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Globalization Globalization is an essential component to the modern world in which we live today. Without the privilege of being interconnected, the world would not be what it has grown to be. Beginning in the nineteenth century, and continuing throughout the twentieth centuries were a series of events, innovations, and advancements that furthered the globalization of the economy with a momentum that was previously unprecedented. The onset of the Industrial Revolution, expansion of trade, and efficiency and timeliness of communication, established a degree of global interconnectedness that continues to advance today. While there is no universally agreed definition of globalization, the term generally refers to international integration, trade and mobility in commodity, capital and labor. Beginning with the onset of the Industrial Revolution, new innovations and advancements surged the markets; and the need to take these products to the global market was imminent. Since the mid-19th century, there have been at least two episodes of globalization. The first began around the mid- 19th century and ended with World War I. The second began after World War II and continues today. Globalization has not been a smooth process and has had periods of rapid integration and reversals including wars and trade disputes. Those challenges were addressed by the development of new organizations like NATO, WTO, and NAFTA that promoted increasing interconnectedness, cooperation and integrations. Coal, a significant driver of the industrial revolution had been primarily used for heating homes, now found significantly increased demand     with the invention of the steam engine by James Watt. Abundant fossil fuels, and the innovative machines they powered, launched an era of accelerated change that continues to transform human society and was the beginning of The Age of Fossil Fuels. Not all countries welcome integration. "The arrival of US Admiral Matthew Perry in 1853, demanding that the country open to foreign commerce and navigation, brought to head the question of Japan's isolationist policy and prompted a considerable debate in Japanese circles," (Primary source 19.2). Globalization required ever increasing resources to fuel the growth and the demand for these resources and was an important driver of both World Wars and the associated problems that occurred. The globalization race between Great Britain and Germany resulted in WWI. The lack of a good resolution between warring countries where the repratirations demanded of Germany led to the Great Depression, the rise of Hitler, WWII, and the Holocuast. Having learned the importance of a good resolution, the United states created the Marshall Plan to rebuild the economy of Europe. The second phase of globalization occurred after WWII, following the expansion of the Marshall Plan and the revitalization of European and Japanese economies. A new era of demand drove trade inevitably driving globalization. America became the leading consumer of natural gas and coal, reaching a skyrocketing consumption level of 20% in the 20th century. The exportation of coal and importation of oil was yet another way we became increasingly interconnected. According to class powerpoints,  "The development of coal-, oil-, or gas fired power stations, alternating current, transformers, and batteries permitted electricity to be generated on a commercial scale, moved across great distances and stored" (Valesey PPT Week 15). The usage of this energy opened up doors to the plethora of new advancements that could be achieved. With the European and American economies booming, the ability and advantage to taking the markets global was too rich to     ignore. However, in light of a growing economy, new conflicts at home began to brew. As we see in the textbook and from class powerpoints, the Industrial Revolution resulted in positive and negative changes in lifestyle. In the 19th century, these new changes also brought about new conflicts, as labor was exploited, and gave way to lower class uprisings, as well as conflicts among values of the upper class emerging in the 20th century. Erik Solheim, Norwegian head of the UN Environment Program expressed his concerns: "humanity's advancement in science, technology and industrialization (is) harming the planet, hence the need to reverse course" (Valesey PPT week 7). Whereas on the other hand, others felt industrialization was an indisputable necessity to the flourishing of countries. As Dr. Lloyd G. Adu Amoah, Professor at the University of Ghana in West Africa stated, "We need to industrialize, because if we don't, we are not adding value to what the African continent has" (Valesey PPT Week 7). These conflicting opinions outline the polarizing views that were felt during the 19th century. However, the need to advance and move forward outweighed the concern. The state of the art attainability and accessibility of transportation was a foothold for the new lengths people could reach. By the end of the 18th century, Great Britain had started to dominate the world both geographically and technologically. With innovations like industrial weaving machines, and the steam engine, Great Britain was capable of creating and exporting on a global scale. The British Industrial Revolution made for a great twin engine of global trade. With their advanced technologies, Britain was able to attack a huge and rapidly expanding market. However, Britain was not the only one to see the opportunity and take it. "...the Japanese discovered that they ... could soon manufacture a variety of goods that people overseas wanted, from raw tea and raw silk to gold leaf and buttons and cotton textiles." (Primary source 19.2). This widespread interconnectedness created a new level of attainability in the global market. "A   revolution in transportation and communication was transforming the world into a global marketplace in the second half of the nineteenth century ..." (Primary source 19.2). Goods were now easily exported and transported like never before and corporations were pumping out products and services to people at an unprecedented efficiency. The industrial revolution was a time of great imagination and progress. The onset of this revolution brought groundbreaking ideas to the public and in the process, changed their lives. The inventions that allowed new products to be manufactured created a demand that caused a virtuous cycle that propelled some people to prosperity, while at the same time, a vicious cycle held people down in poverty. With transportation being redefined and reirnagined with the steam engine and the Communication Revolution bringing forth tools like the telegraph, there was no question that economic globalization was at the forefront of the nineteenth century. The  Industrial Revolution was a monumental factor in contributing to the backbone of today's economy. As John Maynard Keynes, the economist, observed: "The inhabitant of London could order by telephone, sipping his morning tea in bed, the various products of the whole Earth, in such quantity as he might see fit, and reasonably expect their early delivery upon his doorstep." What an interesting foretelling of the corning of the future with Amazon; except we get delivery next day.    

