Transition to the data driven supply chain

On February 21st, EyeOn hosted the network event on ‘Building the data driven supply chain’ in Antwerp, Belgium. Around 30 participants were introduced to selected key elements for successful application of data science to planning and forecasting.


Transition to the data driven supply chain

For most larger companies, technology has greatly improved availability of data. This data holds the potential of better, faster, and more efficient decision making. Turning the available data into better insights for decision making is not straightforward and requires a completely different mindset and capabilities than continuous improvements within a more traditional planning process and toolkit.

A key driver for success is the centralization of analytical skills in a team where subject-matter experts, data ninjas, project managers, and operational specialist work together. The tools these teams use, should support continuous innovation, collaboration, and easy integration with data sources. These tools should have an open architecture and have the ability to tap into the latest open source technologies.


How digitalization will change your company

More companies want to go to a ‘no-touch S&OP process’. Within forecasting we already see a level of autonomy. But is this autonomy possible in every part of the planning process and do people want to have an autonomous decision making process? Moving towards no-touch S&OP requires actions in different fields. You need a number of building blocks to get there, such as the organizational readiness and excellent data.


Innovative data science applications in forecasting, inventory, and supply management

We saw some real case examples of how data science has made its entrance to the domain of forecasting and planning in the following topics:

  • Forecast value add
  • Supply chain optimization using apps
  • Production wheel


Key insights from the day

  1. Start collecting and storing data as of tomorrow
  2. Build strong analytical skills – often centrally organized
  3. Do not make analytics a stand-alone exercise – embed in process
  4. Develop fact-based collaboration & communication – planner as orchestrator

Slides of presentations

Below you can find some of the materials that were shared during the day:

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