The Six Sigma Methodology: Machine Learning

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Introduction

The wide use of machine learning contributes to the growth of this application as an advanced and innovative methodology for improving artificial technology. However, under the influence of social excitement and without a proper understanding of its principles, performers make mistakes in maintaining an uninterrupted operation of the corresponding algorithms. Such implementations are fraught with systematic risks that are to be taken into account at the stage of the development of machine learning systems. In modern enterprises, different approaches to improving productivity are utilized, and one of them is the Six Sigma methodology. The relationship between machine learning and this concept lies in the possibility of avoiding omissions and serious gaps in the development of the technical base and creating an advanced quality control algorithm. This work aims to analyze the Six Sigma methodology, define its basic components and their purpose, assess an opportunity to introduce machine learning in this framework, and identify the key features of this practice. High productivity, lower production costs, a reduced percentage of defects, and stable profits are the most significant prospects for introducing the approach to improving the Six Sigma concept through artificial technologies.

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Background

Since digital technologies are applied in different fields today, modern and innovative learning methods play an essential role in optimizing the work process in different environments. According to Portugal et al. (2018), machine learning is a system based on the work of artificial intelligence. As the authors state, this algorithm “allows computers to identify and acquire knowledge from the real world, and improve performance of some tasks based on this new knowledge” (Portugal et al., 2018, p. 207). In other words, the machine learning method involves utilizing artificial intelligence as a technology that helps increase the productivity of equipment used in various fields through the principle of self-learning. This approach is commonly used not only in the sphere of digital development but can also be utilized in industries where maintaining sustainability and error-free operation are indispensable objectives, for instance, medicine or business.

As a tool for productive entrepreneurial activities, machine learning is a convenient technology that serves to implement one of the most significant tasks – minimizing risks and performing integrated planning. Mohammed et al. (2016) conduct a comprehensive analysis of this practice and note that there are various techniques that are designed to control the operation of the corresponding equipment, for instance, supervised or unsupervised machine learning. Moreover, the use of suitable algorithms allows achieving a number of significant advantages. As Mohammed et al. (2016) argue, the application of data research methods in software development processes allows overcoming a competitive barrier. For instance, in software engineering, a methodology for measuring software characteristics has become widespread. However, without automation, it is impossible to figure out the huge arrays of data obtained by using this technique and take into account all their interdependencies. Machine learning and advances in hardware technology help enterprises to process their data much faster to conduct efficient marketing campaigns, deploy effective logistics operations, and expand a loyal customer base. As a methodology to enhance this optimization practice, the Six Sigma concept may be utilized to cover gaps and potential errors.

Six Sigma: Core Principles

Productive leadership requires not only effective human resources management but also the ability to organize a high-quality change process. For these purposes, the Six Sigma concept may be applied, which is a universal methodology and is utilized in various industries. According to Stamatis (2019), this model is a common technical tool used to improve the quality of products and services in an organization. Six Sigma is a management structure that focuses on business control and process improvement by applying for statistical tools. This approach contributes to analyzing the main causes of business failures effectively and provides suitable solutions for addressing errors and gaps made during the work process. Stamatis (2019) argues that Six Sigma tools are the best way to understand customer needs since each step is reviewed separately to identify and fix specific gaps. Thus, this strategy is designed as an optimization mechanism designed to eliminate defects and plan more effective approaches to creating specific goods or services. The system of six levels shows deviations in the production process, where the sixth level indicates the almost complete absence of defects, and the first one signals an excess of them.

The key objective of the Six Sigma methodology is to create products that exactly match the needs of consumers. This can be done by eliminating all sources that lead to defects and reducing unwanted external influences. The implementation of Six Sigma requires good teamwork because there are many ways to interpret this practice at the planning stage. Stamatis (2019) cites the main elements that form this model and mentions end-user orientation, decision-making based on data and facts, a process approach, planning all the steps, teamwork, and the absence of fear of risks. According to Laureani and Antony (2019), the Six Sigma methodology is a convenient technique for strengthening leadership since these practices are interrelated. Therefore, in order to apply this tool in the work process productively, it is essential to establish an effective monitoring and control regime.