What is Data Science? Business Analytics? What are the differences between these terms? What skills set are required for each? Introduction Data science and business analytics almost go hand-in-hand, with their similarity being in the sense that they are needed to collect data, model and interpret it, and to make projections. Data science is the science of extracting knowledge from data. It involves the use of automated methods to analyze massive data amounts and to extract knowledge from them. It seeks to accomplish this by defining and implementing methods and procedures that extract knowledge and information from sets of data (Earnshaw, Dill & Kasik, 2019). Data science analyzes data using the scientific method, and is mostly concerned with the rigorous data analytics-related work. On the other hand, business analytics, which refers to the psychoanalysis of data using arithmetical concepts to draw conclusions and to get insights and solutions, is about simplifying data upon the solving of a data problem and making it more accessible to deliver insights. By business analytics, there is the focus on business using analytics. The first step in the understanding of what business analytics is all about is the definition of business objectives. Once there are objectives, data can be collected, then analyzed and finally visualized. Analytics creates insights when applied to data. Business analytics as a part of analytics but in business circles is concerned with taking the insights from analytics and using them to create value (Liebowitz, 2013). Data science and business analytics are closely-related in terms of their functions, mostly because both need to collect data, the model and interpret it to create solutions and to make projections. There are certain similarities between the two which explains why there are instances during which the two are used interchangeably. The differences between them make them two different domains, especially in professional circles. It is business analytics which leverages knowledge to provide the right data which is a natural input for data science. Business analytics navigates and addresses the organizational challenges that arise in the adoption and use of data science. It means that business analytics in itself is a set of data science. Business analytics also is the end-product of data science and the two are related because business analysts and data scientists use big data to inform decision-makers and organizational stakeholders for optimal results (Provost & Fawcett, 2013). However, there are major differences between them. Business analytics involves the evaluation of collected data, from which actionable insights are developed, insights which are also solutions to specific problems and roadblocks for businesses. On the other hand, data science mainly uses algorithms and statistics plus technology to give actionable insights on structured as well as unstructured data, thereby solving issues like customer behavior, which are on a broader perspective. While business analytics uses structured data mostly, data science uses both structured and unstructured data. Structured data is the highly organized information which can be located within a defined file or record, for example, point of sales, financial, and customer data. It is usually contained in relational spreadsheets and databases, and compiling and preparing, then storing it for purposes such as analysis is relatively easy (Marr, 2015). Business analytics mainly uses this kind of data. Unstructured data on the other hand is the data that does not work so well in formats such as those of databases and spreadsheets, especially because it cannot be easily slotted into columns, fields, and rows. Text heavy data and text files like PDFs and social media posts, graphic images and photos, videos, and websites, and even PowerPoint presentations are examples of the unstructured data that together with structured data are analyzed using algorithms, statistics, and technology under data science (Marr, 2015). Business analytics leans more towards statistics, which is why the majority of the analysis is based on traditional to some digital statistical concepts, unlike data science which requires the data scientist to combine traditional analytics practices with proper computer knowledge and skills such as coding. More so, statistics in the latter follows coding and algorithm building. Another major difference between the two is that while business analytics works on specific business issues and problems, data science studies and works on trends and patterns. It explains why data scientists should be curious, result-oriented, and possess industry- specific knowledge plus communication skills for them to explain the highly technical results to some non-technical counterparts as the business analysts. The data scientists need a strong quantitative background in such fields as statistics and linear algebra as do business analysts, but the former also need a background in programming knowledge, in particular skills in data mining, warehousing, and modeling (Zhu & Xiong, 2015). These skills enable them to build and analyze complex models and quantitative algorithms that help organize and synthesize big data and information from where questions regarding how to drive strategies by solving business problems can be answered. In addition to modeling and coding skills, data scientists are required to be of great mathematical understanding and aptitude, conversant with data visualization, and that possess significant business knowledge. This allows them to easily translate data into a particular, understandable narrative, data and results they can use to tell a story and show decision-makers and stakeholders just how much the evidence that is provided by the analyzed data is important. Business analysts should mainly be able to define business requirements using analytics and problem-solving skills. They also need to be effective communicators, and they should have developed process modeling skills that are necessary for roles and responsibilities like forecasting, pricing, budgeting, financial analysis, and the passing of regulations and reporting requirements to decision-makers and stakeholders (Aston University Online, 2019). The analytical strengths, business acumen, and efficiency business analysts need are necessary for them to properly and efficiently discern insights and to help companies operate at peak efficiency through their analysis and description of important guidelines as customer bases and purchasing habits. How can Data Science and/or Business Analytics help a business? How can it enhance a business’s key performance indicators? Discuss briefly. Data Science and Business Analytics in Business The descriptive analytics, predictive, and prescriptive techniques that data scientist and business analyst professionals hold and that make them fit for these or closely-related positions in a business are needed because businesses need to collect, analyze, and understand data about their customers, the market, and their industries to make predictions that improve business performance. Data science plus business analytics equal to business intelligence, which businesses need to make smarter decisions. In the decision-making process, for example, data science ensures that the problems after being understood, and data quantified can be solved using the relevant tools implemented by data scientists and that translate the data into insights for better understanding of the business processes and teams. Companies analyze customer reviews using the analytical tools operated by data scientists from where they find the best fit for their products. It is a function that