Statistical Process control is an excellent quality assurance tool to improve the quality of manufacture and ultimately scores on end-customer satisfaction. SPC uses Process monitoring charts to record the key quality characteristics (KQCs) of the component in manufacture. This paper elaborates on one such KQC of the manufacturing of a connecting rod of an internal combustion engine. Here the journey to attain the Process potential Capability index (Cp) and the Process performance Capability index (Cpk) values greater than 1.33 is elaborated by identifying the root cause through quality control tools like the cause-and-effect diagram and examining each cause one after another. In this paper, the define-measure-analyze-improve-control (DMAIC) approach is employed. The definition phase starts with Process mapping and identifying the KQC. The next phase is the measurement phase comprising the cause-and-effect diagram and data collection of KQC measurements. Then follows the analysis phase where the Process potential and performance Capability indices are calculated, followed by the analysis of variance (ANOVA) of the mean values. Finally, the Process monitoring charts are used to control the Process and prevent any deviations. By using this DMAIC approach, standard deviation is reduced from 0.48 to 0.048, the Cp values from 0.12 to 1.72, and the Cpk values from 0.12 to 1.37, respectively.

Process Capability indices show the ability of a Process to produce products according to the pre-specified requirements. Since final quality characteristics of a product are usually interrelated to its previous amounts in earlier workstations, one need to model and consider the relationship among them to assess the Process Capability properly. Hence, conducting Process Capability analysis in multivariate environment is inevitable; unfortunately, the analysis in multivariate environment is usually complex and requires extensive calculations. Sometimes it is preferable to simplify the analysis by assuming independency among quality characteristics and evaluating Process performance with respect to each individual quality characteristic using univariate Process Capability indices such as CP, CPK, CPM, and CPMK. However, this simplification introduces some error in the analysis leading to under or overestimation of the Process Capability index. This paper models the interrelationship among quality characteristics that are produced in different workstations to provide an overall Process Capability index. Ridge residual regression is used as a vehicle to evaluate Process Capability and helps quality engineers to provide a reasonable quality policy for controlling and reducing variation in quality characteristics.

The notion of fuzzy Process Capability indices is studied by parchami et al. [1], were the specification limits are triangular fuzzy numbers. In this note, their results are revised for the general case, where the specification limits are L-R fuzzy intervals.

A profile is a relationship between a response variable and one or more independent variables being controlled during the time. Process Capability Indices (PCI) are measured to evaluate the performance of Processes in producing conforming products. Despite frequent applications of profile and a variety of available methods to monitor its different types, little researches have been carried out on determining Capability indices of profile Process. PCIs such as P C and PK C in profile state, are used to evaluate Process Capability in producing conforming profiles. This paper presents a functional approach for nonlinear profiles which usually expressed as nonlinear regression. Thus, functions such as pertaining technical specification limits, mean and natural tolerance limits are determined as nonlinear profiles and also functional limit is applied to determine Functional Capability Indices (FCI) P C and PK C of functional nonlinear profile. Easy calculation and the ability to calculate FC in each period and at each point are from the advantages of this method over the other methods.

The define–measure–analyze–improve–control (DMAIC) approach is a five-strata approach, namely DMAIC. This approach is the scientific approach for reducing the deviations and improving the Capability levels of the manufacturing Processes. The present work elaborates on DMAIC approach applied in reducing the Process variations of the stub-end-hole boring operation of the manufacture of crankshaft. This statistical Process control study starts with selection of the critical-to-quality (CTQ) characteristic in the define stratum. The next stratum constitutes the collection of dimensional measurement data of the CTQ characteristic identified. This is followed by the analysis and improvement strata where the various quality control tools like Ishikawa diagram, physical mechanism analysis, failure modes effects analysis and analysis of variance are applied. Finally, the Process monitoring charts are deployed at the workplace for regular monitoring and control of the concerned CTQ characteristic. By adopting DMAIC approach, standard deviation is reduced from 0.003 to 0.002. The Process potential Capability index (CP) values improved from 1.29 to 2.02 and the Process performance Capability index (CPK) values improved from 0.32 to 1.45, respectively.