Statistical Process Control Assignment Sample

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 in monitoring the progress of behaviours and identifying 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 controls 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 in 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.

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.

Successfully applying Statistical process control to food industry processes, 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 to massive sets of data and lets organisations visualise their performances and processes.

Table 2. Some Variables in Child Mortality Due to Traffic Accidents in Ardabil Province, Iran, Within 2013 - 2021
Variables	Numbers	Percentage (%)
Condition of the child at the time of the accident
Rear seat passenger in the mother’s arms	16	25
Front seat passenger in the mother’s arms	11	27
Pedestrian	18	28
Tractor passenger	7	11
Front/rear seat passenger without a companion	7	11
Motorcycle passenger	3	5
A passenger in the back of a truck	1	2
Place of the accident
Inner-city streets and passages	6	10
Intercity roads	24	37
Roads inside villages	11	17
Rural roads	10	15
Houses and farms	9	14
Highways outside the city	4	7
Equipment/vehicle
Motorcycle	10	16
Tractor	11	17
Automobiles	35	55
Truck and pickup	9	12
The direct cause of the accident
Pulling over due to the defect of the vehicle or the road or being pulled over for driving carelessly/speeding	5	7
Falling out of a moving vehicle	27	42
Collision with a wall or fixed object in front	1	2
Collision with a moving vehicle	12	19
Being run over	12	19
Being run over by a car as it is leaving the parking lot	2	3
Collision with a car moving backwards	5	8
Indirect cause of the accident
Traffic violation by the driver	28	44
Traffic violation by the other driver in a head-on collision	8	13
Parental negligence	26	41
Unsafe roads	1	2
Vehicle defects	1	2
Place of death
Crash site	46	72
Medical centre	12	19
En route to a medical centre	6	9

Table 1: Some Variables in Child Mortality Due to Traffic Accidents in Ardabil Province, Iran, Within 2013 – 2021

(Source: Shabani et al. 2022)

The above table shows the number and percentage of deaths of children that occurred in Iran from 2013 up to 2021. Certain major indicators of child mortality have been taken such as Condition of the child at the time of the accident, Place of the accident, Equipment/vehicle, Direct cause of the accident, Indirect cause of the accident and place of death. The statistical process control helped the medical sector to improve and manage different areas of healthcare processes (Kadhim et al. 2020).

Figure 1: Number of child mortality in Iran from 2013-2021

(Source: Self-developed)

As per the data collected the standard deviation of the number of child death has been found to be 10.72 and the average number of deaths is 12. So as per the data, it could be said that the standard deviation is not too much scattered away from the mean. 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.

Figure 2: Percentage of child mortality in Iran from 2013-2021

(Source: Self-developed)

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 trends 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 sector

“Statistical process control (SPC)” is also applied in the context of environmental sectors. The environmental sector is those that 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 in 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 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). 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 September 2022. The accompanying logical methodology that was taken is as follows:

Analyses of Data: Important statistics like the minimum, maximum, and average temperature values were derived from the temperature data.

As per Figure 1, it could be seen that the production of milk starts from raw milk, tanker truck, milk reception, the silo, preheat coming to separation. After that, the milk is separated into two parts skimmed milk and cream. Again both skim milk and cream go through the Homogenizer procedure and then comes at last the Standardization.When the milk is separated into skimmed milk and whole milk then they are sent for pasteurization, cooling, product tank, packaging, cooler storage and at last distribution to local stores. 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.

Figure 4: Content of fat in whole milk at 3%

(Source: Bjenning, 2019)

Figure 2 shows the content of fat in whole milk from 2.8 per cent up to 3.2 per cent and the average is found to be 3 per cent. The experiment was started on 2^nd January 2018 up to 1^st April 2019.

Figure 5: Content of fat in whole milk at 1.5%

(Source: Bjenning, 2019)

Figure 3 interprets the fat content in whole milk from 1.3 per cent up to 1.7 per cent where the average is found to be about 1.5 per cent. The experiment was conducted from 2^nd January 2018 to 28^th March 2019.

Figure 6: Fat content in semi-skimmed milk for 45 minutes

(Source: Bjenning, 2019)

Figure 4 shows the fat content in semi-skimmed milk for a time period of forty-five minutes. The fat content ranged from 1.3 per cent up to 1.7 per cent.

Figure 7: Fat content in whole milk for 45 minutes

(Source: Bjenning, 2019)

Figure 5 indicates the content of whole milk for a time period of forty-five minutes. The fat content ranged between 2.8 per cent to 3.2 per cent.

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.

PASTEURIZER	PRODUCTS	S.D. between 0 to 45 minutes (%)	S.D. between 0 to 11 minutes (%) (n-11)	S.D. between 12 to 22 minutes (%) (n-11)	S.D. between 23 to 45 minutes (%) (n-11)
P1	Semi-Skimmed milk	0.035	0.017	0.033	0.012
P1	Whole milk	0.031	0.041	0.035	0.022
P201	Semi-skimmed milk	0.035	0.010	0.009	0.014
P201	Whole milk	0.022	0.033	0.025	0.008
P2	Semi-Skimmed milk	0.023	0.234	0.016	0.014

Table 2: Standard Deviation at various points of time

(Source: Bjenning, 2019)

As per the above data, it could be seen that the Standard Deviation is far away from the average values found earlier. This means that the data in the above table is scattered away from the average values. The time (September 2022) was shown on the x-axis, and the temperature values were shown on the y-axis.

Figure 6 shows the standard deviation at various points in time in regard to the pasteurization of milk for various types of milk such as Semi-kimmed milk and Whole milk. Five types of pasteurization have been utilized P1, P1, P201, P201 and P2 at various times.

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.

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 are 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 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. Keep 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 in the plan to foster a robust data collection process. This can help the plant to identify its risks and early detect the troubleshooting.

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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 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 have been performed for Waterside Lager Limited. A recommendation has been provided based on priorities that need to be done to maintain legally permitted operations and apply changes that were required.

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