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Statistical Process Control (S.P.C)

Statistical Process Control as a function and tool of management aiding the best possible evaluation and implementation of programs, portfolios, and processes in organizations and enterprises by adhering to process centers consistent with outcome expectations. It is possible to engineer the best efficiency of time and product quality, thus optimum customer satisfaction due to its determination of common causes of variations or bottlenecks and utility in configuring requisite benchmarks. Since its inception in the 1920s, the techniques of (S.P.C) have undergone various developments that ensure product standardization chiefly in the manufacturing sector and lately in the service sectors is manageable with great precision.

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It needs to be understood that the statistical techniques are applicable in a field rich in various other managerial decision tools, however, it provides a real-time account of processes and progress necessary for baseline control as it shifts decision practice from opinions to facts. A competent and objective management system is most favourable with (SPC) as it exposes issues some of which are personnel based.

Installation of SPC software and hardware require concerted effort to achieve optimum results, the details of software support, integration of data collection systems and ease of technician familiarization with the system. Technological capability is, however fundamental in excellent practice as little technology is incapable for the installation of adequate statistical process control. Cultural orientations today have earned great progress as women semiskilled employees are find opportunity in the field as technicians.

Importance

Product and service quality have a direct correlation with the balance sheet items of successful companies, the maintenance and improvement of quality at optimal costs, with widening business scopes the world over, there arises a need for impartial standardization of commodities. Different levels of quality enforcement applications exist in different companies with ISO certification. Efforts to integrate laterally and vertically companies, government agencies and the third sector would not be easy. This indicates an urgent need to ratify and integrate quality enforcement agencies for the sake of ethical business practice.

Moreover, professional statisticians and their professional associations need a well integrated work environment. For the sake of audits SPC avails to firms baseline process orientation to management but should be only employed to signal the existence of a system deficiency, unlike for general application in management. The evaluation of alternative solution orientations can also borrow immensely from the facts and figures generate from the statistical process.

There is need to integrate and promote statistical quality control both at the macro and micro-levels of the economy, (Larsen et al, 56) at national and international levels, professional organizations charged with the mandate to supervise implementation of SPC integration, implementation of analytical concepts and for the purposes of ongoing training of employees. Adequate product and process audits demand standard practice, as contained in Section 6.3 of the ISO standard, this regulatory infrastructure require organizations to determine, maintain, and sustain process equipment quality so as to attain product conformity (Robert, p. 142).

Consumers and the general populace exposed to products from diverse backgrounds increasingly need product quality information. To this effect, consumer organizations could be familiarized by quality organizations advertising standard benchmarks, awarding good performance level per industry to steer the wheels of progress. Commodity branding and labeling standards have adequately embraced the progress in SPC which shall have a general pull effect across industries.

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Analysis

Overall, quality control service and management functions to determine areas requiring attention, availing instrument and establishing norms to be followed, to the effect that management in the production sector adheres to standards as well as promote ethical business practice. In management parlance, excellent practice is fast becoming the norm due to the impact on balance sheet items, employee morale, customer satisfaction as well as progress in the entire business community.

Statistical Process Control linked with Continuous Improvement (SPC-CI) is poised as the chief reason for Japanese triumph in product quality in global markets. This is attributable to the fact that it streamlines the production process, cost efficiency enhancement and managerial quality boost. From its founding and incorporation in management, more otherwise unimagined possibilities are becoming widespread and the impact noticeable in a wave of industrial relations transformation signifying a complete paradigm shift, to say the least.

The measurable attributes/ outputs of the system need to be analyzed and interpreted with precision to ensure proper managerial functionality. In many instances, the Six Sigma quality management approaches have been utilized. These include the analysis of variances, cost-benefit analysis, homoscedasticity, analysis of Pareto chart optimality, regression and root cause analysis, market research and enterprise feedback, Taguchi methods among a plethora of approaches. The emphasis should always be rooted in flexibility and dynamic control as opposed to the traditional rigidity of procedure adherence (Joseph, p. 112).

These approaches emphasize monitoring of variations and their probable sources, at the plant level if the machines have wide variation of outcomes, the process stability and process capability needs to be analyzed with an aim of improving precision, or to narrow down the standard deviation of its performance. The ratio of tolerance to the standard deviation defines the process capability. Best utilization of control charts as attention focusing mechanisms is widely accepted as good practice, with these great attentions in management has been shifted from the process mainstream to the narrow outlier processes incident on the production process. Current use of efficient semiautomatic tools reinforces this orientation thus greatly reduces human physical labor, automation of processes hence intensifies more rigorous adoption of statistical process controls hence a general trend of high quality production.

