A Supply Chain Executive’s summary of Deep Learning : With 15+ innovative application opportunities across Supply Chain

What exactly is Deep Learning ?

Deep learning is driving rapid innovations in artificial intelligence and influencing massive disruptions across all markets. This article provides an understanding of the promise of deep learning, the challenges with leveraging this technology, how it is currently solving real-world problems, and more importantly, how deep learning can be made more accessible for data professionals as a whole.

Deep learning, which is a specialized and advanced form of machine learning, performs what is considered “end-to-end learning”. A deep learning algorithm is given massive volumes of data, typically unstructured and disparate, and a task to perform such as classification. The resulting model is then capable of solving complex tasks such as recognizing objects within an image and translating speech in real time.

What are the implications for the world of Supply Chain ?

Deep learning models can be trained to perform complicated tasks such as image or speech recognition and determine meaning from these inputs. A key advantage is that these models scale well with data and their performance will improve as the size of your data increases.


This is the process of an AI application identifying and detecting an object or a
feature in a digital image or video. Image classification has taken off in the retail
vertical which is using deep learning models to quickly scan and analyze in-store
imagery to intuitively determine inventory movement (The basis behind Amazon Go stores). This has lead to streamlined operations, reduced costs, and new sales opportunities.

The following are its applications in Supply Chain, Transportation and Warehousing:

  1. Yard Management (in conjunction with drones)
  2. Warehouse aisle and bay utilization
  3. Loading and unloading dock operations optimization
  4. Trailer utilization
  5. Transit pallet damage identification during unloading
  6. Manufacturing Quality control


This is the ability of a deep learning model to receive and interpret dictation or to
understand and carry out spoken commands. Models are able to convert captured
voice commands to text and then use natural language processing to understand
what is being said and in what context.

The following are its applications in Supply Chain, Transportation and Warehousing:

  1. Pick to voice warehouse systems
  2. Voice enables warehouse operations metrics dashboards
  3. Voice enabled Trailer Management in the yard


This deep learning technique strives to recognize abnormal patterns which don’t
match the behaviors expected for a particular system, out of millions of different
transactions. These applications can lead to the discovery of an attack on financial
networks, fraud detection in insurance filings or credit card purchases, even
isolating sensor data in industrial facilities signifying a safety issue.

The following are its applications in Supply Chain, Transportation and Warehousing:

  1. Freight Audit
  2. Direct material Supplier Invoices audit
  3. Indirect spend audit
  4. Load building plan audit
  5. Warehouse slotting optimization audit
  6. Manufacturing process monitoring


These models analyze user actions in order to provide recommendations based on
user behavior. Recommendation engines are critical components of e-commerce
sites such as Overstock.com which uses a recommendation engine to provide
accurate suggestions of products to users for future purchases based on their
shopping history. This massively reduces the friction for the user and provides
efficient revenue streams for the company.

The following are its applications in Supply Chain, Transportation and Warehousing:

  1. Inventory classification
  2. Optimal Transportation mode selection
  3. Optimal Transportation Service level selection
  4. Optimal product stocking location in the warehouse
  5. Dynamic Inventory Management

What does the Deep Learning process workflow look like?

A generalized workflow for building and training a deep learning model consists of steps that vary in complexity. This spans the ingestion of data, through network architecture choice, to production.


CREATE YOUR TRAINING DATA SET – This can include a wide variety of data types from a wide variety of sources needed to train a model, which may include additional effort to obtain labels or target variable values.

ANALYZE THE DATA – It is critical to clean and organize the data in order to eliminate errors and discrepancies.

DESIGN YOUR ARCHITECTURE – The key is to understand the type of problem you are trying to solve and then choosing the right architecture for the job.

TUNE YOUR HYPERPARAMETERS – In machine learning, a hyperparameter is a parameter whose value is set before the learning process begins. By contrast, the values of other parameters are derived via training. Getting the best results in deep learning requires experimenting with different values for training hyperparameters.

