Management Of Quality

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MANAGEMENT OF QUALITY 7 Management of Quality Professor’s Name Student’s Name Course Title Date Management of Quality 1. What is the purpose of a Statistical Quality Chart (SQC) and how is it used to evaluate the quality performance of an ongoing production process. The statistical quality chart refers to the graph that is used to study changes of a process over time. The data or information available is plotted in time order. The quality chart will ever have the centreline which represents the average, the upper line showing the –upper limits for control and the lower line which shows the lower limits for control (Chakraborty & Mandal, 2019).

The lines are usually determined historically. When you compare the current data with the historical data, you can always come up conclusions on whether the process at hand has variations that are consistent or even the unpredictable, that is the out of control being the special forces of variance. The versatile analysis and the data collection tool is used by many factories and industries in assessing their control processes. This quality chart can be used when trying to control the ongoing processes by correcting and finding the challenges as they happen. It can be used when you expect a large or wide range of outcomes from a particular process (Wei, 2018).

It can be used to determine whether a certain process is table. It can be used to analyse the process variation patterns from the non-routine events or the Special Forces or the causes built in the process. It can also be used to determine whether the quality enhancement project should focus on preventing the specific problems or making the necessary changes to the process. 2. Describe how to use Quality Functional Deployment (QFD) and Value Analysis (VA).

The quality function deployment process starts with collection of customer inputs or the potential customers, mostly through conducting surveys. The surveys sample size ought to be significant because the quantifiable data will have more weight. This avoids allowing the outlier comments drive product strategy in the direction that is wrong. Once the aggregation of data, which comes along with the applicable competitive analysis and surveys are done, it goes to the voice of the customer (Wee et al., 2017). The customer demands, requests, requirements and the preferences are put as specific items and ranked in terms of the urgency.

They are then placed on the left side of the House of Quality matrix and will represent what the customers want to do with the product. The value analysis is the process whereby the firm will identify the support and primary activities that will have value addition to the product and then the analysis is undertaken to improve the differentiation and minimize costs. 3. Discuss the facility location decision process to include the major variables. Explain how factor rating can be used to identify the top location options. Identify any possible problems with using factor rating.

There are 7 major variables that must be considered when the location decisions for businesses are being made. They include labour, competition, the political risk and incentives, facilities and site and the community. These factors that affect the location of the plant will however depend on the business type. The factor rating systems are the most used when determining the location for a plant because it is a system that combine many different factors to a format that is easy to understand.

The challenge with this simple point rating system is that it does consider the wide range of costs that come with every factor. For instance, there could be a few difference in terms of dollars between the worst and the best location on another factor. The initial facto might have many points at disposal but also give little aid in terms of the decisions making. The second factor might have points to consider in terms of availability but much help in making decisions for the proposed location. This can really present a difference in terms of the locations value (Robert & Chase, 2018).

In order to deal with this challenge, a solution has been reached that the costs that are associated with every factor be determined by use of the weighing scale that is based on the cost standard deviations rather than just the total costs and in this way, the relative costs can be determined which sounds more practical. 4. Explain the production planning process from the initial sales estimate to the plant floor. Describe the importance of Johnson's rule in scheduling multiple machines

Production planning is “the administrative process that takes place within a manufacturing business and that involves making sure that sufficient raw materials, staff and other necessary items are procured and ready to create finished products according to the schedule specified” (Böckenkamp et al., 2017). The first step in the production planning process is forecasting demand. This tells the demand expected from the customers (Böckenkamp et al., 2017). The next stage is determination of the potential option for production in the process. The next stage is choosing the option that less uses resources but still guarantees massive sales.

The fourth stage is self-assessment of the performance of the options undertaken to see if objectives are being achieved. The fifth and final stage is adjustment based on the results that have been achieved so far. Johnson's rule is the priority rule that is applied when sequencing jobs across multiple machines where many jobs are involved. Its main goal is to reduce idle time and improve efficiency within the production process by determining the optimal order of jobs to be processed (Charnes & Cooper, 2016).

Paper For Above instruction

Effective management of quality is fundamental for ensuring customer satisfaction and maintaining competitive advantage in manufacturing and service industries. Central to this management is the utilization of various tools and techniques designed to monitor, evaluate, and improve quality performance across processes. These tools include statistical quality charts (SQC), Quality Functional Deployment (QFD), Value Analysis (VA), facility location decision models, and production scheduling methods like Johnson’s rule. This paper explores the purpose, application, and significance of these approaches in the quality management paradigm.

Statistical Quality Charts (SQC) and Their Role in Monitoring Process Performance

Statistical Quality Charts are graphical tools utilized to observe the stability and variability of a process over time. Essentially, they serve to distinguish between inherent process variation and actual signals indicating process shifts or issues. The chart comprises a central line representing the average or mean of the process data, along with upper and lower control limits that delineate acceptable variation thresholds (Chakraborty & Mandal, 2019). These control limits are usually derived based on historical data, providing a benchmark against which current process data can be compared (Wei, 2018).

The core purpose of SQC is to facilitate real-time monitoring of an ongoing process, enabling management to take corrective actions proactively. By plotting process data sequentially over time, operators and quality managers can easily identify points that fall outside control limits, indicating abnormal variation likely caused by assignable causes or special factors (Benneyan et al., 2015). For instance, an unexpected increase in variability might signal equipment malfunction or operator error, prompting immediate investigation and correction.

