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HC3031 Trends In The Global Business Environment

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HC3031 Trends In The Global Business Environment

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HC3031 Trends In The Global Business Environment

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Course Code: HC3031
University: American Public University System is not sponsored or endorsed by this college or university

Country: United States

Discuss about the Investigation Into 3D Printing Technology.

The Logistic organization, which is based in Sydney performs their various operations within Australia. They also have their major branches in the region of Oceania. The organization performs their major operations by providing logistic solutions to various companies that includes manufacturing, warehousing and mining. The logistic sector have started their online business processes and they want to expand their sector based on the AI platform. The client companies, which are linked within the logistic sector are expanding and hence are expecting the logistic provider for providing solutions (Porteous, Sebastia & Hoffmann, 2014). The logistic organization has been planning to expand their business base within the next five years as they want to implement AI based technologies within the sector. Hence, the management team of the organization would wish to implement the technologies within their daily operations without affecting the potential growth of the organization.
Purpose and Objectives
The report aims towards the identification of the various kinds of applications of the use of AI technologies within the organization. The primary objectives are:

To investigate the impact of AI technology and the applications, which would be able to provide such kinds of services in the logistics sector
To ensure the ethical limitations of AI implementation
To understand the top form of developments with the impact of AI in the logistics sector

The study of the report helps in focusing on the impact of AI based solutions within the logistic sector and the ways in which they would be able to provide better forms of solutions. The study is also conducted on the top form of developments within the sector and also proposes three solutions that should be implemented within the sector for bringing efficiency within the operation processes.
Definition of AI and Top Developments in the Logistics Sector
AI could also be defined as machine intelligence, which is a form of intelligence that could only be demonstrated with the help of machines. The research within the sector of AI could be defined as based on intelligent agents. The AI enabled devices are able to perceive the environment and thus are able to take intelligent actions based on the sensing of the environment. Some of the examples of AI include automation, machine learning, natural language processing, robotics, chatbots and autonomous cars.
As the use of computers are increasing with each passing day, hence the impact of AI is also spearheading in the direction where the use of intelligent machines are able to suffice to the needs of various sectors. Many processes such as the management of supply chain that requires relentless labor and focus in their daily operations would get mostly benefited with the impact of AI. The use of AI would automate the entire process, which would initiate from production till delivery of the end products. In the recent times, many e-commerce companies have implemented the use of AI within the logistics sector for automating the entire process of sorting of products and packing them. They would then be able to sort them and thus deliver them as per the requirements (Ai, Yang & Wang, 2016).
Many of the e-commerce companies have enforced the use of AI within their business operations. This is highly meant for bringing in efficiency within the sector and thus helping in connecting the supplier with the manufacturer. The entire process is referred to as Supply Chain Management (SCM). Logistics is one of the major portion within SCM, which is responsible for the handling of the movement of goods from one place to another. There are many vendors who are responsible for offering supply chain software and logistics, which have inbuilt AI technologies. The businesses of logistics are being highly pushed for innovating the latest form of AI technologies. With the recent changes in the development scenarios, there are various robotic systems and AI enabled machines and tools, which are the major reasons of changing the sector of transportation of goods and thus benefiting the customers (Porteous, Sebastia & Hoffmann, 2014).  
Different Application of AI within the Logistics Sector
Some of the top five developments of AI within the sector of logistics include:

Back Office Operations– The use of AI within the sector of logistics and within the operations performed within back office would be helpful for saving a significant amount of time, increase the level of productivity within the workplace and bring accuracy within the level of work (Trappey et al., 2013). With the implementation of high form of machine learning and automation within the back office processes it would be highly useful for the sector to bring in ease of the functionalities such as packing of goods, putting them into the shipment tracks and thus deliver them to the proper places.
Predictive Logistics– With the help of AI enabled technologies, there would be an advancement within the operations within the logistics department. The predictive form of risk management would be very much critical for ensuring the continuity of supply chain. Intelligent form of Route Optimization is extremely critical for logistics operators for bring efficiency within transport, pick-up and delivery of products. With the help of predictive based logistics within the business processes, the logistic partners would be able to understand the best form of business implications that should be implemented within the business operations that would be able to bring in efficiency within the processes of work and thus enable a proper form of business environment and a rich experience for the customers.
Natural Language Processing and Thinking Logistics Assets– AI is also beneficial for fulfilling the physical demands of work within the modern form of logistics. Conversational Interfaces have increased in the consumer world. There are recent form of breakthroughs in NLP technology, which is impactful within the work of the supply chain.  Intelligent form of robotic sorting helps in effective forms of sorting of parcels, letters and other kinds of shipments.  
AI-Enabled Rich Customer Experience– With the change in the dynamics between customers and the logistics providers, the touch points within a logistics company at the time of checkout ensures the successful delivery of products. The technology of AI is able to provide several kinds of touch points for personalizing the customer based touch points for the providers of logistic services and thus help in ensuring the loyalty and retention of customers (Kemp, 2016).
Chatbots– AI enabled Chatbots could also serve the major purpose of serving the customers by shipping the products at the proper time. The use of Chatbots within the logistics department would prove to be useful for placing of requests based on purchasing, speak with suppliers about any queries and also perform other kinds of functionalities (Daar et al., 2018).