helps businesses understand and analyze the current market trends, thereby devising products for the right masses using data science tools. Considering the extent to which businesses today are data rich, they need data science to unearth the patterns hidden inside that data, from where they can predict events and make meaningful analysis. For instance, it is possible to use the raw data that is turned into cooked data by data scientists to predict the success rate of their business strategies, and all they need is key metrics identified by data science and business analytics. Predictive analytics that are carried out using tools like IBM SPSS fall under data science, and they help with customer segmentation, market analysis, sales forecasting, and risk assessment purposes. Business analytics helps businesses leverage data they then use to make calculated and data-driven decisions. Business analytics is primarily concerned with statistical analysis from where actionable recommendations result. Business analytics ensure that the data collected by businesses is centralized and cleaned to steer of problems that may result in poor decision- making as duplication. Business analytics tools filter the collected data to remove any instances of incomplete, inaccurate, or inaccurate data. As a category of business analytics, business intelligence analyzes historical data to gain insight into how a team, department, individual employee, or the whole company has performed over a certain time period (Bichler, Heinzl & van der Aalst, 2017). Statistical analysis works closely with business intelligence as the other category through predictive analysis that is carried out using statistical algorithms and tools to make predictions about that business’ future performance. The predictive analysis is based on the historical data collected. Descriptive analytics too fall under business analytics and involve the tracking of key performance indicators to know where business performance currently stands (Evans & Lindner, 2012). Prescriptive analytics on the other hand are those that use past performance to recommend how the business can handle similar situations should they arise in the future. Company data is used to boost process and cost efficiency, to monitor and improve financial performance, and to drive strategy and change, and all these happen when companies use business analytics and data science correctly to make informed decision-making. They improve overall operational efficiency and may make higher revenue in the long-run because of embracing data and analytics-related initiatives (Gavin, 2019). Illustrate with ONE example how data analytics have been used to help a business. Data Analytics in Use Companies today depend on data they collect from mobile devices, applications, websites, and social media platforms. Companies like Airbnb and Netflix among other streaming platforms use data analytics, in particular data science to improve their service delivery. They collect the data generated by users on the apps and websites and on social media platforms, then process and analyze it to address requirements. The analytics they use help provide premium services to their customers henceforth. They mainly use machine learning, one of the applications of data analytics and data mining to build specific solutions. Airbnb’s Dataportal, for example, captures guests’ and hosts’ metadata information in a graph that shows resources like users, teams, reports, data tables, dashboards and business outcomes. The manner in which they’re shown to be connected reflects their consumption, production, and association relationships (Rodriguez, 2019). Data analytics has helped Airbnb develop workflow management systems to avoid writing scripts regularly and to have scripts call other scripts. They take advantage of big data and data analytics to predict what consumers may like and the market information, from where their decisions on the products and services they should offer result. Major State your major. Pick the best or top 3 data science or business analytics software relevant to your major and briefly discuss why you think they are relevant? Data science and business analytics are now part of core business activities that their software apply in human resources, my major. More companies now use data to support their evidence-based decision making in human resources. They use the software applications for planning, forecasting, recruitment, development, and the retention of members of staff. Python, a programming language, can now be used for people analytics. Companies now have teams dedicate to people analytics, and are using Python, an open source programming language that is relevant for its wide variety of developers and support, to improve HR functions as collaboration among employees. Python is readable, almost better than Excel, because things on it are laid out clearly (Kohli, 2018). HR in a company can easily carry out analysis and reuse scripts they have saved using it. Python makes it possible to create predictive models and to deriver insights from data. It is scalable and people can analyze large datasets in it. Companies that need to predict employee churn can use Python. Tableau, another modern software for HR analytics, can be used to make hiring, retention, and investment decisions. It is an especially important tool for companies that need to visualize the relationship between HR functions like hours, productivity, and tasks. Visualization helps optimize schedules and resources with greater precision. Its relevance in HR comes from its ability to bring together human resources data in a sleek visual interface, which can best help drive insights. Companies like Walmart have noted that Tableau has provided them with efficient people analytics (Diez, Busssin & Lee, 2019). SAP SuccessFactors, the other business analytics software is used as a talent management suite and is now a major human resources technology component in companies. It works as a software as a service (SaaS) software for human capital management and is best suited for functions of talent management as recruiting, performance management, learning and development, and compensation management (Chang, 2015). It is relevant to business analytics for its functionality in people analytics, for workforce planning, and as a time and attendance software with its hubs like Employee Central serving as human resources systems of record and data repository can store employee information like their addresses, social security numbers, and salary and benefits enrolments. Its workforce analytics uses accurate workforce intelligence to make HR decisions (Yang, Smith & Churin, 2018). References Aston University Online. (2019). Data Science: Business Analytics and Big Data. Retrieved from https://studyonline.aston.ac.uk/news/2019/10/25/data-science-business-analytics-and-big-data Bichler, M., Heinzl, A., & van der Aalst, W. M. (2017). Business analytics and data science: once again? Chang, V. (Ed.). (2015). Delivery and adoption of cloud computing Services in Contemporary Organizations. IGI Global. Diez, F., Bussin, M., & Lee, V. (2019). Fundamentals of HR Analytics: A Manual on Becoming HR Analytical. Bingley: Emerald Publishing Limited. Earnshaw, R. A., Dill, J., & Kasik, D. (2019). Data science and visual computing. Evans, J. R., & Lindner, C. H. (2012). Business analytics: the next frontier for decision sciences. Decision Line, 43(2), 4-6. Gavin, M. (2019). Business Analytics: What it is & why it is important. Retrieved from https://online.hbs.edu/blog/post/importance-of-business-analytics Kohli, S. (2018). Innovative applications of big data in the railway industry. Liebowitz, J. (Ed.). (2013). Business analytics: An introduction. CRC Press. Marr, B. (2015). Big data: Using smart big data, analytics and metrics to make better decisions and improve performance. Chichester: Wiley. Provost, F., & Fawcett, T. (2013). Data science and its relationship to big data and data-driven decision making. Big data, 1(1), 51-59. Rodriguez, J. (2019). How LinkedIn, Uber, Lyft, Airbnb, and Netflix are solving Data Management and Discovery for Machine Learning Solutions. Retrieved from https://towardsdatascience.com/how-linkedin-uber-lyft-airbnb-and-netflix-are-solving-data-management-and-discovery-for-machine-9b79ee9184bb Yang, A., Smith, J., & Churin, A. (2018). SAP SuccessFactors Learning: The Comprehensive Guide (SAP PRESS). SAP PRESS. Zhu, Y., & Xiong, Y. (2015). Towards data science. Data Science Journal, 14

