New Product Forecasting using Deep Learning — A unique way


Forecasting demand for new product launches has been a major challenge for industries and the cost of error has been high. Under predict demand and you lose on potential sales, overpredict them and there is excess inventory to take care of. Multiple research suggests that new product contributes to one-third of the organization’s sales across various industry. Industries like Apparel Retailer or Gaming thrive on new launches and innovation, and this number can easily inflate to as high as 70%. Hence the accuracy of demand forecasts has been a top priority for marketers and inventory planning teams.

  • Diffusion modeling
  • Conjoint & Regression based look-alike models
  • It is still a supervised model and the key demand drivers need manual tuning to generate better forecasting accuracy

Case Background

An apparel retailer wanted to forecast demand for its newly launched “Footwear” styles across various lifecycle stages. The current forecasting engine implemented various supervised techniques which were ensembled to generate desired demand forecasting. It had 2 major shortcomings:

  • The heuristic exercise was a significant roadblock in evolving the current process to a scalable architecture, making the overall experience a cost-intensive one
  • The engine was not able to replicate the product life cycle accurately

Proposed Solution

We proposed to tackle the problem through an intelligent, automated, and scalable framework

  • Leverage Recurrent Neural Networks (RNN) to better replicate product lifecycle stages. Since RNN memory layers are better predictors of the next likely event, it is an apt tool to evaluate upcoming time-based performances
  • Since the objective was to devise a scalable method, a cloud-ready easy to use UI was proposed, where users can upload the image of an upcoming style, and the demand forecasts would be generated instantly

Overall Approach

The entire framework was developed in Python using Deep Learning platforms like Tensor Flow with an interactive user interface powered by Django. The Deep Learning systems were supported through NVIDIA GPUs hosted on Google Cloud.

  • Included multiple alignments of the shoe images
  • Standardized the image to a desired format and size
  • Convolution: Conv Net is to extract features from input data. The formation of a matrix by sliding filters over an image and computing a dot product is called “Feature Map”
  • Non-Linearity — RELU: This layer applies an element-wise activation filter leveraged to stimulate non-linearity relationships in a standard ANN
  • Pooling: Reduces the dimensionality of each feature map and retains important information. Helps in arriving at a scale-invariant representation of an image
  • Dropouts: To prevent overfitting random connections are severed
  • SoftMax Layer: Output layer that classifies the image to appropriate category/subcategory/heel height classes
  • Pricing changes
  • Seasonality — Holiday sales
  • Average customer rating
  • Product Attributes — This was sourced from the CNN exercise highlighted in the previous step
  • Product Lifecycle — High sales in the initial weeks followed by declining trend

Demand forecast outcome

Web UI for Analytical Consumption

An illustrative snapshot is highlighted below:

Benefits and Impact

  • Higher accuracy through better learning of the product lifecycle
  • The overall process is self-learning and hence can be scaled quickly
  • Automation of decision-intensive processes like analogous product selection led to a reduction in execution time
  • Long-term cost benefits are higher

Key Challenges & Opportunities

  • The image matching process requires huge data to train
  • The feature selection method can be automated through unsupervised techniques like Deep Auto Encoders which will further improve scalability
  • Managing image data is a cost-intensive process but it can be rationalized over time
  • The process accuracies can be improved by creating a deeper architecture of the network and an additional one-time investment in GPU configurations



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