W2MO provides the ability to create a digital twin for the design, optimization, and operation of warehouses and distribution centers. A 3D model is integrated with business data from the warehouse management or ERP system, including master, inventory, and consumption data, and can be enhanced with live data, such as tracking data. Video process studies with a direct transfer of the required parameters into the Digital Twin further increase the accuracy. This provides the basis for a comprehensive analysis of planning alternatives and the identification or elimination of unproductive processes and procedures. With a digital twin, the distinction between planning, continuous improvement, and dynamic optimization of operational processes becomes blurred.

W2MO offers a comprehensive solution for process studies, providing users with an easy and simple way to record, analyze, and document times and speeds in all logistical processes. Our unique combination of video recording and a digital twin allows users to evaluate not only times but also speeds, enabling us to calculate realistic times based on the chosen route and start and end points. No average times are used. Based on this, processes can be optimized and extremely time-consuming steps can be eliminated. Privacy protection is also considered. With integrated head blurring people can be anonymized in video recordings. W2MO offers the possibility to use the necessary parameters to set targets for the employees; e.g., for a single pick including packing and labeling.

The warehouse capacity optimization process begins with the calculation of optimal storage sizes based on product properties, stock data, and demand behavior and further constraints. In the second step, these optimally sized storage needs are placed in the warehouse in an efficient manner to maximize the effective storage capacity. In addition to this approach, which focuses on the placement of individual storage locations (such as those designed for pallet storage), the innovative algorithms in W2MO can also be used to restructure warehouses using dynamic used bulk areas in order to increase the fill level of the used areas significantly (e.g. in warehouses with a heterogeneous product portfolio). Read more about this in our warehouse capacity optimization section.

The logistics sector has a higher level of data information than other business areas, including product master data, movement data, customer orders, and inventory. This makes it crucial for logistics companies to have a structured approach to big data. This allows them to optimize their layout and processes, identify long-term trends, and evaluate alternatives. W2MO offers a variety of configurable interfaces for each use case. Data for planning tasks can be prepared in Excel and uploaded as a .csv file. Alternatively, data can be automatically transferred from WMS, ERP, or TM systems on a daily basis. The SAP connector provides a standard interface to all SAP systems. W2MO provides algorithmic support to validate the data used and allows automatic correction of data errors. Model errors, data gaps and inconsistencies are avoided and repaired immediately. At the same time W2MO provides a consistent database for simulation and optimization projects.

Logistics planning and optimization are inherently future-oriented. While exact data on product and order structures is only available from the past, W2MO offers the ability to create product and order data using statistical distributions. Furthermore, a valid simulation model can be created with a minimal amount of data. Sophisticated extrapolation algorithms assist in generating goal-oriented, foresighted order structures. The result is based on historical data and accurately reflects the expected business development. This methodology is employed to address any data gaps that may exist.

W2MO offers a range of analytical and evaluative tools for the database and its outcomes. The 2D and 3D layout information enables the creation of heat maps for access times, picking amounts, and labor costs. Such visualizations frequently identify potential for optimization and transparently show inefficiencies in current processes.