The Otto Group is one of the world’s largest online retailers, with total sales of 15 billion euros. As an internationally operating digital retail and service group with 36,300 employees and a wide range of key companies, brands, and holdings in over 30 countries, primarily in the economic regions of Germany, rest of Europe, and North America, it manages highly complex supply chains with individual challenges.
At the top of the list are high customer satisfaction, competitiveness, innovation, and sustainability. Therefore, the Otto Group’s supply chain management is driving the advancement of forecasting processes and AI-based recommendations along the entire supply chain - from inbound logistics to returns processing.
The Challenge: Complexity, Manual Processes
Within the Otto Group’s group companies, the expected order quantities, transport capacities, and returns are forecast weeks in advance - with the goal of delivering orders as quickly, cost-efficiently, and sustainably as possible.
Previously, these forecasts were often manually created by experts - a tremendously time-consuming process based on years of experience and extensive Excel spreadsheets. Each week this meant for the teams:
• Thousands of rows in Excel spreadsheets
• High manual workload
• Limited forecast accuracy
The challenge: Products must be available at the right time, in the right place, in the right quantity and quality - without being able to predict actual customer demand with absolute certainty.
Additionally, numerous external factors such as weather, holidays, or school vacations influence demand. Furthermore, various framework conditions affect the supply chain - especially shipping delays, complex minimum order quantities, and other restrictions in the ordering process. Traditional tools like Excel quickly reach their limits here. The result? Forecast inaccuracies that cause high costs, stockouts, and inefficiencies.
The Impact of Inaccurate Forecasts: High Costs & Bottlenecks
Insufficient forecast quality has direct business consequences:
• Overstock: Excess inventory leads to rising storage and personnel costs, ties up capital, and slows internal goods flow.
• Stockouts: Missing stock because actual demand exceeds forecasts causes delivery bottlenecks. This impairs delivery capability and lowers customer satisfaction.
Accurate quantity planning is therefore essential to avoid bottlenecks, optimize logistics processes, and minimize costs. This applies to all product types - from continuously available NOS articles to seasonal fashion and product launches with little or no historical data.
The Solution: AI-powered Forecasts for Maximum Efficiency
The AI-based platform by paretos plays a key role by almost fully automating forecasts for various group companies of Otto Group companies while simultaneously improving prediction quality to enable better decisions.
Better planning through more precise forecasts
• Less manual work thanks to automated forecasting processes
• Efficiency gains & cost reductions through optimized inventory planning
Together with group companies such as LASCANA, bonprix, Hermes Germany, and Hermes Einrichtungs Service, the Otto Group is further advancing the digital transformation of the supply chain.
AI Creates More Room for Strategic Decisions
Employees benefit from AI-based recommendations that support operational decisions and at the same time create more time for strategic questions. According to McKinsey, AI-based forecasts enable a significant reduction in errors and an increase in forecast accuracy, translating to 65 % fewer revenue losses and up to 40 % lower inventory costs.
Practical Examples: How the Otto Group Uses AI-Based Forecasts
Bonprix
Efficient Workforce Planning through Precise Order Forecasts
Bonprix plans the number of items to be picked per country for the Hermes Fulfilment Center in Haldensleben with AI-supported forecasts. This allows optimal personnel scheduling, avoids bottlenecks, and increases operational efficiency.
LASCANA
Automated AI Forecasts for Optimized Product Availability
Demand and reorder planning for 70,000 to 80,000 articles on external marketplaces and in brick-and-mortar retail is carried out via paretos’ AI-supported forecast optimization. This improves inventory control, reduces costs, and increases availability for customers.
Hermes Germany
Optimized Parcel Logistics through Precise Volume Forecasts
Thanks to AI-based forecasts, the daily parcel volume is accurately predicted. This enables targeted capacity planning so transport and delivery resources can be used efficiently. The improved forecast accuracy results in more stable and plannable logistics, reduces bottlenecks, and increases service quality for end customers. With the new AI-based forecasting methods, forecast accuracy improved noticeably - specifically by about 6 million parcels within one quarter.
Hermes Einrichtungs Service (HES)
Optimization of Operational Logistics Processes
As a logistics service provider for large shipments such as refrigerators and televisions, Hermes Einrichtungs Service uses daily forecasts of expected shipment volumes. These predictions enable precise organization of personnel deployment in hubs and depots, transport capacities between locations are booked early, and bottlenecks are avoided.
Hermes Einrichtungs Service (HES)
Long-Term Planning and Budgeting for Peak Times
In addition to operational daily forecasts, weekly forecasts help optimally plan transport capacities for peak times such as Black Friday and Christmas and make long-term personnel and budget decisions.
"With paretos, we optimize our processes along the entire supply chain data-driven—from demand and returns forecasting to automated order control. The partnership enables us to respond quickly to changes, increase efficiency, and sustainably improve our decision quality."
Katrin Pompe
Head of Innovation and Cooperation
in Supply Chain Management
Otto Group
The Otto Group: A Complex Supply Chain with High Demands
The successful partnership between the Otto Group and paretos shows the potential of data-driven optimization of forecasts and logistics processes. Through the targeted use of AI technologies, significant efficiency gains have already been achieved, manual processes reduced, and operational decisions improved. But development does not stop here.
The collaboration is continuously being further developed to improve forecast quality, implement new process optimizations, and intelligently network additional areas of the supply chain. Existing use cases will be expanded and integrated into even more group companies, while new AI-supported use cases will be identified in parallel to unlock additional potential across the entire value chain. With this clear focus on the development and scaling of AI forecasting solutions, the Otto Group will continue to expand its leading role in digital supply chain management—and help shape a sustainable transformation in the industry.
"Our goal is to deepen the collaboration further in the coming years. Existing projects should become true flagship initiatives, while new use cases with additional group companies will be launched. This will lay the foundation to finally replace manual forecasts with automated, AI-supported solutions and make the Otto Group’s supply chains even more resilient and efficient."
Malte Rehm
Senior Project Manager
Supply Chain Strategy
Otto Group
