Key points at a glance:
- Challenge: Inventory, material flow and network potential often goes unnoticed when data is used only for descriptive purposes.
- Solution: Integrated analysis encompassing inventory management, movement analysis, master data quality, warehouse/location optimization, and supply chain optimization, validated as needed using simulations or digital twins.
- Result: Transparency on performance, reliable decision-making criteria, lower costs, and more stable service levels.
- Advantage: Digital twins and simulations mean risk-free testing before implementing actual process changes.
Do you know your Data Value?
It takes more than just collecting and visualizing data to derive true data value from it. This only really comes into play when operational and strategic decisions in logistics, production, and the supply chain are based directly on data. It manifests itself in various ways in these highly dynamic environments: Sometimes it is simply the speed at which scheduling-related information becomes available. Often, however, the value lies in recognizing hidden patterns – such as identifying unproductive periods in production or bottlenecks in the logistics network. If these analytical insights are integrated directly into operational processes, productivity reserves can be tapped immediately (for example, by using predictive models as the basis to dynamically adjust shift schedules). We therefore combine methods from artificial intelligence, statistics, optimization and simulation in decision-oriented data analysis. These generate actionable insights from complex supply chain data, which then enables clear decision-making.
What is data value?
Data value is the tangible business benefit derived from data. These benefits arise when data is translated into operational decisions through the use of models, which measurably improves productivity, service quality, cost structure, or resilience.
How is data value created in decision-oriented data analysis?
Data sources in decision-oriented data analysis environments are consolidated centrally, continuously evaluated, and synthesized using models, immediately uncovering actionable options. This makes decisions not only faster, but also more robust and transparent.
Key factors for success include:
- A single source of truth for inventory, movements, master data, and network data.
- Rule-based, statistical, and AI-driven model logic for ongoing re-parameterization.
- Scenario and simulation logic to improve reliability prior to roll-out.
- Digital twins enable risk-free testing before process changes are actually implemented.
- Dashboards and alert logic continuously monitor critical parameters.
Why isolated analytical approaches are often insufficient
In practice, inventory logic, movement dynamics, master data quality, warehouse structure, and network design are all interrelated. If these aspects are considered in isolation, interactions and conflicting objectives remain undetected: reducing inventory in isolation can have adverse impacts on service levels, while adjusting warehouse processes can affect transportation. It takes a comprehensive analysis of the entire value case to deliver sound decisions.
Our focus areas for your measurable success
Optimization can only be sustained if all levels of the process are integrated. That is why we take a holistic view of your operational challenges in logistics, production, and the supply chain, then translate these into tangible, data-driven value propositions.
Our methodological toolkit: The io Toolset
We use a wide range of cutting-edge technologies to generate valuable insights from raw data. Find out more about our tools in detail.
Methods for statistics & advanced analytics
Inventory management & optimization ×
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 & material flow optimization ×
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 and product classifications ×
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.
Warehouse and location optimization ×
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.
Supply chain analysis & optimization ×
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.
Advanced analytics methods ×
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.
Optimization & simulation methods ×
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.
AI-driven decision intelligence ×
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.
Digital twins ×
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.
Technology & architecture ×
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).
The specific benefits you gain through data value optimization
Stock parameters are managed in a nuanced and dynamic approach rather than one-size-fits-all. This allows you to specifically avoid excess inventory.
Contingency stock levels and replenishment strategies are based on actual volatility, improving delivery capability and response speed.
Data-driven insights bring unproductive periods, bottlenecks, and inefficient processes out into the open, facilitating systematic reduction.
Scenario and simulation models improve reliability prior to implementation, enabling well-informed, reliable decisions.
FAQ
What sets data value apart from traditional BI reporting? ×
BI primarily describes the current situation. Data value is only created when models actively provide decision-making parameters and drive changes in operational processes.
Which issues should be prioritized first? ×
A typical starting point is an inventory analysis, as this can quickly improve capital utilization, service levels, and the quality of inventory planning.
Why is movement data so important? ×
Movement data reveals load profiles, bottlenecks, and patterns in costs and quality. Opportunities for improvement often go unnoticed in the absence of this transparency. Movement data also form the basis for many inventory management KPIs.
When is a digital twin worthwhile? ×
When there are high implementation risks or multiple factors are at play simultaneously. A digital twin allows for risk-free testing of different options before any operational intervention.
What technology does io use? ×
Python as the analytical core, plant simulation for complex models, and an open platform strategy with AWS, Azure, and SAP SAC.
Ready to systematically boost the value of your data?
Talk to our experts and prioritize your next value case based on your actual data and targets. What is your current number one challenge in inventory management, material flow, or the supply chain?