Furthermore, SQC aids in distinguishing between common cause variation, which is inherent to the process, and special cause variation, which warrants investigation. Recognizing this distinction is crucial for maintaining process stability and continuous improvement (Montgomery, 2019). The ability to detect early signs of potential issues and respond accordingly increases process reliability, reduces waste, and enhances product quality. Especially in high-volume manufacturing settings, these charts are invaluable for enforcing consistent quality standards and optimizing process control (Rerup & Feldman, 2011).

Implementing Quality Functional Deployment and Value Analysis

Quality Functional Deployment (QFD) is a structured method for translating customer requirements into specific technical features of a product or service. The process begins with comprehensive collection of customer inputs, often through surveys and interviews, aimed at understanding explicit needs, preferences, and expectations (Wee et al., 2017). The gathered data must be significant and statistically reliable to prevent outliers from skewing the product development strategy.

Once customer demands are identified, they are prioritized and mapped onto the House of Quality matrix, which visually relates customer requirements to the company's technical responses. This process ensures that product design directly corresponds to customer expectations, fostering customer satisfaction and competitive advantage (Hauser & Clausing, 1988). By focusing on critical attributes, firms can better allocate resources to improvements that will most impact customer perceptions (Kano et al., 2019).

Value Analysis complements QFD by systematically analyzing the functions of a product to identify opportunities for cost reduction and value enhancement. This involves dissecting support activities and primary processes to identify redundancies or non-value adding steps and redesigning them for efficiency (Moore & Brooks, 2017). The ultimate goal of VA is to optimize product value by improving performance while minimizing costs, thereby strengthening market position and profitability.

Facility Location Decision-Making and the Use of Factor Rating

The choice of a facility location significantly impacts operational costs, accessibility to markets, supply chain efficiency, and overall competitiveness. Key variables influencing the decision include labor availability and costs, proximity to markets and suppliers, transportation infrastructure, political stability, incentives, and community factors (Robert & Chase, 2018). Each variable's importance varies depending on the industry and strategic priorities.

The factor rating method is a widely adopted approach that simplifies the complex process of location analysis by assigning scores to each variable for potential sites. Decision-makers rate each location based on predefined criteria, multiply these ratings by weights reflecting the relative importance, and sum the scores to identify the most suitable site (Kennedy, 2014). However, a significant limitation is that the method may oversimplify complex trade-offs and fail to account for dynamic cost factors effectively.

To mitigate these problems, cost-related variables can be incorporated into the factor rating system using weighted standard deviations, which normalize differences and provide a more objective basis for comparison (Sweeney et al., 2019). Nonetheless, decision-makers must exercise caution to avoid biases and ensure comprehensive evaluation by considering qualitative factors alongside quantitative scores.

Production Planning and Scheduling with Johnson’s Rule

Production planning encompasses organizing manufacturing activities to meet forecasted demand efficiently. It begins with demand forecasting, followed by evaluating potential production options. The optimal plan is selected based on resource utilization, capacity, and cost considerations. Continuous performance assessment and necessary adjustments ensure that manufacturing remains aligned with strategic goals (Böckenkamp et al., 2017).

Scheduling multiple jobs efficiently is a crucial component, with Johnson’s rule playing a vital role in optimizing sequence for two-machine flow shop scenarios. The rule prioritizes jobs based on their processing times, scheduling those with the shortest processing time on machine 1 first, and the longest on machine 2, to minimize total makespan and idle times (Johnson, 1954). Successful implementation of Johnson’s rule enhances throughput, reduces delays, and ensures balanced utilization of workstations (Shtub & Talreja, 2014).

Conclusion

In conclusion, effective quality management relies on the integration of various analytical and planning tools. Statistical Quality Charts empower real-time process monitoring, safeguarding against variances and defects. QFD and Value Analysis ensure that customer needs translate into tangible improvements in product design and cost efficiency. Strategic facility location decisions, supported by factor rating methods, optimize operational effectiveness. Lastly, production planning and scheduling techniques like Johnson’s rule facilitate smooth, cost-effective manufacturing operations. Together, these tools constitute a comprehensive framework for continuous quality improvement and operational excellence in manufacturing enterprises.

References

• Benneyan, J. C., Lloyd, R. C., & Plsek, P. E. (2015). Statistical Process Control for Healthcare: Principles and Applications. Quality Management Journal, 11(4), 16-28.
• Charnes, J. M., & Cooper, W. C. (2016). Advances in Manufacturing Systems. Springer.
• Chen, M., & Yang, Z. (2020). Application of Control Charts in Manufacturing Quality Control. Journal of Quality Technology, 52(1), 44-59.
• Hauser, J. R., & Clausing, D. (1988). The House of Quality. Harvard Business Review, 66(3), 63-73.
• Johnson, S. M. (1954). Optimal Scheduling in a Flow Shop with Two Machines. Operations Research, 2(1), 3-22.
• Kano, N., Seraku, N., Takahashi, F., & Tsuji, S. (2019). Attractive Quality and Must-Be Quality. Journal of the Japanese Society for Quality Control, 15(12), 39-48.
• Montgomery, D. C. (2019). Introduction to Statistical Quality Control. John Wiley & Sons.
• Rerup, C., & Feldman, M. S. (2011). Performativity and Quality: How Quality Management Programs Can Affect Quality and Performance. Organization Science, 22(1), 188–209.
• Shtub, A., & Talreja, D. (2014). Project Scheduling: A User-Friendly Approach. Springer.
• Sweeney, D., Heavey, C., & McCaffrey, D. (2019). Location Analysis: A Review of Methods. Journal of Business Research, 103, 250-261.