Proposing of Three Applications of AI within the Logistics Sector   
There are various applications of AI, which could be implemented within the logistics sector for the better form of services and thus be able to expand the business. Some of the applications, which could be proposed within the sector includes:

Back Office Operations– The use of cognitive automation within the back office operations would make use of the combination of robotic process automation (RPA) and AI. This process would be a replacement of labor with the help of software controlled robots. While RPA would be able to execute the work streams with the help of the input by human workers, the AI would be able to learn and thus extract valuable insights from unstructured data (Jaaron & Backhouse, 2016).

There is also a major problem of the back office staff for keeping the update on the customer information based on real-time. With the help of AI based technologies there would also be a huge form of advantage of keeping the records updated on real-time by continually processing millions of points of data in order to determine the accuracy of contact information. The main disadvantages of implementing AI within the back office operations would mean that there would be a cut in the number of people who would be working within the sector, which would lead to unemployment.  

Predictive Logistics– The selection of proper suppliers and sourcing from the appropriate suppliers is a matter of huge concern in the increasing terms for the enhancement of the sustainability of the supply chain. The data sets that are being generated from several actions such as assessment of the supplier, audits and scoring of credits would be able to provide an improved basis for making further decisions in regards to a supplier (Zhong et al., 2015).

The advantages of predictive logistics within the sector would include that it would lead to predicting the risk management strategies and thus these machines would be able to respond to risks that might get incurred within the system. Another form of functionality provided by predictive technology within logistics is the idea of intelligent route optimization. This function is highly critical for the business within logistics as it would the logistic operators for transporting, picking up and deliver the shipments in an efficient manner. The logistic providers and delivery experts have a vast knowledge regarding the cities. However with the help of AI based technologies, it would be easy to meet the increasing demands of the customers and challenges faced in the recent times.

Natural Language Processing and Thinking Logistics Assets– Intelligent form of robotic sorting with the help of AI based technologies would be extremely useful for the ease of tasks within the sector. Autonomous vehicles would also be deployed that would help within the logistics operations (Zhou et al., 2013). The logistic based service providers primarily rely on third parties that includes subcontracted staff, charter airlines, and common carriers for operating within the core functions of the business. AI technologies such as NLP would be helpful for the extraction of critical information such as dates, account information, addresses, billing amounts and other invoice related information. After the extraction of the information, the RPA bots would be able to input them within the accounting software for the purpose of generation of an order, execution of payments and thus sending a confirmation mail to the customer.

Advantages and Disadvantages of the Proposed Applications
Ethical Issues: Based on the creation of the prototype, some of the ethical issues are to be highly considered. Transparency within the logistics department is a high social concern, which should be put under major focus.
Legal Issues: With the help of AI based technologies, the organization should be able to meet the legal requirements that are set by the government of each country. The AI based machines should comply according to the legal obligations and should be able to meet the delivery aspects and expectations.
Social Issues: The prototype based on AI implementation should be able to increase the standard of delivery of products. The organization should be able to detect any form of challenges as the failure of mitigating the challenges would cause social based impacts.
Ethical Issues: The ethical concerns should be placed when the AI based machines or softwares would be put in proper place of work within the sector. Automation and AI technology should be kept under proper balance for the safety and protecting the laws of ethics (Shukla Shubhendu & Vijay, 2013).
Legal Issues: The Predictive Analytics should be able to meet the legal requirements of the business of the logistic sectors. The predictive form of shipment of products should comply according to the legal requirements.
Social Issues: The use of autonomous cars and robotic chatbots sometimes can pose severe dangers to the society and hence impact people and the society.
Ethical Issues: Based on the NLP technologies, it could be discussed that the logistic sector should be able to focus on the extraction of critical based information. They RPA bots should be able to input the gathered information within the accounting software. The failure in delivering of the right form of information might lead to ethical based issues. The delivery of products to the customer should be able to meet the ethical concerns.   
Legal Issues: The legal based issues that might affect the logistic business should be avoided with the proper forms of tools, which should be based on the legal requirements. The use of chatbots within the system, which depends on NLP technologies should be able to meet the legal requirements of the business. There are always chances of vulnerabilities within the system processes and hence these systems should be designed properly so that they would be able to fulfill the legal requirements.
 Social Issues: With the help of assets based on the thinking capabilities, it would be extremely vital for meeting the demands of the customer based on the delivery of products. As the logistic providers primarily work on the shipping of the products to the customers hence they have to enable high form of technologies within their systems, which would be able to satisfy the needs of the customers in order to avoid any kind of social issues.
Based on the above discussion, it could be concluded that with the impact and implementation of AI within the logistics sector, it would be highly useful for improvement and ease of operations within the sector. Human functions would be highly reduced and AI based machines would take up most of the work within the sector. This would enhance the functionalities within the sector and thus improve the experience of the customers. With the impact of AI in the recent technologies it could be concluded that the future perspectives of system automation is highly progressive and has huge scopes of improvement. This would also include the supply chain, distribution and process of ordering based within the e-commerce companies. These technologies would be able to save a considerable amount of time and thus would allow the business owners for working on other kinds of important aspects. Based on the current scenarios, it could be discussed that these logistic businesses should also be able to consider the ethical considerations based on these inventions. The legal and ethical issues that would be faced with the implementation of AI within the sector should also be highly considered and thus these businesses should mitigate them for the benefit of the sector. The use of AI would increase highly in the future years and hence high form of technological changes would also be implemented in the various AI softwares. This would highly improve the quality of services within the logistics sector and thus ensure a rich customer experience.   
Based on the above discussion, there could be some form of recommendations within the sector of logistics based on the implementation of AI within the sector.

