Highly Detailed Performance Management Recommendations
Digital transformation causes higher degrees of uncertainty and variance. Performance management must pivot from coarse-grained to fine-grained. Similar to the notion of “Customer of One” we must evolve towards a “Process of One” performance management mindset.
24 by 365: Cranking Out Realistic Performance Management Proposals
ERP systems hold the transactional reality of an enterprise. Trufa constantly generates meaningful performance management proposals out of this wealth of potential insight.
Interacting Gears Constantly in Action
Underlying ERP systems contain the executed transactions and their business documents—not the actually performed processes.
Trufa reconstructs all operational processes continuously.
The reconstructed processes reflect the sequence of operational process steps. However, they lack information about potential cause-effect relationships between operational process steps and financial outcome. Furthermore, they are missing information about potential management structures and scopes.
To overcome the gap, Trufa continuously scores all potential operational process activities across all potential management structures and scopes.
The scored potential constitutes an overwhelming number of angles to be worked on. As a result, the Trufa Machine ranks all performance management action proposals continuously.
Algorithmic Value Chain Saw
The core gears are implemented via core algorithms.
The Trufa Performance Management Machine continuously learns executed processes, managerial scopes and potential operational drivers in order to model the driver indicator relationships as basis for scoring, and subsequent ranking, of proposed performance management measures.
The core algorithms are designed to answer following questions:
- The “Process Learning” algorithm answers questions like, “Which business documents make up each executed federated process chain?”
- The “Scope Learning” algorithm answers questions like, “Which business documents constitute actionable management subsets?”
- The “Driver Learning” algorithm answers questions like, “Which operational activities drive financial outcome?”
- The “Driver Indicator Modeling” algorithm answers questions like, “How does an operational activity affect the financial outcome?”
- The “Driver Scoring” algorithm answers questions like, “What is the likelihood-to-achieve and potential gain of an operational improvement measure?”
Trufa Performance Score (TPI)
The Trufa core algorithms revolve around the Trufa Performance Indicator (TPI) score. The TPI represents a tuple of potential gain and likelihood-to-achieve values as dependency on before and after driver as well as before and after indicator values.