Data analytics in Manufacturing of Internet of Things
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Data analytics in Manufacturing of Internet of Things
The recent introduction of advancement in information and communication technology has brought new techniques and evolution in the conventional computer-aided in the manufacturing industry, especially in the current era of smart data-driven manufacturing. Data analytics plays a crucial role in such massive manufacturing data, which can yield tremendous business values. At the same time, also it can result in some of the manufacturing challenges as a result of heterogeneous data types, the enormous volume of data as well, as real-time velocity in which manufacturing data is carried out (Dai et al., 2019). Therefore, this paper will provide benefits and challenges associated with big data analytic when it comes to the manufacturing of the Internet of Things.
The reason behind the use of big data analytics for the manufacturing of the internet of things is mainly because of this analytics aid in improving factory operations and production. Besides, the adoption of the data analytics techniques aid in reducing machine downtime and improving the quality of the manufactured products. The technique further enhances the efficiency of the supply chain (Rehman et al., 2019). This, therefore, translates to the promotion and improvement of the customer experience.
There are several challenges associated with the use of data analytics in manufacturing the internet of things. These challenges include data acquisition. There are challenges in undertaking data representation and efficient data transmission during the manufacture of the internet. Besides, there is a challenge related to data processing and storage(Sadeghi et al., 2015). When data analytics is adopted during the manufacture of the internet of things, some challenges are related to undertaking data integration, redundancy reduction, and data cleaning, which requires a lot of resources.
There is also a challenge related to the persistence and reliability of the data storage, causing processing delays and requiring massive investment in resources. In terms of scalability, there is a challenge that causes the distributed efficiency of the storage systems. Some other challenges associated with the data analytics in the manufacturing of the internet of things include the data temporal and spatial correlation, which in most cases, lead to scheme data mining challenges and thus can pose issues in the field of privacy and security assurance.
Data processing approaches in the manufacturing of the internet of things involve data cleaning practices, data integration and the compression of data to ease the process of analysis and save on the storage aspect. (Singh et al., 2015) These measures play a role in ensuring data analytics methods in manufacturing the internet of things are made more accessible and swift, increasing the productivity and quality of the products produced.
In data analytics conducted in internet manufacturing, some methods are employed; these include descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics. These methods play critical roles in the manufacturing industry as it evolves smart manufacturing techniques that will enhance the operational efficiency in the manufacturing sector and ensure the quality of the products produced is high.
Some of the manufacturing sector’s critical characteristics of the internet of things involve the big data life cycle. Manufacturing of the internet of things involves or mainly focuses on improving the manufacturing operations and production and ensuring the machine downtime is highly minimized or wholly eliminated to increase the output and product quality(Dai et al., 2019). Manufacturing of the internet of things is characterized by a harsh industrial environment denoted by noise, vibration, and high or low temperatures. The manufacturing of the internet of things usually requires a high data rate network connection, which is characterized by low delay.
The manufacturing of the internet of things also is associated with mission-critical and sensitive system failure or machine downtime, and therefore with the adoption of big data analytics, this factor can be settled to ensure the systems are running all through and thus, smart objects can result from it since the entire process is highly reliant on the electronic and mechanic sensors, actuators instruments as well as software systems which are programmed to sense as well as collect information from its physical environment of operation and thus makes actions basing on the information collected (Zhong & Ge 2017)
During the manufacturing processes, the data analytics play the most critical role, which is extracting the informative values and predicting the upcoming events to ensure the prediction of the increment or decrement products are using the latest technology and thus improving their efficiency. The data used in analytics regarding the manufacture of the internet of things has the following characteristics; the data is of high volume, mixed up with heterogeneous data types which need data cleaning process to extract the important data for analysis (Sadeghi et al., 2015)
The data types important for manufacturing the internet of things are generated in a real-time fashion and thus require the latest technology to clean, scrutinize and evaluate the content to better the information obtained (Singh et al., 2015). This will ensure the data to be used are in line with both the business-related and social values. These unique features tend to cause research challenges, especially in manufacturing the internet of things products data analysis.
This challenge involves data acquisition, which slows down the issues regarding the data collection process, data transmission, among other issues. For instance, the manufacturing data can be either structured data type or semi-structured data. These data types can become one of the challenges in data analytics for the manufacturing of the internet of things products. This is due to the fact that they tend to have a high level of bandwidth consumption since the actual data transmission of the big data requires high bandwidth to become transmitted effectively. Data analytics and big data are the major bottlenecks of the current wireless communication systems (Rehman et al., 2019)
Another challenge is to associate with energy efficiency. This is one of the significant challenges in most of the wireless industrial systems. This involves the industrial wireless sensors networks, among others. In addition, other challenges arise from the data processing and storage processes. Data used in the manufacturing of the internet of things are associated with some of the research challenges, including heterogeneous features (Sadeghi et al., 2015). This, therefore, means that the integration of various data types is an efficient data analytics scheme and therefore needs further implementation to address the challenge. The other challenge is the techniques to ensure the integration of different data types to facilitate development in manufacturing the internet of things.
The temporal and spatial data redundancy poses a challenge as it leads to data inconsistency, which will affect the subsequent data analysis process. This, therefore, gives rise to the challenge of eliminating the redundancy in the manufacturing of the internet of things data. Since a large volume of data characterizes the manufacturing of internet of things data it, it makes the process of data cleaning a challenge, thus posing the need to design an effective scheme which can be adapted to compress such a large volume of data and thus making even the cleaning process more comfortable and reduce the probability of errors in the Manufacturing of Internet of Things data.
Data storage is a very important aspect when it comes to the manufacturing of the internet of things, data analysis and extraction. However, engaging and designing scalable and efficient data storage systems prevent associated challenges that slow down the entire manufacturing process and lowers the quality of the data. Some of the challenges which are associated with the storage include reliability and data persistence. However, it is challenging to fulfill this challenge since it is associated with a tremendous amount of data (Dai et al., 2019). This, therefore, hesitates the need to develop new storage paradigms that can support extensive data.
Security and privacy are becoming a significant challenge in big data analytics for manufacturing the internet of things. Privacy concerns the utilization of data concerning their utilization to ensure the preservation of rights and ensure data confidentiality, integrity, and data availability. The future concern only revolves around the MIoT data security assurance, especially in the data acquisition, and ensures privacy preservation in storage practices and storage.
References
Dai, H. N., Wang, H., Xu, G., Wan, J., & Imran, M. (2019). Big data analytics for manufacturing internet of things: opportunities, challenges and enabling technologies. Enterprise Information Systems, 1-25.
Sadeghi, A. R., Wachsmann, C., & Waidner, M. (2015, June). Security and privacy challenges in the industrial internet of things. In 2015 52nd ACM/EDAC/IEEE Design Automation Conference (DAC) (pp. 1-6). IEEE.
Singh, S., & Singh, N. (2015, October). Internet of Things (IoT): Security challenges, business opportunities & reference architecture for E-commerce. In 2015 International Conference on Green Computing and Internet of Things (ICGCIoT) (pp. 1577-1581). IEEE.
Rehman, M. H., Yaqoob, I., Salah, K., Imran, M., Jayaraman, P. P., & Perera, C. (2019). The role of big data analytics in the industrial Internet of Things. Future Generation Computer Systems, 99, 247-259.
Zhong, R. Y., & Ge, W. (2017). Internet of things enabled manufacturing: a review. International Journal of Agile Systems and Management, 11(2), 126-154.