The field of cybersecurity has many different subfields, and each one is related to the others in some way. Computer science is the study of computers and their functions, as well as the design and development of computer software. It also includes the study of algorithms and data structures used by computers. The field of computer science is closely related to information systems, which is focused on the management, use, and protection of data. Both fields are related to engineering because they require a lot of math and physics knowledge. There are many tools and technologies that are applicable to computer science. A few of the most common ones are: web development, software development, computer programming and software engineering. Information technology is focused on how information is stored and processed by computers. It includes hardware such as servers and storage devices; software solutions for managing data; as well as user interface design for end users. Information technology also involves systems management such as network security or database management systems. In the field of information technology, the most applicable tools and technologies are those that allow for more efficient and effective use of data. These include databases, analytics, and data visualization tools. Engineering focuses on designing usable products from scratch, from parts that are designed using CAD software or other modeling tools based on industrial design standards like ISO 9000 or ASME standards (American Society of Mechanical Engineers) (Soomro, Shah & Ahmed, 2016). The product might be something physical like a car or an electrical device like an elevator but could also be something abstract like a building design model or a software program. Engineering is based on the scientific method, which emphasizes research and experimentation. Programming and scripting are both used in the IT field. However, they are not the same thing. A script is a short program that executes and interprets commands from another program. A program is a piece of software and generally contains multiple scripts. An IT professional can use a scripting language for many different purposes. For example, an IT professional may write a script that enables them to automate tasks on their computer such as creating new folders or renaming files (Kruse et al., 2017). They may also use scripting languages to create webpages or other content on websites, which would be decoupled from their actual programming languages. This allows them to create new information without having to learn another programming language or toolkit. A potential case where an IT professional could use a scripting language would be when they want to create reports on data using spreadsheets instead of using Microsoft Excel as their primary reporting toolset. In this case, they would need to use some sort of scripting language like Python or RMarkdown (or possibly JavaScript), which allows them to write reports in one file then run it through the script interpreter instead of having two separate files for each step in the process (one for the data itself and one for running the report). If you are an IT professional who is interested in learning a new programming language, then Python or RMarkdown would be a good place to start. Both languages have several advantages over other scripting languages like JavaScript (which is more commonly used for web development) and Ruby (which is more commonly used for creating web applications). A cybersecurity career is a fascinating field that combines the best of both computer science and information systems. It’s a position that requires an in-depth knowledge of how technology works, as well as strong interpersonal skills that allow you to work with others. With the rise of companies like Apple, Google, and Amazon, which have made big strides in cybersecurity as they have become more important to the global economy, there's never been a better time to get involved with this field. The first career option is a cybersecurity analyst. This person would analyze an organization's cybersecurity policy and recommend how best to protect its network (Kruse et al., 2017). This role involves planning and conducting security assessments, researching new technologies to help prevent attacks on networks, performing penetration testing to find vulnerabilities, and implementing new security measures when necessary. Cybersecurity analysts use a variety of tools and technologies to improve their understanding of the cybersecurity landscape, including hardware and software. Another career option is a security engineer. These people are responsible for creating new software to protect networks against attacks by hackers. They might work in teams with developers who specialize in web applications or mobile apps; as such, they'll need strong programming skills and knowledge of network protocols used by various devices connected to the Internet, such as routers (Kruse et al., 2017). A security engineer would use a wide range of tools and technologies to carry out their job. They would have access to a variety of hardware and software; some examples include routers, switches, firewalls, and intrusion detection systems. A third option is a penetration tester, who uses tools such as password crackers and social engineering techniques to break into systems and steal information from them on both sides: attackers and defenders. Penetration testers work closely with security engineers and developers to provide feedback on the effectiveness of their systems, which are then modified accordingly. These people often work for security consulting firms, which help companies improve their defenses against cyber-attacks. They also might work for law enforcement agencies, where they can help track down cybercriminals and identify the source of malware outbreaks. And defenders). A fourth option is a security architect, who designs an organization's network infrastructure architecture. This person might have to ensure that a company's IT systems comply with certain industry standards for security (such as PCI DSS). They'll need deep technical knowledge about how data travels around a network and how it should be secured. And defenders). A person in this role might also be called a security consultant, penetration tester or ethical hacker. The penetration tester is a professional in the field of computer security. They use various tools, including hardware and software, to test the security of a computer system or network.   References Kruse, C. S., Frederick, B., Jacobson, T., & Monticone, D. K. (2017). Cybersecurity in healthcare: A systematic review of modern threats and trends. Technology and Health Care, 25(1), 1-10. Soomro, Z. A., Shah, M. H., & Ahmed, J. (2016). Information security management needs a more holistic approach: A literature review. International Journal of Information Management, 36(2), 215-225.

