Verified Author Share
Modern logistics increasingly relies on technology, data, and artificial intelligence to ensure operational efficiency, predictability, and the ability to scale. What consumers perceive as a simple delivery involves a complex chain of integrated systems, real-time decisions, process automation, and continuous data analysis.
A package may seem simple from the consumer’s perspective: it left the distribution center, it is on its way, and it has arrived at its destination.
Behind that journey is a much more sophisticated technology operation.
Systems need to communicate with one another. Data needs to flow, routes need to be planned, volumes need to be forecast, and exceptions need to be handled. Teams need to make decisions quickly. And all of this happens in an operation where small problems can trigger a chain reaction.
This was the central topic of a conversation on TDM, Tech for Decision Makers, Zallpy’s podcast, featuring Diogo Munhoz, technology leader at Loggi, Renan Deves, Zallpy’s CTO, and Thiago Soldá, Zallpy’s COO.
More than a discussion about tools, the episode raised a strategic question for companies in logistics, supply chain, and complex operations.
The answer involves a combination of systems integration, data, software engineering, automation, and artificial intelligence. Above all, however, it requires a clear connection between technology and business outcomes.
One of the most interesting points in the conversation was the realization that logistics operations begin long before a package physically moves.
They begin with information.
For a package to enter the operational flow correctly, different systems need to exchange data. In a large-scale operation, there is not necessarily a single standard for this communication.
These systems may use APIs, EDI files, CSVs, and various formats defined by each customer.
Based on the experience Diogo shared, Loggi works with more than 40 integrated platforms, along with multiple integration models and customer-specific variations.
This completely changes the scale of the challenge.
Each new platform, marketplace, customer, or partner adds new combinations and exceptions to the operation. Technology needs to be flexible enough to handle this diversity without turning every new scenario into a separate project.
For logistics and supply chain companies, systems integration has become a strategic factor. The larger the operation, the greater the need to connect ERPs, marketplaces, management systems, carriers, and external partners securely and at scale.
Integration, therefore, is not just a technical issue. It is an operational capability and, often, a competitive advantage.
A company may have an extremely efficient physical operation. But if it cannot integrate new customers, receive information correctly, or adapt its systems to different operational flows, its ability to grow is compromised.
In this context, integration needs to be treated as part of the business architecture, not as a collection of one-off connections.
This involves:
The goal is not to eliminate the complexity inherent in logistics. It is to build engineering capabilities that can absorb it.
Another challenge emerges when the volume of data grows faster than the ability to turn it into decisions.
In logistics, a large number of questions need to be answered every day:
Answering these questions should not depend exclusively on manually analyzing dashboards.
During the episode, Diogo specifically discusses the reconstruction of Loggi’s Data Lake to create a structure better equipped to make information available in ways that support business needs.
This initiative reinforces an essential principle of data management: information creates value only when it reaches the point where a decision needs to be made.
The strategic use of data in logistics makes it possible to anticipate demand, optimize resources, reduce costs, and improve service levels. Companies that can turn data into operational decisions gain a significant competitive advantage.
A modern data strategy needs to consider the entire journey from information to action.
This includes:
In practice, the focus shifts from building dashboards to the decisions those dashboards and data need to support.
Artificial intelligence has added a new layer of opportunity for logistics companies.
AI tools can already support different stages of software development, from documentation and analysis to code generation and automated testing.
This creates a tangible opportunity to increase team productivity.
But productivity alone is not an outcome.
During the episode, Thiago Soldá highlighted this point while discussing the use of AI across the development lifecycle. The challenge is not to make every team use AI at every stage, but to find the right balance between speed, quality, governance, and value creation.
This is especially important in logistics operations.
A recommendation system, an integration, or an algorithm that affects an operational step cannot be evaluated solely by how quickly it was developed. It needs to operate reliably within a chain where a single failure can affect several downstream processes.
In other words, AI can accelerate development, but engineering remains responsible for ensuring that this speed produces consistent results.
A more mature approach to adopting artificial intelligence starts with a clear definition of success.
In the episode, Diogo mentions the development of a framework with dozens of indicators that combine engineering metrics and business indicators.
The logic is simple: no single metric tells the whole story.
A team may deliver faster and still fail to create a strategic impact for the company. Likewise, productivity does not necessarily mean stability, scalability, or customer satisfaction.
That is why technology metrics need to be connected to business metrics.
In logistics, this means linking engineering indicators to metrics such as:
This connection is what turns technology productivity into business results.
The application of AI in logistics goes far beyond code generation.
Forecasting, optimization, and simulation are some of the areas with the greatest potential to impact complex operations.
One example shared by Zallpy was its work with Bunge on freight cost forecasting.
The challenge involved dealing with the high variability of grain transportation costs. To address it, the team integrated different data sources, applied Machine Learning models, and developed a tool capable of simulating future scenarios.
The entire pipeline, from data collection to model deployment, was also automated.
The result was not simply the use of artificial intelligence. The technology began supporting three concrete needs:
This is an important example of how enterprise AI projects should start with a business problem, not with the technology available.
Process automation is another important topic for logistics operations.
Many companies still rely on manual tasks, operational checks, information transfers, and repetitive activities.
Automating these workflows reduces operational effort and allows professionals to focus their time on analytical work and strategic decisions.
But there is an important difference between automating tasks and redesigning processes.
Before implementing any technology, it is necessary to understand:
The most effective automation does not start with choosing a tool. It starts with understanding how the process works today, why it was designed that way, and where opportunities for improvement exist.
Digital transformation in logistics requires an integrated view of operations. Data, systems, infrastructure, teams, customers, and suppliers are connected and need to work in coordination to deliver efficiency and predictability. That is why technology solutions should be grounded in the operational context and the business’s actual needs.
An approach focused solely on software delivery is rarely enough to solve challenges of this level of complexity.
Sustainable transformation begins with understanding the current state, mapping processes, and clearly defining the problems to solve. From there, technology and AI can be applied strategically, with metrics aligned to the outcomes the organization wants to achieve.
This approach is reflected in the way Zallpy works on complex projects, combining technology strategy, Data & AI, architecture, software engineering, cloud, modernization, and execution to connect technology with business objectives.
Digital transformation in logistics does not depend solely on adopting new tools. It requires a combination of systems integration, data management, artificial intelligence, automation, and governance to deliver sustainable results.
The sector’s next challenge is to make this infrastructure increasingly intelligent without compromising operational reliability.
That means using AI to improve forecasting, automate processes, support decisions, and expand team capabilities. It also means investing in engineering, architecture, integration, and data to ensure these solutions operate consistently in production.
Companies that connect technology and operations gain greater predictability, reduce costs, improve efficiency, and create the conditions to scale with confidence.
Ultimately, the goal is not simply to have more technology. It is to make technology improve what truly matters to the operation: predictability, operational efficiency, scalability, service quality, and customer experience.
That was the key takeaway from the conversation on TDM: AI, data, and engineering can help logistics companies move faster, but value emerges only when that speed is directly connected to business needs.
A delivery is only the final outcome of a complex technology operation. Behind the scenes, data, systems, and processes connect to coordinate every step of the journey and keep the operation running efficiently.
Verified Author
Verified Author
Verified Author
Verified Author