Develop a semi-structured interview guide consisting of at least eight main interview questions that will help you answer your research question. For each interview question, indicate under which of the four types of qualitative research questions discussed in the textbook (Tolley, Box 4.5) the question falls, and write meaningful appropriate follow-up questions and/or probes. While “can you say more about that” and similar statements are valid probes, do not include these in your assignment. The wording of all questions should be at a literacy and knowledge level appropriate for your interview participants.
Note: It is important to end each interview by asking participants the question, “Is there anything that you feel is important to share that I didn’t ask about?” However, please do not include this question as one of your eight main questions.
Upon completion of the interview guide write a justification section that addresses each of the following concepts in paragraph form:
• For each main question, describe the purpose of the question (intent, what you hope to learn, etc.) and how that contributes to addressing your research question (1-3 sentences per question for each of the eight questions).
• In 1-3 sentences, justify the order in which you are asking your main questions.
• In 1-3 sentences, justify the wording/vocabulary used in your questions, and how it is appropriate for your target population (e.g., grade level, knowledge, language proficiency, etc.).
• In 1-3 sentences, describe how you will use the information gained through these interviews (e.g., to conduct further research, to develop a theory, to inform policy, etc.).
For concept (a) you will provide a 1-3 sentence response for each main question, while for concepts (b)-(d) you will provide a 1-3 sentence answer regarding your overall assignment (i.e., your response will be inclusive of all the questions you wrote for the assignment).
We are asking you to develop interview questions and to justify your decisions in developing this interview guide. We are not asking you to conduct an actual interview with these questions, however your questions should be appropriate and feasible.
Submit a Word document that includes the following:
1. Health topic
2. Priority population
3. Qualitative research question
4. A paragraph justifying your selection of priority population, interview participants and why in-depth interviews are appropriate to answer your research question.
5. You need 8 main interview questions
• For each of the 8 questions indicate the qualitative research question type under which it falls.
6. A minimum of 8 follow-up questions and/or probes (not necessary for each item but must total at least 8).
7. A justification section that includes (a)-(d) above.
(A) For each main question, describe the purpose of the question (intent, what you hope to learn) and how that contributes to addressing your research question (1-3 sentences for each of the eight questions).
(B) In 1 to 3 sentences, justify the order in which you are asking your main questions.
(C) In 1 to 3 sentences, justify the wording/vocabulary used in your questions, and how it is appropriate for your priority population (e.g., grade level, knowledge, language proficiency, etc.).
(D) In 1 to 3 sentences, describe how you will use the information gained through these interviews (e.g., to conduct further research, to develop a theory, to inform policy, etc.).
From translation to research, art creation, fraud detection, and logistics improvement, intelligent
From translation to research, art creation, fraud detection, and logistics improvement, intelligent robots have the potential to change the world. A more prosperous and efficient society may be the result of these machines’ increasing capabilities. There is a growing concern that as these technologies advance, disrupting and improving human life, there will be a devastating impact on human ethics and the moral fiber of our society (Zhang, 2018). As an external representation of the human mind, Green (2017) claims that AI has a wide range of positive and negative characteristics. According to Catrin Misselhorn (2020), the goal of AI is to become as intelligent as the human mind. Artificial systems appear to be capable of replicating human intelligence’s logical component at this time (Braga & Logan, 2017). As a result, the development of autonomous systems has only recently been possible, raising moral and ethical questions. This is due to the fact that automated systems are sometimes required to make decisions without human input (Misselhorn, 2020). This paper therefore evaluates the ethical implications of the transition to AI technology, and assessing whether it is doing more harm than good to society.
Functions of AI and Human Safety
Artificial intelligence (AI) is a term used to describe any technology that is capable of performing difficult tasks or activities that require multiple steps (Wttlestone, Alexandrova, Dihal, & Cave, 2019). An AI system is designed to perform complex tasks, such as optimizing processes and operating autonomously, as well as developing complex behaviors that go beyond their original purpose. A fully functional AI, according to Green (2017), may provide several advantages, including accuracy, speed, convenience, efficiency and quality as well as the ability to destroy, depending on the intended use. When AI is mishandled, it has the potential to wreak havoc and cause irreparable harm, just like a nuclear weapon. Autonomous lethal weapons, such as killer robots, also pose well-known risks to human safety.
Digital technologies may not always work as planned. As with any other new technology, AI must be thoroughly tested to ensure its usefulness and efficacy during its development. Regardless, it is difficult to cover all of the areas in which they are expected to work in practice. According to Green (2017), this means that both human and artificial intelligence can fail. Suppose a self-driving car was unable to come to a halt at high speeds due to a system failure or other unexpected problem. Passengers in such a vehicle, as well as other innocent road users, will suffer a great deal of harm and even die. Many ethical questions have been raised as a result of these flaws, as well as AI technology’s potential negative consequences.
AI Manipulation or Malicious use and their Consequences
The ability of AI to seamlessly integrate a computer program with the data it receives is one of the primary advantages of AI. There is a risk, however, that these functions may bias and manipulate the system in unexpected and potentially harmful ways. Photo captioning and criminal sentencing are among the areas where recent examples of algorithmic manipulation and discrimination have been found. All of these biases and manipulations have had a devastating impact on the lives of those who have been subjected to them. Furthermore, it has eroded confidence in organizations, governments, and businesses that rely on falsified or distorted data (Wttlestone, Alexandrova, Dihal, & Cave, 2019).
