Predicting the Future: Leveraging LSTM for Accurate Product Demand Forecasting

Predicting the Future: Leveraging LSTM for Accurate Product Demand Forecasting
By Himanshu December 24, 2024 10 min read

Introduction

In today’s hyper-competitive business landscape, understanding and predicting customer demand has become more crucial than ever. Effective demand forecasting enables businesses to make informed decisions regarding inventory management, supply chain operations, production schedules, and financial planning. However, the real-world data involved in forecasting is often complex and riddled with challenges, requiring robust preprocessing, feature engineering, and advanced algorithms to ensure accuracy.

As Rob J. Hyndman, a prominent statistician, notes, “Good forecasts lead to good decisions, and good decisions can lead to success.” This highlights the vital role of accurate forecasting in modern business.

This blog will walk you through the process of product demand forecasting using Long Short-Term Memory (LSTM) networks; a powerful deep learning technique ideal for time series data. We will explore real-world data challenges, preprocessing steps, feature engineering, and how LSTMs can unlock actionable insights for your business.

Why is Demand Forecasting Needed?

Accurate demand forecasting is essential for businesses to:

  1. Optimize Inventory Levels: Prevent understocking and overstocking, both of which can have financial consequences.
  2. Enhance Customer Satisfaction: Meet customer expectations by ensuring product availability.
  3. Reduce Operational Costs: Avoid unnecessary expenses in storage, logistics, and production.
  4. Improve Decision-Making: Facilitate data-driven decisions across departments such as procurement, marketing, and sales.

To quote futurist Peter Drucker, “The best way to predict the future is to create it.” Demand forecasting empowers businesses to shape their strategies and operations to align with market trends.

For instance, a retailer who overestimates demand might overstock, leading to excessive inventory holding costs, while underestimating demand can result in lost sales opportunities and unhappy customers.

Business Impact of Good Forecasting

A well-implemented demand forecasting system can drive significant improvements in business performance:

Higher Revenue: By aligning stock with demand trends, businesses can capitalize on sales opportunities.

Cost Savings: Efficient inventory management reduces waste and unnecessary expenditures.

Better Resource Allocation: Accurate forecasts allow for optimal utilization of human and capital resources.

Strategic Planning: Enables long-term planning for market expansions, product launches, and budget allocations.


As Steve Jobs famously said, “You can’t connect the dots looking forward; you can only connect them looking backward.” Forecasting helps businesses connect those dots by analyzing past trends to anticipate future outcomes.

Conversely, poor forecasting can lead to supply chain disruptions, strained customer relationships, and financial losses.

Time Series Data: The Foundation of Forecasting

Time series data represents observations recorded sequentially over time. Examples include daily sales, monthly revenue, or weekly website traffic. Unlike regular data, time series has temporal dependencies, meaning past observations influence future values. This characteristic makes it a perfect candidate for specialized forecasting techniques like LSTM networks.

Key Characteristics of Time Series Data:

Trend: A long-term increase or decrease in data values.

Seasonality: Regular patterns or cycles occurring at specific intervals.

Noise: Random variations that do not follow any identifiable pattern.


Real-World Data Challenges

Real-world data rarely comes clean or ready for analysis. Challenges include:

Missing Values: Incomplete data can distort analysis.

Outliers: Extreme values can skew predictions.

Seasonal and Cyclical Trends: Complex patterns require careful handling.

Stationarity: Non-stationary data, where statistical properties change over time, complicates modeling.


LSTM: The Backbone of Modern Forecasting

Long Short-Term Memory (LSTM) networks are a type of recurrent neural network (RNN) designed to capture long-term dependencies in sequential data. Unlike traditional methods, LSTMs can:

       Handle large amounts of data efficiently.

       Capture complex temporal relationships, trends, and seasonality.

       Mitigate issues like vanishing gradients during training.

How LSTMs Work: LSTMs use a system of gates (input, forget, and output gates) to regulate the flow of information, ensuring relevant patterns from the past are retained while irrelevant ones are discarded.

Steps to Forecast with LSTM

  1. Data Preparation: Split the dataset into training, validation, and test sets. Normalize the data.
  2. Model Architecture: Design an LSTM model with suitable layers and hyperparameters.
  3. Training: Train the model on historical data using backpropagation.
  4. Evaluation: Use metrics like RMSE, MAE, or MAPE to assess model performance.
  5. Forecasting: Generate predictions and interpret results to drive business decisions.

