How data, AI and agents are changing supply chain planning
This article is a second part of a six-part series currently appearing in DVZ (Deutsche Verkehrs Zeitung, dvz.de), analysing the future of each station in the supply chain. Together with SAP Prof. Hoberg and his team spend the last year studying how supply chains will realistically develop and included input from 660 supply chain experts.
In the discussion about the future of supply chain management, one area is increasingly taking centre stage: planning. Supply chain planning not only determines operational efficiency, but also directly influences a company's service quality, tied-up capital and responsiveness.
Supply chain planning is the key lever for anticipating problems before they arise. Companies that identify bottlenecks in their supply chains at an early stage and manage capacities, inventories and resources avoid frantic ‘firefighting’ in their day-to-day business. Effective planning requires the seamless integration and evaluation of a multitude of internal and external data sources – from granular sales data and economic framework data to production, procurement and distribution plans.
Different planning levels and horizons need to be linked together – and this is precisely where enormous opportunities for artificial intelligence (AI) lie. Advanced planning processes will become the norm in the next ten years. Those who do not achieve at least a modern, integrated level of planning by 2035 risk not only competitive disadvantages, but also the loss of operational control.
A survey conducted for the study by SAP and Kühne Logistics University (KLU) among around 120 supply chain decision-makers paints a clear picture of the future (see chart below). 42 percent expect planning to be easier and more intuitive in 2035. More than half (56 per cent) assume that generative AI and GPT models will have a significant impact on planning processes – and 60 per cent see data quality as a critical success factor for planning decisions. In addition, many expect a breakthrough in new AI-supported forecasting tools that incorporate additional data sources and significantly improve sales planning. Around a quarter expect advanced risk analyses to offer the ability to identify and mitigate disruptions.
However, not even one in ten companies consider their planning to be mature. Similarly, few are experimenting with GenAI or GPT applications in the planning environment. Only 7 per cent have accurate planning data that has been harmonised from various sources. Highly automated demand planning with minimal human intervention is the exception, as are planning systems that provide clear recommendations for action in the event of disruptions. This discrepancy illustrates that the road to truly modern, intelligent planning is still long and requires targeted investments, clear priorities and a structured approach.
Various approaches must be pursued in order to close the gap between aspiration and reality. Companies for which planning is a core strategic process or requires individual workflows can realise enormous potential with agent-based AI. With the advent of new tools, much of the repetitive, error-prone work is being automated, allowing planners to focus on value-adding analyses and decisions. Companies with standardised processes and low strategic relevance of planning will increasingly be able to purchase their planning function as a service in the future.
Technology and platform models could enable true end-to-end planning across company boundaries for the first time. Simulations and digital twins will also play a growing role. Today, these often fail due to complexity, modelling effort and scalability. However, AI-supported modelling agents could accelerate the creation and updating of simulations and make them more precise in the future, making their use in larger networks more practical.
Regardless of the individual target vision, there are measures that every company should address immediately. These include, above all, the establishment of robust data governance and the harmonisation of master data as the basis for any data-driven planning. Without high-quality data, even the best AI system cannot deliver meaningful added value. A consistent, company-wide coordinated IBP or S&OP process (Integrated Business Planning or Sales & Operations Planning) creates the basis for a common planning goal. Initial pilot projects with agent-based sales forecasting help to gain experience with AI-supported planning approaches. Finally, it is important to link existing planning platforms with AI services in order to tap into integration potential at an early stage.
By 2035, supply chain planning will become a key differentiator between successful and lagging companies. The technologies needed to make planning smarter, faster and more resilient already exist – but they must be combined with clear goals, a robust database and a willingness to embrace organisational change. Those who lay the groundwork today will not only be able to plan faster and more accurately tomorrow, but also act strategically before others can even react.