WALZ et al. (2019) contends that AI “poses a significant risk of being abused for manipulation, surveillance, or other quasi-legal purposes.” In order to prevent the use of abusive techniques, many people believe that restrictions should be imposed on users. Facial recognition is a well-known societal challenge when it comes to AI advancements. Our democracy and civil liberties could be in jeopardy as a result of this. According to Weinstein (2020), flaws in facial recognition algorithms have the potential to exacerbate existing biases, particularly in light of law enforcement’s already unequal treatment of people of color. Due to racial bias, AI may mistakenly believe that black people are more likely than white people to commit the same crime again. Facial recognition systems also put users’ personal information, such as credit card numbers, at risk. A high number of false positives and a negative impact on a variety of populations may result if not everyone designing facial recognition software is informed about how to create a realistic representation. Data breaches and invasive surveillance harm people because they are made to witness or be observed without their consent.
It is because of this that people have to deal with algorithmic bias, manipulation, and the evil use of AI technology for personal gain and objectives. For this reason, it’s imperative that people work to create artificial intelligence (AI) and other technological advancements that are better than what we currently have. Consider the possible consequences of artificial intelligence-facilitated computer hacking (Martin, Shilton, & Smith, 2019). The aforementioned situations could result in death, invasion of privacy, and data security breaches, all of which could harm human life.
AI and Human Spirit
People’s lives, interpretations of life, and interactions with one another are profoundly influenced by AI. Existential questions abound in light of this. By externalizing their intellect via AI technologies, are humans reducing themselves to inferior creatures in comparison to the creatures they have created? By using artificial intelligence (AI) and enhancing it beyond the capabilities of human intelligence, are we, like ancient philosophers, reducing ourselves to our own creations and losing our sense of self-identity? Humanity’s soul is pierced by questions like these ones concerning artificial intelligence. In almost every way, it addresses the question of what will happen to humans if their creations surpass them (Kambria, 2019). There is a possibility that losing human cognition to machines can help humans realize that intelligence is not as critical to human identity as previously assumed. If artificial intelligence (AI) ever surpasses human intelligence, it will be the end of humanity as we know it. Studies show that AI robots can now perform tasks that are on par with what humans can. Note that corporations, non-profits, and government agencies are increasingly incorporating AI with advanced capabilities that mimic human cognitive processes like speech recognition and planning (Kambria, 2019). Since new AI technologies such as super AI will likely surpass human capabilities in the near future, humans may be forced to fight for their very existence.
Artificial Systems as Moral Agents
It is critical to determine whether or not artificial systems can be considered moral actors when discussing the morality of various forms of artificial intelligence. In order to qualify as an agent, one must be a source of one’s own actions, as well as have the ability to act for a variety of reasons (Misselhorn, 2020). To qualify as a moral actor, there are additional requirements that must be met. Ethics, autonomy, intentionality, responsibility, and values are all required for moral agency in (Wiltshire, 2015).
Artificial systems, on the other hand, will never have phenomenal consciousness, a quality essential to human existence and morality (assuming, as we now know, that phenomenal consciousness is a quality unique to living species) (Misselhorn, 2020). There must be no phenomenal consciousness in order to realize the essence of what an emotion “feels like,” which is a crucial component of emotional experiences. Given that moral judgment is often based on emotional responses, questions arise about artificial systems’ ability to be like the human brain. For artificial systems to become moral agents, it may be necessary to incorporate the emotional features of moral emotions that are essential for moral judgments. The absence of a self-perceived identity in artificial systems also limits their ability to exercise agency, as do the limitations on other human characteristics such as learning from experience, feeling emotions, and using their imagination (Braga & Logan, 2017).
Artificial systems appear to be more trustworthy if they are perceived to have a certain level of mental capabilities. Endowing other species with mental abilities falls under this concept of mind perception. In the conventional sense, artificial systems lack consciousness. Scientific evidence, however, supports the idea that consciousness is a matter of perception. Mind perception is dependent on two dimensions: agential and experiential, according to a study by Bigman and Gray (2018). Since machines are widely believed to be without “agency,” humans are hostile toward robots that make moral judgments. Machines can perform calculations or preprogrammed tasks, but they do not possess the capacity for “thinking.” Humans have this predisposition not because they are afraid that artificial systems will make incorrect moral judgments, but rather because computers are not equipped to do so. As a result, not only are moral judgments and the determination of what is good or bad dependent on the results of mind perception, but so too is the determination of who is authorized to make moral decisions. According to Bigman and Gray (2018), the general public does not yet recognize artificial systems as moral arbiters because of their apparent lack of consciousness.
It’s also been discovered that certain elements and events alter one’s perception of reality. Anthropomorphism, the tendency to attribute human characteristics to non-human organisms, has been documented in research. Artificial systems that appear to be more human-like have a greater chance of being conscious because they appear to have human-like features (such as a human-like voice or face) (Bigman et al., 2019). For an artificial system to be perceived as more reliable, it needs to have human-like characteristics.
Humans’ skepticism and refusal to believe artificial systems’ judgments may be exacerbated by the loss of the human mind (and thus the capacity for moral evaluation and decision making based on the mind). Uber and Lyft, two of the most popular ride-sharing services, clearly demonstrate this conclusion. Young and Monroe (2019) also found that people are wary of autonomous vehicles despite evidence that they are safer than human-driven cars. As a result of these findings, people are more wary of artificial systems because they lack a perceived mind. This could be because moral reasoning is a difficult cognitive ability to grasp. Another finding of the research supports the idea that the appearance of mentality in an agent influences people’s perceptions of its moral competence and trustworthiness (Young & Monroe, 2019). (Young & Monroe, upcoming).)
