International Journal of Statistics and Systems ISSN 0973-2675 Volume 12, Number 2 (2017), pp. 355-362 © Research India Publications http://www.ripublication.com

An Essential Role of Statistical Process Control in Industries

S. Subbulakshmi1, A. Kachimohideen2, R.Sasikumar3 and S. Bangusha Devi4 1Assistant Professor, Dr.MGR Janaki College of Arts and Science for Women, Chennai-28, Tamil Nadu, India.

2Assistant Professor, Periyar EVR College, Trichy, Tamil Nadu, India.

3 Assistant Professor & 4Research Scholar, Manonmaniam Sundaranar University, Tirunelveli-12, Tamil Nadu, India.

Abstract In order to endure in a aggressive market, getting better quality and production of product or process is a must for any Industry. Statistical Process Control (SPC) is an efficient controlling methodology for analyzing, monitoring, managing and recuperating process performance. Biggest benefits for implementing SPC in industries are enhanced quality products and condensed process variation. The objective of this paper is to examine SPC implementation in the industry setting by applying systematic literature review, and to explore the comprehensiveness of SPC applications in the industry. This review systematic identified from more than 41 studies published between 1980 and 2014, depicting evidence of SPC application sparsely spread throughout the industry and a need to pursue more research in this area. Keywords: Statistical Quality Control, Statistical Process Control, Control Charts, Process Variation, Industries

1. INTRODUCTION Control charts are originally developed for use in mass production manufacturing. These techniques are applicable to most other types of activities in all sectors of the 356 S. Subbulakshmi, A. Kachimohideen, R.Sasikumar and S. Bangusha Devi economy including service business, government, education and health care. According to Montgomery (2005), quality is one of the most important verdict factors in the selection of products and services. Therefore, quality leads to big business achievement, development, and increases competitiveness. Ryan (1989) defined quality as a decision- making technique that uses statistics to monitor the consistency of a production process and the resulting product as improves the work environment. Quality Control (QC) is an important task in factory as it deals with product inspection before the product was shipped to customers. SPC is a dominant tool in order to improve the processes that augment productivity and quality. Many researchers have noticed the leaning that service quality improvement has become a inevitability in many industries. Wyckoff (1984) claimed that SPC is an excellent method for service managers to scrutinize service processes, and also supportive for staff to perform self-improvement. Palm et al. (1997) also barbed out that SPC would have great potential in tertiary sector of industry, such as health care and teaching, and has already been established to be useful in healthcare industry. SPC is an approach that has been broadly used in many industrial and non-industrial fields. The primary application domain for SPC charts is in process control and improvement in manufacturing. SPC is a procedure developed based on Shewhart’s formation of process variability, which generally applied not only in manufacturing processes but also in service operations for quality sustainability purposes. SPC is defined by Montgomery (2009) as a powerful collection of problem-solving tools useful in achieving process constancy and improving capability through the lessening of variability. The primary purpose of SPC implementation is to perceive and reduce special cause variations for process solidity.

