Workload estimation mechanism for company management

Abstract

The invention discloses a workload estimation mechanism for company management. First, all major workflows that may be involved in the life cycle of the entire improvement work are determined, and it is necessary to determine the potential impact on the workload or work time occupied by completing a certain improvement function. A detailed list of parameters; the above parameters are presented in the form of questions, and the work is done after many discussions with experts, after each parameter has been determined at the most granular level, the next step is to assign the workflow and the relative relationship of each parameter Weights: A pairwise comparison matrix is ​​established for all workflows and all parameters in each workflow, and the matrix is ​​filled according to expert opinions after repeated discussions for weight calculation. The present invention largely eliminates the possibility of potential missing parameters when evaluating retrofit effort, it can clearly indicate the amount of work required to complete an analytical tool or product retrofit, provide a price estimate for retrofit and expedite decision-making process.

Description

technical field [0001] The invention belongs to the technical field of workload estimation, and in particular relates to the development of a workload estimation mechanism, especially a workload estimation mechanism for company management. Background technique [0002] Effort estimation is an attempt to make the best possible prediction of the effort required to complete a specific task. In a nutshell, there are currently two types of models for effort estimation, algorithmic models and expert-based The former aims at deriving estimates in a quantitative manner with the help of predefined algorithms, such as Function Point Analysis (FPA) and Use Case Points (UCP), while the latter uses the subjective judgment of experts to derive estimates, such as the Delphi method . Existing models fall short in predicting the most accurate estimates because they do not comprehensively cover all relevant evaluation criteria that could affect the required effort. Therefore, there is a sig...

AI Technical Summary

Problems solved by technology

Existing models fall short in predicting the most accurate estimates because they do not comprehensively cover all relevant evaluation criteria that can affect the required effort