Introduction - Utilizing Statistical Process Control for Improved Efficiency
Statistical process control is a statistical technique for controlling the production process. This study contains information on analysing the temperature of combined effluent discharged by a brewery, Waterside Lager Limited (WLL). All the data used in the analysis is collected from the database of the past temperature raised by the process of the organisation. The study also has the potential to present an effective table of calculations along with some graphical representation based on the data as well. The method of analysis and provided recommendations in the study also can be applied to maintain or reduce the temperature generated by the brewery.
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According to Karunakaran et al. 2020, the increasing number of the manufacturing unit has a huge impact on the environment and climate. There is almost a 7% increment in the manufacturing industry of the entire world. Similarly, there is also a differentiation in the temperature and environment. Extreme temperatures will become more common as a result of environmental disruption. Extreme weather can have a variety of effects on individuals, including changes in behaviour, production, cognitive function, mood, healthcare, and well-being. Extreme conditions, both cold and hot, are found to hinder wealth creation. Studies show that productivity losses induced by severe temporal apertures are driven by both manpower input and worker productivity declines. Researchers predict that severe weather now decreases Canadian industrial production by 2.3% annually, and believe that this harm will likely increase as global warming progresses (Ladha-Sabur et al. 2019). It causes a very bad impact on global economic factors as well.
On the use of ARIMA model (Integrated moving model) gives values to smooth down manufacturing time series. It will forecast auto-creation on residual errors in production annually which boost up future values more.
Another study by Ho et al. 2019, shows the relationship between the manufacturing unit and global temperature in an effective way. Total production in China is expected to reduce by 0.7% for every day with a temperature anomaly above 31°C, while daily integration testing in India is expected to fall by 3.4% when the average temp exceeds 24°C. In contrast, there is little evidence that severe conditions have an impact on business sales in the United States. Several studies suggest that hot temperatures have a detrimental effect on economic productivity at the national or sub-national level. The strategy aims to develop empirically determined estimates of the effect of environmental modification on the outcome of interest by combining experimentally estimated temperature-outcome connections with projections of future heat outcomes (Liu et al. 2019). Consequently, rising temperatures diminish the gross national product in developing countries. This particular study has the potential to elaborate on the method of controlling temperature generation. According to author, Quality assurance system must be based on statistical control through average forecasting model. MPC (machine performance check) has the feasibility on business performances (Puyati, et al. 2020). Geometric tests can be approached regarding this relative to baseline, through presenting trends.
The study by Plotkowski et al. 2020, is based on the impact of temperature infatuation on the manufacturing industry of the entire world along with all the other business factors. Notably, researchers discover that the function of heat and industrial output is remarkably equal to TFP. When compared to a day with temperatures between 341 and 60 degrees Celsius, a day with temperatures above 45 degrees Centigrade reduces manufacturing production by 0.41% or $9260 in 2015 dollars for the organisation or company. Temperature impacts on capital goods inputs, on the other hand, are less sensitive. According to this scenario, climate change would cut Chinese industrial production by 11% per year on average between 2040 and 2059 (Mirzababaei and Pasebani, 2019). If China's industrial output share maintains at its present 72% of GDP, climate-related manufacturing losses alone might cut yearly Chinese GDP by 4.1% by the mid-century. This equates to a $43.5 billion loss for the nation.
According to Shah et al. 2020, there are several techniques for resolving SPC synchronization concerns. These possibilities encompass non-standard SPC charts and other advanced procedures that practitioners believe are difficult to execute in real-world settings. The purpose of this research is to choose appropriate tools that most professionals are acquainted with and that are straightforward for them to utilise. As a result, the characterisation of these techniques in an asynchronous scenario must be properly understood. The capacity to simulate different forms of correlation is the very first step toward conventional chart policies made (Wei et al. 2021). These procedures may not be appropriate for ceramic manufacturing practitioners who are conversant with typical standard standards. The adoption of SPC has the potential to provide a suitable method of controlling the production unit and industry along with its heat generation. The method can also be applied in future industries to introduce more innovation by reducing risk factors related to it.
