Role Of Machine Learning In Business Optimization
The role of machine learning is becoming a core technology in the business optimization process of gaining from a given data set, understanding of patterns, behaviours,etc.
Machine learning is one of the newly driven concepts of technological development that is being widely accepted by organisations around the world to enhance their organisational businesses.
The organisations of the current global market are dealing with extreme challenges regarding the maintenance of quality of the product throughout the entire segment, maintaining the integrity of data storage and protecting the valuable information of consumers from getting data breach.
As per existing literary sources, machine learning is being widely adapted and proven to be providing significant results in this matter of facts.
However, the scope and limitations of these machine learning concerts are untouched by the existing authors.
As a result, the aim of this paper is to identify the role of machine learning in business optimization through analysing its future scope and limitations.
Pragmatism philosophy, abductive approach, explanatory design and thematic analysis will be chosen to conclude these research objectives.
Introduction
Businesses are attempting to figure out how to manage a period of trouble as a result of global uncertainty.
That may necessitate a new type of approach.
One of the biggest challenges for organisations in the recent era is to manage data and secure their confidentiality.
However, the fast-transforming technological growth has not only provided a significant method to secure the data; it also enhanced the data security theft in an organisational point of view.
Furthermore, in the respect of the manufacturing industry, the increased quantity of production has also accelerated the invisibility to the prediction of the quality of the products.
Manufacturers often face terrible process-based reduced productivity.
Reliability, productivity, wastage, velocity, energy consumption, pollutants, and, more broadly, any loss caused by inefficiencies are all examples of the production-based losses tackled by manufacturers on a daily basis.
In addition, marketing organisations as well have to deal with significant challenges in order to establish an effective competitiveness in the respective market.
Manual operations to identify different data types such as current market trends, consumer needs, organisational competitiveness and pricing strategy is filled with extreme potential of misleading data and errors.
As a result, these aspects certainly reduce the operational flow of certain organisations and decrease their respective customer satisfaction rate.
In summation, it can be stated that the current market of globalisation and high technological advancements have increased the count of uncertain parameters.
Apparently, this has led the organisations to implement new technological advancements such as machine learning in terms of artificial intelligence in order to manage, analyse, operate and regulate organisational operations.
However, these existing secondary sources have failed to highlight the scope of machine learning in business optimization.
Furthermore, its degree to achieve greater success is not incorporated.
Linking Between Variables
It is clearly observed throughout the analysis of the existing literary sources that organisations around the world are dramatically implementing the aspects of artificial intelligence and machines in order to advance their business operations and adhere to market competitiveness.
As a result, it is clearly visible that the business operations are the dependent variable; whereas, the machine learning aspects are the independent variable in this particular scenario.
The organisations around the world are extremely dependent on the concept and various applications of machine learning in order to optimise their respective businesses.
The previous researches have explained the usefulness of machine learning in numerous aspects of organisational success.
It has been precisely observed from the conclusion of the existing literary sources that the machine learning concept has increased the security and management of data.
Figure 1: Role of machine learning in business decision making
Furthermore, as its aspects are entirely driven by artificial intelligence, it maintains the same pattern and quality of all the products manufactured in certain organisations.
This parameter is extremely helpful in reducing the quality-related issues of the production organisation.
Moreover, the components of machine learning are artificial intelligent based program-driven mechanical parts.
These components are highly upgradable to the new advancements of the technological division of the world, resulting in high security of consumer data and reduction in data theft.
Nonetheless, the market analysis aspect on behalf of the organisational promotional parameters has become significantly easier with the utilisation of the machine learning concepts.
However, the scope of applications of machine learning has not been appropriately discussed.
Its degree of application as well as limitations of the role of machine learning in business optimization is yet to be accessed.
Conclusion
It has been observed that there are numerous benefits of machine learning concepts that help businesses to adhere to numerous successes in respective markets.
As a result, the current paper is solely focused to analyse the importance and significance of machine learning in business optimisation.
Furthermore, its areas of application, benefits and scope will be significantly discovered.
In addition to this, this paper will also assess the limitations and adverse influence of machine learning in business optimisation.
However, it needs to be mentioned that the process to resolve such issues and increase the importance of machine learning in business optimization will be thoroughly examined in this particular paper.
