Chapter 9 Case Review
The problem addressed in the case study was the organization’s adoption of a new SAP system. Versum continues using its IT infrastructure after being split from its parent firm (Laudon, 2021). However, the holding company quickly demanded that Versum converts to an autonomous IT structure within 18 to 24 months, which was a substantial hurdle, so management elected to continue with SAP software. As this was an organization-wide overhaul, the greatest obstacle was making the IT specialists learn a new digital scheme, which could not be accomplished quickly. Consequently, they chose to construct their new system using SAP S/4 HANA.
Versum’s decision to use SAP S/4 HANA was prudent. SAHA, the new SAP ERP system, was built by Versum Materials on a private cloud maintained by a web host inside its own data center (Laudon, 2021). This saved the company substantial money and managerial effort up front. The company’s installing SAP S/4 HANA enabled it to simplify processes, save expenditures, and use real-time data. The business may now function independently as a specialist in materials.
During the implementation of the new system, Versum encountered various obstacles, particularly when converting data from the previous system (Laudon, 2021). The previous SAP ERP system had a different data structure than the latest SAP S/4HANA structure, which featured a new stratified Business Partner complex. Three rounds of data cleaning were required for Versum to collect enough accurate billing, address, and contact information. The business could now streamline its operations, control expenses, and use real-time information. The firm was now prepared to operate as a separate entity focusing on specialty materials.
Chapter 10 Case Review
An SAP system could add a competitive edge if Uber adopts it. Integration through an Uber Freight API, prompt and current pricing data can be tapped into, as well as the freight capacity in the national network. As a result, the freight tendering process would be changed to an intelligent process, creating opportunities for innovation from shippers.
Uber’s competitive advantage in terms of competitive forces derives from its scale and network effects. As Uber expands, it becomes increasingly difficult for new competitors to enter the market. Uber’s clientele is price and quality concerned. Price-conscious consumers want the best pricing across aggregator platforms, while quality-conscious clients want reliability, quick availability, and consistency. Cab drivers connect with service seekers through the platform for a part of the ride’s earnings. Around the world, these drivers’ negotiating potential is escalating as they unionize to establish consensus and pressure the organization to change the course of action. Uber confronts competition from companies with similar business strategies and sizes in each area. These players have created a network of riders, most of whom work for various organizations (Rosenblatt et al., 2017). In terms of the value chain, Uber’s competitive advantage derives from its technological platform. Uber has an efficient technology platform that enables it to connect riders and drivers efficiently, which gives them an advantage over others.
Uber’s business model is driven by technology. Uber has developed a successful business model by leveraging information technology to connect drivers and passengers and establish a ride-sharing marketplace. Uber has also developed an effective method for pricing rides and passenger safety thanks to advances in information technology. Only information technology makes this level of convenience and automation possible.
It is a positive disruptive force in the transportation industry because it connects passengers and drivers more efficiently than traditional methods, such as calling a taxi company, which can be less convenient (Schneider, 2017). It has provided a more convenient, affordable, and adaptable alternative to traditional taxis. Uber has also been disruptive in the regulatory environment, as the company has faced significant resistance from local and national governments regarding its business practices.
There are a variety of reasons why Uber is a viable business. First, it has adopted a dynamic pricing strategy based on the demand-supply economic principle. As demand increases, so does the price. This becomes advantageous for the business and the drivers. Second, Uber possesses a significant competitive advantage. It is difficult for competitors to duplicate the company’s technological platform. Lastly, it has a large customer base and is accessible worldwide in many cities.
Chapter 11 Case Review
The viability of Uber is assured at the moment, but future trends will determine whether it would need t to restructure its strategy to remain relevant to customers. Uber Eats launched its automated vehicle technology in delivering food in the United States in September of 2022. Uber is expected to continue integrating automated cars in other sectors of their business to remain in business; however, drivers would have to lose their jobs. Despite improved overall efficiency, safety, and convenience, automated vehicle technology presents ethical and social issues that people would have to contend with.
