Adapting Internet Of Robotic Things In Manufacturing Industry

The Challenges Facing Manufacturing Industry

Problem Domain

In recent times the manufacturing industry is suffering due to shortage of skilled workers. The author has found that the skilled professionals are aging and retiring, that is why the manufacturing sectors are searching for the solution by which they can retain their top position in the market and can fulfil demand of the customers (Simoens et al., 2016). The manufacturing sectors are opting for the Internet-enabled robots. Internet of Robotic Things allows robots to communicate with each other over the internet. The robots by working together can eliminate the skills gap and can help the manufacturing sectors to prosper.

Purpose and justification

The purpose of this project is to provide the manufacturing industry a solution by which they can increase their productivity and market share. The robots will give them the cost-effective solution and the competitive advantage following which they can prosper in future. Also the manufacturing sectors can be able to reduce of risks which emerge due to lack of skilled labourers (Razafimandimby, Loscri & Vegni, 2016). The paper will demonstrate various advantages that can be acquired due to the adaptation of IoRT in the manufacturing industry.

The robots communicate with each other over the internet and will assist to handle the difficult tasks with ease which is not even possible for human beings even if they work as a team. The manufacturing industry mainly uses those robots to perform pick and place operations.

• What is the main reason behind for not meeting the quality demand?
• What are the issues occurring due to shortage of skilled labourers?
• Does IoRT devices are capable enough to meet the needs of the manufacturing sectors?
• What will be the probable budget for implementing all these smart devices?

The conceptual framework explains several questions which help to conduct the research. The figure 1 has illustrated the major issues which the manufacturing companies are facing. The manufacturing sectors are opting for implementing the smart IoRT devices. They hope that all these robots will give them several benefits. The robots are generally fast and efficient compared to the humans. The humans can give wrong output. However, the robots give fast and efficient results (Ray, 2016). The robots require little maintenance and they can serve for long. The robots are cost-effective. Figure 1 shows the issues associated with the project. The cost also depends on the robots.

Figure 1: Conceptual Framework

(Source: Created by Author)

Research and System Development Methodologies

Research Method: The research methodology aids the researchers to organise their effort and this helps to develop a new product. Qualitative Research method has been chosen for the paper to carry on the research work. The qualitative data analysis is suitable for conducting the research as it will involves collection of data from primary sources in the form of questionnaires or direct interview. The quantitative method is basically a straightforward experiment (Vermesan et al., 2017). In this method the generated results are collected data at first. Then that data are analysed to recognise the relevance of research.

Benefits of Internet of Robotic Things (IoRT)

Systems Development Method: The system development methodology that has been chosen for this paper is the Rapid Application Development (RAD). RAD is basically an Iterative type framework. The framework is responsible to provide them high quality system at relatively lower cost. The system basically divides the tasks into numerous subtasks. RAD analyses the risks and mitigate the risks. The users are involved with the development method and the system design. This methodology also helps to produce documentation. The documentation will help the manufacturing companies in future development and maintenance. System development method comes with several advantages-

• Production of systems at a lower cost
• Facilitates the stakeholders of the projects

Data Collection

The data collection methodology is dependent on defined variables, accuracy and strategy. The data collection methodology also depends on skills of the researcher. The data collection is essential for any research project as incorrect data adversely affect the research project. There are various types of data collection methods. The Quantitative research methodology is being chosen for the project. It comprises of surveys, interviews, questionnaires, documents as well as records. The surveys and the questionnaires will help to determine the issues that erupt due to the several reasons like lack of skilled labourers (Wan et al., 2017). The researchers can get to know the expenses for the project. The documentation will help to showcase the expenses required by the manufacturing sectors for adopting the smart devices.

Ethical Issues

The primary issue that is involved with the research is the privacy of the participants. There are several issues that must be considered while conducting the research and they are-

Anonymity of Respondents- The manufacturing companies must secure the confidential data of the respondents. The personal information must not be disclosed at any cost otherwise data breach can lead can harm the organisation.

Research Misconduct- The respondents provide the data and the data collected must be solely for the research purposes. The sensitive information must not be disclosed at any cost (Al-Fuqaha et al., 2015).

Conflicts of interest- The respondents participating can have different opinions on a definite issue. The researcher should not take into account anyone’s response to manipulate others’ opinions.

Compliance Requirements

The robots development must meet the standards of a particular industry. The compliance aims towards the human safety as well as process development.

Active Compliance: The active compliance is generally set by the users (Benardos & Vosniakos, 2017). The active compliance varies from applications to applications. It is setup with the assistance of the software programming of several components and internet of things.

