AI in Retail: Applications in the Industry 4.0 Market

Currently, we experience the technological advances present in the industries, directly impacting the processes of construction, development, and delivery of products to the consumer. Market competitiveness increasingly focuses on technological and digital pillars, thus making the automation and digitization of processes more recurrent in companies. The impact generated by Industry 4.0 has created a horizon of opportunities for the retail market to get ahead and compete at higher levels with competitors, seeking higher rankings in terms of delivery, quality, efficiency, and effectiveness in the processes until the product reaches the consumer.

AI in retail

In addition, advances in Artificial Intelligence, Machine Learning, and IoT (Internet of Things) provide new horizons for the various branches of the retail industry. The automation of storage processes, route monitoring, material storage strategies, demand forecasting, and customer satisfaction are examples of procedures adopted through these technologies to obtain better results in the market.

We are living in the data age. Being prepared for it, and orienting internal processes to data, will enable companies to immerse themselves in this ocean of opportunities, thus resulting in cost reductions based on analysis of losses and waste, and in more sustainability, competitiveness, and approval in the market.

Supply chain and S&OP

Among the different operational areas of the industry, artificial intelligence stands out strongly in the supply chain, promoting more automation in production processes. Tracing a procedural path of operations with AI, it is clear that the cycle ranges from the implementation of intelligent technologies in sales and operations (S&OP) processes, thus intensifying the analysis for better sales strategies with the help of the marketing area, to better formats to operate and the automation of exhausting and repetitive jobs.

Through the advancement of the Internet of Things, it becomes more efficient to capture data from different stages of production. Obtaining data from the first task to delivery to the final consumer is no longer a problem, with the possibility of extracting production data, for example, robots implemented for storage, applications for drivers, and products connected to the internet, among other ways of collecting data with IoT. Another important point is the bridge between stakeholders through advanced data analysis with Machine Learning and AI, aiming to filter raw material suppliers and final suppliers that are more aligned with the company’s interests and also to obtain a lower loss in the processes. procurement of materials and delivery to the consumer.

AI, ML, and IoT technologies also influence revenue generation, increasing profits and better results with customer and supplier relationship management. An example of this is intelligent dynamic pricing, which uses artificial intelligence and adopts market and consumer-based strategies to determine the best price (not necessarily the highest, but the most appropriate price to compete in the market), aiming at increasing revenue.

Demand Forecasting 

Regarding demand forecasting processes, the implementation of AI and ML produce an assertiveness of around 90%, generating impact and improvements in demand forecasts based on advanced analysis of different data, such as weather conditions, the economic situation of the market, available quantities, consumer desire, and consumption predictability. In addition, advanced analytics and intelligent models that have continuous learning through greater data collection and time provide predictive actions in real-time, helping decisions in a way assisted by professionals. This reduces the failures and risks in operations with decision-making and can change them in case of negative predictions that can generate several impacts.

Furthermore, in the area of ​​dairy and perishable products, AI has great strength, as strategies for goods with short dates and more fragile logistics need to be much sharper. This contribution is supported by collecting data, and information and creating predictive demand models that deliver better strategies for storing products, defining the best routes, reducing fuel waste, and forecasting geolocation in the case of products with greater demand. so as not to keep them in distant stocks, thus facilitating the preservation of the products until their final delivery.

Big Data

This is a term that has been gaining great proportion and space in the context of industry 4.0, representing the large mass of data, intensive collection, and importance of artificial intelligence and machine learning to handle this information that can add a lot of value to companies. Represented by the thousands of data produced by the different stages and experiences of the market, big data includes purchase data, online browsing of consumers, media and marketing data, and customer satisfaction with the service and/or product, among other diverse information.

The process of collecting and storing data is complex and analyzing thousands of data becomes an impossible human task. Thus, AI and intelligent models based on machine learning go hand in hand with big data to integrate external market and internal company data in a way that makes forecasting and planning of demand, greater revenue, profit, reduction of waste, and sustainability.

Logistics 4.0 

It is clear the advances that industry 4.0 has been allocating. For example various automation in production processes, digitalization of products for testing improvements, speed of information, and implementation of results.

With industry 4.0 comes logistics 4.0, aimed at optimizing the loading and unloading processes of goods. Automation and use of AI in various stages of logistics, such as the organization of products in warehouses made by robots that, through AI, strategically and hierarchically organize products to facilitate and increase the speed of cargo operations.

In addition, it is possible to generate forecasts of events on highways, such as works that interrupt routes, using AI and real-time data analysis. This allows the adoption of a better route in the present time, without relying on the historical past and wasting resources, also resulting in customer satisfaction and speed of delivery. Taking advantage of routes, inappropriate and unnecessary use of vehicles, higher gas emissions, and high fuel and maintenance costs are problems interrupted by logistics 4.0 directives, aiming at more assertiveness, intelligence, sustainability, greater revenue, and consumer and supplier satisfaction.

AI in Retail – Final Thoughts

Implementing AI and machine learning through intelligent models is not an easy and instantaneous task. However, the result of all the preparation and construction of these technologies directed to the specifics of the business will result in several benefits.

The power of AI provides intelligent market insight, demand forecasting with higher hit rates, reduced product loss due to expiration or warehouse saturation, and precision in price adjustments supported by different variables that can influence revenue variation. In addition, through advanced data analysis, it is possible to filter suppliers looking for those that deliver the most results and are more aligned with the company’s values.

These positive points are in line with the use of AI to obtain better sustainable results, aiming at the use of routes, continuous delivery, analysis of better routes, reduction in costs, and gas emissions.

The access generated by advanced analytics and AI of the entire supply chain and operations of the company results in great predictability of risks or failures in the initial stages, preparation, and delivery to the end customer. This power of predictability and intelligent strategies consolidates the idea of ​​risk management in a real-time, drastic reduction of failures and waste, and unified control of the stages of sales, operations, production, and delivery of goods. In short, smarter and more sustainable companies have never been so close to being consolidated. The way forward only depends on preparation and organization for greater intelligence and predictability.

What is Aquarela Advanced Analytics?

Aquarela Analytics is the winner of the CNI Innovation Award in Brazil and a national reference in the application of corporate Artificial Intelligence in the industry and large companies. Through the Vorteris platform and the DCM methodology, it serves important clients such as Embraer (aerospace), Scania, Randon Group (automotive), SolarBR Coca-Cola (food retail), Hospital das Clínicas (healthcare), NTS-Brasil (oil and gas), Auren, SPIC Brasil (energy), Telefônica Vivo (telecommunications), among others.

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