The Impact Of IoT On Industrial Sector – Smart Enterprises

Smart Enterprises and Smart Manufacturing

This essay aims to discuss the impact of IoT on the industries. A brief explanation of smart enterprise is provided. A brief discussion of two different aspects of two different companies are provided in this essay. Lastly, this report concludes with an appropriate conclusion for this essay.

The IoT has impacted the modern era at a significant level. It has provided opportunities for the growth of several sectors. Among one of these sectors, the industrial sector has gained immense growth with the introduction of IoT in their systems. The growth of smart enterprises has been on the rise in the industrial sector (Wahi, Medury & Misra 2015, p.5).

Smart enterprises are the combination of smart management, knowledge management IT platform, Multiple data sources, and Real business processes (Townsend et al. 2016, p. 31). The functioning of smart enterprise can be understood as:

• The data from all the sensors are measured by KMIT and it executes the import of data from several databases.
• The processing of raw data, calculation of all indicators and models of sales/production/business/distribution, evaluation of results and the status of business objects (merchandises, clienteles, providers, machineries, economics, public, environment, value, enterprise as whole) is executed by the KMIT (Key Management IT platform).
• The results of smart management are sent to the common mobile devices by KMIT after complete verification of the requirement of information.

The term “Smart” denotes to the enterprises that use and create information and data during the course of the life cycle of product with an intention of creating flexible processes of manufacturing that is aimed to respond efficiently to the changes in the demand of the customers at the lowest cost without damaging the environment (Kusiak 2013, p. 23). The aspect of smart manufacturing allows all information about all the processes of manufacturing that are to be made accessible whenever required. The detailed definition of smart manufacturing ensures several different technologies. Some major technologies in the aspect of smart manufacturing movement includes capabilities of big data processing, services and devices connectivity at industrial level, and developed robotics (Ivezic, Kulvatunyou & Srinivasan 2014, p. 26).

The aspect of smart manufacturing is the broad category of producing with the intention of optimising concept of generation, manufacture, and product business. As manufacturing is the multi-phase process of generating products from raw materials, this aspect of smart enterprises is the subset that empowers computer control as well as higher level of adaptability . The companies who implemented the process of smart manufacturing has the goal of taking advantage of manufacturing technologies and advanced information for enabling flexibility in the physical processes for addressing some dynamic and global market (Weichhart et al. 2016, p. 36).

The French airplane company Airbus has been gaining growth by harnessing IoT (Internet of Things) to increase their revenue and generate more profit. The company claims to have manufacturing facilities all over the world and most of the facilities have implemented the IoT to generate more profit (Lee & Lee 2015, p. 335). This new technology has helped in collecting information from the sensors that are located in all the factories. The company first used the technology of RFID in the tracking parts and now the company is aiming to utilise the IoT tools to track the tools residing in a factory (Mehdi et al. 2013, p. 30). The cycle time of an aircraft stretches over 30 years and if the company is progressing towards the implementation of automation intelligence then it will need to utilise these tools for the entire lifetime of an airplane. The business strategy of airbus is divided into three categories:

• ERP (Enterprise Resource Planning)
• Engineering and manufacturing processes
• Shop floor

Airbus – Implementation of IoT

The company views the idea of digital factory as the method of increasing the production capacity and the economic conditions as a production building and developing aircraft throughout the complete production line lifecycles.

The company Siemens with its headquarters located in Munich and Berlin is the global powerhouse of electronics and electrical engineering. According to a recent survey, it is estimated that the company possesses 377,000 employees that are working for developing and manufacturing products, designing, and installing complicated systems and projects, and create a wide range of services specified for some individual requirements (Alzate & Sinn 2013, p. 944).

The company implemented the smart manufacturing system to exercise the flexibility and holistically allow the human workforce, robot assistants, and manufacturing systems for optimising swift flow of energy and materials with the help of artificial intelligence-powered self-organising IoT (Jaramillo et al. 2013, p. 5). With the help of advances in the fields of simulation and algorithms, most of the products of the company are created in “digital twins”, which means the products are made as duplicates of their counterparts in real world. With the evolvement of this process, this method can be used for more than creation of geometric characteristics of an object. This method is unique for the functional characteristics it possesses, which are heat resistance, contraction and expansion coefficients, and security optimisation, that are being refined and tested in the simulated world. The company gained the ability of automation flexibility. The flow of energy and materials will be optimised and production in the facilities will increase with the implementation of AI in the business processes of the company. For example, the AI driven Siemens gas turbine has been successful in mitigating the nitrous oxide emissions of the turbine with the help of AI. It is believed that the new generation of smart manufacturing will create opportunities for affordable, specific produced parts and supplies that are built with keeping the demands of the customers in mind and meet the requirements of scheduling (Veza, Mladineo & Gjeldum 2015, p. 555).

The smart services term is referred as the developed level of digital service offerings that are based on data. These services are provided to the customers who utilise these smart products. The smart services idea was originally developed in the public research projects framework (Nie 2013, p. 1576). With the utilisation of broadband connection, machines can authorise the movement of data to a specific software platform provided by the provider of smart service. Machine knowledge is produced by aggregation on the platform and the networking of several data is carried out with the help of various techniques like machine learning and artificial intelligence (Kumar & Dahiya 2017, p. 4).

