AI in Retail: Applications in the Industry 4.0 Market

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 Brazilian pioneering company and reference in the application of Artificial Intelligence in industry and large companies. With the Vortx platform and DCIM methodology, it serves important global customers such as Embraer (aerospace & defence), Scania and Randon Group (automotive), Solar Br Coca-Cola (beverages), Hospital das Clínicas (healthcare), NTS-Brasil (oil & gas), Votorantim Energia (energy), among others.

Stay tuned following Aquarela’s Linkedin!


Four Facts that will change the Job Market after industry 4.0 kicks in

Four Facts that will change the Job Market after industry 4.0 kicks in

We are currently living in the best time in the history of humanity, at least in terms of the power of creativity and knowledge flow towards the concepts of Industry 4.0 and Web3.0 (what is Web 3.0?). Jobs, wages, hours, and benefits are far superior in the twenty-first century. Despite the pandemic, job projections show that everyone will have a different future with new trends.

One of the characteristics of this era is digitization, which applies to people and organizations alike. This trend is responsible for all the changes and innovations in our daily routines. Thanks to technology, our life (physically speaking) is much simpler. According to the Wall Street Journal, 50 percent of work activities done by humans can have a robotic replacement using the current technology. 

However, this will not be a problem for job seekers for the next few years. The paragraphs below will talk about the four most important facts that will mark the future of jobs in this decade.

Remote Work and Hybrid Schedules

The pandemic made digitization a necessity for businesses, schools, and governments. The home office will be a common activity for the next few years, especially in digital marketing, health care, software development, and financial consulting. This trend also means hybrid schedules, which simultaneously save resources for companies and employees.

These schedules consist of working hours from home through digital devices such as computers or smartphones. Some companies plan to apply this modality one or two days per week, while others hire workers to cover 100 percent of their time remotely. At Aquarela Analytics for instance 100% of the total workforce operates remotely and only 40% lives nearby the headquarters.

Robotic Growth

Manufacturing and transportation companies will drastically change their production over the next ten years. The growth of robotic technology will be a historic victory in the factories of developed countries (and in the rest of the nations, eventually). You may see the negative side of this story, as thousands of manual jobs are lost, but this trend will also be favorable for many more people.

Companies will invest in personnel to maintain, manage, and repair these mechanical systems. In addition, patent companies will invest millions in hiring robotics engineers to develop these machines. On the other hand, we see the demand for other specialized occupations such as data science, cloud computing, and software design in an ever changing technological stack.

Training Centers Within Companies

Employers know the impact of technology and new trends in the job market. A trained team means better results, efficient production, and more profit for the company. The training centers are technical and cultural training strategies. For this reason, it is common to see companies encouraging their employees to attend a coding bootcamp to learn about advanced technology and current trends.

Competition is the main reason employers want updated staff. This is the rise of the self-taught era.  If you want to know which job offer is right for you, evaluate the training programs, educational resources, and technical training they offer. Your level of preparation helps you become a more efficient professional prepared for any challenge.

AI for forecasts and dynamic pricing

The future may not be more predictable than it used to be, but the number of attempts to crack it will continue to grow as AI mines ever growing datasets to generate ever more complex combinations. This is at least a “predictable” prediction of the 4.0 industrialization process.

The ability to understand the new workflows of Industry 4.0 will have a high impact on the job market. Working to teach a computer has a significant impact on the ability to look at the data wearing a scientific glass to avoid any types of data bias. 

Four Facts that will change the Job Market after industry 4.0 kicks in – Conclusion

In the paragraphs above, we saw the strong impact of technology on each of the trends for the job market in this century or at least this next decade. This field can be a great ally for you, so it is important to consider our own capabilities and interest in joining tech careers. Consider the facts, analyze the patterns of change in the labor market, and prepare for the challenges that lie ahead in the next ten years.

What is Aquarela Advanced Analytics?

Aquarela Analytics is Brazilian pioneering company and reference in the application of Artificial Intelligence in industry and large companies. With the Vortx platform and DCIM methodology, it serves important global customers such as Embraer (aerospace & defence), Scania and Randon Group (automotive), Solar Br Coca-Cola (beverages), Hospital das Clínicas (healthcare), NTS-Brasil (oil & gas), Votorantim Energia (energy), among others.

Stay tuned following Aquarela’s Linkedin!


Syed Ibrahim Imran, from Karachi, Pakistan, started working with Career Karma in July 2020. He is currently enrolled at Iqra University as a BBA student. He has contributed content to some of the best software and IT companies in Pakistan. He hopes to start his own digital marketing agency or real estate firm before he turns 30 years old.


AI for demand forecasting in the food industry

AI for demand forecasting in the food industry

The concept of a balance point between supply and demand is used to explain various situations in our daily lives, from bread in the neighborhood bakery, which can be sold at the equilibrium price, which equals the quantities desired by buyers and sellers, to the negotiation of securities of companies in the stock market.

On the supply side, a definition of the correct price to be practiced and mainly the quantity are common issues in the planning and execution of the strategy of several companies.

