Statistical Process Control Assignment Sample

Understanding the application of Statistical Process Control in food, medical, and environmental sectors with insights from a brewery plant analysis.

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Introduction Of Statistical Process Control Assignment

“Statistical Process Control” can be defined as the statistical approach to control a production process within a business. Statistical Process Control can also be defined as the tool or a set of procedures that assists businesses to monitor the progress of behaviours and identify the problems within the internal processes of a concerned business. This process provides many instruments and machinery to gather quality data from the readings of products (Kear, 2020). In manufacturing businesses, these data are used in monitoring and controlling processes. Statistical Process Control helps to unlock the potential of any business outputs and results in consistent and quality manufacturing. This report will aim to understand the Statistical Process Control application within the food, medical and environmental backdrops. It will also analyse the data derived from a brewery plant and understand its quality measures and processes.

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Application of Statistical process control within food, medical and environmental settings

Application of Statistical process control within the food sector

Quality and process control are highly important for the food industries. The importance of quality management in the food industry has grown significantly over the years as the consumer has now become health conscious. The governmental rules have also become strict and the consumption techniques have also changed over the period (Jacobs et al. 2019). Process control controls all the activities of consumer demands from manufacturing to delivery of the products. Statistical process control for the food industry is that is why based on maintaining a conventional process to control the quality (Lim and Antony, 2019). In response to the demands of fierce markets and consumer expectations, the food industry has started to find solutions to the problems to improve the quality and find legitimate solutions.

The differences in food science and technologies have created few modern approaches to demonstrate the statistical processes into the food industry (Medina et al. 2019). The collected data are used in the process control and simple graphical tools are used. The statistical quality control charts are applied to calculate significant changes to further apply them to the distribution of variables. The control limits are calculated by control charts. Statistical process control helps to employ statistical techniques to control and monitor the processes of food manufacturing. A typical business in the industry of food generates large amounts of data, performs inspections, checks the quality, and packaging and tests the final product before marketing them. The Statistical process control helps to bring the information into reality and get insights into the derived data (Wang et al. 2020). It guides the food businesses and lets them understand if there are any porches that are out of control or need to be handled well.

By successfully applying Statistical process control into food industry processes, it helps to increase customer satisfaction, reduce negative feedback, and decrease the need for inspection in the supply chain. On the other hand, it also helps to decrease the cost of scrap materials, increase the efficiency of the data analysis, and also improve the level of communication within the company.

Application of Statistical process control within the medical sector

Statistical process control in the healthcare or medical sector of the world gives access to the business about massive sets of data and lets the organisations visualise their performances and processes. The statistical process control helped the medical sector to improve and manage different areas of healthcare processes (Kadhim et al. 2020). Statistical process control enabled the patients with therapeutic qualities. “Statistical process control or SPC is a philosophy, a strategy, and a set of methods for ongoing improvement of systems, processes, and outcomes” (Haddad, 2021). SPC has helped in many terms with healthcare organisations and in yield improvement of the firms. SPC is also used in visualising blood pressure charts, homoeostasis, and other measures.

Healthcare is a complex set of networks and has different processes and paths (Reconnet et al. 2022). The quality of healthcare is immensely important to maintain in contemporary measures. The healthcare system is highly relied upon by these complicated networks. Apart from the traditional trial and error phase, the new methods of visualisation can be supported in different ways. Rapid methodologies are needed in the healthcare sector. Statistical process control can help the medical sector with many different tools. The SPC charts for healthcare help the different NHS organisations. There are two different types of charts such as the “run charts" and "control charts” (Marang-van de Mheen and Woodcock, 2023). Run charts are those forms of charts that help with the analysis of trends within an organisation and identify if the process is stable. IT rapidly plots the data with effectiveness to find out the trend and processes within a system. It is known as “run” when the data points are used in the calculation of medians. These are statistically different and consist of too many runs.

The control charts whereas is a more advanced form of the chart within a Statistical process control in the medical sector (Chakraborti and Graham, 2019). It involves a single line of data having an “upper control limit (UCL) and lower control limit (LCL)”. These are the data charts that enable business professionals to measure variations in the data and derive a central line or mean of the calculations.

