This report will mainly analyze and provide detailed information on the performance of Huskie Motor Corporation (hmc) globally and in regions
Download 6.85 Mb.
|
|
Executive Summary This report will mainly analyze and provide detailed information on the performance of Huskie Motor Corporation (HMC) globally and in regions. HMC is an emerging company in the automotive manufacturing industry and it has built its outstanding brand name as well as high customer satisfaction. HMC's business performance was analyzed to be the most effective in North America, especially in the United States. However, there was a reverse shift in 2016 when operational efficiency in South America and Europe increased significantly while that in North America declined. The report also identifies opportunities and suggestions to help HMC improve previously discovered problems and enhance the company's profile. Finally, the issues related to ethical, privacy, security and legal data will also be covered. Table of Contents Executive Summary 2 Table of Contents 3 Introduction 4 Business Overview 5 Current Situations and Challenges 7 Performance and Competition by Regions 7 Profitability by Models 8 Changes in Performance by Regions over the years 8 Other challenges 9 Criticisms and recommendations 11 Ethical Issues 13 Conclusion 14 Reference List 15 Appendix 16 Appendix 1.1 - Average After-Tax Revenue, by Car Model 16 Appendix 1.2 - Total Revenue by Region/Country of Sale 17 Appendix 1.3 - Brand Performance by Sales Volume 18 Appendix 1.4 - Average Net Revenue by Sales Channel 18 Appendix 1.5 - Overall Performance Dashboard 19 Appendix 2.1 - Box and Whisker Plot of Contribution Margin 20 Appendix 2.2 - Total Variable Cost by Quarter 21 Appendix 2.3 - Percentage Contribution by Sales Channel 21 Appendix 2.4 - Minimum and Maximum Variable Cost by Model 22 Appendix 2.5 - Financial Analytics Dashboard 22 Appendix 3.1 - Total Sales Volume by Model 23 Appendix 3.2 - Average Days on Car Lot, by Model 24 Appendix 3.5 - Operation Analysis Dashboard 25 Appendix 4.1 - Four Quarters Sales Volume Forecast 25 Appendix 4.2 - Four Quarters Contribution Margin Forecast 26 Appendix 4.5 - Forecast Dashboard 26 Appendix 5.1 - Tariff Rate by Country of Sale 27 (<>)Appendix 5.2 - Comparison of Gross Sales between 2015 and 2016, by Country. (<>)28 Introduction The automobile industry, and corporations in general, have been accustomed to the formal use of complex forms of data for many years (Levinsohn 1994, p. 337). Using both their own personal customer datasets (Llopis-Albert, Rubio & Valero 2020, p.2), and industry-specific public datasets such as the “Automotive News Market Data Book” (Levinsohn 1994, p.337), they can create specialized analytics, that allow broad and powerful predictions about the futures of their own company, even before a Big Data mindset has been considered (Sheng, Amankwah-Amoah & Wang 2019 p.321). The automobile industry has also commonly been a source of innovation from its early days. The introduction of assembly lines and “Fordism” almost 100 years ago brought forward a new wave of ideas and innovations globally, revolutionising the labour market and society in general, similar to how Big Data affects the world today (Boyd & Crawford 2012, p.666). The need to follow along with the newest innovation of Big Data to stay relevant has also been recognized repeatedly by both researchers and businesses, adding to the sense of urgency (Peters, Chun & Lanza 2016, p.2). Business Overview The primary purpose of analyzing the data is to assist HMC to understand the current condition of the business and enable HMC to make the business decision and strategies more accurate to improve the performance. The analytics of data can be demonstrated as dashboards that indicate different business aspects. HMC needs to collect every data from selling details and information in each of the cars and use the data to establish the business model to predict the future performance of HMC. The performance of HMC can separate into three regions, which include North America, South America, and Europe. The Huskie Motor Corporation (HMC) is not a new start-up company; it came into existence as an offshoot, or “spinoff” of an existing automobile manufacturing corporation, Blue Diamond Automotive (Klepper 2002, p.649). This experience in the industry at a very high level, as well as the location of HMC in US, means that they would have a much greater understanding of data analytics than a newer car company in a weaker market, such as those overseas (Llopis-Albert, Rubio & Valero 2020, p.4, Peters, Chun & Lanza 2016, p.1). Being a relatively new company, albeit a spinoff of an existing one, they may also have production and capabilities models that are less outdated than their larger competitors. Spinoffs are also historically successful, if they are building off the expertise of their parent company (Klepper 2002, p.660). All of these factors, when put together, imply that HMC is in a great position to deal with the many intricacies involved with ‘Big Data’. However, in practice, this is not the case. In the early days of the company, the data remained manageable, despite having a decent size, or “volume” (Sheng, Amankwah-Amoah & Wang 2019, p.321), as well as some complexity. However, Big Data is not just about the volume of data, but the veracity, velocity, value, and other major conditions (Sheng, Amankwah-Amoah & Wang 2019, p.321). As HMC grew over time, the amount of data that came in every day accelerated, or the velocity increased, and the volume of it grew to be unmanageable. Similarly, the data that was coming in had issues