2. ADVANTAGES OF SPC IMPLEMENTATION SPC implementation is important as it could develop process performance by reducing product variability and improves production competence by decreasing scarp and rework. According to Attaran (2000), in their attempts to stay competitive, US business had embarked on Total Quality management (TQM) techniques such as SPC that leads to higher quality product by reducing-variability and defects. Most of the production and quality cost that SPC aims to reduce such as rework lost of sales and proceedings are measurable. The success and failure in SPC operation does not depend on company size or resources, but it relies on suitable planning and immediate actions taken by workers with regards to problem solving. According to Benton and Talbot, the advantages of implementing SPC could be categories into the following categories, viz., maintain a needed degree of conformance to design, raise product quality, eradicate any unnecessary quality checks, trim down the percentage of defective parts purchased from vendors, diminish returns from customers, lessen scrap An Essential Role of Statistical Process Control in Industries 357 and rework rates, afford evidence of quality, facilitate trends to be spotted, ability to reduce costs and lead times. The primary tool of SPC is the Shewhart control chart. The Shewhart control chart quantifies variation as either special cause or common-cause (natural) variation. The control limits on control charts put a figure on variation as that intrinsic to the process (natural variation data inside the control limits), or variation caused by an event or assignable-cause (special cause variation data located outside the control limits). Data outside the control limits are also referred to as “out of control” points. A large variety of SPC schemes have been developed for quality and productivity improvement since the 1960s. SPC utilizes statistical methods to monitor manufacturing processes with an aim to maintain and improve the product quality while decreasing the variance. Much research has been conducted on the issues of SPC and the consequential developments are readily available in the literature, see surveys of research on SPC by Lowry and Montgomery (1995), Woodall and Montgomery (1999). Nevertheless, conventional SPC methods are typically restricted to a single process stage in industrial and service applications are also some obstacles to applying SPC in services, such as what to measure and how to measure. The main difference between a manufacturing system and service system is that customers are involved into service operations. How to measure the customers professed quality is a challenge. Therefore, researchers investigated modification of quality definition in services. Khong sak Srikaeo et.al (2004) analyzed in their paper that the association between raw material variables and product variables. They adopted SPC techniques such as histogram to found that the data were not normally distributed ANOM detect a association between the production line from the grand mean in all product variables, p-control charts showed that the process is not in statistical control for some variables. Since they use the historical data the out of control points where removed to simulate the situation where the process is in statistical control for further studies. With the process can capability studies the process precision and accuracy can be clearly assessed. Gauge R and R studies suggest that the variations found in the process are caused by the measurement system used in the plant. Nigel P.Grigge et.al (1998) presented a case study that the use of statistical process control in fish product packaging in the year 1498 which demonstrated the simplicity of applications of the off-the peg system. Such system can reduce unnecessary check weighs rejections and product give away, assist the packer to maintain the lowest possible legal target quantity. Andras Ittzes (2000) in his paper evaluated that the application of SPC is that of the several variance components of the variable in the control of the dry matter content in butter cream. He evaluated the data of Analysis of Variance (ANOVA) based on the nested model. He also referred Snedecor and Cochran (1967) or Steiner (1984). 358 S. Subbulakshmi, A. Kachimohideen, R.Sasikumar and S. Bangusha Devi

