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The internal transit times for moving the goods between plants are key factor in defining what business can promise to customers. With uncertainty of the global transportation markets, it becomes very critical for business to ensure a minimum highly reliable methods for calculation the internal transit times. The lead time accuracy process improvement would help better serve customers.

What are the real challenges:

The transit lead times are dynamic and can be severely influenced by outlier events – unusual custom holds, the Suez canal case, lockdown situations, Rapid shipments and etc.. Relaying on outdated lead time information results in inaccurate committed ship dates being calculated, ultimately impact the customer requested delivery date.

  • The process for updating lead time was tedious not optimal in SAP.

  • Leadtime is maintained at both logistics level (Routes) and Part Number level (PDT). Data was often out of sync.

  • Missing historical trend analysis of transit time changes.

  • No visibility and control over when people updated lead times in SAP.


With challenges listed above, we identified opportunities to standardize and automate the transit time calculation process.

What needs to be done to improve the process:

We can optimize the manual process by applying a new statistical method and developing an automated solution to compute more accurate planned delivery time and route turnaround time with help of process teams.

The solution is to develop a custom model based on SAP on-time report to combine the data from multiple tables from SAP in EDW and apply the new calculation method (IQR/percentile) and then compare traditional vs new calculations of route TAT/PDT.

The next step is to optimize the route TAT review process and automating the PDT update into SAP.

Solution Design

What does the value add:

  • Data quality improvement by ensuring PDT maintained for all parts.

  • Savings from Manual to automated solution

  • Accurate transit times as the transit times are calculated based on delivery routes.

  • Improve the data frequency refresh.

  • Moving from traditional to new statistical methods help to identify outliers for accurate transit times.

Best Regards


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