Building the data driven supply chain

EyeOn’s network event on ‘Building the data driven supply chain’ was held in Amsterdam, the Netherlands on November 13th. Around 40 participants were introduced to selected key elements for successful application of data science to planning and forecasting. Specific attention was given to pressing issues and innovative solutions, both tool-based and process-based.

Transition to the data driven supply chain

Technology has greatly improved the availability of data in large companies. This data hold the potential of better, faster and more efficient decision making. Data science is a crossroads between business and IT. It requires completely different capabilities of tools and a different mindset than is common in traditional planning processes.

A key driver for success is the central organization of analytical skills in a team where subject-matter experts, data ninjas, project managers and operational specialist work together. Tools used by these teams 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 to kick start a center of excellence?

Teams that facilitate the transition to digital supply chains are often organized in centers of excellence. They develop prototypes which are built, tested and operationalized, almost as a continuous process. As a consequence, payback periods for investments are greatly reduced and risks are much smaller than with traditional systems implementations. At the same time, speed of adaptation required from processes and peoples is big.

Blockchain in the automotive industry

Providing end-to-end supply chain visibility requires high-quality data. To prevent company data to be turned into a commodity, it is important to capture the relevant digital space. In the automotive industry, storing data about finished cars from manufacturer to dealer in a decentral and open blockchain adds an unprecedented level of traceability. Collaborative “co-opetition” with other parties such as government agencies and insurance providers opens up new use cases in the regulatory sphere and in fraud detection.

Innovative data science applications in forecasting, inventory and supply management

Data science has made its entrance to the domain of planning and forecasting. Four concrete company cases and examples illustrated how successful transition to a data driven supply chain can be achieved.












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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