Application of Statistical process control within the environmental setting

“Statistical process control (SPC)” is also applied in the context of environmental sectors. The environmental sector is those who are associated with the measurement of waste and pollution in the world and are associated with monitoring the statistics and implementing changes to reduce pollution and minimise damage to the environment (Inobeme et al. 2022). Therefore, it is obvious that these companies will require the measurement of statistical data in order to foster and facilitate measurable and actionable changes into the environment. These practices also include the statistical process control tool that gauges the early detection of a faulty system that is hazardous to the environment. For example, a faulty machine is releasing huge amounts of CO2 into the air or releasing large amounts of contamination into the sewers or canals (Kumaraswamy et al. 2020). For these measurements, a Statistical process control is needed to be there in order to monitor and review the processes and gather sufficient data to make decisions. SPC can also be used to track these data from different manufacturing industries or any industry that is affecting the environment by any means (Ammar et al. 2022). Statistical process control helps in decision-making processes as well as a visual representation of data such as pie charts and bar charts to show dynamic improvements in the processes. Statistical process control is also combined with environmental laws and ISO 14000 fills the firm’s objectives and targets while maintaining regulatory compliance (Lashitew, 2021). As much literature suggests that the modern techniques of Statistical process control have the capabilities to evaluate the environmental data and evaluate performance measures to enforce changes minimising the risks associated with the processes.

Data visualisation and description

A detailed description of the analytical approach taken

The exhibition of the gushing control process for “Waterside Lager Limited (WLL)” was imagined by investigating the temperature information recorded throughout the period of September 2022. The accompanying logical methodology that was taken are as follows:

  • Analyses of Data: Important statistics like the minimum, maximum, and average temperature values were derived from the temperature data. The level of temperature readings that were inside the ideal temperature scope of 25°C to 35°C and the rate that was over the highest temperature that was permitted, which was 40°C, were determined. The data showed significant patterns or trends, like fluctuations in temperature or periods of constant high or low temperatures.
  • Visualisation of Data: The temperature readings over time were depicted using a line graph. Any significant data variations or trends could have been seen thanks to this graph.
  • The time (September 2022) was shown on the x-axis, and the temperature values were shown on the y-axis. The temperature readings were plotted on the diagram, giving a visual portrayal of how well the emanating control process kept up with the ideal temperature range.
  • Analyses of Compliance: To assess compliance with the desired temperature range and legal limits, a bar graph was created. The percentage of temperature readings that fell into each category was shown on the graph: close enough (25°C-35°C), exceeding the reach (>35°C) and surpassing as far as possible (>40°C). The Statistical process control’s performance in meeting regulatory requirements was clearly depicted in this visual representation.
  • Impact on Maintenance: It was considered how regular maintenance would affect the process of controlling effluents. Maintenance intervals, which occurred on a weekly basis, were denoted by either shaded regions or vertical lines. During and after maintenance, this visualisation made it possible to observe any alterations in temperature patterns.

At Waterside Lager Limited, insights into the Statistical process control’s performance in the past were obtained by analysing and visualising the temperature data. The compliance analysis graph displayed the percentage of readings within the desired range and any violations of legal limits, while the line graph provided a visual representation of temperature variations over time. These visualisations evaluated the Statistical process control’s effectiveness and helped identify areas for improvement.

The degree to which the plant has performed well

It is preposterous to expect to decide the degree to which the plant has performed well on the restricted data that was given. A progression of temperature data of interest, extra setting and explicit execution standards would be expected to do a thorough assessment.

Figure 1: Temperature rise in Waterside Lager Limited (WLL) manufacturing plant of the brewery

(Source: self-developed)

The plant of Waterside Lager Limited has generated a quite controlled range of temperatures in its manufacturing process. The variable data received from the company’s readings has denied that it has maintained its average range of permitted temperature efficiently. However, there are few entries that stated that the temperature hiked more than 40°C which is the maximum legally permitted temperature of its operations. This data has been derived by implementing statistical process control in the manufacturing plants of Waterside Lager Limited. These data indicate that the highest proportion of readings have indicated that the range is lying between the desired range of temperature indicating stability in its manufacturing process irrespective of a few cases of violations. Support's recurrence and effect as referred to, the temperature of the brewery can be impacted by supporting and adjusting the framework. It is fundamental to understand what maintenance activities mean for temperature control (Dias et al. 2022). Adjustments to the procedures or enhancement is required assuming temperature changes happen during or after maintenance. Long-term execution practices can be found and patterns can be checked by dissecting temperature information. It is feasible to acquire knowledge of the plant's general production process by determining the information from the data with the information from an earlier time and deciding if temperature control has improved or diminished. It has been difficult to evaluate the degree to which the plant has performed well without extra data or an extensive investigation of its tasks and execution measures. To give an exact evaluation, a more in-depth assessment in context to the previously mentioned elements would be required.

Priorities for quality improvements that plant management should set

In manufacturing plants, maintaining a quality measure in the production process. However, it is possible to gain insight into the plant's capacity to maintain stable temperatures by examining the data for any changes or reliable high or low temperatures. A plant that operates consistently within the desired range with few fluctuations is equipped with an effective temperature control system. There are also other factors including the guidelines, manufacturing attributes, monitoring, control, and other factors that fluctuate the output of the production. Below are a few recommendations that can be prioritised in order to foster quality improvements in Waterside Lager Limited (WLL).