In order to make a moral judgment, one must first understand the concept of mind perception. The question of whether or not an artificial system can be viewed as a moral agent is irrelevant to this argument. It’s because even if an artificial system doesn’t meet the definition of a moral agent, an individual’s perception of it as having an actual mind suffices for moral agency.
It is well-known that artificial intelligence innovation and adoption are unstoppable forces in both business and society, and they will continue to do so for the foreseeable future. There are numerous reasons to be concerned about untrained artificial intelligence, but there are also numerous reasons to be optimistic if advancements are properly implemented. It is important to ensure that artificial intelligence policies are developed with the greater good in mind and to prioritize the organization of economic, social, and educational systems in order to help many people grow with technological and scientific advancements and changes in the world. With AI, many aspects of our lives could be drastically altered for the better. The discovery of new diseases and treatment methods, for example, may benefit from this technology, as well as the facilitation and security of driving. If we ensure that artificial intelligence policies are designed to benefit the greater good, we can reduce bias and deception. To ensure that AI is used in a responsible and beneficial way, there must be established ethical norms, principles, and moral standards. “Government and business must work together to establish laws and standards that represent societal goals and expectations based on human values, ethics, and social norms,” (Wirtz et al. 2020). Artificial intelligence will only make judgments based on algorithms specified by humans if there is no legislation governing and monitoring the ethical actions of AI. This could have grave consequences for humanity in the near future. AI systems must adhere to ethical and social norms and accept responsibility for their actions when they interact with humans in public contexts and have an impact on them” (Wirtz et al2020). As a further response to the potential impact of AI, the world’s financial, social, and educational systems are evolving (Wirtz et al., 2020). With the help of this prioritization method, many people can keep up with technological and scientific advancements. If these rules and standards are put in place they will help to shift the focus away from nationality and toward humanity’s achievements. This achievement will also ensure that AI advances benefit all people, irrespective of their social or financial circumstances. Data privacy is critical, according to Gilman (2020), not only for autonomy and dignity, but also for economic justice—defined here as guaranteeing that everyone has access to the material resources that provide chances to live a life free of urgent economic worries. Human rights, civil liberties, and the consequences of social and economic inequality must be protected as AI progresses. Because the world faces so many problems, it may appear intimidating that such an agenda lacks the resources necessary to build safeguards that can keep pace with AI’s rapid development. Though it is not a panacea, Gilman (2020) claims that the GDPR has the potential to reduce digital discrimination against poor people while also improving their economic stability and mobility. Gilman (2020). In order for these benefits to materialize, responsible AI must be implemented, and ethical standards must be upheld in the process.
Regardless of where we live or reside, we can legitimately say that technology plays a significant role in our lives. It’s terrifying to think that we could have a slew of super-intelligent AI systems. Accordingly, the most pressing issue is whether or not to limit the development of AI technology. It is the opinion of this article that AI research and deployment must take place in an ethical manner. However difficult it may be to address ethical issues related to artificial intelligence development in a broader society, the preceding discussion has made it clear that doing so is both necessary and important. Research and understanding of artificial intelligence are essential if we are to make informed decisions in the future given the potential benefits and drawbacks of this technology. Artificial intelligence’s daily activities should be overseen by higher authorities, such as the government, and the development and recreation of artificial intelligence should not be abused or manipulated in any way. In order to make the world a better place, we need both human and artificial intelligence.
2 Artificial intelligence and Sustainability in Tesla Supply Chain Name Institution Course
In-depth Interview Questions Essay Writing Assignment Help 2
Artificial intelligence and Sustainability in Tesla Supply Chain
Tesla is an American-based company that specializes in the manufacture of electric cars. The company was founded by Elon Musk and Martin Eberhard in 2003 and has remained at the forefront of everything electric ever since (Kumari & Bhat, 2021). Tesla’s first production car, Model S, a luxury sedan known for its speed, performance, and innovative design, was released on July 19, 2012 (Kumari & Bhat, 2021). In 2013, Tesla introduced their most recent Model: Model X. Models S and X are among the fastest accelerating cars today. The Model X goes from 0-to 60 in 3.2 seconds, making it the third-fastest car in its class. In addition to being recognized for their speed and performance, Tesla vehicles are also known for their safety ratings which far exceed those of conventional cars. Tests have shown that during a collision, the impact is dispersed across the entire vehicle’s width rather than at specific points like crumple zones in conventional cars. It is achieved by having rails between each of its four wheels extending from front to back to absorb impacts, thus reducing damage to key parts responsible for driving and braking (Kumari & Bhat, 2021).
Tesla has produced several different models. These vehicles include the Model S, Model X, and the Roadster, built-in 2008 until it was discontinued in 2012. In 2015, Tesla introduced the lower-priced Model 3. The introduction of this car caused a major change for Tesla as they began to move away from premium cars and into mass-market cars by drastically decreasing the price point (Kumari & Bhat, 2021). Today, Tesla’s primary focus is on producing electric cars, but many of their other models are being used as stepping stones to making their electric cars more reliable and appealing to customers alike.
