Fruit Sales Forecast October 2024

Mei operated a fruit stand selling Bananas, Oranges, Lychees, Starfruits, and Longans. Every month she threw away spoiled fruit that didn't sell and ran out of popular items mid-week. She ordered inventory based on last month's sales, but demand shifted unpredictably. Some weeks Lychees sold out in two days. Other weeks she threw away half her stock. Mei needed a way to predict next week's demand so she could order smarter and waste less.

Mei hired a data analyst to build a sales forecasting model using September 2024 data. The analyst chose ARIMA (AutoRegressive Integrated Moving Average)—a time series model that identifies patterns in historical sales and projects them forward. The goal was to predict the first week of October's sales for all five fruits, allowing Mei to adjust inventory before the week started.

The model trained on September sales data, capturing daily fluctuations, weekly trends, and seasonal patterns. The analyst tested model accuracy using AIC (Akaike Information Criterion) and variance—lower values meant better predictions. Fruits with clear seasonal patterns like Oranges and Starfruits showed more reliable forecasts. Fruits with erratic sales patterns produced less accurate predictions but still offered directional guidance.

The forecast predicted a sales drop across all five fruits in the first week of October. Bananas were forecast to sell 667 units—down from 4,235 units in September. Oranges were predicted at 606 units—down from 4,213 units in September. Lychees, Starfruits, and Longans all showed sharp declines. This wasn't random noise—it was a consistent signal that October demand would be lower than September across the board.

Starfruits and Lychees, which had strong September sales, showed the steepest forecast drops. These were fruits Mei usually overstocked in early October based on September's high sales. The model said that strategy would lead to massive spoilage. Demand was shifting downward, and Mei needed to cut orders dramatically to match the new reality. Ignoring the forecast would mean throwing away money.

Bananas and Oranges, while also forecast to drop, remained in relatively higher demand compared to other fruits. The model suggested these should still anchor Mei's inventory strategy—just at lower volumes. Bananas had consistent baseline demand regardless of season. Oranges showed seasonal patterns but remained steady sellers. These were the safe bets when uncertainty was high.

Mei faced a choice: trust the model or trust her intuition. Her intuition said order the same as September—sales had been strong, so why would October drop? The model said the opposite. Historical patterns showed seasonal dips after September. Ignoring data in favor of gut feeling would cost her thousands in wasted fruit. She decided to trust the numbers.

Mei adjusted her orders based on the forecast. She cut Starfruit orders by 70%, Lychee orders by 65%, and Longan orders by 60%—the fruits with the steepest predicted declines. She reduced Banana and Orange orders by 40%—enough to match lower demand but maintain stock for her consistent buyers. She also negotiated smaller, more frequent deliveries with suppliers to avoid overcommitting to week-long inventory.

Mei didn't know yet if the forecast would be accurate—she was ordering based on the model's predictions. Bananas forecast: 667 units (down from 4,235 in September). Oranges forecast: 606 units (down from 4,213 in September). Starfruits and Lychees showed the steepest predicted drops. She'd cut orders by 40-70% depending on the fruit. If the forecast was right, she'd avoid spoilage. If it was wrong, she'd learn how to adjust the model.

The model wasn't a guarantee—it was a probabilistic guide. Some forecasts would be accurate, others wouldn't. But ordering based on historical patterns was better than ordering based on gut feeling. The ARIMA model identified seasonal trends Mei couldn't see just by looking at last month's sales. It showed that September's high sales didn't mean October would be the same. That insight alone changed her ordering strategy.

Mei planned to track forecast accuracy every week. She'd compare predicted sales to actual sales, identify where the model was right and wrong, and retrain it monthly with new data. She also knew she couldn't rely on the model blindly—if customers started asking for a specific fruit repeatedly, she'd adjust orders upward even if the forecast suggested otherwise. Data would inform her decisions, but she'd stay in control.

Today, Mei orders fruit based on ARIMA forecasts, not last month's sales. She doesn't know yet if it will reduce spoilage significantly, but she knows ordering 4,235 Bananas in October when the forecast says 667 would be wasteful. The model gives her a starting point based on patterns, not guesses. She'll track results, refine the approach, and adjust as she learns. The goal isn't perfect predictions—it's better decisions than ordering blindly and hoping for the best.