A conversation with Sandra Lemańska, Category Management Specialist at Lorenz, about how artificial intelligence is revolutionizing category management in the FMCG industry
ŚWIĘTOKRZYSKIE, POLAND / ACCESS Newswire / December 31, 2025 / Introduction
In the rapidly changing world of FMCG, where "shelf space is not made of rubber," Category Managers face increasingly complex challenges. How do you cope with growing amounts of data, time pressure, and the need to make quick decisions? The answer may lie in the intelligent use of artificial intelligence.
Sandra Lemańska from Lorenz, a company known for brands such as Crunchips, Wiejskie Ziemniaczki, and Monster Munch, shares her experiences with implementing AI solutions in the daily work of a Category Manager.
Category Management in the Data Era
Category Management is much more than just arranging products on shelves. As Sandra Lemańska emphasizes: "Today, Category Management is not just about shelf arrangement and what should be in the store. It's something more, because it's often an increasingly strategic role, as we base our decisions on knowledge from data."
Key KPIs in Category Management
In daily work, a Category Manager must balance multiple metrics:
Distribution - product availability in stores (weighted and numerical)
Rotation - sales per store, category, brand, and SKU
Margin - a key element of profitability
Market share - continuous competitive monitoring
But this is just the tip of the iceberg. Equally important are marketing activities, promotions, activity calendars, and collaboration with various organizational departments.
Challenges of Modern Category Managers
Working as a Category Manager at a company like Lorenz means constantly balancing different business dimensions. The salty snacks market is very diverse - from discount stores, through small shops, to hypermarkets. Each channel has its specifics and requires a tailored assortment.
"There's always a challenge in assortment management from the manufacturer's perspective, because later our recommendations as a manufacturer are passed on to retail chains, which may or may not implement these recommendations," explains Sandra.
The Partnership Between DS STREAM and Lorenz
The collaboration between DS STREAM and Lorenz represents a successful example of how data science expertise can transform traditional category management approaches. This partnership began when Lorenz recognized the need to move beyond traditional business intelligence solutions and embrace more advanced analytics capabilities.
How DS STREAM Helped Lorenz
We are proud to have supported Lorenz in several key areas. We built a robust data infrastructure by creating a Data Lake that consolidated information from various departments and sources. This foundation enabled advanced analytics and machine learning applications.
We introduced advanced analytical models that go beyond basic reporting, delivering predictive and strategic insights rather than just retrospective views of business performance.
We developed custom dashboards that provide Lorenz teams with real-time access to critical business metrics and insights.
We implemented machine learning solutions tailored to category management challenges, including demand forecasting, assortment optimization, and promotional effectiveness analysis.
Our Expertise and Industry Experience in FMCG
Our successful transformation of Lorenz's data capabilities was made possible by our extensive expertise and established position in the FMCG industry. Since 2017, we have grown into a leading AI & Data Analytics company, employing over 150 experts with more than 130 certifications from top technology partners such as Google, Microsoft Azure, and Databricks.
We specialize deeply in the FMCG sector, delivering numerous projects for global Consumer-Packaged Goods companies. Our experience includes implementing MLOps platforms, standardizing machine learning workflows, migrating complex data infrastructures, and creating metadata-driven data lakehouses. This enables us to fully understand Lorenz's specific business challenges and translate them into practical, scalable technical solutions.
Our technology-agnostic approach and commitment to building long-term partnerships ensure that the AI solutions we develop are not only technically sophisticated but also user-friendly and perfectly aligned with the daily workflows of category managers. This collaborative methodology, rooted in transparency and continuous improvement, guarantees high adoption rates and measurable business impact that fundamentally transform how Lorenz approaches data analytics.
The Breakthrough Moment: Why AI?
