Unveiling Insights in Supply Chain Data using Concept Activation Vectors (CAVs)

Introduction: Supply chain data plays a vital role in optimising operations, improving efficiency, and enhancing decision-making within organisations. However, understanding the underlying concepts and patterns learned by neural network models applied to supply chain data can be a complex task. In this article, we will explore how Concept Activation Vectors (CAVs) can help interpret neural network models in the context of supply chain data. We will provide a comprehensive code example in Python, along with proper comments, to demonstrate the practical implementation of CAVs.

Key Audience: Data scientists, machine learning practitioners, and supply chain professionals seeking to interpret neural network models applied to supply chain data. Researchers and practitioners interested in leveraging CAVs for model explanation in supply chain analytics. Anyone looking to gain insights into the inner workings of neural networks applied to supply chain datasets.

Understanding Supply Chain Concepts with CAVs: Concept Activation Vectors (CAVs) offer a unique perspective on interpreting neural network models applied to supply chain data. By associating high-level concepts with specific neurons within the network, CAVs help us uncover the underlying patterns and concepts learned by the model. This understanding enables us to explain the behavior of the model and gain insights into the dynamics of supply chain processes.

Sample Use Case: Predicting Demand in a Retail Supply Chain To illustrate the application of CAVs in the supply chain domain, let’s consider a use case of predicting demand in a retail supply chain. We will demonstrate how CAVs can aid in interpreting the neural network model’s decisions and understanding the concepts driving the predictions.

Data Preparation:

• Load and preprocess the supply chain data, including historical sales, inventory levels, pricing information, and promotional activities.
• Perform feature engineering to extract relevant features such as seasonality, trend, and lag variables.
• Split the data into training and testing sets to evaluate the model’s performance.

Model Training and Evaluation:

• Train a neural network model to predict demand based on the prepared supply chain data.
• Evaluate the model’s performance using appropriate metrics like mean absolute error (MAE) or root mean squared error (RMSE).

Computing CAVs:

• Select a specific neuron within the trained neural network model that represents an important decision-making unit.
• Define a concept of interest related to supply chain dynamics, such as promotional impact or inventory levels.
• Generate reference samples by randomising the selected concept while keeping the other features intact.
• Train an auxiliary classifier to distinguish between the concept and the reference samples.
• Compute the gradients of the concept with respect to the activations of the selected neuron to obtain the Concept Activation Vector (CAV).

Interpreting the Results:

• Analyse the CAV to understand the sensitivity of the neuron to the selected concept.
• Examine the magnitude and direction of the CAV values to identify the concepts that significantly influence the model’s decisions.

Code Example:

import numpy as np
import pandas as pd
import tensorflow as tf
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_absolute_error

# Step 1: Load and preprocess the supply chain data
data = pd.read_csv('supply_chain_data.csv')
# ... data preprocessing steps ...

# Step 2: Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Step 3: Train a neural network model
model = tf.keras.Sequential()
# ... define and compile the model architecture ...

model.fit(X_train, y_train, epochs=10, batch_size=32)

# Step 4: Evaluate the model's performance
y_pred = model.predict(X_test)
mae = mean_absolute_error(y_test, y_pred)

# Step 6: Select a specific neuron in the trained model
selected_neuron = model.layers[5].output

# Step 7: Define the concept of interest
concept = data['promotional_impact']

# Step 8: Generate reference samples
reference_samples = data.copy()
reference_samples['promotional_impact'] = np.random.random(size=len(reference_samples))

# Step 9: Train an auxiliary classifier
aux_model = tf.keras.Sequential()
# ... define and compile the auxiliary classifier architecture ...

aux_model.fit(X_train, concept, epochs=10, batch_size=32)

# Step 10: Compute the Concept Activation Vector (CAV)
with tf.GradientTape() as tape:
    tape.watch(selected_neuron)
    neuron_activations = model.predict(X_train)
    concept_predictions = aux_model.predict(X_train)
    loss = tf.keras.losses.mean_squared_error(neuron_activations, concept_predictions)

gradients = tape.gradient(loss, selected_neuron)
cav = np.mean(gradients, axis=0)

# Step 11: Analyze and interpret the CAV results
# ... perform analysis and visualization of CAV values ...        

References:

1. Kim, B., Wattenberg, M., Gilmer, J., Cai, C., Wexler, J., Viegas, F., & Sayres, R. (2018). Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (CAVs). arXiv preprint arXiv:1711.11279.
2. Olah, C., Satyanarayan, A., Johnson, I., Carter, S., Schubert, L., Ye, K., & Mordvintsev, A. (2018). The building blocks of interpretability. Distill, 3(3), e10.

Keywords: Concept Activation Vectors, CAVs, Neural Network Interpretability, Supply Chain Analytics, Model Explanation, Model Transparency, Deep Learning, Predictive Modeling