Figure 1: Method of STATISTICAL PROCESS CONTROL
In the study by Oliveira et al. 2020, The use of the SPC method also can increase the accuracy and acceptability of the determined result by up to 11%. It has the potential to provide strong and effective evidence along with the outcomes by calculating the statical data. In the modern days, the use of this method of continuously increased for most industries in the modern world. The handbook is one of the most thorough and helpful sources of knowledge about SPC. Today's factories must contend with the fiercer competition. concurrently rising raw material costs A corporation switches from person identification to preventative medicine quality management by using the SPC method (Rodrigues et al. 2019). Vast knowledge is information; ignoring it or failing to acquire it results in a significant opportunity lost. When available, judgments should be supported by evidence and made as objectively as feasible. This method is related to the betterment of the manufacturing industry of the entire world.
SPC techniques will help to synchronies problems creating 2 semaphores on the current manufacturing process with three segments that are exclusions, processing solutions, waiting bounds.
The given table contains all the data and their calculated value in a structured method. The number of considered days for the study is 120. During this time the temperature lay between 45.2167 degrees Celsius and 16.2033 degrees Celsius. After that, the mean and standard deviation of the data had been analysed. After gathering the data there are conducted different calculations based on some accurate formulas (Morice et al. 2021). All the formulas are mentioned in the following section of the study.
Control limit: The control limit of the entire data gathered in the database is calculated in two different methods. In the first part, there is an Upper Control Limit (UCL) calculation. Formula: Mean of entire value + (3 * Standard deviation). The value of UCL is 48.6292. The value stated that the temperature can be raised to 48.6292 degrees Celsius by the production process of the organization. The value of the lower control limit is 12.8852 generated by using the formula: Mean of entire value - (3 * Standard deviation). It refers to the minimum temperature generated by the entire process of production (Hajej et al. 2021).[Reffered to Appendix 1 & 2]
Specification limit: The second calculation of the analysing is related to the specification limit. The overall control limit of the data in the database is calculated in two methods. In the fort portion, the formula for calculating the Upper Specification Limit (USL) is Nominal value + Tolerance. The value of USL is 66.5012. According to the calculation, the organization's manufacturing method can raise the temperature to 66.5012 degrees Celsius. The Lower Specification Limit (LSL) is -4.9868, computed using the formula: Nominal value - Tolerance. It is the lowest temperature produced during the whole production process by the brewery organisation (Saputra et al.2019).
Figure 2: Graph of Day Vs. Temperature
Capability index: The term capability index is directly associated with the capability of both processes to evaluate the suitable value of the temperature. It is also differentiated between the upper and lower limit by which the temperature capacity of the production can be calculated. The formula of the capacity index of the generated hit is (USL-LSL)/(6* standard deviation). The calculated value of the index is 2. It is preferred that the infatuation of the temperature can lie between 2 degrees Celsius (Puyati et al. 2020). The calculated value of the index is based on the dataset of the temperature generated by the production of LLW.
Recommendation
Implementing Standard Operating Procedure (SOP): The implementation of SOP is directly associated with keeping a record of the power generation occurring by the production of the organization. Creating a strong and effective database will help the entire process to be more effective by generating a lower amount of temperature by their production (Bodrud-Doza et al. 2019). It also has the potential to introduce some betterment for the production to be more sustainable by reducing all the negative factors related to the entire production.
Strong monitoring and control: Strong and effective monitoring is very important in the organization. It is applied to the concept of controlling all the factors and operations in the organization with more effort and efficiency. The accuracy of the power generation and other factors can be ensured by this fact (Chou et al. 2020). Apart from that, the strong monitoring and control held by the management can play a very effective role in the betterment of the organization and its entire production process for the upcoming future. The effort and integrity of the management also can be increased or ensured by adopting the proposed approach.
Innovative technologies: Introducing an effective innovative technology might be very helpful for calculating the temperature and power generation occurring by the organization. It also has the potential to provide all the internal and external support to the organization and its manufacturing process (Park et al. 2019). The effort of maintaining and controlling heat generation can be possible by the implementation of innovative technology in the organization along with combined effluent discharged by the brewery.
The mentioned literature review and methodology in the study are directly associated with the method of analysing a temperature by using suitable methods and formulas. All the processes and formulas used in the study are directly associated with the method of keeping an effective record of the generated heat and temperature by the manufacturing process conducted by the organization. There are also mentioned some very important recommendations by which the generation of the temperature can be reduced or controlled. The entire study has the potential to provide a huge effort for analysing the temperature generation of the WLL organization.
References
Journals
Bodrud-Doza, M., Bhuiyan, M.A.H., Islam, S.D.U., Rahman, M.S., Haque, M.M., Fatema, K.J., Ahmed, N., Rakib, M.A. and Rahman, M.A., 2019. Hydrogeochemical investigation of groundwater in Dhaka City of Bangladesh using GIS and multivariate statistical techniques. Groundwater for sustainable development, 8, pp.226-244.