TRAIN THE MODEL – In this step, you provide the data to the learning algorithm which performs optimization routines to produce the best model it can.

EVALUATE PERFORMANCE – This validates the ability of the model to confidently perform forecasting and estimation — the actual “thinking” of the model — against an unseen (“test”) dataset.

Deep Learning frameworks available

If you were to start doing deep learning, which framework would you use? That question can be answered by understanding the problem you are trying to solve. Most frameworks support different programming languages, offer varying levels of architectural complexity, different degrees of performance, and a variety of deep learning algorithms suitable for specific use cases. Here’s an overview of some of the common deep learning frameworks available.


TENSORFLOW – is a powerful, open-source software library for numerical
computation using data flow graphs.
CAFFE – is an open-source deep learning framework designed with expression, speed,
and modularity in mind. It is often used for operationalizing models for prediction in
MXNET – is a deep learning framework designed for both efficiency and flexibility. It
allows you to mix symbolic and imperative programming to maximize efficiency and
KERAS – is an open source neural network library written in Python designed to enable
fast experimentation with deep neural networks, and focuses on being minimal,
modular and extensible.
PYTORCH – is a Python based computing package that provides a wide range of
algorithms for deep machine learning.

It needs to be noted that even with all of the available frameworks and greater understanding of how to pursue AI through machine learning and deep learning, there are still substantial limitations to current abilities of artificial intelligence. Trying to compare the capabilities of a deep learning application against the capabilities of an actual human is going to be disappointing to say the least – it’s currently not an
apples to apples comparison. A reason for this is that while a person may easily recognize the nuance associated with knowing for example how to clearly distinguish the differences between images of a toddler and an infant, a deep learning program will find this incredibly difficult to do with consistent accuracy.

The performance of any artificial intelligence will only be as good as the data that it is being fed, and if the data itself is either incorrect or incomplete, the performance will be simply wrong. Processing a lot of data is easy, but feature learning is very difficult. But, as deep learning algorithms evolve to recognize nuances and overcome their current limitations, the future of AI is bright. What’s most critical is for big data analytics platforms to handle the growing complexities of these algorithms as well as the scale of the data that will be required for better model training

Challenges Ahead

Leveraging the promise of deep learning today is as challenging as big data was yesterday. Deep learning frameworks have unique capabilities and steep learning curves. Scaling out over distributed hardware requires specialization and significant manual work; and even with the combination of time and resources, achieving success requires tedious fiddling and experimenting with parameters.

Additionally, each of these frameworks provide different strengths for different deep learning approaches and their own unique challenges. For example, Caffe is a strong option for image classification but can be resource intensive and the framework itself is difficult to deploy. Things can get very hard, and very complicated, very quickly for Data Science and Engineering teams. Also, the effectiveness of deep learning, or artificial intelligence of any type, rests on the quality of the infrastructure that powers it. The infrastructure should be viewed as a multiplier of the effectiveness of your AI.

Neural networks come in various architectures, the performance of which is a function of the architecture, which can be challenging for traditional engineering teams to manage, let alone a data science team. Also, the processing requirements of an AI infrastructure can be massive, requiring specialized – and expensive – processors such as GPU’s (graphical processing units) in order to perform the mathematical computations that power deep learning models.

From a resource standpoint, training an accurate deep learning model can be extremely taxing. Parameters need to be tweaked to create a great model, and this step can be manually intensive. This puts a lot of pressure on a data science team as the number of decisions required to develop successful deep learning models can be incredibly time-consuming. Significant time and money can be wasted if poor decisions are made. Also, since deep learning models are complex, it requires a significant amount of data to accurately train a model.


Artificial intelligence through deep learning will drive innovations in IT for the foreseeable future. The rise of big data has helped to bring this to reality and the benefits are just beginning to be realized. AI is already being explored across many industries, and as the technology improves, will have a major impact on how it can be used to solve real-world problems. The opportunity to bring AI to the mainstream is here.


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