Relationship Between Machine Learning and Six Sigma

Since the continuous and stable improvement of equipment operation algorithms is one of the main principles of machine learning is, this practice fits into the Six Sigma methodology. Avoiding risks and preventing defects in the work process, which are realized through the introduction of such a technique, contribute to the smooth operation of artificial intelligence tools. According to Uluskan (2018), as innovations are everywhere introduced, machine learning is used along with advanced data mining mechanisms. Statistical methods of collecting and analyzing available information contribute to optimizing the workflow and obtaining the most relevant results of performance evaluations. In the business sector, the interaction between machine learning and the Six Sigma methodology is based on ensuring error-free planning of work activity and interaction with the target audience through automated strategic development systems. Such interaction stimulates the success of entrepreneurship and opens up prospects for expanding the sphere of influence.

The relationship between machine learning and Six Sigma can be seen with a specific example of working with documentation. Fogarty (2015) suggests paying attention to the ways of protecting information through the introduction of artificial intelligence programs and describes successful cases in world practice. For instance, the Intel Corporation that utilizes modern analytical mechanisms monitors performance constantly through the use of innovative control methods and, as the author notes, optimizes its activities regularly (Fogarty, 2015). Failure to organize the operational process with minimal manufacturing defects makes the introduction of machine learning pointless since the mistakes made not only hinder development but also require additional time for analysis. As a result, the Six Sigma methodology is a valuable practice in order to establish productive machine learning. In the business environment, this process should be carried out in accordance with the conventions and nuances of adaptation to the defect minimization technology.

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Implementation of Machine Learning in the Six Sigma Framework

The ways to implement machine learning in the Six Sigma framework can vary depending on the specifics of a particular business and the characteristics of the production process. However, given the widespread use of artificial intelligence tools that are utilized today, some universal strategies may be proposed. Gupta et al. (2020) argue that “machine learning can help to learn the pattern and make smart conclusions” through the use of modern and high-tech analytical networks (p. 4). This means that if the Six Sigma technique is applied, machine learning can not only evaluate a specific problem and confirm its presence with relevant data but also find its cause and determine the optimal correction algorithm. The implementation process for introducing machine learning in the Six Sigma framework may be as follows:

  1. The appropriate equipment is installed, and by setting a specific task, the area of ​​work is tested.
  2. The search algorithm identifies the problem and provides the evidence of its presence.
  3. The artificial network assesses the risks of the transition to a new production regime with minimal defects.
  4. The potential results of the change process offered by the innovative network are measured in accordance with the planned goals and objectives.
  5. The data obtained are recorded in the memory of artificial intelligence, and the specified strategic planning algorithm is used in the future.

The proposed machine learning implementation method may be applied to most business models in which Six Sigma is used as a methodology for minimizing defective production. As Albliwi et al. (2015) argue, there are some restrictions that may affect the implementation process, for instance, a weak innovation base or insufficient technical equipment of the company. To address these limitations, it is necessary to establish a management regime that will stimulate work towards the modernization of the operational process and support the sustainable consolidation of the change model. Otherwise, machine learning will not be introduced effectively because, in order to maintain stable operation, both hardware and software are to be able to function at the required level. The proposed implementation regime has characteristic features and consequences that should be taken into account when planning a change program.

Advantages and Disadvantages of Implementation

As a result of the successful implementation of machine learning in the Six Sigma framework, significant advantages for business development may be achieved due to a well-coordinated risk prevention system. Firstly, according to Sharp et al. (2018), the use of artificial intelligence makes it possible to reduce costs on complex change programs. Given the limited financial resources, this advantage is a valuable perspective. Secondly, Sharp et al. (2018) state that defects elimination programs that are the cores of the Six Sigma framework can be supplemented with artificially developed decision-making algorithms. This technique eliminates errors made due to the human factor and is based on technical planning by taking into account the current results of the work. In other words, short-term and long-term development prospects are determined through accurate calculation tools, which helps bridge the existing gaps. Finally, as another advantage, one should note the availability of information on priority areas of work. As Sharp et al. (2018) argue, machine learning provides specific ways to intervene with the provision of evidence. This advantage enhances the value of artificial intelligence in a given methodology and allows managing risks successfully.