Ali Mostafaeipour et.al (2012) in their paper introduced that SPC technique in ceramic tile manufacturing plant. They suggested SPC for reducing the unwanted ceramic tile defects and wastages. They adopted to group different causes for inappropriate effects and its category. Also they adopted six-pack charts to offer six different packs in the format of one diagram. They applied control chart like R, 푋̅ and range control charts to manage the process variations. They also adopted dispersion diagram to evaluate and to compare process performances during various periods. Data are categorised in a specific format by using Histogram. Also is one of the significant items in production. Finally they recommended complementing the correct SPC frequently in the manufacturing plant to minimise ceramic tile waste. Ranjit Sabhapandit et. al (2014) in their paper described about some of the software tools and their capabilities that are used for applying SPC techniques. They also suggested the need for applying statistical process control in software development process. They discussed about few most repetitively applied software tools such as WinSPC, Zontec, XLSTAT,SCADA and Minitab. They sternly said that the organization has to opt the proper tool that suit best to its needs in order to have the most effective output. Yonatan Mengesha Awaj et. al (2013) recommended to pertain the SPC tools in the production processing in order to condense defects by identifying where the highest waste is occur at and to give idea for improvement. They adopted, Pareto chart/analysis and control chart (p-chart). They mainly explained how to create consciousness to quality team how to use SPC tools in the problem analysis, particularly to train quality group on how to held an successful brainstorming session, and make use of these data in cause-and-effect diagram construction, Pareto analysis and control chart construction. After detail observation and interview they suggested that SPC tools needed to minimize or reduce the problem and the more proficient technique is long run. Finally he sturdily recommended that the company should make every effort for the implementation of SPC tools for productivity improvement. Ved Parkash et.al (2013) described about the SPC, its advantages, limitation, applications and information regarding the control charts. They also introduced the primary groups of input for man, machine, material, method and milieu. They explained the application of SPC into three main sets of activities, the first understands the process and is achieved by business process mapping, the second is measuring the sources of variation assisted by the use of control charts and the third is eliminating assignable (special) sources of variation. They suggested SPC, can be used in various industries for improving the quality of the product and helps in lowering the product costs as it provides a better product and/or service. Rami Hikmat Fouad et.al (2010) in their paper identified the key ingredients for successful quality management in any industrial organization. They also illustrated how An Essential Role of Statistical Process Control in Industries 359 is it important to realize the intergradations between SPC are seven tools, and how to effectively implement and to earn the full strength of these tools. They also carried out a case study to monitor real life data in a Jordanian manufacturing company that specialized in producing steel and found that the steel tensile strength is the vital few problem and account for 72% of the total results of the problems. They specified the major causes of nonconformities and root causes of the quality problems and possible remedies were also proposed. They also explained the necessity of SPC tools in Jordan Steel to introduce ongoing education and training programs of management and line staff. Rallabandi Srinivasu et. al (2011) recognized (SPC) as effective approaches for process monitoring and diagnosis. They suggested that SPC principals and techniques would be at every stage of the production and to control quality characteristics on the methods, machine, goods, equipments both for the company and operators with magnificent seven. He explained the “seven basic quality control tools” which provide a very valuable and cost effective way to meet the objectives. Dr. D. R. Prajapati (2012) adopted some SPC techniques in the industry for various applications in the automotive industry. He explained the power of SPC lies in the ability to examine a process and the sources of variation in the process. He used only two main techniques i.e. cause and effect diagram and control charts are implemented in industry out of seven SPC techniques. Finally he found in his case study that after implementing the SPC tools to remove the root causes, the percentage refutation is reduced from 9.1% to 5% and process capability of 0.953 is achieved. Muhammad Riaz et. al (2012) strongly recommended that the control charts worth in the manufacturing industry. They also suggested that monitoring the key characteristics is most important in any manufacturing industry. They strongly believed that the inefficient of a quality control section may cause poor quality of a product. Ben Mason and Jiju Antony (2000) in their paper discussed the ingredients that are needed for the victorious performance of SPC. They attempt to eradicate the myth that SPC is concerned with only the control charting of processes. A major issue addressed by them in this paper is the need for the organized and realistic methodology for the execution of SPC in industry. Gasper Skulj et.al (2013) in their Illustrated a service approach of SPC and discussed through an industrial case study. They clearly explained the successful development and implementation of SPC as a service. They also discussed how to use the SPC service in a small company to increase its capabilities in process quality management and process control through a case study. Zhonghua li et. al (2012) in their paper designed Conventional Phase II SPC charts using control limits. They suggested designing the SPC charts using p values. They demonstrated the p value approach to consider univariate process monitoring by 360 S. Subbulakshmi, A. Kachimohideen, R.Sasikumar and S. Bangusha Devi cumulative sum control charts in various cases. They also proposed designing control charts using p values and a significance level. Erwin M. Saniga (1989) explained the control charts which may be designed by using a simple rule recommended by Shewhart, by a statistical norm, or by an financially viable criterion. He placed statistical constraints on economic models to provide designs that meet industry's demand for low-process variability and long-term product quality. He illustrated its use in the joint design of an X bar chart and an R chart. He developed a method to find the most economical statistical design for Shewhart-type control charts and have applied it to the joint determination of the parameters of X bar chart and R charts.

3. DISCUSSIONS This paper represents an understanding report on defining and carrying out a systematic review on SPC, which is well known in industrialized contexts. Implementation of SPC tools at the company is expected to develop its processes and condense variability or waste because it may not be possible to completely eliminate variability. With reduced variability the cost of dealing with scrap, rework, and other losses created by defectives which is an enormous drain on the company will be greatly reduced. This review indicates that SPC is applied in the industry with huge benefits in the business to diverse. Although there are limitations and barriers impeding the implementation, if the implementation was done correctly and greatly facilitated, SPC can be a flexible technique for control quality improvement in the industry and sustaining the quality of the product. This review suggests that SPC is a powerful technique when implemented in the industry. The production is under the control and the quality of the component gets improved in automobile industry through Statistical Process Control.

4. CONCLUSION In conclusion the company should endeavour for the execution of SPC tools for productivity development. SPC implementation is significant as it could improve process performance by plummeting product variability and improves production efficiency by lessening scrap and rework. It is concluded that SPC helps the organization to observe the process activities.

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