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5 real data challenges in the food sector

5 real data challenges in the food sector

The food sector and food security are a global concern and Brazil is one of the main countries responsible for the world demand for food (Estadão). In this sense, what are the main challenges related to data management to optimize Brazil’s operational efficiency in the food/agribusiness sector, which today represents 21% of Brazil’s GDP?

This article addresses the issue with the bias of Aquarela’s experience in Advanced Analytics and Artificial Intelligence projects carried out in large operations in Brazil. The risk of a lack of information is as relevant as its excess and lack of analysis, which can impact the efficiency of the sector’s logistics chain as a whole.

Below, we have elaborated on some of these main risks.

Characterization of the food sector

The food sector is quite varied due to the large extension of the production chain, which ranges from agricultural inputs, industrialization, transport logistics to commercialization in consumer markets and finally the final consumer.

As fundamental characteristics, the food sector is directly linked to factors that can have great variability and little control, such as: 

  • Climate (temperature, water volume, luminosity and others);
  • Economic factors such as currency fluctuations;
  • Infrastructure;
  • Domestic/external market demand.

In addition to these factors, below we list some related to data management. We also show how they, if well organized, can help mitigate the effects of uncontrollable variables in the food supply chain.

01 – Incompleteness of information

The supply chain is quite large. This makes the data complex and difficult to interpret due to the different phases of each process, culture and region. In addition, it causes many important planning decisions to take place with very limited information and high risk. In other words, decisions are made without a vision of the complete scenario of the chain, largely following the manager’s intuition.

The lack of quality information is a big risk. If data is lacking today, imagine what the scenario was like 10 or 20 years ago.

In recent years, the industry and retail have shown great advances in their computerization processes with various traceability solutions. With the evolution of Industry 4.0 technologies (IOT and 5G) in the coming years, it is likely that the food market, from the agricultural and industrial sector to the commercial sector, will hold more complete information for decision making than what is currently available today.

02 – Data from multiple sources

If data is becoming more and more present with the development of informatization and communication, then the next problem is trying to analyze data from multiple and disconnected sources.

Different data is often stored on different systems, thus leading to incomplete or inaccurate analyses. Combining data manually to form datasets (what are datasets?) for analysis is quite heavy and time-consuming work and can limit insights into the reality of operations.

What is sought is the construction of Data Lakes adherent to the type of management to democratize access to data by market professionals, thus optimizing their activities with increasingly powerful analytics solutions. This not only frees up time spent accessing multiple sources, it also allows for cross-comparisons and ensures that the data is complete.

03 – Low quality data

Having incorrect data can be just as or more harmful than not having it. Nothing is more harmful to data analysis than inaccurate data, especially if the idea is to use data science and machine learning practices. Without a good input, the output will be unreliable.

One of the main causes of inaccurate data is manual errors made during data entry, especially when information is collected manually. Another problem is asymmetric data: when information from one system does not reflect changes made to another system and thus makes it out of date.

Analytics strategic planning projects seek to mitigate and/or eliminate these problems. This happens from systematic processes of data dictionarization, survey of processes, functions, and so on.

04 – Lack of data talents

Some organizations and companies, in general, are not able to achieve better levels of efficiency in operations, as they suffer from a lack of talent in the area of ​​data analysis. In other words, even if the company has consistent technologies and data, the manpower to execute the analysis and action plans still counts a lot at the end of the day.

This challenge can be mitigated in three ways:

  • Develop an analytical technology stack that is always up-to-date and adherent to the business and with up-to-date training materials.
  • Add analytical skills to the hiring process. In addition, invest in the constant training of the team on new data technologies related to the technological stack of the operation.
  • Use analytics outsourcing to accelerate the process. In this article, for example, we list the main aspects to be considered when choosing a good supplier.

05 – Customization of values ​​and product characteristics in the food sector

Although, according to Embrapa, about 75% of the entire world food sector is based on just 12 types of plants and 5 types of animals, there are thousands of different products, marketed in multiple ways, prices and deadlines in the final consumer market.

Just as an example, in the area of ​​animal protein, the process of marketing cattle meat requires investments, infrastructure, deadlines and processes that are quite different from what would be for the production of pork or even chicken.