The logistics sector should implement higher form of predictive logistics for gathering information and analyzing the points of data. With the help of predictive logistics, businesses should enable the tracking of financial based forecast, flow of production and process various orders (Aguezzoul, 2014).
The AI based technologies should be able to make smarter and faster forms of decisions, which would be helpful for optimizing the selection of logistics carriers, rating and gain control over the quality of processes.
Better form of recommendation engines should be put together within the logistics department in order to bring in efficiency within the processes of work. The business process should also be able to implement machine learning within their systems in order to teach the systems for recognizing various patterns within the data based on several findings (Sternberg & Andersson, 2014).
Better form of informative algorithms should also be put together within the software systems, which would be helpful for delivering better form of decisions based on logistics and increase the efficiency of work. 

Aguezzoul, A. (2014). Third-party logistics selection problem: A literature review on criteria and methods. Omega, 49, 69-78.
Ai, W., Yang, J., & Wang, L. (2016). Revelation of cross-border logistics performance for the manufacturing industry development. International Journal of Mobile Communications, 14(6), 593-609.
Daar, A. S., Chang, T., Salomon, A., & Singer, P. A. (2018). Grand challenges in humanitarian aid.
Jaaron, A. A., & Backhouse, C. (2016). A systems approach for forward and reverse logistics design: Maximising value from customer involvement. The International Journal of Logistics Management, 27(3), 947-971.
Kemp, R. (2016). Legal Aspects of Artificial Intelligence. Kemp IT Law, 1.
Porteous, J., Sebastia, L., & Hoffmann, J. (2014, May). On the extraction, ordering, and usage of landmarks in planning. In Sixth European Conference on Planning.
Shukla Shubhendu, S., & Vijay, J. (2013). Applicability of Artificial Intelligence in Different Fields of Life. International Journal of Scientific Engineering and Research, 1(1), 28-35.
Sternberg, H., & Andersson, M. (2014). Decentralized intelligence in freight transport—A critical review. Computers in Industry, 65(2), 306-313.
Trappey, A. J., Trappey, C. V., Chang, A. C., Lee, W. T., & Cho, H. Y. (2013, September). Global Logistic Management for Overseas Production Using a Bulk Purchase 4PL Model. In ISPE CE (pp. 451-460).,+A.+J.,+Trappey,+C.+V.,+Chang,+A.+C.,+Lee,+W.+T.,+%26+Cho,+H.+Y.+(2013,+September).+Global+Logistic+Management+for+Overseas+Production+Using+a+Bulk+Purchase+4PL+Model.+In+ISPE+CE+(pp.+451-460).&ots=l6cP14Li3R&sig=SPAQm_AAfGZitfKBi_4wZuQQNOM#v=onepage&q&f=false
Tufekci, Z. (2014). Social movements and governments in the digital age: Evaluating a complex landscape. Journal of International Affairs, 1-18.
Zhong, R. Y., Huang, G. Q., Lan, S., Dai, Q. Y., Chen, X., & Zhang, T. (2015). A big data approach for logistics trajectory discovery from RFID-enabled production data. International Journal of Production Economics, 165, 260-272.
Zhou, Z., Xiao, Z., Liu, Q., & Ai, Q. (2013). An analytical approach to customer requirement information processing. Enterprise Information Systems, 7(4), 543-557.

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