Nissan’s vision and mission statements
Discuss about the Artificial Intelligence and Data Science.
Singapore is the most compromising city in Asia on the sector of automobiles. Larger companies of Asia expand their automobile businesses in Singapore. Amongst the different automobile companies Nissan is one the largest brands in Singapore.
The vision statement of Nissan is “Enriching people’s lives, building trust with our employees, customers, dealers, partners, shareholders and the world at large.”
The mission statement of the organization “To provide unique and innovative automotive products and services that deliver superior measureable values to all stakeholders in alliance with Renault.”
A strategic objective of the organization is “to achieve sustainable, profitable growth.”
The three charts represent
- The average amount spent by customers over the six quarters
- The average amount spent by the customers in different brands
- The difference in average amount spent and average revenue earned by different brands
Thus, from the three charts, the management can assess the economic performance of the organization based on the demography, race and marital status of the customers.
From the three charts the management gets an overview of the amount spent by the customers in the five brands over a range of time. This information can be used by the management in understanding customer preferences for the different brands. From the charts we find that the average amount spent by the customers on “Note” is higher than all other brands of cars. We also find that the average revenue earned is also higher for “Note” which is essential in having a sustained growth of the vehicle.
Filters in the dashboard can narrow down how the amount spent and revenue generated by the organization is spread across gender, race and marital status of the customers. In the side panel we find three filters, one each for gender, marital status and race. In each of the filters there are various parameters. When a particular parameter is selected then the average amount spent and revenue generated by the requested parameter is shown. For example, let’s say we want to view the average amount spent and revenue earned during the period for females. We would unselect the males in the gender filter. The dashboard would change to reveal the average amount spent and revenue earned for the period for females. With the help of filters, we can get the data for each segment, which would aid the management in taking decisions.
To build the dashboard the title block was used. Next the three sheets were imported. The first sheet was placed on top. The second and third sheet were placed below. The filters were imported from the sheet. The filters were assigned as global filters. Assigning the filters to be global enabled the use filtering to be shown on all the charts simultaneously.
The current trend of automotive industry is going in the direction of electronics and software upgradation of automobile industry. Manual testing of automotive parts such as carputers and Bluetooth technology are being less popular in vehicle technology. As the outcome of global trend, the automotive industry in Singapore is hampered. It shows the increment of demand for software and hardware engineering. Purchases and sells of new cars of Singapore Automobiles provide car financing and exports used cars. They combinedly deals with automobile designer, production designer, driver instrumentation designer, quality engineering, automotive technician along with supplier warranty recovery specialist.
Charts for customer preferences and segment data
In data mining process, four levels form a framework in which it is probable to categorize data analysis competence and potential benefits for a company in general. The Cross-Industry Standard Process for Data Mining (CRISP-DM) involves no optimization or decision-making. Based on the business understanding, data understanding, data preparation, modelling and evaluation sub-steps, CRISP tends directly to the disposition of outcomes in business methods. Automobile industry propose an additional optimization step that comprises multi-criteria optimization and decision-making support.
Cross value chain analytics is divided in four categories that are:
- Customer behaviour analytics
- Market mix analytics
- Supply chain optimisation analytics
- Predictive quality analytics
CRISP model deals with huge iterative approach used by data experts to manually analyse data. The big data analysis reflects the iterations between business understanding and data understanding. It also controls data preparation and modelling. It evaluates the modelling interpretation of relevant application experts in evaluation step. The fundamental idea of the analysis is that the models can be derived from data with the support of flowcharts and algorithms. This modelling process can run automatically in most of the analytical prospect.
The data mining approach is depicted from the sensors and integrated into the data management system. The companies could forecast the models for the system’s relevant results in terms of quality, variance of process and deviation from target value. Machine learning options could be utilised within the context for predicting outputs of system.
Data-mining approach along with semantic network tools demonstrates market structure, perceptual maps and meaningful insights. A comparison between a market structure based on user-oriented content data with a market structure achieved from more traditional sales and survey-based data for establishing authentic differences.
In automobile industry, the production, marketing, sales and retail optimization must be accessible and helpful for connecting customer. Using software, the analysis of customer and manufacturer occur in the consecutive process of data analysis, analytical knowledge and action. Data mining in simulation data is very critical and at best the object of tentative research approaches at this time.
The automobile industry requires to get learned about the things for which production organisation interpreted. In this approach, utilization of evolutionary algorithms for simulation plausible limited to the probable combinations that can be structured. The needed computing power is available and the inclusion of variables in decreased. The conclusion eliminates the limitations of analytical data processing, monotonous activities and domain of decision-making. It helps to forecast such quality errors and utilise optimizing analytics for decreasing the occurrence. CAD models and simulations typically of technical methods maintain manufacturing in multi-disciplinary optimization.
