In the current competitive business world, businesses are tasked with the difficult task of gaining a competitive edge in their industries to maximize profitability and sustainability. Gaining a competitive edge requires a business to incorporate significant strategies to ensure their products and services are not only superior in the market but also affordable to the target consumers. For achievement of a competitive edge, one of the basic and significant strategies that businesses can have is to ensure they provide a product or service that is better than competition in terms of quality and price. One of the ways in which that superiority can be achieved is through initiatives of Statistical process control (SPC). Statistical process control refers to a statistical analysis process management process used to monitor production and development processes to ensure quality standards are met (Oakland and Oakland, 2019). This paper intends to critically synthesize and apply the statistical process control mechanism with reference to Komuro’s ‘Experiences of Applying SPC Techniques to Software Development Processes.’
According to Komuro (2006), Statistical process control is a process management technique that is geared to define, measure, control, and improve processes. In his work, Komuro established that the statistical process control technique was majorly incorporated in the manufacturing industry which is more product oriented than in the software industry. He cited that the software development processes were more human intensive and required more creativity and time unlike the manufacturing industry that is product oriented. The statistical process control involve certain critical activities that include but not limited to detection, analysis, and stability. The process involves detecting out of control situations with regard to the set limits or patterns as defined by the process control charts. It then goes ahead to analyze the special reasons and causes for the out of control situations and thereafter seeks to stabilize the process by eliminating the causes (Komuro, 2006).
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The application of statistical process control in software development can be successfully applied in test processes, monitoring and control as well as in the peer review process. It is however difficult to apply the process in the software development due to the complexity of the process with consideration to high degree of creativity and mental capability required in the software development. Software development processes are quite human intensive and creative and involve multiple causes which makes it hard to obtain large sets of important homogenous data unlike in the manufacturing processes (Komuro, 2006).
However, despite the difficulties involved in the application of statistical process control, it is likely to successful given the company can gain control of the various significant aspects in its processes. The firm has to control human intensive and creative activities, deal with multiple causes, and perform analysis and control of processes with the available limited data. The process also has to consider the psychological effect of the people involved in it for the necessary motivation to yield success (Caivano, 2005).
I work in the service industry where our company specializes in transport and logistics with product delivery being our main revenue driver. In the industry, our competitive edge lies in the seamless and timely delivery of both general and special products that need specialized handling. Using the statistical process control analysis, I noticed some bottlenecks in the delivery process using a flow chart of the company’s delivery data. The data used was the total time in hours taken to execute a delivery to the customers during the year 2019 as indicated below.
The SPC was completed based on the above data by calculating the lower and upper limit control limits, for which the mean and standard deviation are required. Using the Excel Formula, I calculated the standard deviation to be 73.39.
LCL (Lower Control Limit): Mean + (3 * Sigma)
910.08 + (3* 73.39) = 1130.24
UCL (Upper Control Limit): Mean - (3 * Sigma)
910.08 - (3* 73.39) = 1689.91
The control limits were determined by calculating the total hours driven averages and control limits. Assuming the process was consistent and predictable, those averages and control limits were set. Future performance is judged against those set control limits. Based on the findings, a control chart would show the mean, the upper control limit (3* sigma), and the lower control limit (3*sigma) to determine if the process within our dataset is in statistical control.
I believe the process would benefit from using the Six Sigma tools that helps us focus on developing and delivering near-perfect products and services (Jacobs and Chase, 2006). There are a number of reasons why the company commercial drivers for this company could have a time delays or down time like extreme fog during winter.
Internal memo
From the data, driving hours s seem to deviate from the control limits towards the end of the year, meaning the drivers were driving more hours probably due to the increased service orders. The data provides a significant insight on how the company can drive its revenues up by employing the lean operation strategies that would not only reduce costs but also eliminate time wastage in the delivery process.
I would like to applaud the company for first availing the data that enabled this process and based on my findings would like to recommend a few things. My statistical control project describes the time taken to make deliveries. Using the six sigma and the statistical process control information, the process can be adjusted to a minimum average time through using lean operation strategies that involve automation of processes. The automation would reduce the chances for errors due to human intervention and also reduce time wastage to allow faster deliveries.
The upper and lower specification control limits considered for delivery services involved deciding how to increase the amount of orders seen and adding an upper and lower range to see if we still have to process orders without a negative impact. The goal in managing the process is to keep it within plus or minus three standard deviations of the process mean. It is hence imperative that statistical process control is significant in the efficiency of logistics and transport and the company needs to put that into consideration.
Bibliography
Caivano, D. (2005). Continuous Software Process Improvement through Statistical Process Control. Ninth European Conference on Software Maintenance and Reengineering .
Chase, R. B., & Jacobs, F. R. (2006). Operations management for competitive advantage . Boston, MA: McGraw-Hill.
Komuro, M. (2006). Experiences of applying SPC techniques to software development processes. Proceeding of the 28th International Conference on Software Engineering - ICSE 06 , 577–584.
Oakland, J. S., & Oakland, R. J. (2019). Statistical process control . London: Routledge.