Research Methodology

Passive Compliance: The passive compliance is implemented at the time of the robotic cell development and interconnecting those robots over the Internet. The passive compliance always stay active in the background to meet the embedded safety role.

Data Analysis

Sampling technique: Random sampling is used to create a list of responses. The list of responses is acquired from several respondents. About 80 employees and about 20 managers from several manufacturing industry are considered as respondents. The data will be collected from them in the form of questionnaires and surveys (Kshetri, 2017).

Data Analysis Technique: Quantitative research will be carried out for this research. The data analysis will be carried out with the help of the SPSS software. SPSS software will provide the researcher with accurate data so that they can carry out the research on the research topic.

Deliverables

Conclusion: The internet-based robots are still in development phase and it will take time to develop fully so that the manufacturing industry can use it to a maximum extent. These internet-based robots can be able to save time and money, so they are economically feasible. They can work fast and efficiently so the manufacturing industries can increase their productivity by implementing these internet-based robots.

Recommendations: the introduction of this research needs an extensive research and the robots must be developed in such a way that it fulfils the needs of industry standards.

Project Plan

Work Breakdown Structure

Risk Analysis

The project must be analysed well to detect any kinds of risks involved. The risks analysis help to know whether a project will be completed within scheduled deadline or the budget. Proper risks analysis helps to detect the possible success or the failure of the project. There are several risks associated with the project and they are-

• Inappropriate risk analysis
• Not understanding the required components of the technology
• Inactive development of the stakeholders
• Requirements of additional resources beside estimated budget and time

Duration

The entire duration of the project is 275 days. The project is expected to start at 22 March, 2018 and is expected to end by 10 April, 2019.

References

Akash, S. A., Menon, A., Gupta, A., Wakeel, M. W., Praveen, M. N., & Meena, P. (2014, September). A novel strategy for controlling the movement of a smart wheelchair using internet of things. In Global Humanitarian Technology Conference-South Asia Satellite (GHTC-SAS), 2014 IEEE (pp. 154-158). IEEE.

Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., & Ayyash, M. (2015). Internet of things: A survey on enabling technologies, protocols, and applications. IEEE Communications Surveys & Tutorials, 17(4), 2347-2376.

Benardos, P. G., & Vosniakos, G. C. (2017). Internet of things and industrial applications for precision machining. In Solid State Phenomena (Vol. 261, pp. 440-447). Trans Tech Publications.

Bhatt, C., Dey, N., & Ashour, A. S. (Eds.). (2017). Internet of things and big data technologies for next generation healthcare.

Friess, P. (2016). Digitising the industry-internet of things connecting the physical, digital and virtual worlds. River Publishers.

Kshetri, N. (2017). The evolution of the internet of things industry and market in China: An interplay of institutions, demands and supply. Telecommunications Policy, 41(1), 49-67.

Ray, P. P. (2016). Internet of robotic things: concept, technologies, and challenges. IEEE Access, 4, 9489-9500.

Razafimandimby, C., Loscri, V., & Vegni, A. M. (2016, April). A neural network and iot based scheme for performance assessment in internet of robotic things. In Internet-of-Things Design and Implementation (IoTDI), 2016 IEEE First International Conference on (pp. 241-246). IEEE.

Simoens, P., Mahieu, C., Ongenae, F., De Backere, F., De Pestel, S., Nelis, J., … & Jacobs, A. (2016, October). Internet of Robotic Things: Context-Aware and Personalized Interventions of Assistive Social Robots (Short Paper). In Cloud Networking (Cloudnet), 2016 5th IEEE International Conference on (pp. 204-207). IEEE.

Stankovic, J. A. (2014). Research directions for the internet of things. IEEE Internet of Things Journal, 1(1), 3-9.

Vermesan, O., Eisenhauer, M., Sunmaeker, H., Guillemin, P., Serrano, M., Tragos, E. Z., … & Bahr, R. (2017). Internet of Things Cognitive Transformation Technology Research Trends and Applications. Cognitive Hyperconnected Digital Transformation; Vermesan, O., Bacquet, J., Eds, 17-95.

Wan, J., Tang, S., Hua, Q., Li, D., Liu, C., & Lloret, J. (2017). Context-aware cloud robotics for material handling in cognitive industrial internet of things. IEEE Internet of Things Journal.

Order an Essay Now & Get These Features For Free:

Turnitin Report

Formatting

Title Page

Citation

Outline

Place an Order