Since the processes are different, the data generated by the production chain also becomes different, requiring customizations in information systems and databases. As a consequence, there are changes in models of:

The recommendation is to parameterize the systems based on the most common classifications in the market and focus on the most important products from a strategic point of view (contribution margin, volume or sales price).

5 real data challenges in the food sector – Final thoughts

In this article, we have collected some relevant points about the real challenges of data in the area of ​​food, a sector in which  Brazil stands out as one of the main global players.

It is a complex area with several risk factors and great opportunities for optimization with the increasingly intensive use of data. Previously, we wrote an article related to data strategies for energy trading and which in part has the same challenges related to decision making in the food sector.

We, at Aquarela Analytics, constantly work with these challenges of making the complex  things simple and with good risk mitigation. So if you have any questions, get in touch with us!

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!

Author

AI and Analytics strategic planning: concepts and impacts

AI and Analytics strategic planning: concepts and impacts

The benefits and positive impacts of the use of data and, above all, artificial intelligence are already a reality in the Brazilian Industry. These benefits are most evident in areas ranging from dynamic pricing in education, forecasting missed medical appointments, predicting equipment breakdowns, and even monitoring the auto parts replacement market. However, to achieve these benefits, organizations need to reach a level of analytical maturity that is adequate for every challenge they face.

In this article, we are going to discuss the concepts of AI and Analytics Strategic Planning and also look at which characteristics of the scenarios demand this type of project within the Digital Transformation journey of companies towards Industry 4.0.

What is AI and Analytics strategic planning?

The AI ​​and Data Analytics Strategic Planning is a structuring project that combines a set of elaborate consultative activities (preferably by teams with an external view of the organization) for the survey of scenarios, mapping of analytical processes, elaboration of digital assets (systems, databases, and others) to assess the different levels of analytical maturity of teams, departments and the organization as a whole.

As a result, shared definitions of the vision, mission, values, policies, strategies, action plans, and good data governance practices are accomplished to leverage the organization’s analytical maturity level in the least possible time and cost.

Symptoms of low analytic maturity scenarios

Although there are many types of businesses, products, and services on the market, here we present emerging patterns that help to characterize the problem of companies analytical maturity and can generate interesting reflections:

  1. Is it currently possible to know which analytics initiatives (data analytics) have already taken place and are taking place? Who is responsible? And what were the results?
  2. In analytics initiatives, is it possible to know what data was used and even reproduce the same analysis?
  3. Does data analysis happen randomly, spontaneously, and isolated in departments?
  4. Is it possible to view all data assets or datasets available to generate analytics?
  5. Are there situations in which the same indicator appears with different values ​​depending on the department in which the analysis is carried out?
  6. Are there defined analytic data dictionaries?
  7. What is the analytical technology stack?
  8. Are data analytics structuring projects being considered in strategic planning?

Other common problems

Organizational identity

Scenarios with low analytic maturity do not have data quality problems in isolation. There are usually systemic problems that involve the complexity of business processes, the level of training of teams, knowledge management processes, and finally, the choice of technologies for operating ERP, CRM, SCM and how these transactional systems are related.

Security Issues

Companies are living organisms that constantly evolve with people working in different areas. Thus, over time, control of the access levels of each employee is lost, causing unauthorized people to have access to sensitive information and also the opposite when people cannot access the data they need for their work.

Excessive use of spreadsheets and duplicates

Spreadsheets are one of the most useful and important management tools and for that reason, they are always helping in various processes. The big side effect of excessive use of spreadsheets is the maintenance of knowledge of each process. When there are two or more people and the volume of information and updates starts to grow, it becomes difficult to manage the knowledge that travels in blocks with spreadsheets. Additionally, many duplications occur and make it virtually impossible to securely consolidate data in large volumes.

What are the benefits of AI and Analytics strategic planning?