Is AI displacing human jobs? Over time, the world witnessed the revolution of Supply Chain Management (SCM) in the name change, from Industrial Management to Production Management to Operations Management, to be the present name (Soni & Soni, 2019). With the changing in designation, the scope of the field is also evolving thanks to advanced technology, especially Artificial Intelligent (AI). According to Balan (2019), “Artificial intelligence refers to a broad group of technologies, among which range the following: computer vision, natural language, virtual assistants, robotic process automation, and advanced machine learning” (p.17). Recently, AI adoption to SCM has gained the public attention due to its diverse of application and potential. AI can help boost work productivity, cut costs, and improve work efficiency. In short, AI makes human life easier. However, it raises a concern about the job displacement and the need of workforce declining. This essay will discuss the innovation of AI to SCM in companies in terms of Inventory, Warehousing, and Transportation, and conclude with the concern that AI is more and more displacing human jobs. Our world had gone through a severe Covid-19 pandemic which greatly impacts on driving enterprises to change their supply chains management. Recently, a survey of 200 senior- level supply chain executives conducted by Ernst & Young Canada (2023), in late 2020 and September 2022, has found that strategic planning can help the enterprises more resilient, collaborative and networked with their customers, suppliers and stakeholders. During the survey, 52% of executives have disclosed that they started their strategic planning up to the year of 2025 on robotic warehouses and stores, transportation solution and fully automatic planning. Typically, Amazon, IBM, or Walmart, etc. are considered pioneers in data analytics, robotic warehouse and delivery drone. Approaching AI in inventory control and planning can reduce sustainable cost and increase revenue of enterprises. As AI can collect and analyze automatically historical, present, and future data, it provides precise and reliable forecasting demand, which allows enterprises to optimize their sources in terms of inventory or customer orders (Dash et al., 2019). Advertising campaigns or entertaining programs on social media are being running with underlined machine learning engine to analyses customer’s activities (Bughin et al. 2017, as cited in Dash et al., 2019). From there, the enterprises can have evidence of consumers’ near-real-time demand for their inventory planning. Walmart, one of the largest retailers, is another example of investing on Big Data analytics to catch customers’ preferences and behaviours for inventory management to reduce overstock and remain sufficient stock on most in-demand products (ProjectPro, 2023). However, an occurring challenge that may put Walmart on risk of falling behind its competitor is the limited number of professionals with experience in cutting-edge analytics and programming language like Python and R (ProjectPro, 2023). Therefore, any new team members join Walmart must participate in their designed program to gain the necessary knowledge in big data analytics. To gain such remarkable result in the industry, enterprises require experts in understanding and utilizing analysing data. Retraining workers to capture such dynamic complexity database is also a fundamental. Another advanced branch of AI, robotics, is also a promising approach to help perform heavy and repetitive tasks with high precision and speed. Thus, the use of robots in warehouse potentially helps increase efficiency and productivity, reduce manual labour costs (Dash et al., 2019). Let’s look inside Amazon’s warehouse where there are more than 200,000 mobile robots working alongside with hundreds of thousand human workers. Their mobile robots carry shelves of products from worker to worker to help pick, pack and ship the items. This is operating in a massive warehouse where Amazon workers used to walk more than miles a day for order-picking (Rey, 2019). Higher expectations may require in workers to work along with automated tasks in the warehouse. “The robots have raised the average picker’s productivity from 100 items per hour to a target of 300 to 400”, (Scheiber, 2023). Also, there are always issues during performing of Amazon picking and stowing robots that only can be solved by workers (Rey, 2019). Hence, as per Amazon CEO, “Amazon announced plan to upskill 100,000 of its US employees, including warehouse workers” (Bezos, 2023). This is a great chance for company’s employees to develop their technical skill to move into better-paying jobs. Additionally, it is essential to adopt AI and machine learning to avoid cost adding and shorten delivery time in logistic management. At the meantime, AI and machine learning can analyse and predict the duration of delivery. This helps to facilitate the delivery to customer timely and meet the requested delivery date as agreement (Dash et al., 2019). Also, other advanced technology in transportation solution, such as drone delivery, driverless truck, etc., which is safer, faster and economic for enterprises. After Amazon successfully delivered a pilot to Cambridge, there is a surge in this area (Dash et al., 2019). However, Amazon’s drone still cannot cross the stress and cannot come near or fly over people. It needs six people to monitor each flight, including observers and ground station operators, which proves that the innovation is still on experiment procedure and requires a lot of workers involving on its performance (Hollister S., 