Nevertheless, despite the aforementioned obvious advantages, the introduction of machine learning in Six Sigma can have some negative manifestations and slow down the process of eliminating defects in doing business. As a rationale, Makridakis et al. (2018) note that today, there is little evidence to support the benefits of machine learning over traditional and alternative planning methods. Except for the context of minimizing the human factor, in academic literature, more attention is paid to the specifics of implementing artificial intelligence than comparing the indicators of its functioning with those in traditional systems. For the Six Sigma framework, uncertainty is unacceptable since this methodology requires accurate calculations and clear statistical correlations. As another limitation, one can note an inadequately configured machine learning algorithm, which, in turn, is fraught with errors in forecasts. According to Makridakis et al. (2018), the accuracy of forecasting and determining development plans is higher in traditional statistical practices if an applied digital model is not suitable for a specific change program. These potential barriers and shortcomings are crucial to consider when introducing appropriate equipment into the workflow.

Future Research

When taking into account the features of machine learning in Six Sigma and considering potential barriers, future research can aim to assess the accuracy of forecasting and compare the artificial planning method with traditional approaches. As a background, alternative mechanisms may be applied, for instance, drawing up business plans or competition models. This research will make it possible to determine how effective and accurate the process of using machine learning is in a transition to defect-free production. Also, particular attention can be paid to the analysis of significant artificial networks to identify the optimal tools for implementation in different Six Sigma programs. Despite the uniqueness of the strategy, the nature of the business or services provided determine specific planning models, for instance, the struggle with competition, the production of consumer goods, and other patterns. Therefore, as a background for research, several distinctive entrepreneurial strategies may be utilized, and based on the Six Sigma framework, individual machine learning algorithms can be reviewed and assessed.

Conclusion

The introduction of machine learning in the Six Sigma concept may have many valuable implications due to the achievement of such positive outcomes as reducing production defects, increasing productivity, and minimizing costs. The Six Sigma methodology is an advanced mechanism utilized in change projects, and machine learning can be a driver to ensure a steady and error-free transition to updated operating modes. The implementation process itself is universal, although some features are essential to take into account, in particular, the specifics of the business and the artificial network used. At the same time, the aforementioned advantages may also be accompanied by some limitations and disadvantages. The inconsistency of the learning algorithm with the declared project changes or, for instance, a poor innovation base can lead to unintended planning errors. Future research may focus on addressing these barriers, analyzing alternative strategies to strengthen the Six Sigma framework, and comparing individual approaches.

References

  1. Albliwi, S. A., Antony, J., & halim Lim, S. A. (2015). A systematic review of Lean Six Sigma for the manufacturing industry. Business Process Management Journal, 21(3), 665-691. Web.
  2. Fogarty, D. J. (2015). Lean Six Sigma and big data: Continuing to innovate and optimize business processes. Journal of Management and Innovation, 1(2), 2-20. Web.
  3. Gupta, S., Modgil, S., & Gunasekaran, A. (2020). Big data in lean six sigma: A review and further research directions. International Journal of Production Research, 58(3), 947-969. Web.
  4. Laureani, A., & Antony, J. (2019). Leadership and Lean Six Sigma: A systematic literature review. Total Quality Management & Business Excellence, 30(1-2), 53-81. Web.
  5. Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2018). Statistical and machine learning forecasting methods: Concerns and ways forward. PloS One, 13(3), e0194889. Web.
  6. Mohammed, M., Khan, M. B., & Bashier, E. B. M. (2016). : Algorithms and applications. CRC Press.
  7. Portugal, I., Alencar, P., & Cowan, D. (2018). The use of machine learning algorithms in recommender systems: A systematic review. Expert Systems with Applications, 97, 205-227. Web.
  8. Sharp, M., Ak, R., & Hedberg, T. (2018). A survey of the advancing use and development of machine learning in smart manufacturing. Journal of Manufacturing Systems, 48, 170-179.
  9. Stamatis, D. H. (2019). Six Sigma fundamentals: A complete introduction to the system, methods, and tools. CRC Press.
  10. Uluskan, M. (2018). Artificial Neural Networks as a quality loss function for Six Sigma. Total Quality Management & Business Excellence, 1-18. Web.

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