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Business value being addressed by the Process Observer (POB) for the SAP Business Suite

You might have seen already a couple of blogs on the new solution ‘Process Observer for the Business Suite (POB)’ which has been introduced by Bernd Schmitt and Jens-Christoph Nolte.

Introducing New Component: Business Process Monitoring & Analytics for Business Suite Processes (PMA), Architecture of Business Process Monitoring & Analytics for Business Suite Processes (PMA), Setup of Component Business Process Monitoring & Analytics for Business Suite Processes (PMA), Monitor Sales Order Processing with Business Process Monitoring & Analytics for Business Suite Processes (PMA), Create Process Definition for Business Process Monitoring & Analytics for Business Suite Processes (POB)

I would like to outline the underlying value proposition of the solution and recap on the example Perfect Order Dashboard.  

With the new solution being introduced in the previous blogs the foundation has been built within the SAP Business Suite to gain process visibility for ‘built-in’ processes. In the SAP Business Suite the implemented business logic indicates the execution of processes normally by writing business documents and keeping reference to the previous documents.

Process visibility of the running business processes and its context information (process instance data, process steps, activities, process status information, process flows, timestamps, systems and users involved etc.) results in transparency on the process quality and costs and helps businesses to understand how an enterprise performs. It is important to know which actions need to be taken to gain productivity and to improve the business e.g. by …

  • speeding-up and streamlining most critical business processes (process automation)
  • detecting problem situations as early as possible in order to solve them as fast as possible (monitoring and error handling)
  • adapting management by exception strategies and foster automation together with a selected set of process KPI’s
  • allowing identification of improvement areas via dash-boards based on statistics, pre-defined analytics, analysis of run-/ lead time

POB can address multiple use cases by providing transparency of process steps in an end-to-end process across automated steps, rule-based decision or by human centric interactions.

Manage business process performance real-time at process instance level:

  • A business user tracks a process instance at real-time or right-time. For example, the customer calls, the end user wants to understand the overall process flow and involved parties (organizations, users, documents…) in order get the needed insight to be able to answer the customer query.
  • A business user reacts on thresholds, SLAs, and alerts. Resolution strategies are proposed as context for raised alerts, for example, specific business actions and technical resolution proposals.

Manage overall business performance and fine tune processes:

  • Measure efficiency of processes on an aggregated level to support business activity monitoring (BAM) and compare it to operational targets, including navigation to instance level if needed.
  • A business user wants to identify and prevent process instance breaks to ensure process completion and consistency. For example, breaks which can be solved by automation or because manual intervention is needed.

A good example to show the benefit of correlating process data and transactional business data is the calculation of a Perfect Order Rate. The Perfect Order Rate calculates the error-free rate of each stage of a sales order along an Order-to-Cash process and provides hints how mature this business process has been implemented in an enterprise. For almost all issues that occur along the sales order life cycle a corrective measure and weight is issued. Process instance specific measures are for example the calculation of the ‘Delivered on Time’ which is based on cycle times or the ‘Pick & Pack Accuracy’ which represents the number of goods which were picked and packed correctly in relation to the total number of pick and pack transactions for a certain period in time.

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