Joint KLU/Bayer Research on next-generation supply chain planning

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KLU and Bayer recently joined forces for an extensive study on the next generation of supply chain planning. How will automation and artificial intelligence technologies shape supply chain planning in the future? How will new data sources help us manage the virtual “explosion” in the number of decisions to be made? Which new tools like process mining or machine learning will improve the decision-making process? And how will all these trends impact the future of human work in supply chain planning? These were just some of the questions addressed.

While many companies are still struggling with supply chain management, industry leaders have a clear vision of the value that outstanding supply chain practices can offer. In particular, supply chain planning processes have the potential to boost operational performance in various contexts, and have recently received considerable attention. New digital technologies, new data sources, and new software solutions are paving the way for broader and more fundamental optimization.

As part of an extensive study on next-generation supply chain planning, over the past few months a joint KLU/Bayer project team has gathered substantial material and interviewed many leading supply chain experts. The project team – including KLU researchers Prof. Dr. Kai Hoberg, Prof. Henrik Leopold and former KLU faculty Prof. Dr. Jan Fransoo, as well as numerous Bayer experts led by Luisa Franzone, Head of Global Supply Chain Management and Galina Gray, Head of Digital Transformation & IT for Product Supply at Bayer Consumer Care – has identified eight core aspects that will shape next-generation supply chain planning. According to Prof. Dr. Kai Hoberg, head of the Operations and Technology department: “It’s wonderful to work so closely together on a topic that will redefine supply chain planning in the years to come, and I’m glad to say that this is just the start of our collaboration.”

Automation is becoming essential and is driven by planner's trust

One of the key conclusions: supply chain planning workflows will become more automated and shift from manual, routine activities to system-guided decision-making and exception-based interventions. Many planners will increasingly focus on refining algorithms and fine-tuning the input parameters needed to create forecasts, production schedules, and resupply orders. This automation is vital to dealing with the skyrocketing number of decisions produced by more SKUs, more frequent updates and a higher granularity of planning. However, with regard to this automation it is also important to foster planners’ trust in the system, so as to ensure that the advanced black box algorithms function optimally without manual input.


Process mining is an essential new tool for boosting data quality and arriving at insights for supply chain planning. By applying process mining, the process flow and deviations from the intended process can be identified fully automatically. Process mining also makes it possible to detect bottlenecks and other inefficiencies as a basis for effective process improvement initiatives. In the planning context, it can be used to better understand and improve the planning process, including the use of planning systems. Moreover, process mining can help us to better understand the data maintenance planning process and, in the long term, to automatically determine and update planning parameters.


The digital transformation is accompanied by structural changes in planners’ workflows and responsibilities. By automating the repetitive, reactive, and manual tasks within the planning process, future planners will be free to concentrate on strategic and predictive tasks that require uniquely human capabilities. Human planners, therefore, will improve decision-making by contributing insights based on their understanding of cause and effect, as well as relationships and context. In order to accomplish this, the job profiles for supply chain planners will also change. Besides becoming more data-science-oriented, they will require soft skills, strategic and management tasks, and cross-functional work.


Download the summary of the report, which highlights the eight key aspects concerning best practices for next-generation supply chain planning, here.

#1 Supply chain planning workflows will become more automated

#2 The automation of processes and critical supply chain decisions will be driven by trust and incentives for the planners

#3 AI/rule-based approaches will play an important part along the way, but will require experimentation

#4 The quality of transactional and master data will be critical

#5 Process mining will be an essential tool

#6 The required supply chain planning technologies are maturing rapidly and will support the new processes

#7 More holistic, strategic roles will emerge from supply chain planners’ current job profiles

#8 New dedicated roles concerning data science and data stewardship will be needed in order to support supply chain planners