Chou, S.H., Chang, S., Tsai, T.R., Lin, D.K., Xia, Y. and Lin, Y.S., 2020. Implementation of statistical process control framework with machine learning on waveform profiles with no gold standard reference. Computers & Industrial Engineering, 142, p.106325.
Hajej, Z., Nyoungue, A.C., Abubakar, A.S. and Mohamed Ali, K., 2021. An Integrated Model of Production, Maintenance, and Quality Control with Statistical Process Control Chart of a Supply Chain. Applied Sciences, 11(9), p.4192.
Ho, A., Zhao, H., Fellowes, J.W., Martina, F., Davis, A.E. and Prangnell, P.B., 2019. On the origin of microstructural banding in Ti-6Al4V wire-arc based high deposition rate additive manufacturing. Acta Materialia, 166, pp.306-323.
Karunakaran, R., Ortgies, S., Tamayol, A., Bobaru, F. and Sealy, M.P., 2020. Additive manufacturing of magnesium alloys. Bioactive Materials, 5(1), pp.44-54.
Ladha-Sabur, A., Bakalis, S., Fryer, P.J. and Lopez-Quiroga, E., 2019. Mapping energy consumption in food manufacturing. Trends in Food Science & Technology, 86, pp.270-280.
Liu, Z., Liang, H., Shi, T., Xie, D., Chen, R., Han, X., Shen, L., Wang, C. and Tian, Z., 2019. Additive manufacturing of hydroxyapatite bone scaffolds via digital light processing and in vitro compatibility. Ceramics International, 45(8), pp.11079-11086.
Mirzababaei, S. and Pasebani, S., 2019. A review on binder jet additive manufacturing of 316L stainless steel. Journal of Manufacturing and Materials Processing, 3(3), p.82.
Morice, C.P., Kennedy, J.J., Rayner, N.A., Winn, J.P., Hogan, E., Killick, R.E., Dunn, R.J.H., Osborn, T.J., Jones, P.D. and Simpson, I.R., 2021. An updated assessment of near?surface temperature change from 1850: the HadCRUT5 data set. Journal of Geophysical Research: Atmospheres, 126(3), p.e2019JD032361.
Oliveira, J.P., LaLonde, A.D. and Ma, J., 2020. Processing parameters in laser powder bed fusion metal additive manufacturing. Materials & Design, 193, p.108762.
Park, J.E., Park, S.Y., Kim, H.J. and Kim, H.S., 2019. Reproducibility and generalizability in radiomics modeling: possible strategies in radiologic and statistical perspectives. Korean journal of radiology, 20(7), pp.1124-1137.
Plotkowski, A., Sisco, K., Bahl, S., Shyam, A., Yang, Y., Allard, L., Nandwana, P., Rossy, A.M. and Dehoff, R.R., 2020. Microstructure and properties of a high temperature Al–Ce–Mn alloy produced by additive manufacturing. Acta Materialia, 196, pp.595-608.
Puyati, W., Khawne, A., Barnes, M., Zwan, B., Greer, P. and Fuangrod, T., 2020. Predictive quality assurance of a linear accelerator based on the machine performance check application using statistical process control and ARIMA forecast modeling. Journal of Applied Clinical Medical Physics, 21(8), pp.73-82.
Rodrigues, T.A., Duarte, V., Avila, J.A., Santos, T.G., Miranda, R.M. and Oliveira, J.P., 2019. Wire and arc additive manufacturing of HSLA steel: Effect of thermal cycles on microstructure and mechanical properties. Additive Manufacturing, 27, pp.440-450.
Saputra, T.M., Hernadewita, H., Prawira Saputra, A.Y., Kusumah, L.H. and ST, H., 2019. Quality improvement of molding machine through statistical process control in plastic industry. Journal of applied research on industrial engineering, 6(2), pp.87-96.
Shah, S., Dhawan, V., Holm, R., Nagarsenker, M.S. and Perrie, Y., 2020. Liposomes: Advancements and innovation in the manufacturing process. Advanced Drug Delivery Reviews, 154, pp.102-122.
Wei, H.L., Mukherjee, T., Zhang, W., Zuback, J.S., Knapp, G.L., De, A. and DebRoy, T., 2021. Mechanistic models for additive manufacturing of metallic components. Progress in Materials Science, 116, p.100703.
