First, the production manager should consider whether his business will fulfill the computer hardware company's requirement that all computer chips are 1.2 centimeters long. Unless the standard is met, there will be potential quality defects, quality-related sues, and sale losses subsequently. The manager has to monitor the duration of the chips to ensure that the chips fulfill the specifications. If the manager cannot, another factor to evaluate is whether or not corrective efforts can be introduced to minimize the difference between the duration of the output and the length needed by the computer hardware corporation ( Volchok, 2020) . In conclusion, the manufacturing manager's components to consider in evaluating the capacity of his company to manufacture chips that meet specifications include retraining personnel requirements, machinery, potential modification tools, parts, and manufacturing equipment, and procedures and processes review.
The chips do not meet the specifications desired. There is a 5% probability that this estimate is wrong, but because the chip specification is 1.2 cm, it informs us that the manufactured chips may not meet the desired requirement. For the average size of the computer chip, at 95 percent confidence interval level is about 1.1 centimeters and 0.9 centimeters. This suggests that the firm is 95% positive that the average length (mean) of the computer chips they make ranges between 1.1 and 0.9 centimeters in length. We already have information that the computer chips' size must be at least 1.2 centimeters in length size to satisfy the requirement. Anything less or more than that will imply that the computer chips manufactured will not meet the targeted provisions and that 95 percent of the chips produced are below the necessary specifications of the computer hardware manufacturing firm ( Volchok, 2020) . Last month's production range results in a 95 percent confidence interval varying from 0.9 to 1.1 cm in length. 95 percent confidence in the performance indicates that the length of a computer chip that does not fulfill the requirement is between 0.9 cm and 1.1 cm.
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The manufacturing department can justify meeting the production team's requirements by saying that while confidence intervals are generally assumed to be of 95% confidence interval, if the organization wants more confidence that the interval has the required parameter, the intervals should be high 99 percent. In other words, the outcomes gained from the 95% confidence interval might not be large enough to generate reliable results ( Volchok, 2020) . The production manager's suggestion is to make the vice president is to change the sample size. If the production manager had adequate samples, the 1.2 cm size should have been included in the confidence interval. It could not be an accurate reflection of the overall output based on the production run for just one month. The result also indicates a 95% confidence interval (0.9 cm, 1.1 cm), and I am 95% sure that the required chip size 1.2 cm is not included in the interval from 0.9 cm to 1.1 cm.
This decision would affect the chip manufacturer's revenue and net profit by giving the company a poor reputation for its efficiency. This is because the computer chip does not fulfill the specifications, so the customer agreement cannot be fulfilled by the company. In order to minimize the company's sales and benefit, the customers may terminate the contract. In terms of lost revenue and low net income, the above justification could be catastrophic ( Volchok, 2020) . It is easier if they evaluated whether or not the computer chips produced to meet the needed criteria and if they do not comply with the specifications, look for ways to correct the defection, instead of trying to challenge the outcomes collected from a confidence interval level of 95 % in an attempt to aid justify the efforts of the production department.
References
Volchok, E. (2020). Clear-Sighted Statistics: Module 14: One-Sample Hypothesis Tests (slides).
