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The paper analyzes the impact of the sensor reading errors on parameters that affect the production costs of a leading US industrial gas supply company. For this purpose, a systematic methodology is applied first to determine the relationship between the system output and input parameters and second to identify the assigned input sensors whose readings need to be improved in a prioritized manner based on the strength of those input-output relationships. The two main criteria used to prioritize these sensors are the decrease in production costs and the decrease in production costs’ volatility obtained when the selected sensor’s precision is improved. To illustrate the effectiveness of the proposed approach, we first apply it to a simplified version of the real supply network model where the results can be readily validated with the simulated data. Next, we apply and test the proposed approach in the real supply network model with historical data.