7123MAA Industry 4.0 and Engineering Digital Environment Assignment Sample 

1.Introduction

Implementation of advanced technology in various business operations is the key strength for industry 4.0 in terms of achieving sustainability. Organizations in the technology industry use additive manufacturing, artificial intelligence (AI), robotics, machine learning, augmented reality, or virtual reality (VR) technologies in order to enhance productivity, products quality, energy consumption, and marketing. The study is going to shed light on the impact of implementing specific technology on organizational performance. A case study of Amazon is selected to analyze the impact of utilizing artificial intelligence (AI) in business operations. Besides this, the sustainable development of the organization is going to be analyzed in the context of three main sustainability pillars (environmental, social, and economic).

2.Brief description of the case and selected technology

Amazon is one of the leading technology-based organizations that push its innovation boundaries in order to develop management strategies. As per the view of Dhanabalan & Sathish (2018), customer experience is one of the key strengths of Amazon that helps in developing customer retention and organizational performance as well. Amazon uses artificial intelligence and machine learning technology in several business operations such as logistic management, order tracking and dispatching, and production of technical gadgets all around the world. Amazon has used AI technology to store rotational energy effectively. This artificial technology helps in spreading innovation in all the departmental layers across the globe. This technology has helped the organization in integrating all the systems and innovation to perform every business operation sustainably across the globe (Hofmann et al. 2019). AI technology has powered three main pillars of Amazon which are Alexa, “Amazon recommendation engine”, and “Amazon go store”. Amazon uses AI technology for working together with these three pillars in order to create a developed customer experience.

Amazon has implemented AI technology to enhance customer experience with effective management strategies. The main purpose of implementing AI technology is to integrate all the systems and innovations in order to monitor all the business activities suitably around the globe. Amazon aims to develop customer satisfaction by implementing Advanced technology in the organization.

The study has focused on Amazon’s approach in developing artificial intelligence which is known as the flywheel. Flywheel technology has helped the organization to save rotational energy and make all the systems around the globe integrated. This technology assists in storing energy and spreads constant energy to different parts of systems without wasting energy by turning it on and then off (Mhlanga, 2020). Thus, innovation in a specific area regarding machine learning empowers the effort of other teams through flywheel technology.

3.Impact of selected technology on business performance

Amazon uses AI technology known as the flywheel in order to integrate the overall system all around the globe. As opined by Yao et al. (2017), silos and bureaucracy are two major factors that affect the organizational performance as a unit. Maximum organizations in the 4.0 industry have suffered business issues due to a lack of effective technology in order to integrate the entire organization to push innovations and management strategies. Flywheel technology is a simple tool that has been designed for storing rotational energy in a convenient manner. This automated technology stores energy even when a machine is not being operated constantly (Cioffi et al. 2020). This technology helps in keeping the energy constant for spreading it to different parts without turning on or off a machine.

Flywheel technology helps The organization in keeping a steady continuous innovation and spreading knowledge and energy in all the departments globally. As per the view of Benotsmane, Kovács, & Dudás (2019), Amazon is one of the leading organizations that focuses on machine learning in order to produce efficient tech gadgets for satisfying customers. The concerned technology helps to empower the efforts of all the teams by keeping them updated with the details of new innovations. Henceforth,  all the teams utilize new innovation in driving their products which improves the overall organizational performance all over the world. Usually, a new innovation created in one part of Amazon works as the catalyst in other parts for ensuring growth regarding machine learning or artificial intelligence through flywheel technology (Angelopoulos et al. 2020). AI and machine learning are the major focus zones of the research and development team of Amazon since starting. However, flywheel technology has become the keystone in the business expansion of amazon by connecting all the departments and systems together.

AI technology is located in every business sector in Amazon, it helps to spread information throughout all the departments. Thus, main insights regarding machine learning discovered by the “product recommendation team” have been shared through the organization to improve overall innovation and product quality in different parts of the globe. Machine learning and AI technology empower three major products of Amazon that are “Amazon recommendation engine”, “Amazon go store”, and Alexa. As per the studies of Frank, Dalenogare, & Ayala (2019), AI bot Alexa is the first virtual assistance technology based on voice-power that has been created through machine learning. However, it faces initial challenges regarding customer experience. Therefore, flywheel technology has helped to integrate all the teams to work on new innovations integrated and make Alexa beneficial for other Amazon products. Initially, Alexa was integrated with prime video, Amazon music, and various personalized products. However, continuous improvement of machine learning allows Alexa to add value in customers’ lives such as capital one and Liberty mutual. Capital one allies customers to make digital payments through Amazon, while Liberty mutual provides automated insurance-related information.