2023). Therefore, Human intervention is still needed to monitor and troubleshoot issue to ensure the ongoing operation of enterprises. Overall, the impact of AI in SCM has been and will be significant. AI is step by step replacing non-customer-facing jobs in a positive way. It not only brings benefit to a company but also creates more job opportunities to company’s workers. By investing state-of-the-art technology to warehouse, inventory or transportation, company can reduce physical workload, minimize human error, eliminate manual jobs, and save time, which is benefit to the business development and expansion. At the same time, workers have precious opportunity to be retrained and to upgrade themselves to an advanced level. By enriching knowledge in data analysis, basic coding, programming language, engineering, etc., employee will be able to master in AI implementation. Thus, to achieve the best success of technology adoption to the SCM, it should be the collaboration from both AI and human workers.   References Dash R., McMuntrey M., Rebman C., & Kar K. U. (2019). Application of Artificial Intelligence in Automation of Supply Chain Management. Journal of Strategic Innovation and Sustainability 14(3). http://www.m.www.na-businesspress.com/JSIS/JSIS14-3/DashR_14_3_.pdf Ernst & Young Canada (2023). Ernst & Young Global Limited. Research shows severe disruption through the pandemic is driving enterprises to make their supply chain more resilient, collaborative and networked. https://www.ey.com/en_ca/supply-chain/how- covid-19-impacted-supply-chains-and-what-comes-next Helo P. & Hao Y. (2020). Artificial intelligence in operations management and supply chain management: an exploratory case study. Production Planning and Control 33(16), 1573-1590. https://www.tandfonline.com/doi/epdf/10.1080/09537287.2021.1882690needAccess=true&role=button Holister S. (2023). The Verge. Amazon’s delivery drones served fewer than ten houses in their first month. https://www.theverge.com/2023/2/2/23582294/amazon-prime-air-drone-delivery Rey, J. D. (2019). Voxmedia. How robots are transforming Amazon warehouse jobs – for better and worse. https://www.vox.com/recode/2019/12/11/20982652/robots-amazon-warehouse-jobs-automation Ph.D. Balan C. (2019). Potential influence of artificial intelligence on the managerial skills of Supply Chain Executives. https://www.srac.ro/calitatea/en/arhiva/supliment/2019/Q-asContents_Vol.20_S3_October-2019.pdf#page=9 Projectpro (2023). How Big Data Analysis helped increase Walmarts Sales turnover? https://www.projectpro.io/article/how-big-data-analysis-helped-increase-walmarts-sales-turnover/109 Schieber, N. (2019). The New Your Times. Inside an Amazon Warehouse, Robots’s Ways Rub Off on Humans. https://www.nytimes.com/2019/07/03/business/economy/amazon-warehouse-labor-robots.html Soni R.G. & Soni B. (2019). Evolution of Supply Chain Management. Ethical Issues for Leaders. The Competition Forum 17(2). https://d1wqtxts1xzle7.cloudfront.net/64517433/2019_Competition_Forum_Vol_17_No._2-1-libre.pdf?1601011458=&response-content-disposition=inline%3B+filename%3DEditor_in_ChiefAbbas_J_Ali_Indiana_Unive.pdf&Expires=1684621006&Signatur e=g~n6WrnV2LdOvjIqQQBLYl465IrbgdzYvF9za4une4FAynWxGvS3BqjCRpqOwe9b I2ikwESa7hksHvhR350DXn1a23WYgkeEciU4YwS0fQVjoSPuK0n51EWMDWF5gJb 60B~we7hSxKcbHsJ09s2acAUaywNhZj824G3Iiutx39w5qnCKr3rk~Pcm0Y~9FSmOZ lvnHwm9KvwvjbJkk2Wp2OohE0ZxI1oZjIgxQSAzFvX62L8- 9OYJeQNJfXgYvK4eTijfsBzEV5~YYoQy19A4g35HkeeVTUam9fWZnEs9b5manicv DpdlrVRG-EoUimzaoo5xmaQI1DmOIOAdBqy2PA &Key-Pair- Id=APKAJLOHF5GGSLRBV4ZA#page=63

Abstract The complexity has quadrupled in every sector and the emergence of modern technologies and tools, be it physics, mathematics, social work, mechanical, business, or any other field. And, it has impacted information systems a lot, since information systems touch thoroughly or deeply into any other areas in this modern day. It has increased the complexity in information systems so much so that it is virtually impossible to live without it in this day, since it tracks a human being day and night, online and offline, from a person's birth to his death or beyond. This complexity impacts not only human beings but also big organizations. Intricacy is surrounding us in this undeniably computerized world. Worldwide advanced framework, online media, Web of Things, mechanical cycle computerization, advanced business stages, algorithmic dynamic, and other carefully empowered organizations and environments fuel intricacy by cultivating hyper-associations and shared conditions among human entertainers, specialized ancient rarities, cycles, associations, and establishments. Intricacy influences human organizations and encounters in all measurements. People and associations go to carefully empower answers for adapting to the underhanded issues emerging out of digitalization. In the computerized world, intricacy furthermore, mechanical arrangements present new freedoms and difficulties for data frameworks (IS) research. The motivation behind this uncommon issue is to encourage the improvement of new IS speculations on the causes, elements, and outcomes of intricacy in expanding computerized sociotechnical frameworks. This article examines the key hypotheses and techniques for complexity science and delineates new IS research difficulties and openings in complex sociotechnical frameworks. These articles trace how IS specialists expand on hypotheses and methods from intricacy science to contemplate evil issues in the arising computerized world. They likewise represent how IS scientists influence the uniqueness of the IS setting to create new experiences to