Digital twins in the supply chain: How DISKOVER enables informed decision-making
Many companies are familiar with this paradox: warehouses are full, yet customers are still waiting for their deliveries. At the same time, the supply chain is expected to become faster, more flexible and more cost-effective. One possible cause lies in the way planning and scheduling decisions are made: based on experience and assumptions rather than on reliable data.
This is precisely where a digital twin of the supply chain and its planning and control mechanisms comes into play. Using the DISKOVER software and Abels & Kemmner’s consultancy methodology, companies can model and simulate their own supply chain before implementing any measures in live operations. This enables companies to assess how decisions affect stock levels, delivery readiness and costs.
This article explains what a digital twin is, how simulation and scenario planning work, and how they contribute to supply chain optimisation.
What is a digital twin in the supply chain?
A digital twin is a virtual representation of the real-world supply chain, including its planning and control mechanisms. To achieve this, operational data from the ERP system – such as SAP – and, where applicable, from other systems is transferred into a unified model capable of simulation.
Depending on the specific requirements, this model depicts products, sites, storage levels, material flows and capacities. The result is a test environment in which companies can explore alternative planning approaches and scheduling strategies.
The key advantage is that the simulation is decoupled from the operational system. Changes can be simulated without disrupting day-to-day operations. At the same time, the calculations are based on real company data. This provides a practical basis for decision-making in inventory management, production planning and materials planning.
The DISKOVER approach: simulation rather than estimation
Traditional planning often relies on static parameters, isolated systems and empirical values derived from historical data. Interactions between stock levels, service levels and lead times are not adequately taken into account. This can lead to decisions whose consequences for costs, capital tied up and delivery capability only become apparent after implementation.
DISKOVER takes a simulation-based approach to supply chain optimisation. Rather than examining individual control variables in isolation, it analyses their interactions within the supply chain.
This makes it possible to assess in advance the likely impact of a measure under the relevant assumptions. This is particularly useful for stock optimisation, the optimisation of planning parameters and the alignment of stock levels with delivery readiness.
Four core elements of the digital twin
The development and use of a digital twin with DISKOVER can be broken down into four components:
Data integration: Operational data from SAP or other ERP systems, and from other data sources where applicable, is consolidated and structured.
Modelling: The relevant supply chain and its planning and control mechanisms are mapped as a consistent, simulable model.
Simulation: Alternative scenarios are calculated and compared with one another.
Decision-making: The results are evaluated on the basis of key performance indicators such as service levels, stock levels and costs, and translated into concrete actions.
This results in a transparent decision-making process that can be repeated should the framework conditions change.
Scenario planning and ‘what-if’ scenarios
The added value of the digital twin is particularly evident in scenario planning. Rather than implementing a change directly whilst operations are ongoing and waiting to see its effect, it can be simulated in advance using the model.
Typical ‘what-if’ scenarios include, for example:
How do the required stock levels and safety stock levels change if the target service level is adjusted?
How does a change in safety stock levels affect delivery readiness?
What impact would a relocation of warehouse sites have on stock levels, delivery times and delivery readiness?
How do changes in demand or disruptions in the supplier supply chain affect the value chain?
Which planning strategy offers the best balance between costs, capital tied up and availability?
These questions can be examined in the digital twin without interfering with day-to-day operations. If a scenario proves convincing under the tested conditions, it can be implemented in practice. This provides a quantitative basis for investment decisions, network changes and new planning rules.
Practical examples: Reducing stock levels, improving delivery readiness
Projects from the industrial supply chain demonstrate how the method is applied in practice. A common starting point is high stock levels coupled with insufficient delivery capacity. Possible causes include inconsistent planning parameters, unsuitable stocking strategies or a lack of transparency regarding the actual interdependencies.
ebm-papst: Optimising component availability and lead times
At ebm-papst, a digital twin was developed in collaboration with Abels & Kemmner, based on ERP data, to analyse component availability and lead times.
A decoupling point analysis identified which components need to be held in stock and when order-specific parts must be available. The simulation also examined where stock levels could be eliminated and where delivery or in-house production times could be reduced by one to two working days.
For the components identified as requiring stockholding, stock levels and forecast parameters were subsequently optimised. The calculations revealed the potential to reduce component stock levels by around 28 per cent. At the same time, it became apparent that component availability could be increased from 89 per cent to a target of around 98.5 per cent.
Sihl: Reviewing stockholding strategies and reducing working capital
The method was also applied at Sihl, a supplier of high-quality printable materials. A two-stage potential analysis was supported by a specialised simulation system utilising a digital twin of the ERP system.
Detailed master and transaction data were imported from the ERP systems and analysed in the simulation system. This made it possible to identify excessive stock levels, insufficient availability and unsuitable items in the product portfolio.
A decoupling point analysis was used to determine which items needed to be held in stock in view of the promised customer delivery times. This revealed unsuitable stocking strategies. Based on the revised strategies, the simulation system calculated the required stock levels and identified potential for reducing stock and working capital.
wolfcraft: Simulating sales planning and inventory strategies
At wolfcraft, the digital twin was used to dynamically simulate the interplay between the value stream and planning and control mechanisms based on real ERP data.