Advantages
Due to 360-degree vision, networked cars, and continual contact, disaster will be drastically minimized. Although accidents will not be eliminated, they will be far less frequent than those caused by manual human driving (Abraham &Rabin, 2019). Since the vehicle will be automated and will need little human input to operate, even those with visual or auditory impairments will be able to get one. Their speed is expected to be slower in large cities, but their traffic performance will improve. Vehicle-to-vehicle (inter-vehicle) communication is a tool that can be used to coordinate platooning and prevent collisions. Vehicle-to-road management systems can deliver current local information on accessibility and traffic.
Disadvantages
The first issue is that, due to being constantly linked to the whole environment, data protection might become a cyber issue. Infrastructure for autonomous vehicles depends on 5G cellular service, which is still costly and may take an undefined timeline to implement (Yoo &Managi, 2021). Although significant work has been done in decreasing the cost of creating their tools, these reductions are insufficient to make them a financially feasible option for the typical household. People have been struggling with solving ethical dilemmas that come up while driving. These dilemmas cannot have any real solution even when self-driving cars are widely adopted.
Automated cars are set to make decisions as programmed by algorithms, which removes the possibility of being flexible depending on the situation. It would be preferred that accidents should occur naturally but not because algorithmic decisions predetermined it. Nonetheless, human error is expected to be more frequent than in automated systems. In another instance where the only options are ramming into a pedestrian or saving one’s life, there is no valid answer in this scenario. Self-driving cars still cannot offer a solution to such challenging instances.
Yes, I would invest in the development and marketing of autonomous vehicles because they have the potential to transform the automotive industry. Automated vehicles have the potential to significantly decrease accidents and traffic congestion while simultaneously improving fuel economy. In addition, autonomous vehicles might deliver significant environmental advantages by decreasing emissions.
Chapter 12 Case Review
Among the core systems in self-driving cars are automated systems and algorithms, which significantly eliminate human error. The algorithms are programmed to estimate viewpoints and proximate cars’ blind spots to generate a map mimicking a bird’s eye view of the nearby environment. The generated map aids in detecting obstacles and in understanding how other automobiles move to accurately execute predetermined decisions without causing accidents. Machines learning algorithms allow automated systems to learn and apply these data in real time.
Using algorithms and automated systems for decision-making presents various issues. These systems are often opaque, making it difficult to comprehend how they make judgments. This might result in a lack of responsibility in the event of an error. In addition, these systems may be biased due to their training data or the algorithm’s design. This may result in the making of discriminating judgments. The systems may be fragile, meaning they might abruptly fail when presented with new information or circumstances (Cobbe, 2021). Some of these systems may be pretty sophisticated, making debugging them challenging.
Several distinct elements have contributed to the algorithm’s decision-making difficulty. The firm did not effectively supervise the development and release of the algorithm. (Binns, 20022) The second organizational problem is that the corporation lacked a clear plan or strategy for using the algorithm after it was created. Lastly, the technology may be obsolete or poorly matched to the job at hand, increasing the likelihood that the findings may be erroneous or prejudiced.
There is no proper response to this question; the answer depends on the context in which it is posed. Suppose, for instance, that automated systems are employed to make financial judgments. In such a scenario, there is a more significant chance of mistakes and misuse than when automated algorithms choose what website material to show. Before introducing automated decision-making systems, weighing their advantages and disadvantages is necessary.
References
Abraham, K. S., & Rabin, R. L. (2019). Automated vehicles and manufacturer responsibility for accidents. Virginia Law Review, 105(1). Web.
Binns, R. (2022). Human Judgment in algorithmic loops: Individual justice and automated decision‐making. Regulation & Governance, 16(1). Web.
Cobbe, J., Lee, M. S. A., & Singh, J. (2021). Reviewable automated decision-making: A framework for accountable algorithmic systems. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. Web.
Laudon, K. C., & Laudon, J. P. (2021). Management Information Systems (17th ed.). Pearson Education (US). Web.
Rosenblatt, A., Levy, K. E., Barocas, S., & Hwang, T. (2017). Discriminating tastes Uber’s customer ratings as vehicles for workplace discrimination. Policy & Internet, 9(3). Web.
Schneider, H. (2017). Creative destruction and the sharing economy: Uber as disruptive innovation. Edward Elgar Publishing. Web.
Yoo, S., & Managi, S. (2021). To fully automate or not? Investigating demands and willingness to pay for autonomous vehicles based on automation levels. IATSS Research, 45(4). Web.