From firefighting to foresight: transforming logistics through AI and analytics

The client managed complex logistics operations where fluctuating demand, limited space, and manual coordination created daily friction. Much of the team’s effort went into solving problems as they happened, reacting to urgent bottlenecks, reallocating resources on the fly, and dealing with recurring inefficiencies.

While the operation was resilient, it lacked systematic visibility and foresight. Performance indicators were tracked inconsistently, making it difficult to understand where time and resources were being lost.

Without a unified data layer or shared performance view, managers could not easily distinguish between structural issues and day-to-day noise. Furthermore, the company was facing a period of geographical expansion, which imposed a greater need for tools that standardize processes throughout the organization. The result was a cycle of firefighting that drained time, energy, and focus.

Leadership recognized that the path to stability and growth required a more analytical way of working, one that could both clarify what was happening and predict what would happen next.

LTPlabs partnered with the client to design a roadmap anchored on two complementary tracks: descriptive analytics for structure and AI for transformation, always keeping people and adoption at the center.

Our approach revolved around three key conditions:

1. Enablement: Descriptive analytics for alignment and control - The first track focused on creating clarity and a monitoring culture to continuously improve existing processes. LTPlabs worked with teams to define key KPIs, unify data definitions, and build interactive dashboards that brought transparency from the warehouse floor to management meetings. This foundation provided a single source of truth and reduced the time spent collecting and reconciling information.
2. Empowerment: AI for transformation and foresight - In parallel, AI models were developed to tackle the organization’s most critical pain points, from optimizing warehouse layouts and picking paths, to predicting daily workloads and resource needs, to linking activity costs with profitability. These solutions turned reactive management into proactive planning, directly improving operational performance.
3. Engagement: Involving people in the process - Adoption was built through collaboration. Teams participated in workshops and gamified sessions to explore how AI worked and how it could support them. This open, participatory approach turned curiosity into confidence and ensured that technology strengthened, rather than replaced, human expertise.

The descriptive analytics layer gave the organization a common analytical language. Dashboards made operational performance visible in near real time, allowing managers to identify trends, spot inefficiencies early, and make informed decisions without waiting for manual reports.

Meanwhile, AI brought tangible transformation:

• ‍Optimization models reorganized storage and picking processes, reducing travel distances and improving throughput.‍
• Resource forecasting models provided visibility on future workload, enabling teams to plan rather than react.‍
• Profitability analytics connected effort to financial outcomes, highlighting which operations created the most value

By combining structure with intelligence, the client gained both control and foresight, transforming daily operations from reactive to predictive.

The initiative delivered measurable and lasting impact. Operational efficiency rose significantly, with productivity increasing by more than 25% and unnecessary movement across the warehouse sharply reduced.

Forecasting and planning improved, giving managers the ability to anticipate peaks rather than chase them. Moreover, by comparing forecasted needs to workforce capacity, managers were given more visibility of areas systematically under stress and those with more idleness.

Financial visibility helped direct attention toward the most profitable activities and clients. This increased visibility allowed the company to monitor the unitary cost per added value element (e.g., storage and picking movements, warehouse space, temperature maintenance, transportation), giving managers more information to negotiate existing and future contracts.

Beyond numbers, the most meaningful shift was cultural. AI became part of how people worked, guiding priorities, informing conversations, and building confidence in data-driven decision-making. The organization emerged more predictable, more efficient, and better prepared for the future, with analytics as a permanent capability, not just a project outcome.