Market factors greatly influence production processes and quality requirements. In such dynamic processes, work supervision shifts from the monitoring of efforts to the management and facilitation of change, (Jaikumar, p. 128). Taylorism departs from SPC in its insistence on static approaches as opposed to the current dynamic standards. The intellectual underpinning dominant with the statistical process control is integration other than reductionism and specialization postulated by Taylor, (Jacob et al, p. 250).

Numerical control(NC) approach has found little application, however studies have found it more applicable where technology combines the versatility of general-purpose machinery with the precision of automated machinery (Demmy, p. 68). The interface between product and machine remains man while best practice today is almost wholesomely utilizing computer interfaces with minimal operative engagement of human effort. Moreover, microprocessors technology greatly improved efficiency and applicability of automated statistical process controls (Western Electric Company, p. 221).

Certain machines experienced a problem of forced vibrations which interfered with the quality of product; however, mechanistic intervention is possible to solve the anomaly, (James et al, p. 89). It is possible to mechanically control virtually all mechanical anomalies that may hamper production process from time to time.

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The difference or variations between sectors is less marked in practice as pertains statistical process control, however, industrial specific variances occur. Capability studies show standard deviations between the process mean and nearest specification limits, the consequent decrease in the sigma number signifies great applicability across sectors. Managerial practice in terms of leadership, sociology of organizations, hierarchical structures, industry-specific influences as well are the regulatory environment need be well integrated for optimal outcomes with a statistical process control (Jacob et al, p. 57).

The DFSS (Design For Six Sigma) strategy, is fundamentally integral in its implications to management context. The premises of integrating executive leadership, champions, master black belts, black belts and green belts variably and appropriately to the six sigma process yields best practice outcomes. Moreover, the use of software that suits process as well as technician and personnel education and training is requisite (John, p. 137).

Synthesis /Integration

SPC based on the use of control charts are better appreciated when they exclusively address process trends other than personnel nonconforming orientations, all processes to be evaluated are documented and familiarized to employees or relevant departmental technicians. Such measurements only need to be appropriate other than exhaustive; in addition, they ought to be inexpensively traceable. Direct formulas are then employed to interpret such trends of distribution to establish control limits without addressing control techniques too exhaustively.

Capability indices of different applications and software as well as industrial establishment efficiency are crucial to greater adaptability and integration of statistical processes in production or commercial undertaking situations. Quality, process variations, capability indices of appliances/ tools are all of great importance in sustaining adequate and productive statistical processes. The state of statistical data is often random, requiring an analysis of time series to be able to grapple with emerging autocorrelations and other substantial systematic time series incidental to production lines (Joseph p. 45).

Trend(S)

The use of control charts as U, P, X-bar, S and XmR- charts is a new trend, the use of computer programs with complex parameters of analysis.

The use of time series modeling for statistical process controls , the systems are currently being integrated in engineering, contemporary research in the field have yielded overwhelming new areas of practice to review of develop.

In a new era of competition in volume of output and quality as well as quality benchmark regimes Software like SuperQ and many others are being adapted to monitor or evaluation of performances of much analytical industrial equipment. They promote systems in organizations that are self regulating; they do more to demonstrate the commitment of firms to national and international quality benchmarks put in place by quality regulating institutions.

The methodologies of DMADV (Define, Measure, Analyze, Design, Verify) and DMAIC (Define, Measure, Analyze, Improve) ushered in an era of business process improvement through schematic program management. The paradigm inspired vigorous process measurement, analysis, and evaluation of correlations between cause-and-effect of measurable inputs and outputs.

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Challenges

Statistical process is an entirely mathematical and technical process arousing many contextual challenges to tacticians, as well as persons meant to use such data for managerial purposes. The eclectic field integrates mathematics, operations research and computer science, and multiple algorithms and physical systems. Moreover, consumer priorities and tastes keep changing and clashing with production process economies often and with standard maintenance agencies. Production technologies change often in terms of machinery or chemical process, such changes need be contained by cost budgets in companies as well as personnel matrices.

Some process control software are inherent with operational errors as incompatibility with other packages, graphical display interpretation misinterpretation, on-line and offline inadequacies, it should not be assumed that the tools available for statistical process are adequate in themselves, sometimes the variables in question can only be established by rigorous manipulation of multiple cooperating factors. Shrewdness in conceptual interpretation and postulation of variable factors to study in given problem situations is an invaluable discipline in the field.

Process design

Design of statistical process has to have close correlation with the afore conceived difficulties and expectations out of the system, after the design stage the project is implemented. Implementation process entails myriad tasks as training of technicians, and management, developing a control plan, charts, adjustments, and capability indices, improving and implementation of the process.