with accuracy, or veracity, which often went unnoticed due to the size of the data, and the quality decreased (Boyd & Crawford 2012, p.669, Sheng, Amankwah-Amoah & Wang 2019 p.321). This unclean data hides some awkward truths about the business – from a basic sanitised data set, even the simplest analysis shows cars which, on average, are losing large amounts of money. There is low profits in multiple countries, many of which are presumably attempts at expanding the company to a global audience, which are not performing up to the usual standard of profitability of the US, and the North American region. This also includes attempts to enter the South American market, which is an issue as “South American market shares are decreasing” (Peters, Chun & Lanza 2016, p.1). There is also a sales forecast, and a contribution margin forecast that are stagnant for 3 quadrants, even with seasonal variation, before sharply plummeting Q4. These failings contrast strongly with their supposed strong brands and customer satisfaction and may be the key reasons why they are so popular – they are paying for popularity, and in doing so losing money on too many of the cars they manufacture and sell. Current Situations and Challenges Performance and Competition by Regions When evaluating HMC's operation by regions in general and countries in particular, North America was the area where HCM generated the highest operational efficiency (Appendix 1.2). Nearly 60% of HMC's total profit after tax came from three North American countries such as the United States, Canada, and Mexico. In particular, the United States was the country with the top amount of profit generated, accounting for 40% of the total. Ranked 2nd and 3rd in terms of profit were Canada and Mexico, with 9.40% and 8.17% respectively. When looking at the bar chart (Appendix 5.1), the leading after-tax profit position of the United States and Canada can be understood by the fact that the tax rates in these two countries were among the lowest, around 2.6%. Meanwhile, Mexico imposed a tax rate 3 times higher than that of the two previous countries, nearly 8%. Besides, HMC's performance in Europe seemed to be quite even when compared across countries. Profit after tax of HMC in each country ranged from 3.1% - 4.3% of the total. The region was also considered stable as the tax rates across the entire region are equal, exactly 2.1% (Appendix 5.1). Finally, South America was the country where HMC had the worst business efficiency, with 4 countries in this region listed Bolivia, Argentina, Venezuela, and Brazil had the lowest after-tax profit (Appendix 1.2). When HMC operated business in the South American market, although it had a lot of efforts to increase sales, the profit would not be as high as other regions due to the high tariff rate. The automotive manufacturing industry is highly competitive. Globally, there are many car manufacturers from Japan and Korea that are well-branded and occupy a large market share at mid-range prices such as Toyota, Honda, Hyundai, etc. While in Europe, German carmakers at a mid-to-high level such as Volkswagen and BMW also have a considerable market share. Although the USA was the nation where HCM had the most profitable business activities, there is a big competitor which is American branded and renowned - Ford. In the European and North American regions where taxes are low, the market would become extremely competitive when many brands could easily enter. This is probably a challenging problem for HMC to seek solutions and improve its operational efficiency. Profitability by Models In terms of performance by model, Advantage was the model that brought the highest sales as well as profit for HMC. This could be considered as a strategic product model of the company when the profitability of this product line (about 4 million dollars) was twice as large as the second most profitable model (Bloom) and eight times more than the model that had the lowest profit (Mortimer) (Appendix 1.1). Considering total sales, two models named Jespie and Mortimer were in the top models with the highest sales rates (13.2% and 8.5% respectively) (Appendix 3.1). However, it is seen that these were also the only 2 models that brought negative profits for the company. Their negative margins were all around -500 thousand dollars while the rest generated positive profits (Appendix 1.1). In contrast, Chare and Summet were two product lines with high gross profit (about 1.6 - 1.7 million dollars) (Appendix 1.1) but the sales rate was in the lowest group (just over 5%) (Appendix 3.1). The problem posed here is that profitable models Chare and Summet were not customer’s favourite products. Meanwhile, customers were extremely fond of models like Jespie and Mortimer which negatively affected the company’s overall profit. Changes in Performance by Regions over the years Appendix 5.2 shows HMC’s total gross sales over the years by regions and countries. During the period, the company always had the best business performance in North America. However, 2016 saw a significant decrease in the total gross sales in North America, compared to 2015. Specifically, the total gross sales in the two countries Canada and Mexico in 2016 was only about 50% as in 2015. Contrastly, Europe and South America experienced an increase in the company’s business performance across nations. Especially this figure in Europe doubled from 2015 to 2016. The tax rate in North America is considered low, so the competition in this area would be extremely high as more and more famous automotive firms from Asia or Europe intend to enter. The drop in HMC’s North American