Development and application of process capability indices

In order to measure the performance of manufacturing processes, several process capability indices have been proposed. A process capability index (PCI) is a unitless number used to measure the ability of a process to continuously produce products that meet customer specifications. These indices have since helped practitioners understand and improve their production systems, but no single index can fully measure the performance of any observed process. Each index has its own drawbacks which can be complemented by using others. Advantages of commonly used indices in assessing different aspects of process performance have been highlighted. Quality cost is also a function of shift in mean, shift in variance and shift in yield. A hybrid is developed that complements the strengths of these individual indices and provides the set containing the smallest number of indices that gives the practitioner detailed information on the shift in mean or variance, the location of mean, yield and potential capability. It is validated that while no single index can fully assess and measure the performance of a univariate normal process, the optimal set of indices selected by the proposed hybrid can simultaneously provide precise information on the shift in mean or variance, the location of mean, yield and potential capability. A simulation study increased the process variability by 100% and then reduced by 50%. The optimal set managed to pick such a shift. The asymmetric ratio was able to detect both the 10% decrease and 20% increase in µ but did not alter significantly with a 50% decrease or a 100% increase in σ, which meant it was not sensitive to any shift in σ. The implementation of the hybrid provides the quality practitioner, or computer-aided manufacturing system, with a guideline on prioritised tasks needed to improve the process capability and reduce the cost of poor quality. The author extended the proposed hybrids to fully measure the performance of a process with multiple quality characteristics, which follow normal distribution and are correlated. Furthermore, for multivariate normal processes with correlated quality characteristics, process capability analysis is not complete without fault diagnostics. Fault diagnostics is the identification and ranking of quality characteristics responsible for multivariate process poor performance. Quality practitioners desire to identify and rank quality characteristics, responsible for poor performance, in order to prioritise resources for process quality improvement tasks thereby speeding up the process and minimising quality costs. To date, none of the existing commonly used source identification approaches can classify whether the process behaviour is caused by the shift in mean or change in variance. The author has proposed a source identification algorithm based on mean and variance impact factors to address this shortcoming. Furthermore, the author developed a novel fault diagnostic hybrid based on the proposed optimal set selection algorithm, principal component analysis, machine learning, and the proposed impact-factor. The novelty of this hybrid is that it can carry out a full multivariate process capability analysis and provides a robust tool to precisely identify and rank quality characteristics responsible for the shifts in mean, variance and yield. The fault diagnostic hybrid can guide the practitioners to identify and prioritise quality characteristics responsible for the poor process performance, thereby reducing the quality cost by effectively speeding up the multivariate process improvement tasks. Simulated scenarios have been generated to increase/decrease some components of the mean vector (µ2/µ4) and in increase/reduce the variability of some components (σ1 reduced to close to zero/σ6 multiplied by 100%). The hybrid ranked X2 and X6 as the most contributing variables to the process poor performance and X1 and X4 as the major contributors to process yield. There is a great challenge in carrying out process capability analysis and fault diagnostics on a high dimensional multivariate non-normal process, with multiple correlated quality characteristics, in a timely manner. The author has developed a multivariate non-normal fault diagnostic hybrid capable of assessing performance and perform fault diagnostics on multivariate non-normal processes. The proposed hybrid first utilizes the Geometric Distance (GD) approach, to reduce dimensionality of the correlated data into fewer number of independent GD variables which can be assessed using univariate process capability indices. This is followed by fitting Burr XII distribution to independent GD variables. The independent fitted distributions are used to estimate both yield and multivariate process capability in a time efficient way. Finally, machine learning approach, is deployed to carry out the task of fault diagnostic by identifying and ranking the correlated quality characteristics responsible for the poor performance of the least performing GD variable. The results show that the proposed hybrid is robust in estimating both yield and multivariate process capability, carrying out fault diagnostics beyond GD variables, and identifying the original characteristic responsible for poor performance. The novelty of the proposed non-normal fault diagnostic hybrid is that it considers quality characteristics related to the least performing GD variable, instead of investigating all the quality characteristics of the multivariate non-normal process. The efficacy of the proposed hybrid is assessed through a real manufacturing examples and simulated scenarios. Variables X1,, X2 and X3 shifted away from the target by 25%, 15% and 35%, respectively, and the hybrid was able to select variables X3 to be contributing the most to the corresponding geometric distance variable's poor performance

Innovation determinants in manufacturing firms

In this paper the findings of an empirical study concerning the innovation determinants in manufacturing firms is presented. The empirical study covers 184 manufacturing firms located in the Northern Marmara region of Turkey. The types of innovation considered here are product, process, marketing and organizational innovations. An extensive literature survey on innovation determinants is provided. A model is proposed to explore the probable effects and the amount of contribution of the innovation determinants to firm’s innovativeness level. Among all possible determinants considered, intellectual capital has the highest impact on innovativeness followed by organization culture

Acceptance sampling plan for multiple manufacturing lines using EWMA process capability index