Dataset Overview

Dataset I used for blog is taken from kaggle. The dataset contains historical product demand data for a manufacturing company with a global footprint. This company offers thousands of products across dozens of categories and operates four central warehouses, each responsible for fulfilling regional demands. The company manufactures its products at various locations worldwide, which means it typically takes over a month to ship products via ocean freight to different warehouses.

If accurate forecasts can be made for each product’s demand at these warehouses on a monthly basis; specifically for the month after next; it could lead to significant benefits for the company, including optimized inventory management, reduced shipping costs, and improved customer satisfaction.

This dataset consists of five key variables:

Product_Code: Unique identifier for each product.

Warehouse: The central warehouse responsible for fulfilling the order.

Product_Category: The category to which the product belongs.

Date: The date of the order.

Order_Demand: The quantity of demand for the product.


Steps to perform demand forecasting

Step1: Import libraries and read the data

    

    

Step 2: Basic preprocessing on the data:

       Test for null and missing data and drop them.

       Outlier detections and handle them with utmost care.

       Augmented Dickey-Fuller (ADF) test is used to check for stationarity. If the series is non-stationary, apply transformations like differencing or detrending to make it stationary.

       Use moving averages to smooth the time series and reduce noise. This can help in identifying trends more clearly.

       If your data is in a high-frequency format (e.g., hourly), you might want to resample it to a lower frequency (e.g., daily or weekly) to reduce noise and make patterns more apparent.

       Sort the data as per the date related information

Step 3: Perform EDA to explore the data and visualize the data

       Below graph depicts the count of samples according to Product Category

       Below graph shows count of samples according to Warehouse

     

       Percentages of Order Demand according to the years can be depicted as under.

                 
             

       Extraction of statistical information about warehouse data 

           

       Each product category vs various warehouse based analysis can be given as under

 Product Category

       We can finally ensure and visualize the data as time series as under

                           

Step 4: Build LSTM model

The model is built using the Keras Sequential API, enabling a straightforward stacking of layers. The model begins with a Bidirectional LSTM layer consisting of 128 units. This layer processes input sequences in both forward and backward directions, enhancing the ability to understand complex temporal dependencies in the data. To prevent overfitting, a Dropout layer is applied, randomly deactivating 20% of the neurons during training. A second Bidirectional LSTM layer with 64 units follows, refining the features and outputting a single value since return_sequences is set to False. Another Dropout layer is added to regularize the network further. Following the LSTM layers, the model includes a Dense layer with 32 neurons and a ReLU activation function, introducing non-linearity to help extract more meaningful patterns from the data. 

     

The final output layer is a single neuron, responsible for producing the prediction, typically a regression value. The model is compiled with the Huber loss function, known for its robustness against outliers by combining the benefits of Mean Squared Error (MSE) and Mean Absolute Error (MAE). It employs the AdamW optimizer, an improved version of Adam with decoupled weight decay for better generalization, and uses Mean Absolute Error (MAE) as the evaluation metric. After carrying out many experiments it is concluded that the combination of Bidirectional LSTMs, Dropout, and robust loss functions makes the model highly effective for extracting meaningful patterns while maintaining resilience against overfitting and noisy data.

Step 5: Visualize the forecast with LSTM Model

Conclusion

Product demand forecasting is more than just a numbers game—it’s a strategic tool that can make or break a business. By leveraging the power of LSTMs, businesses can unlock the full potential of their time series data, making predictions that are not only accurate but also actionable. While challenges exist, a robust framework involving data preprocessing, feature engineering, and LSTM modeling can overcome them and deliver impactful results.

As renowned physicist Niels Bohr remarked, “Prediction is very difficult, especially if it’s about the future.” Yet, with the right tools and methods, businesses can navigate uncertainty and seize opportunities with confidence.

Ready to transform your business with better demand forecasting? Connect with us to get more details and improved methods for improvements.

About the Author

Himanshu

Machine Learning Expert

Himanshu is the AI and Machine Learning Content Strategist at FutureWebAI, where they lead the creation of cutting-edge content that drives innovation in AI and full-stack development. With a focus on forward-thinking strategies, Himanshu crafts impactful narratives that translate complex AI technologies into compelling, actionable insights