⦁ ⦁ ⦁ ⦁ ⦁ ⦁ ⦁ ⦁ ⦁ ⦁ ⦁ ⦁ ⦁ ⦁ ⦁ ⦁ ⦁  ⦁ Introduction In this project, SPC has been studied; the inspection has been carried out for sampling process control. Moreover, description of statistical process control has been provided. The efficient and effective ways of using Statistical process control have been mentioned in the project. The two major sampling techniques used in the process control have been defined and mentioned. Justifications on the use of use of these techniques have been provided. In this regard it also needs to be noted that the appropriateness in selecting the approaches are vital, as these will determine success or failure for the organisation. ⦁ Section A: SPC / Quality: THEORY Describe SPC The statistical process control is a way of controlling the quality of a project in which statistical methods are used. The process helps in handling the system in an efficient and effective manner. It can be stated here that there are various types of statistical measure which can be used to monitor an organisation efficiently. This process ensures that the institution operates at full potential. One of the key tools that have been used for this process is control charts; the tool focuses on continuous improvement in the designing of the experiments (Ab Rahman et al., 2015). It can be stated that quality data in form of product and process management is obtained in real time during manufacturing in statistical process control system. The analysis is based on the establishment of the process where there are decisions to take so that the work can be done in an effective manner. The 5M&E processes with evaluation of man, machine, material, method, measurement and the environment for manufacturing and quality control is one such example. Usage of Statistical Process Control (SPC) to be most effective It has been evident from the definition in the above segment that it is a measure of quality control and can be used for any process having non conforming products. In this regard it can be stated the process can be used in the manufacturing process of an organisation. This will help in minimising the cost and optimising the output as per available resources. Moreover, it can also be stated that statistical process control will also help in developing an effective production design. This will help in assuring quality and reduce warranting and reworking on the products manufactured. (Thomopoulos, 2016). In this regard it can be further stated that there is higher non-conformance cost involved with the scrap and the rework, the application of the process will prevent down gradation of the system and giving away to others. The SPC also brings in the confidentiality with the assured delivery of the quality along with handling the reliable patterns that are set for the short leading times, small batches along with competitive pricing methods. The continuous improvement with the reduced costs helps in processing the variability methods that helps in attaining the best usage for SPC (Jiang, 2015). P1.1 The two basic types of inspection used in sampling for process control In improving the inspection system, there is a need to use the process improvement techniques which are based on lean manufacturing with waste elimination, structured problem solving, and the techniques that have been set with APQP. The process is also based on finishing the product and the quality with the raw materials which are set to handle the different parameters with the processing or the rejection of the data. Here, with this, there are also certain machine breakdowns with the fuel and the power consumption. SPC is able to manage and monitor the different processes with the effective delivery process along with handling the time of the transaction. The forms are set for the data recording of all the errors as well as the other forms of the network faults of the computer. With this, the team is also able to exercise the data that is based on monitoring and controlling the different forms of the processes for the collection of the data with the assessing of the control and the capabilities. (Brown, 2016). The setup of the system is mainly based on the improvement through the process structure. It has been evident from here that the two most important methods in inspecting the data is data collecting and data recording and gauging. Data collection and recording this involves collection and recording of data in an efficient manner. This help in portraying the varying situations in the different parts of the organisation. This form of inspection not only helps in dealing with the present problems prevalent in the system but also with future problems. The records helps in this regard and produce efficient result. Gauging Gauging is the process of detecting whether the product is defective, effective and cost efficient or not. This helps in adopting measure that increases the efficiency for an organisation (Brown, 2016). P1.2 Sigma is a measure of variation and the significance of natural and assignable causes of variation Variation Variance can be defined as the process of being different, divergent or being inconsistent with the normal process. Statistically it can be said that it is the expectation of the squared deviation of a random variable from the mean. It can be further stated that variation process is important to handle the different processes which includes the manufacturing as well as the production in same and at the different times. With this, there is a possibility in the variations that mainly arise to work on the different times. With this, there is a need to process the data of the company, with the distributorsand the suppliers to handle the customers as well as the other people. (Markovic et al., 2017). The variations are based on working towards types of variations with the batch and the machine to machine system with the temporary settings. Analysis Analysing the data in this regard is also very important. This enables in understanding the magnitude of the variations existent within the system. It also portrays the fact about how much the process is deviating from the normal levels. This is depicted by the mean of the collected data. The machine faults with the forms that include the incorrect methods as well as the untrained operators is the cause of variation (Markovic et al., 2017). Common and special causes of variations Variations in a system can occur because of various reasons. Variations in the regaed can be differentiated into common and special causes of variations. The common causes of variations in the system can occur because of different reasons. These variations in the system are regular events and therefore can be easily detected. On the other hand the special causes of variations can also occur because of varied reasons. These causes are not repetitive and therefore cannot be identified easily. The deviation from normalcy is only evident but the cause remains undetermined analysing the data reveals the true cause of anomaly (Hawkins and Zamba, 2005). The common cause of the variation that has been evident is for the machine designing and the construction segment. This is because it works on clearances and fits, alignments and the end plays. The special causes could not be identified, but are generally due to external factors like resource input, market conditions and weather. Controlling Variance In order to reduce the variations, there is a need to identify the standards of the variation where there are patterns for the specific variations that are due to the common causes. The SPC is set for the improvement action with the response in problem as per the identified approach. Figure 