AI4DA in Retail Operations

The retail sector is currently facing significant challenges in Canada and around the world, such as inflation, supply chain disruptions, armed conflicts, labour shortages, environmental concerns and changing consumer preferences. To succeed in this competitive and dynamic market, retailers need to use artificial intelligence and decision analytics to optimize their operations and provide personalized, seamless shopping experiences across channels. 

At the AI Centre for Decision Analytics, we aim to develop and apply state-of-the-art machine learning/data analysis techniques to solve various retail problems to help retailers boost their revenue and profits, lower their costs and waste and improve customer loyalty and satisfaction.

At a glance

$752B revenue: Retail is a crucial sector of the Canadian economy, generating over $752 billion in revenue in 2021, an increase of more than 12% from 2020.

E-Commerce growth: While physical store sales grew by 11%, accounting 4

for 91% of total retail revenue, e-commerce sales surged by 25%, representing 9% of total retail revenue

Significant GHG emissions: The last-mile delivery sector in Canada holds substantial value, accounting for a notable 53% of total shipping expenses. However, it carries the weight of environmental impact, contributing to 37% of the country’s transportation-related greenhouse gas emissions. 

How AI Can Help

Demand Forecasting and Inventory Optimization. AI4DA uses machine learning algorithms to predict future demand and optimize stock levels, locations, and replenishment policies. This reduces waste and stockouts, lowers inventory costs, and increases customer satisfaction. AI4DA can also use optimization algorithms to determine the best inventory levels for each product and location, taking into account factors such as lead time, holding cost, service level and replenishment frequency. 

Dynamic Pricing and Personalized Promotions. AI4DA uses AI to adjust prices in real time based on market conditions, demand elasticity and inventory levels. AI4DA also creates individualized promotions and product recommendations based on customer preferences and purchase history. This maximizes revenue and profits, and enhances customer loyalty. AI4DA can use ML models to estimate the optimal price for each product and customer segment, taking into account factors such as competitors’ prices, market trends, customer willingness to pay, product life cycle and seasonality. 

Agile Supply Chain Management. AI4DA enables collaborative, data-driven management of suppliers, logistics and fulfillment through AI-powered models. These models also help mitigate pandemic-related disruptions in supply chains by predicting changes in demand, assessing supplier risks, optimizing delivery routes and schedules, tracking goods in real time and planning for scenarios. AI4DA can also use predictive analytics to forecast the impact of various factors on the supply chain, such as demand changes, supplier disruptions and weather events, and provide contingency plans for different scenarios. AI4DA can also use optimization techniques to find the best delivery routes and schedules for each order, taking into account factors such as traffic conditions, fuel costs and customer preferences.