Process control

Controls entail all actions of measurement used to assess signal outputs to determine the changes they undergo with time. Such changes have a great bearing on product quality which is the chief concern of statistical process control. Also control practices assist management to track timely and with precision changes that would otherwise lead to asset deterioration and depreciation. The control mechanisms adopted per particular process is crucial in the sustenance of the process.

Control mainly functions to identify variation within the process, the accurate documentation of data and their interpretation identifying areas that demand immediate investigation. Common cause variations and special cause variations can be distinguished and appropriately taken care of, hence dynamic processes and static process incident upon the production process are discernible. Most variations occur from determinate sources as people, materials, methods, measurement, and environmental sources. Systematic improvement of the process need to be undertaken through the entire lifespan of the system to ensure its validity. Various software and hardware are applicable in this undertaking with great outcomes.

Process operations

Contemporary practice has greatly been influenced by the use of quality assurance audits and quality measurement plans. However, its often difficult to conceptualize a state of statistical control or a deviation from it because of the constant experience with random data, the difficulty lies often in establishing from the patterns separate special causes and common causes of discrepancy.

Process approaches as cross-docking have proven beneficial particularly as relates distribution and consumer loyalty according to (Jacob et al, p. 78). Software applications in process appraisal, evaluation, and budgeting are productive when integrated with appropriate personnel competence and engagement. Complex operations benefit from software applications which are greatly simplified for technician use. Incorporation of all attendants to the process yields synergies that perform tasks in a cost effective and simple sequence.

Modeling

The model of SPC need to concur with manufacturing or service process it is meant to monitor, In chemical process, the Multivariate Statistical Process Control (MSPC) is most appropriate, other advanced methods like Moving Principal Component Analysis (MPCA) and (DISSIM) are applicable.

There is a rich account of the various models of SPC, more strikingly is the Six Sigma, designed to improve manufacturing process and pioneered by Bill Smith of Motorola Inc. Other approaches like quality control, zero defects and T.Q.M developed in close succession. Others include DMAIC and DMADV utilized in improving existing business processes and the ration of products or product designs, respectively.

The identification of requisite control parameters that adequately inform the engineering of appropriate (SPC) is absolutely necessary particularly in light of the fact that it addresses the fact of greatest importance; as the sales-force of a firm’s brand and consumer loyalty.

Integration

Support methods and tools/ equipment that promote best implementation is preferable at the process modeling stage as well as evaluation stages. It should be appreciated profoundly that all the stages of statistical process control are super conjoined and successful execution of one stage has great bearing on all other stages.

Applications

The various software used in SPC find best application when the personnel are well trained in their use and management, an elaborate implementation is requisite that allows managers to integrate well in new skills both at the plant level and at corporate levels. Moreover, currently there is wide application of SPC in fields other than manufacturing.

SPC has been applied in social science research in both theory and practice. More profoundly is the current influence of the discipline in management theory transforming the traditional procedural and strict controls theorized by Taylor. In addition, the accelerated rate of technological progress experienced currently shuts out from commerce entities and enterprises based on the non-dynamic practices.

The standard monitoring organizations greatly benefit from statistical process control in their duties. In effect the trade unions are gradually coming in to industrial synergy with the business community to effect ethical practice as well as safeguarding worker safety particularly in risk prevalent undertakings. Moreover, employees in industries embracing the progress generally account profitability as a result of the increased work satisfaction.

Work Cited

  1. John S, O. Statistical Process Control, Butterworth-Heinemann: London. U.K 2003.
  2. James R, T and Jacek Koronacki. Statistical Process Control, CRC Press: USA, 2002.
  3. Western Electric Company. Statistical Quality Control Handbook, Indianapolis; U.S.A
  4. Joseph A. & William Barnard. JURAN Institute’s Six Sigma Breakthrough and Beyond – Quality Performance Breakthrough Methods, Tata McGraw-Hill Publishing Company Limited, 2005
  5. James Harrington, Robert R. and Glen H. Statistical Analysis Simplified: Easy-to-Understand Guide To SPC and Data Analysis.
  6. Jacob A. and Pillai A. K, “Statistical Process Control to Improve Coding and Code Review,” IEEE Software, Volume. 2003,
  7. W. S. Demmy, “Statistical Process Control in Software Quality Assurance”, Proceedings of the IEEE National Aerospace and Electronics Conference (NAECON 1989), 1989,

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StudyCorgi. (2021, October 22). Statistical Process Control (S.P.C). Retrieved from https://studycorgi.com/statistical-process-control-s-p-c/

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StudyCorgi. (2021, October 22). Statistical Process Control (S.P.C). https://studycorgi.com/statistical-process-control-s-p-c/

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