The project team developed and tested a three-stage sales planning process. Demand was broken down by market segment, suitable forecasting methods were tested, and trends and promotional campaigns were taken into account.
In addition, the simulation compared various stockholding strategies. This revealed a potential for reducing stock levels by between 36 and 46 per cent without falling below the required delivery readiness.
Benefits at a glance
A digital twin can help businesses achieve the following improvements:
Reduce stock levels and working capital: Required stock levels are determined whilst taking into account the targeted delivery readiness.
Improve service levels and delivery capability: Stockholding and procurement strategies are coordinated.
Optimise warehousing and ordering costs: Alternative strategies are compared in terms of their cost implications.
Create transparency: Interactions between sales planning, materials planning, stock levels and delivery capability are made visible.
Inform decisions: Comparable scenarios provide a clear basis for operational and strategic decisions.
Reduce decision-making risks: Measures are tested under defined conditions before implementation.
Facilitate implementation: Simulations based on empirical data show how proposed solutions would have played out under the conditions examined.
The improvements that can actually be achieved depend on the initial situation, data quality, model assumptions and implementation. Simulations make the potential improvements quantifiable and help to prioritise measures.
From a one-off project to continuous management
A digital twin can be used for a one-off optimisation project. However, it can also serve as a tool for continuous analysis and management.
Three different modes of use can be distinguished:
Project mode: Setting up the digital twin, conducting initial simulations and identifying levers for optimisation.
Decision-making mode: Evaluation of specific investment, structural or planning decisions based on comparable scenarios.
Control mode: Regular simulations to adjust planning parameters and to continuously optimise stock levels, delivery readiness and costs.
The introduction to supply chain optimisation with Abels & Kemmner is structured in a modular way:
Quick Scan: Based on existing data, initial anomalies and potential areas for optimisation are identified.
Analysis project: Improvement strategies and economic optimisation potential are examined and evaluated using the digital twin.
Implementation project: Selected approaches are developed in detail and implemented. Further simulations and external support can accompany the roll-out.
Ongoing use: Where required, the digital twin is set up as an ongoing analytical tool and supply chain control tower. In conjunction with other DISKOVER functionalities, the solution can be expanded into a comprehensive Advanced Planning and Scheduling (APS) system.
This allows the approach to be integrated step by step into ongoing supply chain planning and management.
Conclusion: Supply chain optimisation based on reliable data
Experience alone offers only limited insight into the interdependencies within complex supply chains. The targeted configuration of planning and control mechanisms requires reliable data and a systematic assessment of potential measures.
The digital twin illustrates how decisions can affect stock levels, delivery readiness, costs and capital tied up. Simulations help to assess conflicting objectives and reduce the risk of poor decision-making.
DISKOVER and the consultancy services provided by Abels & Kemmner combine this analysis with concrete improvement strategies for stock management, sales planning and materials planning. This makes supply chain optimisation transparent and allows its economic impact to be assessed.
FAQ – Frequently Asked Questions
What is a digital twin in the supply chain?
A digital twin is a virtual representation of the real-world supply chain, including its planning and control mechanisms. It is based on operational data from ERP systems and, where applicable, other systems. It can be used to simulate scenarios and assess their impact on stock levels, service levels and costs before measures are implemented during ongoing operations.
What is DISKOVER?
DISKOVER is a modular APS software solution for supply chain management. Among other things, it supports inventory management, forecasting and sales planning, Sales & Operations Planning (S&OP), material requirements planning and simulation in the digital twin. In combination with consultancy services from Abels & Kemmner, DISKOVER is used to analyse supply chains on the basis of data and to optimise planning and control processes.
How does simulation in a digital twin differ from the traditional optimisation of planning mechanisms?
Traditional approaches often rely on static parameters and consider individual planning areas in isolation. Simulation using a digital twin, on the other hand, examines the dynamic interplay of the processes modelled. This reveals the interrelationships between stock levels, delivery readiness and costs. Companies can evaluate alternative planning parameters and strategies using comparable scenarios.
What are ‘what-if’ scenarios in the supply chain?
‘What-if’ scenarios are simulated alternatives to existing plans. These include, for example, changes to safety stock levels, different warehouse locations or fluctuating demand. They show how a particular measure might affect service levels, stock levels and costs under certain assumptions. The analysis is carried out within the model and does not disrupt day-to-day operations.
What added value does scenario planning offer businesses?
Scenario planning provides a transparent basis for decision-making in supply chain optimisation. It highlights conflicting objectives between costs, capital tied up and delivery capacity, and enables measures to be assessed before they are implemented. This allows companies to prioritise areas for optimisation and reduce the risk of making the wrong decisions.
Which businesses would benefit from a digital twin with DISKOVER?
Companies with complex, multi-stage supply chains, high stock levels or inadequate supply availability stand to benefit particularly. A digital twin helps to analyse the root causes and develop appropriate stock management and planning strategies. The modular approach – comprising a quick scan, analysis and implementation – makes it possible to tailor the scope and methodology to the specific circumstances.