Research Methodology
State of the art
Research Objectives
● To examine the importance of machine learning in business optimisations
● To investigate the limitations and adverse effects of machine learning in business optimisation
● To analyse the scope of application of machine learning in business optimisation
● To find the most suitable solutions to enhance the degree of machine learning application in business optimisations
Research Questions
● What are the applications of machine learning in order to optimise business?
● What is the importance of machine learning in business optimization?
● What are the limitations of the utilisation of machine learning in business optimisation?
● What are the most suitable solutions to enhance the usage of machine learning in business optimization?
In the current era of globalised market, it is highly important for any organisation to adapt to new technological parameters in order to ensure successful business.
As a result, machine learning incorporated with artificial intelligence is one of the most advanced concepts in the current market.
Thus, this topic has been chosen for this research paper, in order to understand the role of machine learning and its area of applications in business optimization.
Methodology And Research Timeline
Research methodology can be described as the approach that in summation provides a contextual framework in order to conclude particular research through establishing and creating links among values, beliefs and hypotheses.
Research methodology is constructed with several parts such as philosophy, design, approach, data collection and analysis methods and so forth.
Research philosophy is the overall framework of a research and the processes to be incorporated in order to conclude research.
The overall strategy for conducting research that provides a brief and logical plan to address a specific research topic through the gathering, processing, evaluation, and exchange of data is referred to as research design.
On the other hand, the research approach describes the procedures that lead from general assumptions to precise data collecting, analysis, and evaluation methodologies.
For this current research, the realism research philosophy, abductive approach, and explanatory research design will be taken into consideration.
Data collection is addressed as the process of gathering data based on the extracted variables of the research. Furthermore, data analysis is defined as the process to analyse, modify, cleanse, rectify or alter a gathered data.
There are primarily two types of data source: primary data source (Data collected first-hand during the assessment of a research) and secondary data source (Data collected from existing sources such as articles and journals).
In addition, a process that deals with only statistical and numerical data analysis is called quantitative data analysis. Whereas, a process that deals with more theoretical information is known as qualitative data analysis.
For this particular research study, thematic analysis in terms of secondary-qualitative will be incorporated.
Methodological Relationship
.Pragmatism is a means of evaluating assumptions and discovering truths in a realistic manner.
This distinguishes it from other epistemology philosophies, which are frequently preoccupied with information or the concept of knowledge.
Through the assessment of beliefs and opinions, pragmatism offers a route to actuality.
As a result, it will be extremely beneficial to uncover the hidden facts about the application of machine learning in businesses.
Furthermore, abductive reasoning allows for a more thorough examination of evidence,resulting in a more complete grasp of reality in research study, resulting in understanding the scope of machine learning applications.
In addition, explanatory design aids in improving comprehension of a topic, determining how or why a specific event occurs, and forecasting future events.
This particular method will be useful to identify the limitations of machine learning in business optimization.
Furthermore, it will be beneficial to foresee the future rate of business optimization through machine learning adaptation.
The application of thematic analysis will be helpful to access the secondary as well as existing literary sources to grasp information regarding the stated objectives of this research and further evaluate them to conclude the study
(Source: Created by researcher)
Research Timeline
|
Parameters |
Time frame |
|
Preparation of the research methods |
3 Weeks |
|
Data collection via secondary sources |
4 Weeks |
|
Data analysis via thematic approach |
2 Weeks |
|
Reviewing overall activity |
1 Week |
|
Preparing dissertation structure |
1 Week |
|
Project completion report |
1 Week |
|
Total |
12 Weeks |
Expected Outcome
This research has been planned to understand the significance of machine learning in business optimization. Furthermore, through the evaluation of the limitations of machine learning, this paper will be directed to predict its future certainties as well as the suitable methods to adapt to machine learning will also be interpreted.
Conclusion
The current paper has dealt with stating the problem and respective challenges associated with various organisations in the current globalised era.
Furthermore, this paper has also described the aim, objective and research questions associated in order to conduct the research. Furthermore, the most appropriate methods under the research methodology section have been precisely described.
Nonetheless, the justification for the methods in order to conclude the research area significantly has been accessed in this current paper.
In conclusion, it can be stated that, through implementing the stated methods and approaches, the aim of this study is to identify the role of machine learning in business optimization.
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