Data-driven management is expected to provide not just drawings and sketches of operations or market conditions, but a high-resolution photograph of present and future reality. Thus, it provides subsidies for corporate strategic planning in the short, medium, and long term with the following gains:

  • Procedural and technological readiness for data lakes projects and Advanced Analytics and AI labs.
  • Increased intensity of application of scientific techniques to businesses, such as comparative analysis, scenario simulations, identification of behavior patterns, demand forecasting, and others.
  • Increased accuracy of information.
  • Security of access to information at different levels.
  • Acceleration of the onboarding processes (entry of new team members) who in turn learn more quickly the work scenario and also begin to communicate more efficiently.
  • Greater data enrichment from increased interaction of teams from different sectors for analytical challenges.
  • Increased visibility into analytics operations, Organization for localizability, accessibility, interoperability, and reuse of digital assets.
  • Optimized plan of change for data-driven Corporate Governance.
  • Incorporation of Analytical and AI mindset in different sectors.
  • Homogenization of data policies and controls.

AI and Analytics strategic planning – Conclusions and recommendations 

The preparation of strategic AI and Analytics planning is an important step to reach the level of data governance that allows the intensive use of analytics and artificial intelligence in operations since the high failure rate of analytical projects is linked to low quality of data, processes, and even the correct use of technologies (training).

Structuring projects, such as AI strategic planning and Analytics are, or at least should be, the first step in the journey of digital transformation of traditional companies. Therefore, we are convinced that in the future every successful company will have a clear and shared idea (vision, mission, and values) of what data means to them and their business model, in contrast to investments in data technology purely and simply because of the competition.

We believe that the focus on orchestrated (tidy and synchronized) data will be reflected in almost every area, for example: in the range of services, in revenue models, in key resources, processes, cost structures, in your corporate culture, in your focus on clients and networks, and in its corporate strategy.

Last but not least, it is worth pointing out that, for a successful structuring to happen, a long-term holistic approach must be taken. This means investments in optimized technology, people, and processes to enable continued business growth.

How Aquarela has been acting

Developing new technologies and new data-driven business models in a vision that the amount and availability of more data will continue to grow, taking the business to new heights of optimization.

What we do specifically for companies:

  • We analyze data-generating enterprise ecosystems.
  • We determine analytic maturity and derive action fields for data-driven organizations and services.
  • We develop and evaluate data-based services.
  • We identify and estimate the data’s potential for future business models.
  • We design science-based digital transformation processes and guide their organizational integration.

For more information – Click here.

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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!

Authors

Automotive Aftermarket: AI in the auto parts industry

Automotive Aftermarket: AI in the auto parts industry

Do you know the Aftermarket market segment of the auto parts industry? This term refers to the automotive aftermarket segment, which supports the continuity of operations of approximately 42.6 million vehicles (motorcycles, cars, trucks and even agricultural machinery) in Brazil. The turnover of this industrial segment ranges between 75 and 85 billion reais per year (data by Issuu).

The automotive aftermarket process involves a large amount of data on a large number of parts (SKUs) produced and sold over decades. This usually makes it difficult to identify market share and new business opportunities. But, how to overcome this challenge?

Market share case on the auto replacement aftermarket

To answer this question, we prepared a material presenting our success story in the automotive sector in the Aftermarket segment, in which we show how advanced analytics and artificial intelligence strategies can result in great benefits to the commercial operation.

The case study addresses our client’s business problem, immersed in several challenges, such as the difficulty in understanding the sales behavior of some part groups; the journey, marked by the development of a system capable of presenting the evolution of the organization’s market share; and the results generated for our client.

Our goal is to assist marketing managers, commercial managers and administrators who work in large-scale operations.

Automotive Aftermarket – Conclusion

Identifying market share and new business opportunities in the auto parts sector is a challenge, but that can be overcome through tools like AI and Advanced Analytics.

However, its implementation process is complex, demanding artificial intelligence as well as qualified, market-recognized data analytics providers.

Also read – How to choose the best AI and Data Analytics provider? 

Do you have any doubts about our success case in the automotive aftermarket? So leave your comment.

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!

Authors

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Pioneer of Advanced Analytics and  Artificial Intelligence in Brazil. We’re Brazilian pioneers who set the benchmark for Artificial Intelligence (AI) applications in industry and large, national, and multinational enterprises, directly impacting Brazil’s gross...