What is the need for both the process capability index and the process capability ratio?
Controlling the manufacturing process is a challenging task that requires using a range of resources that an organization has at its disposal. More importantly, it is crucial to make sure that the essential processes are supervised accordingly and that every single element thereof is under the control of the people involved. Herein lies the significance of establishing a system that permits a detailed analysis of the processes identified.
The Process Capability Ratio (PCR) is typically rendered as the means of measuring the performance of an organization from a purely quantitative perspective. However, the characteristics of the production process that the above index provides are not enough to maintain high product quality and facilitate the environment, in which compliance with the existing production standards can be a possibility. While providing companies with a range of options concerning the use of the available resources for a detailed evaluation of the firm, the specified tool cannot be credited as the most accurate one due to the excessive focus on the quantitative aspects of the evaluation (Pyzdek, 2014).
By definition, the Process Capability Index (PCI) helps identify the relation between the known process spec limits and the natural variation thereof (Shenvar & Toan, 2012). As a result, the standard deviation of the process can be located, and the deviation from the required outcome can be identified comparatively easily. Seeing that the Process Capability Index allows for not only quantitative but also a qualitative assessment of the company’s success, it is traditionally viewed as the most efficient tool for measuring the performance of entrepreneurship.
How one can calculate the yield of a sequence of processes if the yield of each process in the sequence is known?
As a rule, the yield of a process is typically linked to the performance efficiency of an organization or a particular project. However, the given assumption is not quite right. While the yield may be comparatively high, the company may at the same time be experiencing certain financial issues, facing a rapid drop in its revenues (). The specified phenomenon can be attributed to the fact that the yield process must be viewed as a sequence instead of a single process. Therefore, to identify the opportunities that entrepreneurship may get and the threats that it may face, one will have to calculate the yield as a sequence instead of viewing it as a solid piece of information. However, the task becomes increasingly high in case the yield of each process segment is unknown (Carroll, 2013).
Nevertheless, the calculation process still can be carried out. For these purposes, one should assume that each stage of the process returns a yield of approximately 90% of the possible outcome. By multiplying the percentages of the unit yields and averaging the result, one will be able to retrieve the general yield percentage and, thus, locate the risks that a company may encounter once entering the target market or launching a specific project. For instance, in case the process includes three stages, the yield will equal 93 , or 0.729 (roughly 0.73). The framework provided by Pyzdek, therefore, allows suggesting that the feasibility of a project shrinks with the increase in the number of stages necessary to complete to reach the final goal. It should be noted, though, that the concept of the final yield is still approximated, and, therefore, the introduction of resources for the project support should be considered even in the cases that guarantee a 90% possibility of success.
Reference List
Carroll, C. T. (2013). Six Sigma for powerful improvement: A Green Belt DMAIC training system with software tools and a 25-lesson course. New York, NY: CRC Press.
Pyzdek, T. (2014). Measurement systems evaluation. The Six Sigma handbook (pp. 393-426) (4th ed.). New York City, NY: McGraw‐Hill 4th Edition.
Shenvar, O., & Toan, H. (2012). Process capability and Six Sigma methodology including fuzzy and lean approaches. In I. Fuerstner, Business, management, and economics: Products and services: From R&D to final solutions (pp. 153-178).Rijeka: InTech.