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The Synergy of Logistics, Retail & Artificial Intelligence

Updated: Aug 4, 2020

Advancements in information technology have led to a dramatic surge of artificial intelligence applications. With data streaming in from all directions, machine and deep learning have enabled the analysis, evaluation and employment of data to optimize our systems’ performances – from loan default forecasting by small finance banks to on-spot essay analysis by educational institutions. Automation and augmentation in every industry have their foundations in artificial intelligence.


However, some industries stand out in their potential to exploit artificial intelligence and benefit from optimization like no other: namely, Logistics & Retail. With centuries of traditions dictating supply chain development, consumer retention and other integral industrial pillars, artificial intelligence is – in the literal meaning of the phrase – here for disruptive business.


Demand Forecasting & Supply Chain Enhancement


Long-Short Term Memory (LSTM) models are at the forefront of time-series prediction. A type of neural network, LSTM, allows machines to more accurately predict a time-dependent input as compared with Recurrent Neural Network (RNN) – more details on how LSTMs solve the vanishing gradient problem here.

In light of LSTM models’ abilities to reliably predict patterns, demand forecasting has become their go-to application. Most items vary cyclically – fluctuating in demand throughout the year – and this makes forecasting supply a particularly tough job.

We can see the trend line with a steady gradient – but there are cycles throughout the year. (Kaggle, 2018)


However, with the help of artificial intelligence – especially LSTM models – the complete trend of each item in every store can be taken into account. This would empower producers to supply the near-perfect amount, lowering costs associated with stocking, wastage and rushed production.


P.S. another exciting, upcoming model which can help with time series prediction is a “transformer model.” These models use an encoder-decoder system along with an “attention” module to appropriately weigh a massive amount of data. Check them out here!


Review Aggregation & Sentiment Analysis

Understanding consumer preferences is a crucial part of all industries. However, it is only in retail that we can understand its true value to businesses – and what better way than to ask the customers themselves. With the advent of online reviews, ratings and other engagement metrics, every firm has the ability to better engage with their customers. Fueled by the power of Natural Language Processing (NLP), product optimization and performance marketing have gradually embraced artificial intelligence.


We can extract a simple 3-word summary from a relatively long review. (AnalyticsVidhya, 2019)


Deep learning models, such as LSTMs with an encoder-decoder architecture, can be used to summarize colossal amounts of reviews a product receives online or in-person to a mere 5-10% of the original text. This can significantly expedite the process of strategists, designers and managers who simply want to know what, how and where to better their products or services.

Likewise, another amazing prospect of NLP is its ability to conduct accurate sentiment analysis of texts. Extracting emotions and linguistic elements such as sarcasm, sadness, anger or humor from thousands of reviews with minimal latency leads to astronomical enhancements on the efficiency end. Knowing how customers feel without scraping through bewildering amounts of text is a godsend for marketing and designing teams throughout retail.


Sentiment analysis on product reviews (KDNuggets, 2017)

Product Analysis through Image Recognition


Sourcing is essential for both logistics providers and retailers – the final product will only be as good as the founding ingredients. Therefore, the primary product must be thoroughly analyzed and evaluated for quality control and only then passed on to the consumer. However, with a diverse set of materials being used to develop most products and the sheer amount of material required for industrial, mass processing – it’s becoming increasingly more complicated for a small team of quality controllers.

Artificial Intelligence is here to save the day. With advancements in Convolution Neural Networks (CNNs), image processing is crossing over from mundane facial recognition systems to detecting the quality of products on an industrial scale. CNNs allow one to process images with latency, and this value can be used to see if a product matches specification or not, without being monitored by a human.


A corn leaf with the Northern Corn Leaf Blight being identified by an artificial intelligence system (Martinez, 2018).

These are just some of the ways in which artificial intelligence can disrupt the world of retail and logistics. With the introduction of quantum computers, continuously bettering hardware and creation of more efficient algorithms, deep learning will allow us to completely revolutionize industries.


At Neuralastic, we’re already putting artificial intelligence to use in most industries. We’re helping our clients, in retail predict what trends will be in demand the coming season, optimizing price based on historical data and news for wholesalers and enabling our logistics partners to optimize product delivery based on demand forecasts – we’re doing it all with our state-of-the-art, personalized yet affordable AI models. Head on to neuralastic.com to know more!


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