Operations Management Quiz 3 Question 1 - 1 Point

Operations Management Quiz 3question 1 1 Pointpeople Often Evaluate

People often evaluate the quality of a service on the basis of psychological impressions. Question 1 options: True False

Regarding control charts, changing from three-sigma limits to two-sigma limits: Question 2 options: increases the probability of concluding nothing has changed, when in fact it has. increases the probability of searching for a cause when none exists. decreases the probability that the process average will change. decreases the probability that defects will be generated by the process.

One of the main challenges in developing the proper culture for TQM is to: Question 3 options: define customer for each employee. suspend reward systems based on quantity. institute an equitable employee recognition program. get buy-in from the customer.

One chart commonly used for quality measures based on product or service attributes is the chart. Question 4 options: True False

When a process fails to satisfy a customer: Question 5 options: it is quite often the customer's fault. it is considered a defect. it is time to reengineer the process. it is usually half the customer's fault and half the company's fault.

The UCL and LCL for an chart are 25 and 15 respectively. The central line is 20, and the process variability is considered to be in statistical control. The results of the next six sample means are 18, 23, 17, 21, 24, and 16. What should you do? Question 6 options: Nothing; the process is in control. Explore the assignable causes because the second, fourth, and fifth samples are above the mean. Explore the assignable causes because there is a run. Explore the assignable causes because there is a trend.

Which of the following can be used to eliminate "common" causes of variation? Question 7 options: statistical process control acceptance sampling traditional statistical techniques they cannot be eliminated.

Increasing the quality level by better products and processes may: Question 8 options: allow a company to raise the price of the product. move a company closer to a competitive priority of price. reduce prevention costs. ensure that the trade-off between prevention costs and other costs of poor quality is worthwhile.

Which of the following statements relating to total quality management and Six Sigma is true? Question 9 options: The only effect of internal failure is loss of material. Poor quality generally does not increase the inventory level or lead times. To produce 100 good units in a process with a 20 percent defective rate, the company must produce a total of 120 units. High product quality in manufacturing can have significant market implications for a firm.

An example of a type I error would be: Question 10 options: throwing away a perfectly good banana. counting a student's multiple choice response correct when it is actually incorrect. releasing a guilty defendant. counting a student's multiple choice response correct when it is actually correct.

The notion of internal customers applies to all parts of a firm. Question 11 options: True False

Which one of the following is considered to be an appraisal cost? Question 12 options: cost of quality audits cost of supplier programs cost of rework cost of process design

What is one reason that Six Sigma is more difficult to apply to service processes? Question 13 options: There is no manual that suggests how Six Sigma can be applied to services. The work product is more difficult to see. There is no way to measure process capability of a service product. The National Six Sigma Society cautions against using it for services.

On a control chart, a type I error occurs when the employee concludes that the process is in control when it is actually out of statistical control. Question 14 options: True False

"Quality at the source" implies: Question 15 options: less expensive raw materials. lower scrap. higher rework costs. more final-test inspectors.

A professor, dissatisfied with a product he's purchased, bad mouths the product to his class, resulting in decreased market share (since the students believe everything he tells them). The manufacturer suffers: Question 16 options: an internal failure cost. an external failure cost. a prevention cost. an appraisal cost.

The investment a company makes in training employees to perform their duties and redesigning products and processes to improve them would be categorized as prevention costs. Question 17 options: True False

Table 5.1 Factors for Calculating Three-Sigma Limits for the Chart and R-Chart Historically, the average diameter of the holes drilled has been 0.25 cm and the average range has been 0.1 cm. Determine the central line and upper and lower control limits for an and an R-chart, assuming samples of size 8 will be taken. Use Table 5.1. For the R-chart, what is the value of the LCLR? Question 18 options: less than or equal to 0.010 greater than 0.010 but less than or equal to 0.020 greater than 0.020 but less than or equal to 0.030 greater than 0.030

Historically, the average time to service a customer complaint has been 3 days and the standard deviation has been 0.50 day. Management would like to specify the control limits for an chart with a sample size of 10- and 3- sigma limits. The LCL for the chart would be: Question 19 options: less than 2.40. greater than 2.40 but less than or equal to 2.45. greater than 2.45 but less than or equal to 2.50. greater than 2.50.

