-World Impact

Here’s what this means for you, our valued customers:

1. Always Available – The right tires when you need them
2. Better Prices – Efficient inventory management means cost savings
3. Wider Selection – Confidently stocking a greater variety of tires
4. No Winter Rush – Pre-ordered winter tires mean no panic buying

The Human Touch

While we love our data and algorithms, we know that tire selection is both a science and an art. Our system is designed to support—not replace—the expertise of our staff. Think of it as giving our experienced team a crystal ball; they still make the final calls, but now with superhuman foresight!

Future Improvements

Could our system be better? Absolutely! We’re considering:

• Machine learning models for individual customer preferences
• Integration with weather forecasting data
• Real-time supplier inventory tracking
• Customer feedback loop integration

Conclusion

Is this the perfect system? Probably not. But is it helping us serve you better? Absolutely! We’re constantly refining our approach and would love to hear your thoughts and suggestions.

At the end of the day, our goal is simple: to ensure that when you need tires, we have exactly what you’re looking for. No stress, no waiting, no compromise on quality or choice.

P.S. If you’ve read this far, you might be wondering if we named our prediction model "The Prophet of Tires" just to make tire puns. The answer is yes, yes we did. We’re not even sorry about it!

Want to learn more about how we’re using technology to serve you better? Drop by our shop—we love talking about tires almost as much as we love talking about data!

Note: No tires were harmed in the making of this prediction system, though several spreadsheets did experience mild anxiety.