Myths and realities of S&OP

Manufacturer corrected Supply Chain data with AI and saved on costs and emissions

Johan Öhlin
Head of Advanced Analytics

It is time to say goodbye to manually corrected Supply Chain data
A vast majority of Supply Chain data correction is still today handled either manually in Excel or via static and manually-defined rule-based processes in SQL or other types of data applications. These ways of correcting data is typically very time-consuming, error-prone and do not handle changes in the data very well since it is by default static. Since clean and accurate data is the cornerstone of effective Supply Chain planning and optimization, we want to empower our clients with a data correction solution that quickly and automatically handles this process, reducing overall risk and cost in the process.

Robotic Data Correction utilizes Machine Learning
Robotic Data Correction (or RDC in short) uses Machine Learning models that automatically detect data inconsistencies without any human-defined rules and learns over time from user acceptance of corrections recommendations and new data values. it has not seen before, ultimately being able to quickly to correct any relational data values.

One of our valued customers utilized RDC to improve the quality of their addresses and achieved a 3% annual domestic cost improvement on their transportation costs, as they were able to consolidate their shipments. At the same time it reduced the data correction project time-to-completion with 95%. The accuracy of the model showed up to 90% data correction accuracy.

What data can RDC correct?
RDC can correct any type of Supply Chain data that has relations to other held data, including but not limited to:

  • Customer and supplier addresses
  • Item data such as lead time and cost
  • Network data such as routing codes
  • Material data such as status codes
  • Service data such as service class
  • Demand data such as incorrect peaks
  • Transport data such as delivery mode
Where is RDC data used?
RDC corrected data can be directly integrated with several line-of-business systems and business processes including:
  • ERP source systems such as SAP, Infor M3, Microsoft Dynamics and IFS
  • Planning processes such as demand forecasting and production planning
  • Operational TMS and MES systems

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