When does an organization decide to invest in AI? According to the experience of DS STREAM and Lorenz, the crucial moment comes when:
Traditional business intelligence capabilities are exhausted
Reports only provide a retrospective view of the situation
There's more and more data but less and less time to analyze it
There's a need to scale faster than company resources allow
"We were already at a point where we were processing a lot of data, because we had it in various departments. Later, we created a Data Lake together in cooperation with you, dashboards were created, but everyone wanted to have something more," recalls Sandra.
AI in Practice: It Doesn't Take Away Jobs, But Changes Them
One of the biggest concerns related to AI implementation is the fear of job loss. Sandra Lemańska addresses these concerns: "Certainly, what's worth noting is that we definitely don't take away work. If someone was worried that artificial intelligence would take away work, I have the impression that it adds a bit to me, but completely different work."
Specific Benefits from AI:
Time Savings: - Automatic report updates - Elimination of manual data collection from various sources - Automatic analysis generation
Better Insights: - Deeper understanding of promotional mechanisms - Advanced customer segmentation - Data-driven assortment optimization
Faster Decisions: - Lightning-fast recommendations for retail chains - Real-time response to market changes - Support in making strategic decisions
How DS STREAM Transformed Lorenz's Data Capabilities
The partnership with DS STREAM fundamentally changed how Lorenz approaches data and analytics. Before the collaboration, Lorenz had data scattered across different departments and systems, making it difficult to gain comprehensive insights. DS STREAM's intervention created a unified data ecosystem that enabled:
Integrated Data Analysis: By consolidating data from sales, marketing, supply chain, and external market sources, Lorenz gained a 360-degree view of their business performance.
Predictive Analytics: Moving from reactive to proactive decision-making through advanced forecasting models that could predict market trends, consumer behavior, and optimal promotional strategies.
Automated Insights: Implementation of automated systems that could identify patterns, anomalies, and opportunities without manual intervention, allowing category managers to focus on strategic decision-making rather than data processing.
Scalable Solutions: The infrastructure and methodologies implemented by DS STREAM were designed to grow with Lorenz's business, ensuring long-term value and adaptability to changing market conditions.
Areas of ML Application in Category Management
Artificial intelligence can support Category Managers in many areas:
Analysis and Segmentation - better understanding of consumer behavior
Assortment Optimization - matching offerings to store specifics
Promotion Management - predicting the effectiveness of promotional campaigns
Partner Collaboration - automating communication and reporting
Demand Forecasting - better production and distribution planning
Practical Insights for Category Managers
1. Start with Data
Before implementing AI, make sure you have organized and high-quality data. As the Lorenz example shows, often the first step is creating a Data Lake and basic dashboards.
2. Think of AI as a Support Tool
AI won't replace the experience and intuition of a Category Manager, but it can significantly support the decision-making process by providing insights impossible to obtain through traditional methods.
3. Focus on Specific Business Problems
The best results come from implementing AI in response to specific challenges - whether it's assortment optimization or promotion management.
4. Prepare for Role Change
AI will change the nature of a Category Manager's work - less time on routine tasks, more on strategic analysis and decision-making.
Conclusion
Lorenz's experience shows that implementing AI in Category Management is not a revolution, but an evolution. The key to success is the gradual introduction of solutions that solve specific business problems.
As Sandra Lemańska summarizes: "Such additional tools that can support us in this work, these tools are key for us."
For Category Managers in the FMCG industry, the message is clear: AI is not a threat, but an opportunity to increase work efficiency and make better business decisions. Companies that are already investing in these technologies today are building a competitive advantage for the future.
The partnership between DS STREAM and Lorenz demonstrates that successful AI implementation requires not just technical expertise, but also deep understanding of business processes and collaborative approach to problem-solving. With experts like Kuba from DS STREAM working closely with industry professionals like Sandra from Lorenz, the transformation of category management through AI becomes not just possible, but highly effective.
Paweł Szczepanik
pawel.szczepanik@dsstream.com
Królowej Jadwigi 49, 28-100 Busko-Zdrój, Świętokrzyskie, POLAND
SOURCE: Paweł Szczepanik
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