Tesla’s long-term goal is to create a fully electric car that would run on a battery that stores energy from renewable resources such as solar and wind. They have begun working on the Gigafactory in Nevada, USA, which is designed to produce the batteries for their cars. The Gigafactory will be capable of producing batteries for 500,000 vehicles per year (Kumari & Bhat, 2021). The Gigafactory has been cited as one of the largest projects undertaken by an American start-up company, and it is expected to be highly profitable, with an annualized gross profit of $5 billion.
Tesla’s business model can be summarized as follows:
The Model is based on a referral system where customers refer friends and family as a marketing method to existing customers. As a result, Tesla can continually generate new revenue streams. Although Tesla does not have to satisfy their initial customers through direct advertising costs, it does have to build markets for its products by paying for each sale; higher volumes from new sales should eventually offset this cost (EBITDA) (Kumari & Bhat, 2021).
Current Supply Chain Framework
To better understand the current supply chain structure at Tesla, a brief overview of the design of Tesla’s supply chain can be given. With the development of a few core exceptions, Tesla’s supply chain is generally vertical integration in design. It means that the company’s suppliers provide parts and services for multiple production steps instead of having multiple tiers of suppliers. A horizontal supply chain was an intentional goal for Tesla due to its need for rapid development and high production rates. During the design phase of the production process, Tesla will ask suppliers to perform a few tasks to allow them to allocate the supply chain appropriately. One such task is batch-to-batch tracking, whereby all parts are tracked from beginning to end through each produced order. It allows Tesla to capture data such as how long each step takes and how much electricity each step uses for this data to be analyzed for future optimizations. Another task that suppliers will perform during the design phase: is routing, which involves mapping out where all parts are located throughout their facilities to be distributed accordingly based on demand and production rates.
Tesla’s main components are made in various countries. One of the companies that will supply the batteries for the company’s Model 3 is China’s Ganfeng Lithium. Other companies that provide Tesla include Australia’s BHP and Switzerland’s Glencore (Maverick, 2022). These suppliers have been selected as they produce goods superior to those made in the United States. The effect of this supply chain is that when a part or component is imported from overseas, the cost will be far below the cost of production in the United States. To attain a high-quality product at a low price, Tesla has imported its suppliers’ human resources and workers. For example, Tesla has had to import key personnel from Europe, such as its head of engineering and vice president of manufacturing, who oversees its supply chain. The production costs for this wave of employees came at $100 million, which led Tesla to hire more than 800 workers at an average salary level of $114,000 annually.
Tesla’s main manufacturing unit is in Fremont, California, US, with a 5.5 million square meters total floor area. The facility encompasses operational activities such as Assembly of parts, painting, and stamping. The assembly plant requires over two thousand parts from over three hundred suppliers for the Model S only. Most of the design and build are maintained in-house. It benefits the company as they can keep the quality while achieving economies of scale while reducing the dependency on external suppliers. However, this supply chain management plan impacts flexibility.
Tesla’s value chain is composed of various components and services. The company’s main business model is to disrupt the traditional manufacturing industry by developing and delivering innovative products. Its massive Gigafactory 1 is in Nevada and can produce over 500,000 batteries a year. This plant’s vertical integration ensures that its operations are maintained.
Tesla’s base camp is in Fremont, California, with creation and gathering offices in Lathrop, California, and Tilburg, the Netherlands. They work their special Tesla retail storefronts without the need of sellers or promotional firms, disturbing the vehicle business’ conventional deals approach. In 2016, the office delivered only 83,922 autos, bringing about efficiency of under 20% compared to the Toyota-GM organization’s earlier result (which fabricated around 400,000 vehicles). The two Gigafactory facilities in Nevada and New York are helping with the sub-gathering of battery packs.
Through its partnership with Toyota, Tesla has reduced its inventory costs by implementing the Just in time philosophy, which allows it to keep its outbound inventory at a minimal level as it assembles (Boggild, 2016). It eliminates the need for additional space in the finished products. The information system is very important to improve the processes involved in a company’s production and supply chain. It can be used for various purposes such as planning projects, managing contracts, and ensuring that the facility is functioning properly.
Tesla’s core competencies are its people. The company has many highly skilled individuals who are responsible for its various operations. Its collaboration with other tech companies has helped it develop new technologies. Tesla’s vehicles are also equipped with a sophisticated software system called the Computer on Wheels. It makes them incredibly intelligent. The company has partnered with other companies to develop new products and solutions.
Current Supply Chain Challenges
Despite a disruptive and profitable supply chain framework, Tesla faces several challenges in its current supply chain.
Inventory: Tesla’s most pressing issue is insufficient on-hand inventory. It stems from the company’s failure to forecast demand and manufacturing capabilities accurately due to a lack of historical data. Tesla says this has led to significant overstocking, an oversupply of shipments and a loss in revenue, and operational costs that are too high and unsustainable.
Forecasting: Tesla is not employing forecasting tools effectively because its forecasting methods were highly volatile and inconsistent and did not follow accepted scientific standards for best practices. The company also lacked an effective way for cross-functional team collaboration on forecasting. As a result, Tesla was frequently experiencing over shocks in demand and manufacturing, which caused the company financial losses.
Supply Chain Planning: Tesla failed to set realistic targets in its inventory planning and production planning practices. When developing its manufacturing plant, the company did not effectively align capacity, labor, and material requirements. The company also did not have an effective method for forecasting demand that ensured a true understanding of the market. Finally, Tesla did not effectively utilize demand signals to plan accordingly.
