To manage supply chain ecosystems, end-to-end transparency must be achieved, ideally in real-time. A supply chain digital twin connects to the operational and planning systems of the players in the ecosystem and builds a digital model of the current supply chain situation.
A supply chain control tower uses this model to control the operational processes of the supply chain, to manage the cooperation of the players, and to detect deviations, events and risks at an early stage.
Supply chain analytics to get end-to-end transparency across the ecosystem enables all participants to make informed and optimized decisions collaboratively with all other members of the network.
Increasingly individual and volatile demand drives the shift from classical waterfall towards an agile product portfolio management that allows to quickly react to business cycles in a VUCA world. Supply Chain Segmentation aligns a portfolio of products, customers, and suppliers while harmonizing internal processes or policies.
Modular product structures lead to a higher degree of reuse and simpler supply structures while at the same time expanding the variety of products and offerings. Ecosystem partners may take over engineering or production, while assembly and logistics may be outsourced to other partners.
Product portfolio management is shifting towards ecosystem portfolio management based on coopetition, including startups, suppliers and competitors. This will achieve a broader functional offering, more flexibility and agility, lower costs and a faster time-to-market.
Supply chains are the largest consumers of natural resources and energy. Circular supply chain structures will help reuse components and materials from used products instead of making products completely from new components and raw materials.
Sustainable processes in the supply chain avoid waste and environmental pollution. A regionalization of supply chains additionally improves sustainability but is also inevitable when assessing the risks of globally integrated asset networks.
Cloud-based ecosystems support the building of circular and sustainable supply chains. Through the transparent network structure, information can be transferred directly from suppliers to customers. Unnecessary production is avoided, effort is saved on transports, and quality problems are quickly identified. The carbon footprint is continuously monitored, and operational processes are optimized to avoid emissions, and energy and resources are saved.
Consistent and accurate master data are a prerequisite for any decision support and optimization system in supply chains. In a cloud-based ecosystem, master data is managed overlappingly in many systems. Each company typically has a central system where essential master data is maintained.
At the same time, material numbers and designations are often distinct, so a common semantic layer is needed. This layer is pronounced based on the cloud-based infrastructure of the ecosystem. It enables AI algorithms to recognize synonyms and relate different master data related to the same objects in the supply chain.
AI-based algorithms support identifying synonyms, generating uniform classification schemes, and correcting inconsistent and erroneous master data in the network. The result is a uniform language for all companies involved in the ecosystem and seamless communication based on uniform master data semantics.
The integration of all parties involved in the supply chain ecosystem via the cloud-based infrastructure and the unified semantics layer enables the automation of many planning and decision-making processes. For example, forecasting processes can be largely automated by machine-learning-based algorithms. About 20% of the products still require further planning by humans; the rest is planned automatically.
In the area of supply planning, bottlenecks can be identified automatically by integrating all companies involved in a supply chain, and suggestions for eliminating the bottleneck can be generated. Rule-based systems help prepare the decision for the best option.
The determination of inventory parameters such as safety stocks, minimum order quantities, and reorder points can be determined automatically by optimization methods. Machine-learning-based algorithms are used to monitor the quality of the planning results and to learn from deviations.
Automated supply chain processes require continuous monitoring, parameterization, and adaptation. If an automatism delivers poor results, the causes must be analyzed and appropriate countermeasures initiated. This requires supply chain experts who have mastered the underlying algorithms like machine learning or operations research and are familiar with the data structures used. In industrial companies, such experts are often hard to recruit and also difficult to utilize on a permanent basis. Furthermore, experts who work on the supply chain of one company only do not have experience from other situations and therefore are not able to transfer expertise.
We are convinced that complex mathematical methods for optimizing supply chains in conjunction with expertise in cloud technology will increasingly be offered by service providers in the future, especially planning and fact-based decision making such as:
• Supply chain planning as a Service
• Product availability & allocation as a Service
• Sales & operations planning as a Service
All aspects of the future of supply chains discussed so far generate decisions and measures for tactical optimization of supply chain structures and processes. Today, tactical decisions are managed and executed manually after they have been made - e.g., via Excel and email. Employees are not digitally supported during the execution of the necessary steps. It is not possible to receive evaluable feedback on the success of the measures, and therefore we cannot learn from the situation.
A digital execution system is required to support the management of tactical decisions in the network and supporting the continuous improvement in the network. The more companies participate and are integrated via the cloud-based infrastructure, the more powerful will a digital execution management will be.
The future of supply chains will feature the continuous improvement of all operations across all functions and participants in the ecosystem. It will allow for closer integration, better collaboration, as well as avoidance of waste and losses to boost the supply chain performance.
Our pre-built solutions and managed services enable you to solve challenging improvements faster, more efficiently, and more accurately. But this is only the beginning. We are continually developing solutions for supply chain challenges where execution has been a pain.
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