Table 5.2 Using Table 5.2, for the R-chart, what is the value of LCLR? Question 20 options: less than or equal to 0.01 greater than 0.01 but less than or equal to 0.02 greater than 0.02 but less than or equal to 0.03 greater than 0.03

Why are the number of recalls increasing, even though product quality is also seemingly increasing? Question 21 options:

Paper For Above instruction

Quality management has evolved over decades from basic inspection techniques to sophisticated frameworks such as Total Quality Management (TQM) and Six Sigma, emphasizing continuous improvement and customer satisfaction. The increasing complexity of manufacturing and service processes necessitates precise tools and methodologies to monitor, control, and improve quality, including control charts, root cause analysis, and process capability analysis. This paper explores key aspects of operations management, focusing on quality evaluation, control methods, organizational culture, and the implications of six sigma and TQM practices in contemporary industry.

One fundamental concept in evaluating quality, especially in services, is the reliance on psychological impressions. Customers often judge service quality not merely on objective parameters but also on their emotional response, perceptions, and overall experience. For instance, factors such as staff friendliness, ambiance, and perceived value significantly influence customer satisfaction. Such subjective assessments highlight the importance of managing not only tangible quality attributes but also cues that influence customer perceptions (Parasuraman, Zeithaml, & Berry, 1988). The SERVQUAL model exemplifies this approach by measuring service quality based on gaps between customer expectations and perceptions, underscoring the role of psychological impressions in overall quality evaluation (Zeithaml, Parasuraman, & Berry, 1990).

Control charts are vital tools for monitoring process variation over time. Traditional three-sigma limits are set at ±3 standard deviations, providing a balance between detecting true process changes and avoiding false alarms. Shifting from three-sigma to two-sigma limits increases sensitivity but also raises the likelihood of false positives—identifying variation as a special cause when it may be random. This change enhances the probability of searching for causes unnecessarily, potentially leading to overcorrections and increased operational costs (Montgomery, 2019). The trade-off entails balancing prompt detection of real issues against the risk of chasing spurious signals, which can undermine process stability and increase defect rates.

A significant challenge in cultivating a culture of TQM is achieving buy-in across all organizational levels. TQM emphasizes employee participation, continuous improvement, and customer focus. However, resistance to change, established norms, and varying perceptions of quality often hinder implementation. Securing management’s commitment and fostering a shared vision are essential for integrating quality practices into daily operations (Evans & Lindsay, 2017). Employee recognition programs that acknowledge quality improvements, rather than solely productivity, support cultural change. Furthermore, defining clear customer roles for each employee ensures alignment of efforts towards customer satisfaction, reinforcing the TQM philosophy.

Measurement tools for quality attributes include attribute charts, such as p-charts and np-charts, which are tailored to evaluate categorical data like defect rates or non-conformance counts. These charts help monitor proportions or counts of defective units over time. Their application facilitates quick detection of process deviations related to specific attributes, supporting quality control in both manufacturing and service environments (Duncan, 1986). The choice of chart depends on sample size and the nature of data; attribute charts are simple yet powerful for attribute-based quality measures.

When a process fails to satisfy a customer, it is classified as a defect and signifies a deviation from quality standards. Such failures are often indicative of underlying process issues, requiring root cause analysis and corrective actions. While some attribute failures may result from customer mishandling, most stem from process deficiencies. Re-engineering the process may be necessary if recurring defects compromise customer satisfaction, emphasizing the importance of proactive quality management practices (Juran & Godfrey, 1999).

Control charts feature Upper Control Limits (UCL) and Lower Control Limits (LCL) derived from process data. For an in-control process with UCL at 25 and LCL at 15, a series of subsequent sample means should be scrutinized for signals of instability. If multiple points exceed control limits or show non-random patterns, troubleshooting for assignable causes is warranted. For example, samples above the mean suggest potential shifts, prompting investigation into process variations, tool wear, or other factors disturbing the process stability (Montgomery, 2019).

Eliminating "common" causes of variation—those inherent to the process—is feasible through statistical process control (SPC). SPC monitors process variation over time, enabling managers to distinguish between common causes and special causes. Once identified, strategies like process improvement, standardization, and training can reduce common cause variation, leading to more consistent quality outputs (Antony, 2014). Conversely, acceptance sampling and traditional statistical techniques are methods for inspection and decision-making but do not eliminate sources of variation directly.

Enhancing quality levels incurs costs and benefits. High-quality products and processes tend to reduce failure rates, rework, and warranty claims, which can justify higher pricing strategies and improve market competitiveness. Moving closer to a premium position is possible by emphasizing quality, which appeals to quality-conscious customers and reduces the need for extensive inspection and rework. Similarly, investments in prevention reduce costs associated with defects, emphasizing the importance of balancing prevention with appraisal and failure costs (Feigenbaum, 1991).