Supply and Demand Chain Integration: Tesla is experiencing issues with operational coordination among different supply chain teams and departments. It has led to the uncoupling of its supply chain and demand chain functions, such as inventory management and production planning, resulting in a lack of visibility into demand.
Strategies Inventory control: Tesla’s ability to effectively manage its inventory is one of the prime reasons for its overstocking. As a result of differing departmental effort levels and poor allocation strategies, Tesla’s inventory levels remain excessively high. The company also failed to generate accurate forecasts for raw materials and finished goods, which led to excess inventory during the quarter (Sathish, 2019).
Globalization: Tesla’s need for rapid international growth requires the company to work with many more partners. However, its current supply chain cannot keep up with Tesla’s growing manufacturing needs. As a result, the company is often experiencing operational delays leading to lost revenue and opportunity. Tesla is working to implement new processes and procedures that are more efficient, effective, and scalable. The company is also exploring ways to leverage its vast network of suppliers and distribution centers to reduce costs and increase the pace of innovation.
Supply Chain Processes: The supply chain at Tesla has inefficient processes and procedures and a lack of appropriate analytical tools and expertise to allow for effective coordination at all levels of the supply chain. It has resulted in silos of information and data, which has impeded the cross-functional collaboration necessary to identify and resolve supply chain issues. Supply chain processes also lack visibility into demand signals, which has limited the company’s ability to adjust production and inventory as needed (Sathish, 2019). Tesla needs to invest in its supply chain processes to ensure appropriate visibility and communication throughout the entire supply chain and identify and resolve supply chain issues.
Strategic Sourcing: Tesla has not developed a strategic sourcing strategy. Rather than sourcing strategically, the company is focused on reducing overall cost per unit, which is a reactive approach to purchasing rather than a proactive one. It is an issue as it doesn’t allow all the companies in its supply chain to have an equal opportunity to provide products and services at a fair price.
Transparency: A lack of transparency in Tesla’s supply chain results in mistrust within the company and its supply chain partners, decreasing transparency among industry stakeholders. Its lack of transparency leads to inefficient and unproductive collaboration among stakeholder groups.
Despite putting a lot of effort into eliminating manual interventions during the production stage, Tesla does not seem to be effective in its use of automation. Artificial intelligence is known for being easily manipulated, eliminating all traces of human error. While some companies rely on manual processes, Tesla believes that automation is necessary for their current production line. It may be true as there are certain instances where human intervention and decision making is needed during the process. However, most of their operations rely heavily on individual skills and tasks that require machines to perform those tasks through artificial intelligence. It can be seen in the early stages of production where they are still adding more devices to their facility. It means that Tesla will have to invest a lot in training the machines and operating them correctly.
Tesla is known for its efforts in making eco-friendly products. However, they must be aware that their products are only as green as their electricity. Also, the manufacturing of their batteries and other components must be considered. One of the most important factors that companies can consider when addressing climate change is using renewable energy sources such as wind and solar (Sathish, 2019). It can help reduce their greenhouse gas emissions and improve the supply chain.
Tesla has a significant international presence and is currently penetrating various countries like China, the Netherlands, and China. It may face challenges in these regions due to the potential impact of political instability on its supply chain (Sathish, 2019). For example, in the past, there have been reports of strikes, protests, and other labor-related disruptions in Tesla’s major manufacturing facilities in China. If these events continue to occur, it could significantly impact Tesla’s supply chain. In addition, if there is a political event in the Netherlands, such as the current Dutch government crisis, it could also significantly impact Tesla’s supply chain. (For example, Tesla’s only car production plant in Europe has been in the Netherlands.)
In China, for example, Tesla recently encountered delays in the delivery of Model 3 vehicles from the factory to the distribution centers. The company’s ability to maintain its current production rate in growing demand is critical to its future operating margins. If the company cannot increase its manufacturing capacity, it will risk losing sales to its competitors, who can better manage their supply chains in the face of disruptions in their respective regions.
Implementing Artificial intelligence in Tesla
Artificial intelligence poses advantage in overcoming challenges in supply chain management. Applying AI in supply chain management plays an important role in optimizing supply chains. Tesla can implement artificial intelligence in the following ways:
First, Tesla can create a machine that can learn incrementally rather than being taught everything simultaneously. It would make the process more efficient since it does not have to take up time from the employees. The machine itself will be able to detect problem areas and fix them through its advanced algorithm (Pournader et al., 2021). Therefore, some experts believe that artificial intelligence is a better alternative to human labor because of its ability to solve problems independently and learn continuously.
Tesla has a massive database of big data on its suppliers that can be leveraged to form deep learning neural networks and algorithms for forecasting. The neural network will be able to identify patterns in these data sets and predict future events that may have a long-term impact on their supply chain. Artificial intelligence can also manage the supplier’s capacity utilization rate. It is possible through predictive analytics and machine learning. Tesla can use this when they encounter situations where they need to shift production from one supplier to another (Sathish, 2019). It would involve deep prediction of the future sales and demand for their products. Tesla’s opened an office in China to manage its supply chain operations there.