Flywheel technology also helped in developing machine learning technology used in “Amazon Go store”. Cashier less “Amazon Go store” helps in collecting data to track down shopping trends of customers. Besides this, “Amazon’s recommendation engine” is one of the most successful implementations of AI technology that produces around 35% of the organization’s revenue (forbes, 2021). The flywheel approach helps in integrating all the data from customer purchase and preference and browsing history to prepare a personalized list of most required products according to the customer’s perception. Moreover, “Amazon web service” is one of the most successful AI-based approaches that play a significant role in the sustainability of the company (Candanedo et al. 2018). Thus, flywheel technology allows the organization to encourage research and innovation to improvise in expanding the business performance of the entire organization.

4.Sustainable development in business in reference to sustainability pillars

Table: Sustainability of Amazon

5. Conclusion

It has been concluded from the above study that artificial intelligence helps Amazon in achieving immense success in industry 4.0. The study has focused on the brief description of the selected case study on Amazon in order to evaluate the main purpose of this assignment.  Besides this, the selected technology of artificial intelligence has been evaluated here briefly. The impact of artificial intelligence on the management strategy of Amazon has been critically analyzed in the study. The benefits of implemented advanced technology have been illustrated in the context of business performance. Furthermore, the sustainable development of the organization has been assessed in reference to social, economical, and environmental aspects.

Reference

Journals

Angelopoulos, A., Michailidis, E.T., Nomikos, N., Trakadas, P., Hatziefremidis, A., Voliotis, S. &Zahariadis, T., (2020). Tackling faults in the industry 4.0 era—a survey of machine-learning solutions and key aspects. Sensors, 20(1), p.109.https://www.mdpi.com/602472

Benotsmane, R., Kovács, G. &Dudás, L., (2019). Economic, social impacts and operation of smart factories in Industry 4.0 focusing on simulation and artificial intelligence of collaborating robots. Social Sciences, 8(5), p.143.https://www.mdpi.com/458724

Candanedo, I.S., Nieves, E.H., González, S.R., Martín, M.T.S. &Briones, A.G., (2018), August. Machine learning predictive model for industry 4.0. In International Conference on Knowledge Management in Organizations (pp. 501-510). Springer, Cham.https://link.springer.com/chapter/10.1007/978-3-319-95204-8_42

Cioffi, R., Travaglioni, M., Piscitelli, G., Petrillo, A. &De Felice, F., (2020). Artificial intelligence and machine learning applications in smart production: Progress, trends, and directions. Sustainability, 12(2), p.492.https://www.mdpi.com/613200

Dhanabalan, T. & Sathish, A., (2018). Transforming Indian industries through artificial intelligence and robotics in industry 4.0. International Journal of Mechanical Engineering and Technology, 9(10), pp.835-845.https://www.researchgate.net/profile/Dhanabalan-Thangam/publication/329515585_Transforming_Indian_industries_through_artificial_intelligence_and_robotics_in_industry_40/links/5e0c7a2392851c8364a93804/Transforming-Indian-industries-through-artificial-intelligence-and-robotics-in-industry-40.pdf

Frank, A.G., Dalenogare, L.S. &Ayala, N.F., (2019). Industry 4.0 technologies: Implementation patterns in manufacturing companies. International Journal of Production Economics, 210, pp.15-26.https://www.sciencedirect.com/science/article/pii/S0925527319300040

Hofmann, E., Sternberg, H., Chen, H., Pflaum, A. &Prockl, G., (2019). Supply chain management and Industry 4.0: conducting research in the digital age. International Journal of Physical Distribution & Logistics Management.https://www.emerald.com/insight/content/doi/10.1108/IJPDLM-11-2019-399/full/html

Mhlanga, D., (2020). Industry 4.0 in finance: the impact of artificial intelligence (ai) on digital financial inclusion. International Journal of Financial Studies, 8(3), p.45.https://www.mdpi.com/782264

Sima, V., Gheorghe, I.G., Subić, J. &Nancu, D., (2020). Influences of the industry 4.0 revolution on the human capital development and consumer behavior: A systematic review. Sustainability, 12(10), p.4035.https://www.mdpi.com/716612

Yao, X., Zhou, J., Zhang, J. & Boër, C.R., (2017), September. From intelligent manufacturing to smart manufacturing for industry 4.0 driven by next generation artificial intelligence and further on. In 2017 5th international conference on enterprise systems (ES) (pp. 311-318). IEEE.https://ieeexplore.ieee.org/abstract/document/8119409/

Websites

forbes, (2021), How Amazon Has Reorganized Around Artificial Intelligence And Machine Learning, retrieved on: 29^th October, 2021, from: https://www.forbes.com/sites/blakemorgan/2018/07/16/how-amazon-has-re-organized-around-artificial-intelligence-and-machine-learning/?sh=3d15ffcd7361

aboutamazon,, (2021), home page, retrieved on: 29^th October, 2021, from: https://www.aboutamazon.com/planet

amazon, (2021), home page, retrieved on: 29^th October, 2021, from: https://www.amazon.in/

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