However, data mining has its own disadvantages such as privacy, security and misuse of information. The issue of personal privacy is being enormously increased as the internet is booming with social cites and e-commerce. Because of the lack of personal privacy security is also at high risk. Lots of cases regarding hacking of big data of customers is assumed to be a big problem.
Automobile companies work towards the requirement of customers and provide them with quality services with top priority. They serve car servicing, repair and replacement of parts, accident claimed insurance and advise to maintain cars to our clients. Another prominent improvement within the automobile industry is the enhancement about the concerns of environment that leads to lower carbon emissions. This attempts the boosting of fuel efficiency by manipulating the weight of vehicles through its materials. Hence, automotive companies should strike a proper balance between standard safety measures, compressed energy consumption and responsibility of environment.
Data filters for gender, race, and marital status
The most popular text mining process of extracting necessary and supreme-quality information is text mining. It is an analysis where data is contained in natural text language. With the help of it, unstructured textual data through the identification and exploration is identified. Text mining is also referred as text data mining. Acquiring information from text is a suggested field of research to the automobile industry.
The text mining procedure targets to advertise technological companies in the platform of data management, customer retargeting, demand-side platforms and cross-device advertising. Unstructured data analysis helps statistical modelling and machine learning techniques in text mining. It could help unorganised data set to be connected with structed data in a database. An automobile organization can appropriately utilise text analytics for achieving content-specific values. Text analytics technology is still considered to be an upbringing technology.
Automobile industry developers and significant stakeholders certainly has become big game changer. As big data permeating in our day to day lives, there has been prominent transformation of focus from the typical surrounding to its real value. Big data analysis provides better understanding, behaviour and preferences of the customers. Automobile companies stay interested to expand traditional data sets with social media data, browsing history, text analysis and sensor data for getting a complete picture of the consumers. The main objective is to establish predictive models.
Big data analysis has taken a vital role in the branch of engineering that deals with designing, manufacturing and operating automobiles. Government agencies, businesses, consumers, data storage providers and data aggregators in Automotive engineering industry are taking support of big data analysis.
The companies maintain their target marketing and enhance business operations by taking inferences from big data analysis. It helps to enhance talent retention and make customer experience more efficient. In automobile industries, big data analysis helps to detect fraud and identify individual customer trials too. Overall it can be seen that by applying big data, operational engineering data, manufacturing organisations can identify faulty equipment and regulate optimal control parameters. By data mining, the marketing companies build models based on historical data for estimating the new marketing campaign. With the help of market basket analysis, an organisation can arrange proper prediction in a way that customer can buy automobile products.
- Big data analysis helps to achieve the strategic objectives:
Many objectives of big data analysis use the new tools required a term that distinguish the previous technologies. It is the best possible way to analyse huge amount of organisational data for managing critically. Strategically, big data companies extend their foot by enhancing decision-making capabilities. It suggests to gain an edge over their competition. Automobile industries continuously seek experts with a big data certification for the prosperity of their company. The analysts are likely to have the efficiency and the expertise for analysing huge datasets.
The strategic focus of an automobile company is to maintain problem solvers that study large data streams and establish automated systems for recovering data. The companies maintain their businesses usually to have data in various forms of market research, logistics, sales figures and communication costs. A data analyst uses the big data and creates insights for enhancing decision-making. Database management manages the database of company and sort out day to day issues. A database administrator understands the latest technologies and criterion.
In a company, a data scientist should be business oriented with the skills to conduct effective data evaluations. They should also be capable to make recommendations about increasing trends and advise companies on the needed actions. As per strategy:
- Companies should ensure the database tools and services remain active throughout their use.
- Project managers of automobile companies must monitor data compilation and make sure that it happens accordingly with legal regulations.
- A particular automobile industry should make sure that the data remains backed-up safely.
- The automobile organisations should check the data entry methods and help to build new databases. Data Description:
The data set consists 9 variables. These are CustID, Age, Gender, MaritalStatus, Race, Date, Amount, Product and Revenue. The age, Amount, Product and Revenue are numerical variables whereas others are categorical variables.
What is text mining (text analytics)? – Definition from WhatIs.com. (2018). SearchBusinessAnalytics. Retrieved 10 March 2018, from https://searchbusinessanalytics.techtarget.com/definition/text-mining
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Mine Your Own Business: Market-Structure Surveillance Through Text Mining | Marketing Science. (2018). Pubsonline.informs.org. Retrieved 10 March 2018, from https://pubsonline.informs.org/doi/abs/10.1287/mksc.1120.0713
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