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Analytical Platform
The Analytical Platform as a central analytical environment in your organization
Discover the Analytical Platform
One platform, full predictability
Planning based on intuition is no longer enough. Demand volatility, cost pressure, supply chain instability, and increasing operational requirements mean that organizations need predictability based on internal data as well as external information.
The Analytical Platform is a central analytical environment that:
- integrates historical and operational data,
- analyzes seasonality patterns and trends,
- incorporates external factors (calendar, promotions, weather),
- builds demand forecasts using AI/ML,
- delivers recommendations supporting purchasing and production decisions.
Data-driven planning automation
How does automated planning work?
In many organizations, the forecasting process is still based on:
- manual analysis of fragmented data,
- individual planner experience,
- reacting to shortages and surpluses,
- time-consuming approval of orders.
The result?
- excessive inventory or out-of-stocks,
- hidden operational costs,
- extended time-to-decision,
- lack of consistency between departments.
The Analytical Platform transforms planning from reactive to predictive and supports:
- demand forecasting,
- inventory optimization,
- reduction of out-of-stocks,
- reduction of overstock,
- order automation,
- better utilization of working capital.
Measurable operational benefits:
- less manual work for planners,
- more predictable operations,
- higher product availability,
- lower warehousing costs,
- consistent data-driven decisions.
Applications of the Analytical Platform
What can the Platform forecast?
Retail
- Demand forecasting for assortment planning and replenishment, reducing shortages and excess inventory.
- Separate forecasts for baseline and promotional sales (uplift), enabling better inventory and logistics planning during campaigns.
- Sales forecasting for new products or assortment changes (e.g., rotations, pilot tests in selected stores), supporting listing and allocation decisions.
Healthcare
- Forecasting patient inflow (e.g., emergency departments) to plan staff and resources and reduce overcrowding – often using calendar and weather data.
- Forecasting demand for hospital beds or ward capacity and planning treatment schedules.
- Forecasting consumption of medicines and medical supplies to support procurement planning and reduce shortages or waste.
Manufacturing
- Order and demand forecasting for production planning, capacity leveling, and reducing overproduction.
- Forecasting demand for spare parts to maintain production line availability and reduce emergency procurement costs.
- Predicting material consumption (linked to production plans and demand variability) for better purchasing and more stable delivery timelines.
FMCG
- Demand forecasting in short cycles (high variability) to support product availability and distribution planning.
- Forecasting promotion effects and their impact on other products or categories, which is critical for inventory, production, and logistics during peak periods.
- Forecasting for production and inventory with shelf-life considerations to reduce losses and expired products.
Banking
- Forecasting cash flows and liquidity to support financing planning, limits, and cost of capital.
- Predicting credit risk and expected losses (both portfolio and segment level) to support decision-making and reporting.
- Predicting fraud probability or transactional anomalies (near real-time) to prioritize alerts and operational actions.
Leveraging data to build more accurate forecasts
What does it use?
.
The platform analyzes data in a continuous and consistent way. When delivering recommendations that automate planning and ordering, it uses different types of data tailored to the specific needs and context of each client.
As a result, models are adapted to real business conditions and consider both the organization’s context and broader market trends.
Historical data
- sales / production,
- SKU / product / location,
- seasonality and trends.
Operational data
- current inventory levels,
- order fulfillment data,
- delivery lead times,
- production capacity utilization.
External factors
- business calendar,
- promotions and special campaigns,
- weather,
- industry variables (e.g., BOM in manufacturing).
Analytical Platform implementation stages
How do we implement the Platform?
01. Proof of Concept (2–6 weeks)
We start with a PoC to:
- verify the effectiveness of models using the client’s data,
- compare AI forecasts with current methods,
- estimate the potential return on investment,
- build organizational acceptance.
The PoC covers a limited product scope (e.g., 30 SKUs) and ends with a report containing Forecast Accuracy metrics and recommendations for full implementation.
02. Scaling and automation
After successful validation, we move to:
- automated data ingestion,
- forecasts for the full assortment,
- integration with systems such as ERP, MES, etc.,
- implementation of MLOps (monitoring, audit, SLA),
- full process automation:
Data → Models → Forecasts → Recommendations → Operational Decisions
The solution can be implemented in on-premises, cloud, or hybrid environments.
Discover our case studies
- Artificial Intelligence
- FMCG
Automation of sales forecasting in the FMCG industry
We created a Proof of Concept project for a leading FMCG company in Central and Eastern Europe, focused on automating sales forecasting using Machine Learning and AI. The solution improved weekly…
Read more
- Artificial Intelligence
- Manufacturing
Demand forecasting and production planning automation in the furniture industry
We delivered an automation project for demand forecasting and production planning for a furniture manufacturing company managing a broad portfolio of interior finishing products. The Machine…
Read more
Support for people responsible for key decisions in the organization
Who is the Analytical Platform for?
The Analytical Platform supports professionals responsible for managing key business processes who need reliable data for decision-making, such as:
- COOs and Operations Directors,
- Supply Chain Managers,
- CFOs responsible for working capital,
- CIOs / CTOs building data analytics architecture.
If your organization wants to move from reactive planning to automated and predictive decision-making – the Analytical Platform is the right step.
Frequently Asked Questions regarding the Analytical Platform
FAQ – Analytical Platform
What is an Analytical Platform for demand forecasting?
An Analytical Platform is an integrated data environment that collects, processes, and analyzes historical and operational data in order to build demand forecasts and operational recommendations using AI and Machine Learning.
How does demand forecasting work?
Demand forecasting uses statistical models and machine learning algorithms to analyze sales history, seasonality, trends, and external factors (such as promotions or weather) to predict future product demand.
How long does it take to implement the Analytical Platform?
The first stage, Proof of Concept, usually takes 2 to 6 weeks. Full implementation depends on the organization’s scale and the scope of integrations.
Can the platform be integrated with ERP systems?
Yes. The Analytical Platform can be integrated with ERP systems and procurement processes, enabling automatic generation of order recommendations.
Can the solution run on-premises?
Yes. The Platform can be implemented in on-premises, cloud, or hybrid environments, depending on security requirements and IT architecture.
Which industries most commonly use the Analytical Platform?
The Platform is particularly applicable in industries such as manufacturing, retail, FMCG, healthcare, and banking, especially in the context of forecasting financial flows and risk.
Let’s talk about forecasting in your organization
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