The problem of developing a product acceptance determination procedure for multiple characteristics has attracted the quality assurance practitioners. Due to sufficient demands of consumers, it may not be possible to deliver the quantity ordered on time using the process based on one manufacturing line. So, in factories, product is manufactured using multiple manufacturing lines and combine it. In this manuscript, we present the designing of an acceptance sampling plan for products from multiple independent manufacturing lines using exponentially weighted moving average (EWMA) statistic of the process capability index. The plan parameters such as the sample size and the acceptance number will be determined by satisfying both the producer's and the consumer's risks. The efficiency of the proposed plan will be discussed over the existing sampling plan. The tables are given for industrial use and explained with the help of industrial examples. We conclude that the use of the proposed plan in these industries minimizes the cost and time of inspection. Smaller the sample size means low inspection cost. The proposed plan for some non-normal distributions can be extended as a future research. The determination of sampling plan using cost model is also interested area for the future research. ? 2017 The Japan Society of Mechanical Engineers.11Ysciescopu

Production Quality for Process Capability with Multiple Characteristics on the Chip Resistor Production

There are many journal papers about process capability indices with multiple characteristics in certain manufacturing assemblies including Cp, Cpk, Cpu, and Cpl. However, all of them assume the data is normal distribution and there is no product level process capability with an example chip resistor. This paper will discuss the affection of sample mean and standard deviations on process capability indices for multiple quality characteristics and its product assembly instead of assuming as normal distributions with the data from simulation. Furthermore, it will present several methodologies to calculate the product process capability with weighted arithmetic mean technique so that we can see how each characteristic effect on the product process

The Land Use in Rural New Zealand Model Version 1 (LURNZv1: Model Description)

This paper documents the first version of the Land Use in Rural New Zealand Model (LURNZv1). It describes the overall modelling approach, the database underlying the model, and the construction of each module within the model. The model is econometrically estimated from national time series data and spatially extrapolated using economic and geophysical variables. It is primarily a simulation model but is also set up to produce predictions based on future price scenarios. The model output includes projections of four types of rural land use under different scenarios and 25 ha grid maps of where land use, and changes in land use, are likely to occur.simulation model, land use, dairy, sheep, beef, forestry, reverting indigenous forest

Understandings and Misunderstandings of Multidimensional Poverty Measurement

Multidimensional measures provide an alternative lens through which poverty may be viewed and understood. In recent work we have attempted to offer a practical approach to identifying the poor and measuring aggregate poverty (Alkire and Foster 2011). As this is quite a departure from traditional unidimensional and multidimensional poverty measurement – particularly with respect to the identification step – further elaboration may be warranted. In this paper we elucidate the strengths, limitations, and misunderstandings of multidimensional poverty measurement in order to clarify the debate and catalyse further research. We begin with general definitions of unidimensional and multidimensional methodologies for measuring poverty. We provide an intuitive description of our measurement approach, including a ‘dual cutoff’ identification step that views poverty as the state of being multiply deprived, and an aggregation step based on the traditional Foster Greer and Thorbecke (FGT) measures. We briefly discuss five characteristics of our methodology that are easily overlooked or mistaken and conclude with some brief remarks on the way forward.

Ecosystem properties and principles of living systems as foundation for sustainable agriculture – Critical reviews of environmental assessment tools, key findings and questions from a course process

With increasing demands on limited resources worldwide, there is a growing interest in sustainable patterns of utilisation and production. Ecological agriculture is a response to these concerns. To assess progress and compliance, standard and comprehensive measures of resource requirements, impacts and agro-ecological health are needed. Assessment tools should also be rapid, standardized, userfriendly, meaningful to public policy and applicable to management. Fully considering these requirements confounds the development of integrated methods. Currently, there are many methodologies for monitoring performance, each with its own foundations, assumptions, goals, and outcomes, dependent upon agency agenda or academic orientation. Clearly, a concept of sustainability must address biophysical, ecological, economic, and sociocultural foundations. Assessment indicators and criteria, however, are generally limited, lacking integration, and at times in conflict with one another. A result is that certification criteria, indicators, and assessment methods are not based on a consistent, underlying conceptual framework and often lack a management focus. Ecosystem properties and principles of living systems, including self-organisation, renewal, embeddedness, emergence and commensurate response provide foundation for sustainability assessments and may be appropriate focal points for critical thinking in an evaluation of current methods and standards. A systems framework may also help facilitate a comprehensive approach and promote a context for meaningful discourse. Without holistic accounts, sustainable progress remains an illdefined concept and an elusive goal. Our intent, in the work with this report, was to use systems ecology as a pedagogic basis for learning and discussion to: - Articulate general and common characteristics of living systems. - Identify principles, properties and patterns inherent in natural ecosystems. - Use these findings as foci in a dialogue about attributes of sustainability to: a. develop a model for communicating scientific rationale. b. critically evaluate environmental assessment tools for application in land-use. c. propose appropriate criteria for a comprehensive assessment and expanded definition of ecological land use