While this weeks topic highlighted the uncertainty of Big Data,the author identified the following as areas for future research. Pick one of the following for your Research paper.: Additional study mu
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Data Driven Manufacturing
University of Cumberland’s
ITS 836 – Data Science and Big Data Analytics
Dr. Kelly Wibbenmeyer
July.
Abstract
Technology has changed manufacturing. In the past, manufacturing decisions were made based on theories, assumptions, and expectations. With the advancements in computer technology, the manufacturing industry is now being driven by smart data. These new advancements are aimed at extracting tremendous business values that help improve the profitability margins by reducing the wastage. However, there have been concerns over the challenges associated with the use of smart data-driven in manufacturing, such as heterogeneous data types, large volumes, and real-time velocity in the manufacture of data. With the advent of technology, internet, and the use of computers in different areas, the manufacturing industry has been working on making advantage of all advancements in their different areas of operation. The aim is to ensure that they create business value by minimizing on costs and improving their profit margins.
Keywords: Smart data driven manufacturing, Internet of things
Internet of Things adoption advantages and challenges
Smart Data in the manufacturing industry is a recent advancement that aims at improving decisions working in manufacturing. Smart Data in the manufacturing industry implies that the decision making process during manufacturing is purely based on data. The new decision-making process leads to drive efficiency and effectiveness during the manufacturing process (Weber, et al., 2017). One of the factors that influence the performance of a manufacturing company is the level of production. With data-driven manufacturing, more production is made possible, which increases the amounts of the particular product being manufactured and made available in the market. In addition, smart driven manufacturing improves efficiency in the levels of production. The level of production is determined by the level of demand in the market. With the ever-changing levels of demand, it is vital for organizations to create a decision-making framework in production that ensures that the production is based on the level of demand. With smart data in manufacturing, the computers are able to capture the level of demand in the market and regulate the level of production from the plants. This way, efficiency and effectiveness in production are maintained at optimal levels depending on the data collected.
Advantages of Big Data analytics for manufacturing Internet of Things
Analysis 1. Digital transformation in the manufacturing industry leads to accountability and transparency within organizations. A smart data framework involves the engagement of different individuals and devices within the organization during the manufacturing process. Each of these individuals makes decisions that are in line with the needs identified (Abell, et al., 2017). With smart data, the real-time being collected has to be analyzed and efficient decisions made. With unique data based on real-time observations in the market and the manufacturing industry, these individuals have to make decisions based on similarities. Since the decisions are not based on theories or expectations, it becomes easier to enhance transparency and accountability among all the individuals making decisions that are related to the manufacturing process.
Data based manufacturing leads to the continuous improvement of the organization. As noted, data-driven manufacturing leads to improvement in decision making in manufacturing. Based on the collected and analyzed data, organizations implement incremental changes, monitor sensitive metrics, and implement further changes that are based on the decision-making process that is based on data (Abell, et al., 2017). With the continued making of highly efficient and effective data, the overall performance and efficiency of an organization are attained. In addition, the decisions made based on actual data creates a higher capacity in the scaling of any changes that might be as a result of rapid implementation.
Analysis 2. Data-driven manufacturing improves quality management within organizations. In the manufacturing line, one factor that influences the costs is on material consumption. For any input within the manufacturing line, the output should add value at a minimal cost. In the past, organizations have incurred costs for unnecessary material consumption, waste material, breakages, warehousing for excess products. With data-driven manufacturing, decisions are made in real-time, which reduces the chances of incurring increased costs as a result of waste material, breakages, or storage due to excess products being manufactured. With reduced costs, an organization stands a better chance of optimizing its resources, which leads to improved performance.
Data-driven manufacturing improves organizational culture. With a culture that is based on actual data, improved decision making, improved transparency, and coordination, the employees improve in their motivation to work, which improves the culture within the organization (Abell, et al., 2017). Data-driven decision making helps the employees to understand their mistakes, any inefficiency that affects manufacturing, and the general working environment that leads to effective and efficient manufacturing. Improvement in organizational culture creates a positive working environment that enhances the performance of other areas within the organization.