contribute back to complexity science. Introduction When we direct a hunt on Google, it returns hundreds of thousands, results momentarily. The outcomes mirror the interests of the person who is doing the quest, yet likewise the large numbers of web clients who made or tapped on hyperlinks of sites. As more client’s search, connect and snap with comparable watchwords, the outcomes will evolve per client area and search time. A primary Google output is an emanant property, a mind-boggling web of communications among clients, sites, themes, publicists, and numerous other social or specialized elements. So, our everyday experience of utilizing commonplace computerized apparatuses is a powerful new result of complex sociotechnical frameworks. Advanced innovations did not just lead to complex sociotechnical frameworks; they likewise recognize sociotechnical frameworks from other complex physical or social frameworks. While intricacy in physical or social framework is dominatingly determined by either material activities or human organization, intricacy in sociotechnical frameworks emerges from the proceeding and developing entrapment of the social (human office), the emblematic (image based calculation in advanced advances), and the material (actual antiques that house or interface with registering machines). The elements of cutting-edge advances and the jobs of social entertainers are interminably characterized and reclassified by one another (Faulkner and Runde 2019; Zittrain 2006). This sociotechnical snare restricts the generalizability of intricacy experiences acquired from nondigital frameworks to complex advanced frameworks. Moreover, while material activities or human organization either increment or hose intricacy in physical or social frameworks, cutting-edge innovations can moderate and strengthen intricacy. This is because people and associations with complex sociotechnical frameworks regularly go to advanced innovations (e.g., information investigation) to answer complex issues. However, the use of an answer can affect another round of carefully empowered collaborations that lessen the proposed impact of the arrangement. This double impact of advanced advances on intricacy can deliver dynamic cooperation examples and results that are subjectively not quite the same as those in other complex frameworks. Development is a unique cycle of communications among heterogeneous specialists that unfurls and advances over the long run, bringing about different sorts of sudden novel individual-and- gathering level arrangements as well as more extensive social constructions (Benbya and McKelvey 2016). Through downturn, emergency, hierarchical change, etc., social frameworks put under pressure can show comparable stage advances and emanant results. Numerous social researchers have made a direct numerical equal among physical and social frameworks to reason the cycle systems inborn in miniature connection elements yield the more elevated level request and rising novel results. They have distinguished two types of rise: synthesis or aggregation (Kozlowski and Klein 2000). Developing cycles permit people's discernments, sentiments, and practices to get like each other in arrangement models. Coevolution alludes to the concurrent development of substances and their surroundings, regardless of whether these elements being organic entities or associations (McKelvey 2004). Ehrlich and Raven (1964) acquainted the term coevolution to portray the shared hereditary advancement of butterflies and related plant species. Such an interaction envelops the twin ideas of interdependency and common variation, with the possibility that species or associations develop corresponding to their surroundings while simultaneously these conditions advance comparable to them. Furthermore, to the above attributes, coevolutionary measures have three principle properties. To begin with, coevolutionary wonders are staggered. They incorporate at any rate two distinct degrees of examination. Second, coevolutionary wonders set aside some effort to show. This suggests that longitudinal plans are essential to comprehend coevolutionary measures. Third, bidirectional causality or two-way connections (e.g., Yan et al. 2019) are fundamental to coevolutionary measures. Bedlam's hypothesis was at first evolved with Lorenz's (1963) work in light of an oddity in climatic science. Turbulent frameworks are delicate to beginning conditions. This affectability to beginning conditions called the "butterfly impact," infers that even a slight change, closely resembling a butterfly's wing-beat, can prompt revolutionary outcomes on a lot bigger scale. As well as unsteady and touchy to beginning conditions, tumultuous frameworks are deterministic because the framework's direction is compelled. Such tumultuous frameworks have a weird attractor, a worth, or a bunch of qualities that framework factors incline toward over the long haul, however never fully reach (Lorenz 1963). Unexpected irregular changes in tumultuous frameworks drive them starting with one attractor then onto the next, going subsequently to calamities furthermore, terrible cultural outcomes. Versatile elements allude to the self-likeness of fundamental designs across various degrees of investigation (Manderbrot et al. 1983). This idea of self-comparability across scales has become a centre fundamental of intricacy science. It has prompted different speculations to portray how a solitary reason can increase into positive or negative limit occasions and drive comparative results at different levels (for surveys, Adriani and McKelvey 2006; Benbya and McKelvey 2011). The