Accurately Forecasting Sales Months in Advance with ML

Business Solution Series

The Business Solutions Series is a compilation of solutions to various business challenges that I have encountered throughout my professional journey.

Context

We were engaged by a leading clothing company that operates hundreds of stores worldwide and is renowned for its affordable custom clothing designs. The company sought to accurately forecast the number of clothing items, in various colors, they would sell in advance of each season.

Problem

The company updates its clothing lines and colors every three months, making it crucial for them to accurately forecast sales in order to optimize revenue and avoid excess inventory. The company needed to forecast sales several months in advance to order the necessary fabric and produce clothing with their manufacturing partners before each season began.

Objective

To provide the company's planners with highly accurate quarterly sales forecasts for all items, in all colors, to enable optimal production decisions.

Solution

To address the challenge of accurately forecasting sales for the clothing company, we utilized an AutoML managed service to develop a complex regression model. This model outperformed the more traditional forecasting methods that the company had previously employed. While the company only required quarterly forecasts, we found that training a more granular model and aggregating simulated numbers for the quarter resulted in more accurate predictions.

We utilized the company's historical sales data to train the model to make predictions for each day, store, item, and color. Our model included three types of features: date features (day of the week, day of the month, month of the year, holiday, etc.), store features (country, city, store size, etc.), and item features (type, subtype, color, standardized description, etc.). Using the new items in new colors, we then used the model to simulate sales on all stores for each day in a future season and aggregated the data to the appropriate granularity for the planners.

To ensure the model's real-life accuracy given the granularity of our training data, we were careful to prevent data leakage between the train, validation, and test datasets by splitting them by quarter. This ensured that the model was validated and tested on full quarters with new products/colors that it had never seen during training.

One of the biggest challenges was convincing the planners to trust the model's predictions, even when we had data to prove its accuracy and superiority. To address this, we implemented what-if simulations, using items from previous seasons to forecast sales for future seasons on these past items. This helped the planners identify which new items the model predicted would sell similarly to past items/colors, increasing their confidence in the model's predictions. Additionally, the model allows the planners to run simulations of new items on past seasons, providing further insights on how they would have performed.

Impact

The implementation of these new models had a significant impact on the company's operations. The planners began utilizing these models for their decision-making, resulting in millions of dollars in revenue and significantly fewer leftovers.