1-Suboptimization applies when there are parameter discrepancy between global variables and local variables. Solution is modification of local variables according to global variables structure.
2-Suboptimization applies when local variables cannot execute activities according to global structure; besides, local variables cannot respond to complexity parameters on local mode and innovation models. Solution is modification of local variables according to balance complexity of possible external changes and innovation parameters. Parameter modification can postpone breakdown mode to a certain time.
This approach is cost effective, save a time, create fast affiliate turning point, easy to perform but it would retrieve poor quality properties, low accountability, and response inconsistency. Economy perspective and ROI may urge systems owners to conduct suboptimization in non-biological systems and project management process. Suboptimization can be an optimal approach for a trivial subcomponent, which does not interact with rest of system platform.
Systems owners suppose to perform total optimization with synergy solutions with system competitors due to high levels of dynamic complexity on suboptimization approaches.
Total optimization, complete consolidation, and synchronization process with system competitors might be possible when biological system values prioritize on all global variables in non-biological systems.
Parameter criteria on total optimization process, complete consolidation, and synchronization would eliminate a suboptimization method.
System owner and system competitors would not have confidential relationship due to parameter vulnerability on global variables on biological systems; therefore, system owners apply just parameter suboptimization in future system performances.
Systems owners cannot easily adjust and tackle suboptimization parameters in order to return to previous system status when suboptimization parameters fail to respond to possible external changes and innovation models.
Partial consolidation between systems owners and system competitors is not solid and reliable value for quality of total optimization process.
It is quite popular to conduct suboptimization approach for biological systems despite the fact that biological systems return to breakdown mode again on the best case scenario.
Suboptimization will not be a good performance improvement strategy for biological systems and parameter suboptimization can reduce harmonic balance in non-biological systems. Systems owners would confuse with cost savings in short term; therefore, approach improve parameters apply for biological systems in the future.