Background
Otto Group, with an online revenue of around 12 billion euros, is one of the world's largest online retailers. As a globally operating trade and service group with approximately 41,000 employees in 30 major group companies, it is a highly complex organization with various supply chains and individual challenges. At the top of its agenda are always high customer satisfaction, strong competitiveness, sustainability, and innovation. Otto Group's Supply Chain Management is driving the improvement of forecasting processes and the use of AI-based recommendations for action along the supply chains – from inbound deliveries to returns processing.
"With innovative AI solutions such as paretos, we optimize the forecasting processes along the Otto Group supply chains and use AI-based recommendations for actions to improve the customer experience."
Malte Meistering
Division Head
Supply Chain Excellence
Otto Group
Let's start with a practical example from everyday life: In the group companies of Otto Group, the expected number of ordered items, vehicles, and materials for delivery and pickup, as well as the expected returns, are forecasted weeks in advance. All of this is done to ensure that orders arrive at the customers as quickly, cost-efficiently, and environmentally friendly as possible. Up to now, these forecasts are often manually conducted by experts. This requires years or even decades of experience, as well as very complex and often numerous Excel documents. For the group companies of Otto Group, this means a plethora of manual predictions every week, resulting in several thousand lines in Excel spreadsheets and a tremendous amount of manual labor.
Challenge
The processing of orders along the end-to-end supply chains in the group companies of Otto Group is highly complex and time-consuming. The central challenge is that products must be available at the right time, in the right place, with correct quality and quantity, without precisely knowing which customers will order which product and when.
Moreover, making good business decisions in this environment depends on numerous external factors, such as weather forecasts, holidays, or vacation periods. Traditional tools, such as Excel, regularly reach their limits in terms of performance, functionality, and clarity. This is precisely where errors and inaccuracies can occur with a large variety of products. Furthermore, the complex forecasts and manual effort tie up valuable personnel who could instead focus their expertise on analyzing and implementing solutions for tricky challenges in inbound, fulfillment, outbound, or returns processes.
In this context, the critical factor for success is the forecast quality or accuracy of the predictions of required quantities in the coming days and weeks.
An inaccurate forecast accuracy directly impacts operating costs and efficiency. If too much inventory is on hand because the expected demand did not materialize, additional personnel costs and storage costs occur, and storage capacities are unnecessarily blocked, which can also slow down the internal flow of goods. Additionally, the effort for warehouse management and logistics increases because more goods need to be moved, sorted, and possibly relocated.
If too little inventory is on hand because the actual demand was higher than expected, it can lead to shortages in the supply chain, impairing the readiness to deliver and the ability to fulfill customer orders on time. Consequently, the level of service and customer satisfaction can decrease. Therefore, precise quantity planning is crucial to optimize logistical processes and minimize costs.
Solution
The AI-based forecasts by paretos play a key role by almost fully automating the forecasts for various group companies of Otto Group while simultaneously improving the prediction quality to make better decisions. Our software supports the Supply Chain Management of Otto Group and group companies such as Lascana, bonprix, OTTO, Hermes Germany, and Hermes Furnishing Service. Together, we are driving the revolution of forecasting processes and the operational implementation of AI-based action recommendations forward.
The use of AI in the professional daily routine will provide employees with decision support and also significantly more time for strategic questions and challenges. According to McKinsey, AI-based forecasts allow for a significant reduction in errors and an increase in prediction accuracy, which can translate into 65% less revenue loss and up to 40% lower inventory costs.
Practical Example from Otto Group
The team at Lascana, a leading brand for lingerie, swimwear, fashion, shoes, and accessories, is responsible for forecasting 70,000 to 80,000 items. Already today, the demand and reordering for a large part of the portfolio are implemented through automated AI forecast optimization by paretos. This exceeds the previous level of manual predictions. As a result, Lascana achieves cost savings in logistics and can increase sales through better product availability.
"With paretos, we have automated the replenishment calculation for the external marketplace business at LASCANA and can make highly precise decisions. Through the forecast by paretos, cost savings, a better service level, and lower logistics costs are realized."
Steffen Köhn
Senior Merchandise
Manager Marketplaces
LASCANA
"The goal is to further deepen the collaboration between Otto Group and paretos in the coming years. This will involve developing existing projects into true showpieces, launching new use cases with additional group companies, and thus finally replacing manual forecasts in Excel documents," summarizes Malte Rehm, Senior Project Manager for Supply Chain Development, for the Otto Group Supply Chain AI team.