Siemens – Implementation of IoT

The company Amazon was the first company to display the customer reviews, which helped in creating transparency in the website (Todorovi?, ?udanov & Komazec 2015, p. 300). Earlier, customers had no idea about the quality of the product that they are trying to purchase. With the introduction of customer reviews, it is easier for the customers to have a basic idea about the products that are available on the e-retail platform. The consumers had to take a significant extent of risk before purchasing an item. Another feature of Amazon that helps the customers hugely, is the feature of real-time recommendation. This feature provides an idea about the various kinds of products that are similar to product that the customer is buying. In this case, the customer is connected to several suppliers at the same time. The options of buying are increased for the customers. The company has also developed the personalised recommendations for the customers on the basis of the purchasing habits of the consumers and the behaviours of browsing of the consumers. Another feature that is implemented in the e-retail website of the Amazon company, is the feature of streamlined checkout (Zhou et al. 2016, p. 147). This feature diminishes the burden of the user to provide their shipping address and billing address each time they purchase an item. The AI implemented in the website automatically provides these addresses from analysing the previous billing address and shipping address.

The company recently launched several new applications for iPhone and iPad users (Gu et al. 2013, p. 4484). These applications are designed to provide tools to the users for planning their weekly trips of shopping and innovative ways for accessing Walmart in both online and in-store platforms. It is believed that the new features of Walmart will provide the customers with a seamless, enhanced experience of shopping across the stores. The application of iPhone features new updates that includes smart shopping lists that are integrated with access to the coupons of manufacturers and QR code is implemented for scanning (Oliveira et al. 2015, p. 6082). The application of iPad allows the customers to search, browse, and buy the items that are suitable for the customers. The new app on iPad of Walmart has these features:

• Shopping using touch-optimised technology
• Search the store
• Extended inventory

• Voice
• Budgeting tools
• Coupons of manufacturers
• Information of products
• Sharing
• Beta store item finder

The company has the goal of reaching every customer and provide uninterrupted services to the customers for creating better shopping experiences.

Conclusion

Therefore, it can be concluded that the implementation of IoT in the business models of the companies helps in increasing revenue, provide better services to the customers, and create better environment for working. The IoT has impacted the modern era at a significant level. It has provided opportunities for the growth of several sectors. Smart enterprises are the combination of smart management, knowledge management IT platform, Multiple data sources, and Real business processes. The aspect of smart manufacturing is the broad category of producing with the intention of optimising concept of generation, manufacture, and product business. The company implemented the smart manufacturing system to exercise the flexibility and holistically allow the human workforce, robot assistants, and manufacturing systems for optimising swift flow of energy and materials with the help of artificial intelligence-powered self-organising IoT. The company Amazon was the first company to display the customer reviews, which helped in creating transparency in the website. The company recently launched several new applications for iPhone and iPad users. These applications are designed to provide tools to the users for planning their weekly trips of shopping.

The Benefits of Implementing IoT in Business Models

References

Alzate, C., & Sinn, M. (2013, December). Improved electricity load forecasting via kernel spectral clustering of smart meters. In 2013 IEEE 13th International Conference on Data Mining(pp. 943-948). IEEE.

Gu, Y., Liu, T., Wang, D., Guan, X., & Xu, Z. (2013, June). Bad data detection method for smart grids based on distributed state estimation. In Communications (ICC), 2013 IEEE International Conference on (pp. 4483-4487). IEEE.

Ivezic, N., Kulvatunyou, B., & Srinivasan, V. (2014). On architecting and composing through-life engineering information services to enable smart manufacturing. Procedia CIRP, 22, 45-52.

Jaramillo, D., Katz, N., Bodin, B., Tworek, W., Smart, R., & Cook, T. (2013). Cooperative solutions for bring your own device (BYOD). IBM journal of research and development, 57(6), 5-1.

Kumar, T. V., & Dahiya, B. (2017). Smart economy in smart cities. In Smart Economy in Smart Cities (pp. 3-76). Springer, Singapore.

Kusiak, A. (2017). Smart manufacturing must embrace big data. Nature News, 544(7648), 23.

Lee, I., & Lee, K. (2015). The Internet of Things (IoT): Applications, investments, and challenges for enterprises. Business Horizons, 58(4), 431-440.

Mehdi, M., Sahay, R., Derguech, W., & Curry, E. (2013, October). On-the-fly generation of multidimensional data cubes for web of things. In Proceedings of the 17th International Database Engineering & Applications Symposium (pp. 28-37). ACM.

Nie, X. (2013, March). Constructing smart campus based on the cloud computing platform and the internet of things. In Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013), Atlantis Press, Paris, France (pp. 1576-1578).

Oliveira, R. R., Cardoso, I. M., Barbosa, J. L., da Costa, C. A., & Prado, M. P. (2015). An intelligent model for logistics management based on geofencing algorithms and RFID technology. Expert Systems with Applications, 42(15-16), 6082-6097.

Todorovi?, I., ?udanov, M., & Komazec, S. (2015). Improvement of Organizational Knowledge Transfer through Integration of Functional Silos in Smart Network: Case Study of Public Enterprises. In Knowledge Management for Competitive Advantage During Economic Crisis (pp. 299-309). IGI Global.

Townsend, L., Wallace, C., Smart, A., & Norman, T. (2016). Building virtual bridges: How rural Micro?Enterprises develop social capital in online and Face?to?Face settings. Sociologia ruralis, 56(1), 29-47.

Veza, I., Mladineo, M., & Gjeldum, N. (2015). Managing innovative production network of smart factories. IFAC-PapersOnLine, 48(3), 555-560.

Wahi, A. K., Medury, Y., & Misra, R. K. (2015). Big data: enabler or challenge for Enterprise 2.0. International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), 6(2), 1-17.

Weichhart, G., Molina, A., Chen, D., Whitman, L. E., & Vernadat, F. (2016). Challenges and current developments for sensing, smart and sustainable enterprise systems. Computers in Industry, 79, 34-46.

Zhou, X., Li, R., Chen, T., & Zhang, H. (2016). Network slicing as a service: enabling enterprises’ own software-defined cellular networks. IEEE Communications Magazine, 54(7), 146-153