Tesla is working to improve its forecasting capabilities and reduce overstocking through more accurate planning, more advanced manufacturing, and more efficient logistics. Tesla is also experimenting with new business models and supply chain structures optimized for rapid production and delivery, such as just-in-time manufacturing and fully automated distribution. Tesla is also exploring new ways to utilize its massive network of suppliers and distribution centers, such as manufacturing and delivering parts directly to end customers. Tesla is working to improve its forecasting capabilities and reduce overstocking through more accurate planning, more advanced manufacturing, and more efficient logistics. Tesla is also experimenting with new business models and supply chain structures optimized for rapid production and delivery, such as just-in-time manufacturing and fully automated distribution. Tesla is also exploring new ways to utilize its massive network of suppliers and distribution centers, such as manufacturing and delivering parts directly to end customers. It would allow the company to reduce costs, improve service and increase the pace of innovation (Kumari & Bhat, 2021). It would enable the company to reduce costs, improve service and increase the rate of innovation.
Tesla is known for its eco-friendly approach to vehicle technology. As a result, the company has avoided using toxic chemicals and low-grade materials in the production of its cars, such as batteries, glass, and paint. These factors lead to the production of 100% recyclable and recyclable materials that can be used over again. It has helped the company achieve sustainable business practices important to environmentally conscious companies like Tesla.
Tesla’s “Master Plan, Part Deux,” is an example of its commitment to its environmental responsibility (Boggild, 2016). It talked about the company’s goal to rely on renewable energy to meet its electricity needs for manufacturing and energy storage. The company also expressed its plans to expand into other areas of sustainable energy, such as biotechnology and storage.
Tesla expressed its commitment to be a catalyst in global warming measures by reducing greenhouse gas emissions to zero, one of the objectives outlined in President Obama’s Climate Action Plan. In addition to its green approach, Tesla introduces smart technology in its vehicles for convenience purposes. It includes autonomous driving, allowing car owners to pick up and drop off their passengers without ever touching or interacting with them. It can save a lot of time spent on human interaction and increase the quality of life for people on the road all day.
Tesla has already proven that it can create sustainable products by introducing new technologies into the electric vehicle industry and is one of the fastest-growing car companies today. Tesla has invested in the development of manufacturing processes and is now concentrating on the research and development of new energy-efficient technologies that are crucial to the success of its electric car business.
The supply chain’s long-term viability
A few of the emerging technologies currently in use or under development are relevant to the supply chain. These technologies or trends include Artificial intelligence, Blockchain, Augmented reality and Virtual reality, and the Internet of things. All these technologies will benefit a supply chain in one way or another. And as these technologies develop and become more widespread, there will be many more ways to satisfy our supply chains today and in the future (Holdowsky & Hanley2018, 2018). These technologies can be applied in supply chain management and are described in the following sections. They will provide enhanced capabilities for tracking and managing products, improving efficiencies, automating processes, reducing costs, and increasing productivity.
We can get more out of our existing data sets and make them more useful to supply chain managers with Artificial Intelligence. The benefits include:
Speech recognition is used in the shipping industry. Companies like IBM use speech recognition systems to collate and analyze a large volume of voice data in real-time, for example, to monitor ship-to-shore communications and make them more efficient (Pournader et al., 2021). Speech recognition can also be used onboard ships or trucks as mobile phone apps, translating speech into English or other languages. It can be useful for drivers or seafarers who have difficulty reading maps or operating their phones with flags. It could save lives.
Computer vision (also called visual perception) is the science of controlling machines by feeding information from cameras into a computer system capable of analyzing and interpreting that information or handling some device according to what it sees through the camera lens. Computer vision is processing images, such as photographs, motion pictures, and other information from visual sensors to extract useful information or perform tasks based on what is being seen through the camera lens (Pournader et al., 2021).
Artificial intelligence can be used in supply chains by monitoring and analyzing data collected through the various sensors or devices attached to a supply chain. Certain products or parts could have sensors that monitor temperature, humidity, location, vibration, and movement. The introduction of AI into supply chains will be beneficial for several reasons. The first is that it can save resources by reducing the effort and human expertise required to perform certain tasks (Holdowsky & Hanley2018, 2018). AI algorithms can also be trained to handle different kinds of data sets, so they can do things that are impossible or very difficult with what humans alone could do. Artificial Intelligence will increase efficiency in supply chain management while reducing costs and increasing productivity.
The costs associated with Artificial Intelligence are mainly associated with the time taken to develop it, but they will be beneficial once created and put into use. They include a huge cost in the development of AI software. The initial investment in this can be as much or more than developing a new application for the software. The hardware will also be costly, especially for environments where Artificial Intelligence would run 24/7, so power costs and space are likely to increase (Sathish, 2019). Accelerated processing is another cost associated with Artificial Intelligence: to enable more complex tasks and complex data sets to be analyzed faster, more computing power and memory resources must be added to a system.
Certain externalities support AI technology adoption in supply chain management, including economics, environmental and societal, pollution reduction, natural environment preservation, how we use resources, and the 3R’s (Reduce, Reuse, Recycle). Societal and ecological perspectives on implementing AI tell us that AI requires the consideration of ethical and legal issues and how to address the impact on human capabilities. One example of how AI is currently being used negatively is in autonomous weapons systems. Another good example is AI being used in robotic cars, which can deliver goods without any assistance or input from a human driver (Holdowsky & Hanley2018, 2018). The ability to decide what to do with the car and where to go also improves efficiency.
Tesla should adopt the Sustainable Supply Chain Management framework, which focuses on the triple-bottom-line concept. This approach involves considering the various domains of the economy and the environment. The three pillars of this framework are risk management, transparency, and strategy & culture. Tesla has also applied the social and environmental performance framework.