  • As the company has prioritised on temperature control more, giving priority to its importance to the firm, it can be stated that the management of the plant must carefully optimise its temperature control regulations by applying necessary frameworks. Keeping in mind that the current equipment for the process may not be capable of procuring the desired outcome. The monitoring and control of the management is quite good enough to maintain a permissible temperature control system; it may be improved by adding more precision to the manufacturing process.
  • Waterside Lager Limited must implement a comprehensive program to prevent failures and downtimes within the production process which are crucial for the optimum output from the firm. It must regularly investigate the temperature controls, perform thorough maintenance, and calibrate the equipment and processes if required or possible. This program must also address the potential risks by closely monitoring the temperature and proactively maintaining a level of consistency.
  • A “data-driven decision-making system” can also be prioritised into the plan to foster a robust data collection process. This can help the plant to identify its risks and early detect the troubleshooting.

Conclusion

In conclusion, this report has given a highlighting perspective on Statistical Process Control in the production processes. It has been seen that it is a statistical approach to control a production process and as quality control is deeply important to the manufacturing plants, the activities are bound by the consumer demands and fierce market conditions. The Statistical Process Control has been evaluated in the context of food, medical and environmental sectors. A detailed visualisation of the manufacturing process and the implication of Statistical Process Control in the company has been performed for Waterside Lager Limited. A recommendation has been provided based on priorities that needs to be done to maintain legally permitted operations and apply changes were required.

References:

Ammar, M., Haleem, A., Javaid, M., Bahl, S. and Verma, A.S., 2022. Implementing Industry 4.0 technologies in self-healing materials and digitally managing the quality of manufacturing. Materials Today: Proceedings, 52, pp.2285-2294.

Chakraborti, S. and Graham, M., 2019. Nonparametric statistical process control. John Wiley & Sons.

Dias, C., Santos, J.A., Reis, A. and da Silva, T.L., 2022. Impact of brewery wastewater inhibitors in pure and mixed cultures of the yeast Rhodosporidiumtoruloides NCYC 921 and the microalga Tetradesmus obliquus ACOI 204/07. Biochemical Engineering Journal, 185, p.108518.

Haddad, T., 2021. Quality Assessment of Concrete Production Using Statistical Process Control (Spc) Techniques. Proceedings on Engineering, 3(2), pp.233-240.

Inobeme, A., Nayak, V., Mathew, T.J., Okonkwo, S., Ekwoba, L., Ajai, A.I., Bernard, E., Inobeme, J., Agbugui, M.M. and Singh, K.R., 2022. Chemometric approach in environmental pollution analysis: A critical review. Journal of Environmental Management, 309, p.114653.

Jacobs, T.G., Robertson, J., van den Ham, H.A., Iwamoto, K., Bak Pedersen, H. and Mantel-Teeuwisse, A.K., 2019. Assessing the impact of law enforcement to reduce over-the-counter (OTC) sales of antibiotics in low-and middle-income countries; a systematic literature review. BMC health services research, 19(1), pp.1-15.

Kadhim, K.T., Alsahlany, A.M., Wadi, S.M. and Kadhum, H.T., 2020. An overview of patient’s health status monitoring system based on internet of things (IoT). Wireless Personal Communications, 114(3), pp.2235-2262.

Kear, F.W., 2020. Statistical process control in manufacturing practice. CRC Press.

Kumaraswamy, T.R., Javeed, S., Javaid, M. and Naika, K., 2020. Impact of pollution on quality of freshwater ecosystems. Fresh water pollution dynamics and remediation, pp.69-81.

Lashitew, A.A., 2021. Corporate uptake of the Sustainable Development Goals: Mere greenwashing or an advent of institutional change?. Journal of International Business Policy, 4, pp.184-200.

Lim, S.A. and Antony, J., 2019. Statistical process control for the food industry: A guide for practitioners and managers. John Wiley & Sons.

Marang-van de Mheen, P.J. and Woodcock, T., 2023. Grand rounds in methodology: four critical decision points in statistical process control evaluations of quality improvement initiatives. BMJ Quality & Safety, 32(1), pp.47-54.

Medina, S., Perestrelo, R., Silva, P., Pereira, J.A. and Câmara, J.S., 2019. Current trends and recent advances on food authenticity technologies and chemometric approaches. Trends in Food Science & Technology, 85, pp.163-176.

ReCONNET, E.R.N., Talarico, R., Aguilera, S., Alexander, T., Bulina, I., D?vidsone, Z. and Mosca, M., 2022. The added value of a European Reference Network on rare and complex connective tissue and musculoskeletal diseases: insights after the first 5 years of the ERN ReCONNET. Clinical and experimental rheumatology.

Wang, B., Hu, S.J., Sun, L. and Freiheit, T., 2020. Intelligent welding system technologies: State-of-the-art review and perspectives. Journal of Manufacturing Systems, 56, pp.373-391.

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