  We distinguished between the expected and actual profit of a firm. We
proposed that, beyond maximizing profit, a firm's goal also encompasses
minimizing the gap between expected and actual profit. Firms strive to enhance
their capability to transform projects into reality through a process of trial
and error, evident as a cyclical iterative optimization process. To
characterize this iterative mechanism, we developed the Skill-Task Matching
Model, extending the task approach in both multidimensional and iterative
manners. We vectorized jobs and employees into task and skill vector spaces,
respectively, while treating production techniques as a skill-task matching
matrix and business strategy as a task value vector. In our model, the process
of stabilizing production techniques and optimizing business strategies
corresponds to the recalibration of parameters within the skill-task matching
matrix and the task value vector. We constructed a feed-forward neural network
algorithm to run this model and demonstrated how it can augment operational
efficiency.
