Do you know your Data Value?

Analytical models in inventory management are used to define operational control parameters throughout the entire supply chain based on data rather than on intuition. For example, the models calculate optimal order quantities, batch sizes for production, and contingency stock levels that are precisely tailored to specific service levels required and demand volatility.

Projections and simulations of various replenishment strategies also provide a glimpse into the future. When used in structural planning (such as the construction of a new logistics center), these calculations – which take growth projections into account – form the mathematical basis for determining the size of future storage areas and staging areas for production.

Movement analysis distinguishes between micro-movements (material flow within a production or logistics site, often with a focus on peak times) and macro-movements (routes to clients, global production supply, material procurement).

Complex physical processes can be measured digitally by analyzing seasonal trends, transportation costs, and key material flows. This movement data serves as an excellent basis for forecasting, enabling companies to statistically validate future sales volumes, incoming goods, or the expected workload in warehousing and manufacturing, as well as to plan resources efficiently.

Master data is often a neglected area in corporate logistics and production practice, with only the absolute basic necessary fields being maintained in the ERP system. In analytical practice, however, they are fundamental to any reliable calculation. Products and components must be segmented based on their availability, value, regularity, or status in the lifecycle.

AI-based estimation functions, for example, can be used to supplement missing data points in planning projects that infer unknown values based on known attributes. An aggregated maintenance status indicator makes it possible to quantitatively measure the quality of master data and identifies areas where maintenance efforts are actually necessary to ensure smooth operations.  

This section combines insights from inventory and movement data analysis. The goal is to identify systemic bottlenecks during peak intralogistics periods or any inventory items stored in inefficient locations.

The methodology relies heavily on scenario analyses: How do material flows change when items are compacted, stored in different containers, or handling steps are eliminated? Simulation models can be used to analyze infrastructure, personnel, and operational logic in combination. The effects on travel and cycle times, particularly in order picking (e.g., through reallocation) or in production and assembly processes, can be mathematically demonstrated before any actual changes are made to the warehouse layout.

A supply chain analysis examines issues that go beyond the individual logistics or production site. Key topics include complex supply and logistics relationships, assessments of resilience to global disruptions, and strategic positioning within the network.

Strategic “what-if” questions are often analyzed using data and presented to management: How would additional hubs or consolidations affect network capacity and service levels? What are the logistical implications of structural changes to the product range? Does it make sense to physically separate or consolidate production and logistics within the network? A thorough mathematical analysis evaluates these influencing factors and their interactions. Optimization measures can then be analyzed and identified.

Data mining – going beyond traditional descriptive BI reporting. Traditional statistical methods are often used for initial analysis: Pattern recognition reveals seasonal trends, clustering groups client structures, and forecasting models estimate workloads or future sales.

The AI toolbox offers specific solutions for more complex requirements: For example, it can calculate the likelihood of returns at the time an order is placed in e-commerce, allowing optimization parameters to be adjusted proactively. The advantage of AI-based models often lies in the high quality of the results and, in some cases, a significantly faster computation speed.

Analytical methods reach their limits when the mathematical optimum is required instead of a plausible value. In such cases, optimization algorithms or specialized tools for network and site planning are used.

Simulation models create virtual representations of logistics centers, production facilities, or multi-stage supply chains. The system’s behavior is tested in this environment for changes (such as fluctuating workloads or capacity outages). While analytics aims to condense and simplify data, simulation allows for detailed testing of “what-if” scenarios in high resolution. This provides a solid understanding of the actual system dynamics.

Modern supply chain management processes – from planning processes to order allocation in logistics facilities – increasingly call for decisions based on data patterns and rules. Algorithms process these complex data structures – such as movement data, inventory, time series, and master data – and combine them into reliable decision-making parameters. You can also make decisions on your own or use a “man-in-the-loop” system to help you make informed decisions.

A transparent analysis of the current situation reveals the extent to which manual, slow, or error-prone decisions inhibit the efficiency of processes. It clearly demonstrates the potential that remains untapped as long as decision-making processes are not supported by automation.

Time series and performance metrics serve as benchmarks for improvements. However, the successful implementation of analysis results is often hindered by economic risks, technical constraints, or cultural reservations. Digital twins in the form of simulation models solve this problem, serving as a sandbox in which new algorithms and system behaviors can be tested without any real-world risk. This makes it possible to demonstrate causal relationships and provide data-driven evidence of the benefits of proposed solutions.

When modeling data-driven use cases, the focus is on flexibility of methodology and system stability. The analytical core is based on Python, while state-of-the-art simulation software is used to build complex simulation models and digital twins. The visualizations are then customized for management to suit the specific client and target audience.

We also actively work with popular analytics platforms (e.g., AWS, Microsoft Azure, SAP).