Challenges of Big Data analytics for manufacturing Internet of Things
The current manufacturing systems are planned to run on-demand signals, which are then tied to the execution systems in manufacturing. The data-driven systems in manufacturing are based on time triggers, which influence the level of production. The model of data-driven manufacturing is purely based on an event, which implies that the manufacturing systems are fed with information depending on data collected from outside. This model may, at times, be ineffective, given the many aspects that determine data-driven decision making. There is a lot of data collected that determines the demand signal. Given that data is collected from all significant sources, there are chances that the decisions made might not be effective. The market is dynamic, and so are the demand signals. Given that the model only runs based on planning, any ineffective decisions might affect the level of production. Over time, continued ineffective decisions might largely affect the financial performance of the organization. There is a major challenge with the integration of the data-driven systems in manufacturing with other existing systems within the manufacturing line.
Analysis 3. The introduction of the new data-driven systems does not imply that organizations should dispose of the existing systems that collaborate in improving manufacturing. The introduction of the new data-driven systems in manufacturing is in conflict with the existing systems (Weber, et al., 2017). In such a system, it would be financially infeasible to roll out new systems that can collaborate to work with the new manufacturing system. In some instances, the failure of these systems to integrate leads to further complications that negatively affect the manufacturing process. This challenge creates more future problems on how systems will interoperate, given the different phases in which they are created and the need for them to integrate with improvements in manufacturing.
Analysis 4. The new manufacturing systems are usually intertwined and work alongside other areas of operation within an organization over a similar internet network. The use of the internet exposes the system to a security attack, which might affect the normal functionality of the system (Zhang, Ren, Liu, Sakao, & Huisingh, 2017). The security challenges within the systems keep on increasing as attackers are finding new ways of attacking system networks; there is a major challenge of keeping up with these security issues. Data-driven manufacturing is only feasible for major companies with enough resources. The infrastructure needed for the implementation of data-driven manufacturing is highly costly for small manufacturing companies to afford. Large companies will, therefore, create a competitive advantage by producing more while the small companies continue to produce less at a higher price (Tao, Qi, Liu, & Kusiak, 2018). The implementation of data-driven manufacturing implies that market competitiveness will be unfair and that the small companies might be edged out or the large companies might lower the prices of their commodities due to increased efficiency and reduced costs in manufacturing.
Conclusion
Big data analytics is the future of business technology. With the advent of smart data in the manufacturing sector, organizations are set to increase their production and efficiency in manufacturing. With the advantages associated with smart data systems in manufacturing, organizations should consider their implementation to increase their competitiveness in specific industries. Despite the effectiveness and efficiency in the implementation of the infrastructure, it is important to evaluate the integration with other systems within the organization and the level of impact that the new system will have on the organization. In addition, it is also important to constantly evaluate the security of the system and ensure that proper measures have been put to ensure that any unauthorized access has been blocked.
References
Abell, J. A., Chakraborty, D., Escobar, C. A., Im, K. H., Wegner, D. M., & Wincek, M. A. (2017). Big Data-Driven Manufacturing—Process-Monitoring-for-Quality Philosophy. Journal of Manufacturing Science and Engineering, 139(10).
Tao, F., Qi, Q., Liu, A., & Kusiak, A. (2018). Data-driven smart manufacturing. Journal of Manufacturing Systems, 48, 157-169.
Weber, C., Königsberger, J., Kassner, L., & Mitschang, B. (2017). M2DDM–a maturity model for data-driven manufacturing. Procedia CIRP, 63, 173-178.
Zhang, Y., Ren, S., Liu, Y., Sakao, T., & Huisingh, D. (2017). A framework for Big Data driven product lifecycle management. Journal of Cleaner Production, 159, 229-240.
Zhou, Y., & Saitou, K. (2017). Topology optimization of composite structures with data-driven resin filling time manufacturing constraint. Structural and Multidisciplinary Optimization, 55(6), 2073-2086.
