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.
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.
Founder – Commercial Director, Msc. Business Information Technology at University of Twente – The Netherlands. Lecturer in the area of Data Science, Data governance and business development for industry 4.0. Responsible for large projects in key industry players in Brazil in the areas of Energy, Telecom, Logistics and Food.
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.
Founder – Commercial Director, Msc. Business Information Technology at University of Twente – The Netherlands. Lecturer in the area of Data Science, Data governance and business development for industry 4.0. Responsible for large projects in key industry players in Brazil in the areas of Energy, Telecom, Logistics and Food.
The stack represents a set of integrated systems to run a single application without additional software. In this way and above all, one of the main goals of a technology stack is to improve communication about how an application is built. In addition, the chosen technology package may contain:
the programming languages used;
structures and tools that a developer needs to interact with the application;
known performance attributes and limitations;
survey of strengths and weaknesses of the application in general.
As a rule, stacks must have a specific purpose. For instance, if we look at the the web 3.0 stack (what is web 3.0?), you will see how much different it is in relation to a data analysis stack in statistical R language. That is, the construction of a stack you should always ask: What is the underlying business purpose?
Where does this term come from?
The term comes from the software development community and along with it it is also quite common to speak of a full-stack developer.
A full-stack developer is, in turn, the professional who knows how to work in all layers of technologies of a 100% functional application.
Why is the technological stack so important?
Firstly, on the one hand, the accountant has all company transactions registered for financial management, on the other hand, developers and project leaders need the information of the development team.
Secondly, developers cannot manage their work effectively without at least knowing what is happening, what are the available technology assets (systems, databases, programming languages, communication protocols) and so on.
The technological stack is just as important as lifting inventory control from a company that sells physical products. It is in the technological stack that both the business strategy and the main learning (maturity) of system tests that the company has been through are concentrated.
The technological stack the working dictionary of developers in the same manner data analytics look at their data dictionaries to understand the meaning of variables and columns. It is an important item of maturity in the governance of organizations.
Without prior knowledge of the technological stack, management is unable to plan hiring, risk mitigation plans, plans to increase service capacity and, of course, the strategy for using data in the business area.
Technology stacks are particularly useful for hiring developers, analysts and data scientists.
“Companies that try to recruit developers often include their technology stack in their job descriptions.”
For this reason, professionals interested in advancing their careers should pay attention to the strategy of personal development of their skills in a way that is in line with market demand.
Technological stack example
The professional social network, Linkedin, for example: it is composed of a combination of structures and programming languages and artificial intelligence algorithms to be online. So, here are some examples of technologies used in their stack:
Technological Stack – Linkedin for 300 million hits – Author Philipp Weber (2015)
Is there a technological stack for analytics?
Yes, currently the area of analytics, machine learning, artificial intelligence are known for the massive use of techniques and technologies of information systems. Likewise, analytical solutions require very specific stacks to meet functional (what the system should do) and non-functional (how the system will do – security, speed, etc.) business requirements for each application.
As the foundation of a house, the order in which the stack is built is important and is directly linked to the maturity of the IT and analytics teams, so we recommend reading this article – The 3 pillars of the maturity of the analytics teams (in Portuguese).
In more than 10 years of research in different types of technologies, we have gone through several technological compositions until we reached the conformation of the current Aquarela Vortx platform. The main stack results for customers are:
Reduction of technological risk (learning is already incorporated in the stack);
technological update;
speed of deployment and systems integration (go-live);
maturity of the maintenance of the systems in production and;
the quality of the interfaces and flows in the production environment as the stack makes the maintenance of technicians’ knowledge more efficient.
Conclusions and recommendations
In conclusion, we presented our vision of the technological stack concept and how it is also important for analytical projects. Which, in turn, impacts strategic planning. Yet, it is worth bearing in mind that technological stacks are just like business, always evolving.
Regardless of the sector, the decisions involved in shaping the technological stack are a factor of success or failure in IT and analytics projects. Because, they directly interfere in the operation and in the business strategy.
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.
Founder – Commercial Director, Msc. Business Information Technology at University of Twente – The Netherlands. Lecturer in the area of Data Science, Data governance and business development for industry 4.0. Responsible for large projects in key industry players in Brazil in the areas of Energy, Telecom, Logistics and Food.