dimensionality of such self-closeness across scales can be estimated utilizing a numerical planning method alluded to as fractals. In different terms, fractals measure the "thickness" of a nonlinear informational index, for example, financial exchange practices or the state of a coastline (Casti, 1994). When such measures are taken at expanding significant degrees, every fractal measurement is "self- comparative" to the ones when it, which means that the hidden examples are very similar across levels of investigation. These connections are constantly represented by a force law (Cramer 1993). A forecast of possible results in each socio-technical framework is one of the lasting inquiries in IS writing. It has gotten considerably more significant with late advancements in huge information, and computerized reasoning (AI) advances. In any case, intricacy of sociotechnical frameworks present significant difficulties for forecast. Cooperation’s among an assorted arrangement of associated, commonly reliant, and versatile specialists in a sociotechnical framework lead to the rise of unforeseen results that resist the extrapolation strategies at the core of forecast models. Properties of complex sociotechnical frameworks, like nonlinearity, self-association, coevolution, bifurcations, and so on, lead to unusual states. Reductionist methodologies that expect a few components and connections in the unpredictable framework could make formal forecast models doable to carry out. Albeit a few practices of complex frameworks can be perceived through proper models, those models can't anticipate how a given framework will develop. Conclusion Carefully actuated intricacy is unavoidable in sociotechnical frameworks. Intricacy presents principal difficulties to IS exploration like the trouble surrounding the limits of a perplexing framework, the staggering idea of the unpredictable wonders, the problem of causal surmising, the restricted solidness of new information claims, and the cut-off points to consistency. By and by, intricacy science offers hypothetical and methodological instruments to address these difficulties and transform them into favourable circumstances. The five articles in this unique issue delineate how IS scientists utilize the theoretical and methodological devices of intricacy science to consider the fiendish problems emerging out of carefully initiated intricacy in the advanced world. Every one of these great issue articles perceives that the new mind-boggling wonder it centres around would not have been attainable to concentrate with regular hypotheses and strategies. By expanding on speculations and plans from intricacy science, these investigations could consider the complex new wonders and create new bits of knowledge and clarifications. Be that as it may, these articles are not simple utilization of known intricacy ideas in the IS setting. They likewise influence the uniqueness of the IS setting to create new bits of knowledge to contribute back to intricacy science. In particular, these IS considering illuminate intricacy science how the carefully empowered hyper-associations, hyper speed, and hyper-choppiness in sociotechnical frameworks make beforehand remarkable degrees of intricacy and dynamism and posture significant difficulties to people, associations, and society. In the regular and organic universes concentrated by intricacy science, significant developmental and extraordinary changes require a long period to unfurl. In the examination, significant developmental and remarkable changes trigged by carefully instigated intricacy occur surprisingly fast, if not months, days, and even hours, in present-day sociotechnical frameworks. The articles in the special issue utilized the remarkable properties of advanced advances, digitized measures, items, stages, environments, and plans of action to concentrate on how and why these changes happen. They additionally joined their hypotheses and strategies with those of intricacy science to produce new clarifications concerning how mischievous issues made by carefully initiated intricacy could be subdued. The methodologies created by these IS studies might actually illuminate intricacy concentrates in different pupils. References Faulkner, P., and Runde, J. 2019. “Theorizing the Digital Object,” MIS Quarterly (43:4), pp. 1279-1302. Zittrain, J. 2006. “The Generative Internet,” Harvard Law Review (119), pp. 1974-2040. Benbya, H., and McKelvey, B. 2016. “Advancing Our Understanding of Emergent Phenomena: A Multidisciplinary Review and Research Directions,” in Academy of Management Annual Meetings Proceedings, J. Humphreys (ed.), Anaheim, CA, pp. 1-30. Kozlowski, S. W. J., and Klein, K. J. 2000. “A Multilevel Approach to Theory and Research in Organizations: Contextual, Temporal, and Emergent Responses,” in Multilevel Theory, Research, and Methods in Organizations: Foundations, Extensions, and New Directions, K. J. Klein and S. W. J. Kozlowski (eds.), San Francisco: Jossey-Bass, pp. 3-90. McKelvey, B. 2004. “Toward a Complexity Science of Entrepreneurship,” Journal of Business Venturing (19:3), pp. 313-341. Ehrlich, P., and Raven, P. 1964. “Butterflies and Plants: A Study in Coevolution,” Evolution (18:4), pp. 586-608 Yan, K., Leidner, D., Benbya, H., and Zou, W. 2019, “The Interplay between Social Capital and Knowledge Contribution in Online User Communities,” Decision Support Systems (12:7), pp. 113-131. Lorenz, E. 1963. “Deterministic Non-Periodic Flow,” Journal of Atmospheric Science (20), pp. 130-141. Cramer, F. 1993. Chaos and Order: The Complex Structure of Living Things (trans. D. L. Loewus), New York: VCH.