1: Normal distribution curve for common cause of variance and special causes of variance. (Source: Hawkins and Zamba, 2005) The above diagram shows the difference in distribution that the common causes of variance have and special causes of variance have. It can be further stated from the above diagram that the common causes of variance signifies uniformity while the special causes of variance show dissimilarity over time. This makes the task of evaluating the magnitude and the cause of the special variance difficult. It also needs to be mentioned that apart from the common and the special causes of variance, there are natural causes of variance which are obvious to occur over periods of time. It can be stated here that one of the most important natural cause of variance is capital depreciation over time. This causes lag in the output quantity and quality, if not replenished properly with time and effect. Variance in personal data collection Personal data of people needs to be collected under certain circumstances. This is beneficial for an organisation in order to estimate its strength and also formulate effective marketing and sales strategies for the organisation. In this context it can be stated that variations in personal data collection can occur because of number of reasons (Hawkins and Zamba, 2005). ⦁ Non homogeneous sample selection. ⦁ Wrong information provided by the interviewee. ⦁ Mis-interpretation of facts ⦁ Error in the calculation mechanism and analytic tools. ⦁ Wong choice of the method. If the above mentioned causes can be minimised the variance can also be reduced to a great extent (Bersimis, Psarakis and Panaretos, 2007). P 3.1 Goalpost Mentality and Why does it give producers false hope that they are satisfying their customers and a better way of looking at performance capability Goal post mentality can be defined as the thought that the performance between targets is always equally acceptable (Oakland, 2007). The process or the product which is either good or bad is able to handle the specifications in easy manner is the goal post. The view is mainly about the specifications where there is a need to work on the performance measures with the requirements of the customer. Along with this, there are patterns for handling and working on the degree of the displeasure which has been mainly for the product and the service performance. To work on the different forms of the process over the time period. (Rodriguez et al., 2014). This is based on the samples that have been taken for the processes as well as handling the control and the uncontrolled variations which are resulting mainly from the special and the common causes. There are different forms of the variables which include the inconsistency and the arguments are also common for the visualised standards as and when possible. The standards are for the measurement of the variables and then checking the different attributes which are mainly to work on maintaining the control along with the processes of the stability. Better ways of looking at performance capability. Better ways for evaluating performance capabilities are IRR, CFROI and DCF modelling techniques. This techniques help in complete enumeration of the performance figures. In this regard it can be stated that these technique play an important role in analysing the situation relating to company performance in order to numerically depict it. Taguchi method Vs Goalpost Mentality Method. Taguchi method is a statistical method, developed by Genechi Taguchi to improve the quality of manufactured goods. The theory is based on the principle of statistical loss function. In this regard it can be further stated that the process is also based on the control factors. On the other hand the goalpost mentality method is also based on the theory of statistical loss function but the objective of the goal post mentality is that the performances between targets are always and equally acceptable. In this regard it can be further stated in reference to the Taguchi method that the goalpost mentally method has a fair level of tolerance in the services received as defined by customers. The quality journey has been associated with various champions/gurus, who have appeared in three distinct waves. The three waves There are simple tools, mass education and teamwork which is a new wave with the Japanese industrial success and increased quality awareness. Three waves of the Western gurus with the industrial success, increased quality awareness in the West where there is an amazing turn around of Japanese industry. The three gurus were Philip Crosby, Tom Peters and Claus Møller. The gurus The leaders who have pioneered in this field have been introduced below. Image: Phillips Crosby Phillips Crosby was a businessman born in 1926 had contributed significantly to management theories and management quality practices. Among his main achievements the zero defects program have been most acclaimed which he initiated at the Martin factory. Image 2: Tom Peters Tom peters is an American business management writer and is well known for his in search of excellence. Peters was born in 1942 , among his works are In search for excellence and others. Where he has talked about quality being the key attribute of business success. Image 3: Callus Moller Claus Moller is one of the worlds leading business consultants and key note speaker. His works mainly in areas of leadership, he has also works in spheres of quality management, service management, and requirement of emotional intelligence among workers. The above mentioned factors that he works on helps in increasing quality of products and businesses. Image 4: W. Edwards Deming Deming was the American Statistician, professor, author and the lecturer. Deming is credited to improve the production in U.S. Deming has been able to make a significant position with improving the different statistical methods with the contribution to the regulation for a higher quality of the products and economic power. The regards are related with the high quality products with the innovation where the advocacy is based on the consideration to meet the influence outside the manufacturing with the sales process engineering. ⦁ Part B: SPC Implementation Provided in a separate folder.  ⦁ Reference Ab Rahman, M.N., Zain, R.M., Alias, A.M. and Nopiah, Z.M., 2015. Statistical process control. Maejo International Journal of Science and Technology, 9(2), pp.193-208. Bersimis, S., Psarakis, S. and Panaretos, J., 2007. Multivariate statistical process control charts: an overview. Quality and Reliability engineering international, 23(5), pp.517-543. Brown, D.B. and WETHERILL, G.B., 2016. Statistical process control. Hawkins, D.M. and Zamba, K.D., 2005. Statistical process control for shifts in mean or variance using a changepoint formulation. Technometrics, 47(2), pp.164-173. Jiang, R., 2015. Statistical process control. In Introduction to Quality and Reliability Engineering (pp. 251-266). Springer Berlin Heidelberg. Markovic, G., Schult, M.L., Bartfai, A. and Elg, M., 2017. Statistical process control: A feasibility study of the application of time-series measurement in early neurorehabilitation after acquired brain injury. Journal of rehabilitation medicine, 49(2), pp.128-135. Oakland, J.S., 2007. Statistical process control. Routledge. Rodríguez-Borbón, M.I. and Rodríguez-Medina, M.A., 2014. Statistical Process Control. In Lean Manufacturing in the Developing World (pp. 47-63). Springer International Publishing. Thomopoulos, N.T., 2016. Statistical Process Control. In Elements of Manufacturing, Distribution and Logistics (pp. 183-209). Springer International Publishing.