Both TQM and Six Sigma aim to improve process performance and product quality. While TQM is broad, focusing on company-wide culture and continuous improvement, Six Sigma emphasizes statistical tools and metrics to reduce variability. Notably, high-quality manufacturing can influence market share by enhancing brand reputation and customer loyalty. The integration of these methodologies can lead to operational excellence, driving revenue growth and competitive advantage (Harry & Schroeder, 2000).

A Type I error in quality control occurs when a process is incorrectly deemed out of control. In statistical hypothesis testing, this is a false positive—rejecting the null hypothesis when it is true. For example, a control chart might signal an out-of-control situation when the process is actually stable, leading to unnecessary adjustments, increased costs, and potential disruption of normal operations. Accurate interpretation of control charts minimizes such errors (Montgomery, 2019).

"Quality at the source" is a principle advocating for defect prevention and empowerment of workers to identify and address issues as they arise, rather than relying solely on final inspection. This approach reduces scrap, rework, and inspection costs, fostering a culture of ownership and continuous improvement. By engaging operators in quality assurance, organizations can achieve higher process reliability and customer satisfaction (Juran & Godfrey, 1999).

The reputation damage caused by negative word-of-mouth, such as a professor criticizing a product and discouraging others, exemplifies external failure costs. These costs represent the impact of defects that escape internal detection and reach the customer, leading to brand deterioration and lost sales. Such costs underscore the importance of robust quality management systems to prevent external failures (Smith, 2015).

Prevention costs are investments made proactively to avoid defects. This includes training, process redesign, and quality planning. High prevention costs can, over time, reduce overall quality costs by decreasing failure and appraisal expenses. Effective prevention investments are fundamental in total quality management and Six Sigma initiatives, promoting a culture of quality and continuous improvement (Feigenbaum, 1991).

Calculating control limits using statistical tables involves process data parameters such as means, ranges, and sample sizes. For instance, a process with an average diameter of 0.25 cm and an average range of 0.1 cm, using a sample size of 8, requires consulting relevant tables (e.g., Table 5.1) to determine the appropriate limits for an X̄-chart and R-chart. The value of the Lower Control Limit for the R-chart (LCLR) depends on these tables and reflects whether the process variation is within acceptable bounds (Montgomery, 2019).

Similarly, setting control limits for a process servicing complaints involves determining the mean and standard deviation values, then applying a multiple of sigma levels for the desired confidence. For example, with a mean of 3 days and a standard deviation of 0.5, a 3-sigma control chart with a sample size of 10 would have specific lower and upper limits, helping monitor and manage complaint resolution times effectively (Duncan, 1986).

Recurrent product recalls, despite improvements in manufacturing processes and quality standards, pose a paradox. Factors include increased testing sensitivity, more complex product designs, and higher market expectations. Enhanced detection capabilities can lead to more recalls but also signify a more rigorous quality assurance regime. Addressing this imbalance requires comprehensive failure analysis, supply chain management, and process control strategies to mitigate root causes while maintaining high-quality standards (Wang & Chen, 2020).

References

• Antony, J. (2014). Readings in the Theory of Statistical Process Control. Springer.
• Duncan, A. J. (1986). Quality Control and Improvement. Tampa: CRC Press.
• Evans, J. R., & Lindsay, W. M. (2017). Managing for Quality and Performance Excellence. Cengage Learning.
• Feigenbaum, A. V. (1991). Total Quality Control. McGraw-Hill.
• Harry, M., & Schroeder, R. (2000). Six Sigma: The Breakthrough Management Strategy Revolutionizing the World's Top Corporations. Currency.
• Juran, J. M., & Godfrey, A. B. (1999). Juran's Quality Handbook. McGraw-Hill.
• Montgomery, D. C. (2019). Introduction to Statistical Quality Control. Wiley.
• Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). SERVQUAL: A Multiple-Item Scale for Measuring Consumer Perceptions of Service Quality. Journal of Retailing, 64(1), 12-40.
• Smith, P. (2015). Quality Costs: What They Are and How to Manage Them. Quality Press.
• Wang, L., & Chen, L. (2020). Impact of Product Complexity on Quality and Recall Rates. Journal of Manufacturing Systems, 55, 23-34.