sales in 2016 showed a negative sign of business performance. Addressing this challenge requires the company to review and change its operations strategy in the region immediately. Besides, although there had been significant growth in business operational efficiency in Europe and South America, the political and economic instability of the countries in the region is also a matter of concern. More specifically, the separation of the UK from the European Union (EU) could also seriously affect the output of the automotive industry and cost hundreds of millions of dollars. According to CNN, in the first half of 2019, car production in Europe decreased by 20%, while investment in the industry plunged 70%. Also, two countries in South America named Venezuela and Argentina involve in many problems of political instability, economic crisis, or serious inflation. Therefore, the challenge poses for HMC is to constantly consider and change its operational strategy in unstable areas to ensure the company’s business performance as well as to avoid negative effects when political changes happen. Other challenges Lack of employees skilled in big data: The fact in case study that HMC was hiring R&D for big data governance shows that the company lacks personnel with high expertise in big data analysis and data visualization. To develop the company’s performance by generating insights from big data, companies need highly skilled professionals in the field. The 2017 Robert Half Technology Salary Guide reported that big data engineers were earning between $ 135,000 and $ 196,000 on average, while data scientist salaries ranged from $ 116,000 to $ 163,500 (Fosster.com, 2017) This means that HMC would have to increase their operating costs and their recruitment to meet human resources for big data analysis. HMC should also invest in purchasing more analytics solutions with self-service or machine learning capabilities and offer additional training programs for current employees in the attempt to develop talent that the company needs. However, HMC currently lacks all the above solutions. Dealing with data growth: One of the most obvious challenges when working with big data is storing and analysing that huge scale of data, especially for companies that are new to the industry like HMC. It has been estimated that the amount of information stored in IT systems globally will double every two years (Loskin, 2013). Much of that data is unstructured such as audio, images, and videos which requires a lot of effort to search and analyse. Criticisms and recommendations A concern regarding HMC’s dataset is what has been left out. Even in the unclean state it was delivered in, it lacked important details and specifics, which are very important for analytics. For example, the location data in the transactional database we are given is only country level. This is ok for certain, smaller countries in the dataset such as Chile or Poland. But for larger countries with more dynamic and diverse populations such as the US and Brazil, the amount customers will pay, what cars are bought, and other details such will vary wildly depending on the area of the country. Even smaller countries will have these variations. The inability to analyse these specific minor variations are especially worrying, considering the US is one of the most diverse marketplaces globally and has most of HMC’s profit/sales, yet is treated as a single location. This regional variance is crucial for comparing what works and what does not, especially at higher levels and systems of the HMC (Llopis-Albert, Rubio & Valero 2020, p.8). Going further, although a possible overreach for the tactical levels of the organisation, the dealers themselves, or the equivalent for sales that are not retail, are not listed in the dataset. This is important to note, as there is a large discrepancy between net revenue purely by sales channel, that may be explained by some more detailed data. Even within one sales channel and one country, there may be large disparities between the performance of individual retail dealerships, that warrant further investigation. Continuing with HMC’s potential issues with their transactional data, transactions do not end after the initial purchase. There are still mechanical faults, car check-ups, and many other transactions to note both ongoing expenses and profits with a car model, as well as the model’s reliability over time. These indicators may show a different side to a car model – one that may be profitable being sold, but is unprofitable due to low reliability and systemic faults, or one that is slightly unprofitable on sale, but lasts for years and can be profitable through the many compulsory services it will undergo. These post-purchase transactions are also going to be linked to the many sources of data HMC should theoretically have, such as manufacturing and distributing data, but their omission from the transactional data is worrying, and implies more systemic gaps in the organisation’s data collection strategy. One of the most important aspects of Big Data is semi-structured and unstructured data (Sheng, Amankwah-Amoah & Wang 2019 p.322). Although the transactional data is unclean and messy, it is still heavily structured data. A cleaned transactional dataset with sanitised data entry systems, especially after an extended period of consultancy and data sanitisation, may even cease to be considered as ‘Big Data’, having lost many of the current problems that make it ‘Big Data’, and becoming a part of the operational standard. Levinsohn (1994, p.337), mentions that an important facet of data, even before the concept