Additionally, the company should avoid its suppliers who damage the environment and workers’ rights. Having a proactive supply chain system is very important for Tesla as it will help it manage its risks and improve its efficiency. It can also help it reduce its carbon footprint. It can also involve various stakeholder groups to improve performance.
The company should also implement a social and environmental performance framework and a self-publishing strategy. Tesla should also avoid its suppliers who damage the environment and workers’ rights. The quality management system of Tesla should be based on ISO 9001:2015 standard, which is a specific system for the development of an assurance process to develop goods or services that meet customer requirements.
Developing a culture that is aligned with sustainability initiatives is also a must. Tesla should also invest in developing new materials that can be used to produce more energy-efficient batteries. Doing so will help address the challenge of depleting the lithium reserves.
Tesla should also employ a supply chain management system to facilitate its sustainable practices. The company should improve its existing production capabilities and reduce the cost of manufacturing by producing low-volume models to avoid large losses. It will also help Tesla avoid direct advertising costs.
Tesla has implemented sustainable practices in many areas ranging from quality to their marketing strategies, and is continuously working on other areas such as their supply chain management system and environmental sustainability. Tesla should minimize its carbon emissions by switching to renewable energy. Switching to solar power will help reduce their carbon footprint, and it can be achieved through a service called Powerpacks. Through its subsidiaries, such as SolarCity, Tesla is working on creating an ecosystem that supports sustainable energy options (Boggild, 2016). It should also encourage its customers to use renewable energy sources to recharge their cars.
Tesla has already invested in Artificial Intelligence, Robotics, and the Internet of Things. These technologies will help improve the efficiency of its processes. The future of transportation will involve the use of autonomous vehicles. Out of 5 levels of autonomous cars, Tesla has already reached level 3 and is still pushing the envelope further. It contributes to sustainable practices that support the company’s growth (Sathish, 2019).
Tesla has expanded its services to include diverse areas such as energy storage and renewable energy, and hence, it has a diversified business portfolio. Implementing sustainable practices in these areas will help Tesla reduce costs while at the same time maintaining its profitability.
Tesla’s self-publishing strategy is another area where they are helping the environment. It enables them to communicate directly to their customers without spending too much on advertisement costs. Tesla must also adopt policies and procedures that complement its value chain strategies to be environmentally and socially conscious. Employees must be given priority as they are the company’s most lucrative asset (Sathish, 2019). Suppliers must also be involved in the loop between in-house manufacturers and customers to develop a holistic approach. It will help the company consider the three Ps: Planet People and Profit.
Tesla has a vertical integration and a horizontal supply chain that is unique and profitable. However, the company has experienced some bottlenecks in its supply chain framework stemming mainly from a lack of forecasting tools for smooth manufacturing, production, and delivery of its highly sought-after products. Adoption of Artificial Intelligence can avoid the listed bottlenecks and ensure that Tesla becomes a sustainable, environmentally and socially conscious company. There should be close collaboration between the production network’s partners, especially its strategic suppliers. Using the triple bottom line method to have a comprehensive view of the manufacturing network can benefit Tesla in the long run.
Boggild, L. (2016). Investors Watch Tesla. Alternatives Journal, 42(3), 11.
Holdowsky, J., & Hanley2018, T. (2018). The supply chain paradox. Deloitte Insights. Retrieved April 23, 2022, from https://www2.deloitte.com/us/en/insights/focus/industry-4-0/challenges-on-path-to-digital-transformation/supply-chain-paradox.html
Kumari, D., & Bhat, S. (2021). Application of Artificial Intelligence Technology in Tesla-A Case Study. International Journal of Applied Engineering and Management Letters (IJAEML), 5(2), 205-218.
Maverick, J. B. (2022, March 16). Who are Tesla’s main suppliers? Investopedia. Retrieved April 23, 2022, from https://www.investopedia.com/ask/answers/052815/who-are-teslas-tsla-main-suppliers.asp
Pournader, M., Ghaderi, H., Hassanzadegan, A., & Fahimnia, B. (2021). Artificial intelligence applications in supply chain management. International Journal of Production Economics, 241, 108250.
Sathish, S. (2019). Case Study of Tesla.
Financial Mathematics and Business Statistic 11 Financial Mathematics and Business Statistics Students
Financial Mathematics and Business Statistic 11
Financial Mathematics and Business Statistics
Determine the Economic Order Quantity given the data above
Economic order quantity = square root of [(2 x demand x ordering costs) ÷ carrying costs]
Q is the economic order quantity (units). D is demand (units, often annual), S is ordering cost (per purchase order), and H is carrying cost per unit.
At normal situation, the EOQ for the four scenarios are as shown above.
Produce sensitivity analysis assuming a change of up to 10% up or down on each of the factors individually and on all factors simultaneously.
Make a final recommendation to the board of the company, as to the number of units it should include in each order.
Considering the analysis done above, the company should consider the fast recovery scenario where it should be able to add 335 more units.
Formulate this problem as a linear program and use Excel’s Solver to arrive at a solution. Write a short report describing your procedure, justify your formulation and give a recommendation to the firm on the best daily production mix.