In this context, how are technological innovations in the data area establishing themselves in the food sector?

The construction of the demand forecast

The projection of demand is often built through historical sales data, growth prospects for the sector or even targets set to engage sales of a certain product.

When considering only these means of forecasting, without considering the specific growth of each SKU (Stock Keeping Unit), companies can fall into the traps of subjectivity or generalism.

The expansion of a sector does not result in a growth of the same magnitude for the entire product mix. For example, does a projected annual growth of 6% for the food sector necessarily imply equivalent growth for the noble meat segment?

Possibly not, as this market niche may be more resilient or sensitive than the food sector, or it may even suffer from recent changes in consumer habits.

Impacts of Demand Forecasting Errors

For companies, mainly large ones with economies of scale and geographic capillarity, an error in the forecast of demand can cause several consequences, such as:

  • Stock break;
  • Perishable waste (What is FIFO?);
  • Drop in production;
  • Idle stock (slow moving)
  • Pricing errors

Adversities like these directly impact the companies’ final results, as they result in loss of market share, increase in costs or low optimization in the dilution of fixed costs, growth in the loss of perishable products, frustration of employees in relation to the goals and mainly break in the confidence of recurring customers who depend on supply for their operations.

The demand forecast in the food sector

The food industry is situated in a context of highly perishable products with the following characteristics:

  • High inventory turnover;
  • Parallel supply in different locations;
  • Large number of Skus, points of production and points of sale;
  • Verticalized supply chain;
  • Non-linearity in data patterns;
  • Seasonality.

These characteristics make the sector a business niche that is more sensitive to deviations in demand forecast and adjacent planning.

Supply chain opportunity

As an alternative to the traditional demand forecast format, there are opportunities to use market and AI data to assist managers in the S&OP (Sales & Operations Planning) process, as well as in the S&OE (Sales and Operations Execution) process.

During the S&OP process, demand forecasting supported by AI facilitates the work of the marketing and sales areas, as well as reducing uncertainty and increasing predictability for the supply chain areas.

In the S&OE process, AI can be used to identify new opportunities and to correct deviations from what was planned.

In addition to the technical attributes that AI can add to the process, the data base reduces points of conflict between teams, reduces historical disputes between preferences for SKUs and makes the process more transparent between areas.

Previously, in our blog, we addressed the challenges of forecasting demand in our view (pt. 1 in portuguese). In the articles, we cite the differentials of the predictive approach in relation to demand, taking into account factors such as seasonality, geographic / regional preferences and changes in consumer behavior.

We understand that the need for a predictive approach through data, mainly external to the company, is increasingly latent.

The role of machine learning in the food sector

The use of AI through machine learning techniques associated with a coherent technological stack of analytics (What is a technological stack?) Provides greater information speed, data organization with different granularities (region, state, city and neighborhood), adjustments seasonality, exploration of opportunities and decision making in real time.

In the case of the food sector, the greatest accuracy in forecasting demand means:

  • Inventory optimization among Distribution Centers (CDs);
  • Reduction of idle stocks;
  • Decrease in disruptions that cause loss of market share due to substitute products;
  • Direct reduction in losses with perishability (FIFO).

The great technical and conceptual challenge faced by data scientists (The profile of data scientists in the view of Aquarela), however, is the modeling of analysis datasets (what are datasets?) That will serve for the proper training of machines.

Please note that:

“Performing machine training with data from the past alone will cause the machines to replicate the same mistakes and successes of the past, especially in terms of pricing, so the goal should be to create hybrid models that help AI replicate with more intensity and emphasis the desired behaviors of the management strategy “.

In the case of Aquarela Analytics, the demand forecast module of Aquarela Tactics makes it possible to obtain forecasts integrated into corporate systems and management strategies. It was created based on real national-wide retail data and algorithms designed to meet specific demands in the areas of marketing, sales, supply chain, operations and planning (S&OP and S&OE).

Conclusions and recommendations

In this article, we present some key characteristics of the operation of demand forecasting in the food sector. We also comment, based on our experiences, on the role of structuring analytics and AI in forecasting demand. Both are prominent and challenging themes for managers, mathematicians and data scientists.

Technological innovations in forecasting, especially with the use of Artificial Intelligence algorithms, are increasingly present in the operation of companies and their benefits are increasingly evident in industry publications.

In addition to avoiding negative points of underestimating demand, the predictive approach, when done well, makes it possible to gain market share in current products and a great competitive advantage in forecasting opportunities in other niches before competitors.

What is Aquarela Advanced Analytics?

Aquarela Analytics is Brazilian pioneering company and reference in the application of Artificial Intelligence in industry and large companies. With the Vortx platform and DCIM methodology, it serves important global customers such as Embraer (aerospace & defence), Scania and Randon Group (automotive), Solar Br Coca-Cola (beverages), Hospital das Clínicas (healthcare), NTS-Brasil (oil & gas), Votorantim Energia (energy), among others.

Stay tuned following Aquarela’s Linkedin!