of Big Data became mainstream, is consumer-level data. This often comes in the form of reviews, and customer surveys, or the presumed reliability rating of cars. A simple survey, to be done at the time of sale, would add many more elements of data to analyse, and provide key insights as to how the company is perceived directly by clients, on a country and brand basis. Going further, a full dataset related solely to customer surveys and other similar methods, of getting semi-structured and unstructured data about the company from users, would offer unprecedented predictive capabilities if utilized and analysed correctly. Importantly, it would allow HMC to prove that customer satisfaction is one of their key strengths, measure the popularity of their brands upon clients, and strive towards maintaining those key corporate advantages. Admittedly this is a large endeavour, and it may be worth considering a strategic alliance with one of the many consumer-level survey companies that exist, to have them collect the data and help the organisation analyse it. Ethical Issues Being an automobile industry, Orsato and Wells (2007) mentioned that ethical issues could be one of the most significant elements to keep the business sustainable. Ethical issues would relate to both customers and employees to deal with in different aspects. Firstly, HMC requires making sure the environment of the workplace is safe and suitable for employees working. Also, to give the employees a reasonable salary, superannuation, and vacation are necessary. Moreover, the ethical issues related to the customer include advertising, the price of the cars and components, and the repair price. All of the prices should be reasonable for every customer without any bias. Furthermore, collecting data is one of the necessary actions for HMC in the business, which means it is HMC's responsibility to protect customers' personal information. To avoid privacy and security issues, HMC needs to keep updating the security systems of both software and hardware to make sure the data can be protected in the systems. The repairing standard, along with more and more cars on the road, Wang et al. (2019) stated the emission of cars' gas had caused significant pollution for the environment. This would cause the circumstances of affecting human's health and living quality. Therefore, HMC needs to follow the standard of repairing each car and ensure all the vehicle reaches the gas emission standard before driving on the road. Conclusion In conclusion, HMC has done decent performance overall in the market. The analytic has given the challenges that HMC will need to face and overcome. The data analysis details in the dashboards would provide more understanding and knowledge for HMC to develop better business strategies and decision-making in the future step. Also, the recommendation will assist HMC to manage the aspects of addressing the data issues and gain market advantage through the data. Reference List 1. Teli, S., Majali, V., Bhushi, U. and Surange, V., 2014. Impact of poor quality cost in automobile industry. International Journal of Quality Engineering and Technology, 4(1), p.21. 2. Orsato, R. and Wells, P., 2007. The Automobile Industry & Sustainability. Journal of Cleaner Production, 15(11-12), pp.989-993. 3. Fosster.com. 2016. 2017 Salary Guide For Technology Professionals. [online] Available at: 4. Sheng, J, Amankwah-Amoah, J & Wang, X 2019, "Technology in the 21st century: New challenges and opportunities", Technological Forecasting and Social Change, vol. 143, pp. 321-335. 5. Wang, J., Wu, Q., Liu, J., Yang, H., Yin, M., Chen, S., Guo, P., Ren, J., Luo, X., Linghu, W. and Huang, Q., 2019. Vehicle emission and atmospheric pollution in China: problems, progress, and prospects. PeerJ, 7, p.e6932. 6. Boyd, D & Crawford, K 2012, "CRITICAL QUESTIONS FOR BIG DATA", Information, Communication & Society, vol. 15, no. 5, pp. 662-679. 7. Klepper, S 2002, "The capabilities of new firms and the evolution of the US automobile industry", Industrial and Corporate Change, vol. 11, no. 4, pp. 645-666. 8. Peters, S, Chun, J & Lanza, G 2016, "Digitalization of automotive industry – scenarios for future manufacturing", Manufacturing Review, vol. 3, no. 1, pp. 1-8. 9. Llopis-Albert, C, Rubio, F & Valero, F 2020, "Impact of digital transformation on the automotive industry", Technological Forecasting and Social Change, vol. 162, no.120343, pp. 1-9. 10.Levinsohn, J 1994, "International trade and the U.S. automobile industry: Current research, issues, and questions", Japan and the World Economy, vol. 6, no. 4, pp. 335-35 11.Loskin, D., 2013. Big Data Analytics. Waltham, Mass.: Morgan Kaufmann. Appendix Appendix 1.1 - Average After-Tax Revenue, by Car Model Appendix 1.2 - Total Revenue by Region/Country of Sale Appendix 1.3 - Brand Performance by Sales Volume Appendix 1.4 - Average Net Revenue by Sales Channel Appendix 1.5 - Overall Performance Dashboard Appendix 2.1 - Box and Whisker Plot of Contribution Margin Appendix 2.2 - Total Variable Cost by Quarter Appendix 2.3 - Percentage Contribution by Sales Channel Appendix 2.4 - Minimum and Maximum Variable Cost by Model Appendix 2.5 - Financial Analytics Dashboard Appendix 3.1 - Total Sales Volume by Model Appendix 3.2 - Average Days on Car Lot, by Model Appendix 3.5 - Operation Analysis Dashboard Appendix 4.1 - Four Quarters Sales Volume Forecast Appendix 4.2 - Four Quarters Contribution Margin Forecast Appendix 4.5 - Forecast Dashboard Appendix 5.1 - Tariff Rate by Country of Sale Appendix 5.2 - Comparison of Gross Sales between 2015 and 2016, by Country. Download 6.85 Mb. Share with your friends:
|