X1-1.8 litre engine
X2-2.0 litre engine
X3-2.5 litre engine
Objective Function is
The model seeks to maximize the profitability from the engine manufacturing plant given different product mix. The costs and all other expenses related to the production have been considered. From the model above, it’s clear that the company should not produce any of the 1.8 litre engines. Further for maximum profits, the company should produce 2223 units of the 2.0 litre engines and 2000 units of 2.5 litre engines.
What is the maximum John and Julia can borrow while taking advantage of the bank’s best mortgage rate;
Considering a joint salary, the total earnings will be 81,500 for the couple. Using an eligibility multiplier for this total, they will be able to qualify 3.25 times this amount that gives a total of £ 264,875.00.
The amount you advise them to borrow, given their financial and professional situation;
From the results shown above, the couple should consider going for the 2-bedroom mortgage as its just within the amount they can jointly qualify from the bank.
Which is the best mortgage that John and Julia to take out (assume they take out the amount you recommended in b);
Since the second option is the best for John and Julie whereby they will be required a total of 243,750 for the 2-bedroom, and the interest paid for the second option is a repayment fixed rate of 5 years of 2.34% whereby after that period the rates converts to the normal bank rates of 3.69%, the repayment fixed rate for 5 years is the best which is option 2.
Whether that advice would change if interest rates went up or down by up to three percentage points.
If the interest rates went up by 3 points, then the third option will be best for them whereby there will be interest only mortgage at 5% for the loan life where an investment fund needs to be created to pay an interest rate of 3.9% to cover mortgage repayment. Otherwise, if the rates go down by 3 points, Option 1 will be the best so that they pay a fixed rate of 1.89% for two years and then normal banks rates thereafter.
Discuss and compare the different types of investment appraisal methods Garnett can use, including a discussion of the advantages and disadvantages of each.
There are three main investment appraisal techniques that Garnett can use namely; Accounting Rate of Return (ARR), Payback Period, and Discounting Cashflow (Ye, 2008).
This method compares the expected profits from an investment to the sum needed for an investment. ARR is normally calculated on average per annum on the expected life of a project investment capital. This method has several advantages. The method is easier to calculate and one can understand the payback period. It also recognizes the concept of net earnings as well as facilitating the comparison for a new product and the reducing product by cost. It also gives a clear picture on the profitability and satisfies the interest of the investors. However, the method presents contradicting results when one calculates return on investment and ARR at the same time. Further, the technique doesn’t consider time factor as well as other external factors that affect profits.
A technique that is used for assessing an investment by considering the length of time for repayment. The method is simple to use and presents simple concepts to understand. The technique is used for smaller projects and to make quick evaluations in projects. However, the method tends to ignore the time value of money as it doesn’t consider any cash flows from an investment as well as focusing on short term profitability.
The method utilizes a discounting rate that is used to work on the current equivalent for a future cashflow. The method has two methods of discounting; Net Present Value (NPV) and Internal Rate of Return (IRR). The technique considers all the underlying factors of a project (cost of equity, average cost of capital, growth rate, etc.) and can determine the intrinsic value of the asset. The method also relies on the free cash flows an aspect that is considered to eliminate the accounting policies. Further, the method allows project investors to incorporate the changes for the project in the valuation model. However, this technique is extremely affected by any assumptions made and are related to growth and discount rates. Also, the method is only suitable for long-term projects.
If Garnett had a rule that all investment projects need to payback within 3 years, what project would be chosen? Comment.
Option A would be the most suitable since its able to give returns within that period (In the second year-as it recovers by the end of the first year).
Make a recommendation as to which project should be undertaken.
As shown in the Table above, project B is more profitable as it provides a long-term solution even though the initial capital investment is very high. The payback period for project B is 4 years. Thereafter there is increased sales annually. Option A provides a quick response but not long lasting.
If Garnett believes there is an opportunity to start exporting its product line to another country once sales are finished in its home country (i.e. from year 5), and it thinks it will be able to generate cash flows of £250,000 in the first year, £750,000 in the second and £1,250,000 in the subsequent four years, would your answer to part c) change? How? (Note: production can’t be further increased in the future if option B is chosen now)
Yes. This is because, with a small investment the cashflow is still realized even after the payback period is reached. Compared to Option B where sales are decreasing from year 7, the first project is more profitable considering the amount that was invested in.
Summarise the distribution of profits of the twenty branches and comment on the results, including identification of any particularly good or poorly performing branches.
From the results below, the mean profit is £43,530 while the maximum profit is £91,000 and minimum profit is £8,600. The total profit from the 20 branches is £870,000.
Identify whether there is evidence that the average number of lines stocked per branch is significantly different from 150.
Results as shown above reveal that the p-value is 0.021 which is less than 0.05 and therefore we reject the null hypothesis and then conclude that the average number of lines stocked per branch is not significantly different from 150.
Identify whether there is a significant difference between the profits of two groups of branches, split by the level of sales, with the threshold being £600,000.
Results as indicated in the table above show that the p-value is 0.451 and that means its greater than 0.05. therefore, we accept the null hypothesis and conclude that the profits of the groups of branches have a significant difference between the profits.
Based on this sample, provide a 98% confidence interval for the profits of the twenty branches and comment on the outcome.
Results as shown in the table above reveal that confidence level at 98% results to 14.23 interval for the 20 branches.
Ye, X. (2008). Property Investment Appraisal (3rd edition)20081Andrew Baum and Neil Crosby. Property Investment Appraisal (3rd edition). Oxford: Blackwell 2008. Journal of Property Investment & Finance, 26(6), pp.577-578.