VRIO Analysis of Sample Size and Modeling Accuracy of Decision Tree Based Data Mining Tools

Posted by Zachary Edwards on Mar-22-2018

The VRIO Analysis of Sample Size and Modeling Accuracy of Decision Tree Based Data Mining Tools will look at each of its internal resources one by one to assess whether these provide sustained competitive advantage. The Sample Size and Modeling Accuracy of Decision Tree Based Data Mining Tools VRIO Analysis also mentions at each stage whether these resources could be improved to provide a greater competitive advantage. Lastly, the resources analysed are summarised as to whether they offer sustained competitive advantage, has an unused competitive advantage, temporary competitive advantage, competitive parity or competitive disadvantage.

Valuable

• The Sample Size and Modeling Accuracy of Decision Tree Based Data Mining Tools VRIO Analysis shows that the financial resources of Sample Size and Modeling Accuracy of Decision Tree Based Data Mining Tools are highly valuable as these help in investing into external opportunities that arise. These also help Sample Size and Modeling Accuracy of Decision Tree Based Data Mining Tools in combating external threats.
• According to the VRIO Analysis of Sample Size and Modeling Accuracy of Decision Tree Based Data Mining Tools, its local food products are a valuable resource as these are highly differentiated. This makes the perceived value for these by customers high. These are also valued more than the competition by customers due to the differentiation in these products.
• The Sample Size and Modeling Accuracy of Decision Tree Based Data Mining Tools VRIO Analysis shows that Sample Size and Modeling Accuracy of Decision Tree Based Data Mining Tools's employees are a valuable resource to the firm. A significant portion of the workforce is highly trained, and this leads to more productive output for the organisation. The employees are also loyal, and retention levels for the organisation are high. All of this translates into greater value for the end consumers of Sample Size and Modeling Accuracy of Decision Tree Based Data Mining Tools's products.
• According to the VRIO Analysis of Sample Size and Modeling Accuracy of Decision Tree Based Data Mining Tools, its patents are a valuable resource as these allow the firm to sell its products without competitive interference. This results in greater revenue for Sample Size and Modeling Accuracy of Decision Tree Based Data Mining Tools. These patents also provide Sample Size and Modeling Accuracy of Decision Tree Based Data Mining Tools with licensing revenue when it licenses these patents out to other manufacturers.
• The Sample Size and Modeling Accuracy of Decision Tree Based Data Mining Tools VRIO Analysis shows that Sample Size and Modeling Accuracy of Decision Tree Based Data Mining Tools’s distribution network is a valuable resource. This helps it in reaching out to more and more customers. This ensures greater revenues for Sample Size and Modeling Accuracy of Decision Tree Based Data Mining Tools. It also ensures that promotion activities translate into sales as the products are easily available.
• According to the VRIO Analysis of Sample Size and Modeling Accuracy of Decision Tree Based Data Mining Tools, its cost structure is not a valuable resource. This is because the methods of production lead to greater costs than that of competition, which affects the overall profits of the firm. Therefore, its cost structure is a competitive disadvantage that needs to be worked on.
• The Sample Size and Modeling Accuracy of Decision Tree Based Data Mining Tools VRIO Analysis shows that the research and development at Sample Size and Modeling Accuracy of Decision Tree Based Data Mining Tools is not a valuable resource. This is because research and development are costing more than the benefits it provides in the form of innovation. There have been very few innovative features and breakthrough products in the past few years. Therefore, research and development are a competitive disadvantage for Sample Size and Modeling Accuracy of Decision Tree Based Data Mining Tools. It is recommended that the research and development teams are improved, and costs are cut for these.

Rare

• The financial resources of Sample Size and Modeling Accuracy of Decision Tree Based Data Mining Tools are found to be rare according to the VRIO Analysis of Sample Size and Modeling Accuracy of Decision Tree Based Data Mining Tools. Strong financial resources are only possessed by a few companies in the industry.
• The local food products are found to be not rare as identified by Sample Size and Modeling Accuracy of Decision Tree Based Data Mining Tools VRIO Analysis. These are easily provided in the market by other competitors. This means that competitors can use these resources in the same way as Sample Size and Modeling Accuracy of Decision Tree Based Data Mining Tools and inhibit competitive advantage. This means that the local food products result in competitive parity for Sample Size and Modeling Accuracy of Decision Tree Based Data Mining Tools. As this resource is valuable, Sample Size and Modeling Accuracy of Decision Tree Based Data Mining Tools can still make use of this resource.
• The employees of Sample Size and Modeling Accuracy of Decision Tree Based Data Mining Tools are a rare resource as identified by the VRIO Analysis of Sample Size and Modeling Accuracy of Decision Tree Based Data Mining Tools. These employees are highly trained and skilled, which is not the case with employees in other firms. The better compensation and work environment ensure that these employees do not leave for other firms.
• The patents of Sample Size and Modeling Accuracy of Decision Tree Based Data Mining Tools are a rare resource as identified by the Sample Size and Modeling Accuracy of Decision Tree Based Data Mining Tools VRIO Analysis. These patents are not easily available and are not possessed by competitors. This allows Sample Size and Modeling Accuracy of Decision Tree Based Data Mining Tools to use them without interference from the competition.
• The distribution network of Sample Size and Modeling Accuracy of Decision Tree Based Data Mining Tools is a rare resource as identified by the VRIO Analysis of Sample Size and Modeling Accuracy of Decision Tree Based Data Mining Tools. This is because competitors would require a lot of investment and time to come up with a better distribution network than that of Sample Size and Modeling Accuracy of Decision Tree Based Data Mining Tools. These are also possessed by very few firms in the industry.

Imitable

• The financial resources of Sample Size and Modeling Accuracy of Decision Tree Based Data Mining Tools are costly to imitate as identified by the Sample Size and Modeling Accuracy of Decision Tree Based Data Mining Tools VRIO Analysis. These resources have been acquired by the company through prolonged profits over the years. New entrants and competitors would require similar profits for a long period of time to accumulate these amounts of financial resources.
• The local food products are not that costly to imitate as identified by the VRIO Analysis of Sample Size and Modeling Accuracy of Decision Tree Based Data Mining Tools. These can be acquired by competitors as well if they invest a significant amount in research and development. These also do not require years long experience. Therefore, the local food products by Sample Size and Modeling Accuracy of Decision Tree Based Data Mining Tools provide it with a temporary competitive advantage that competitors can too acquire in the long run.
• The employees of Sample Size and Modeling Accuracy of Decision Tree Based Data Mining Tools are also not costly to imitate as identified by the Sample Size and Modeling Accuracy of Decision Tree Based Data Mining Tools VRIO Analysis. This is because other firms can also train their employees to improve their skills. These companies can also hire employees from Sample Size and Modeling Accuracy of Decision Tree Based Data Mining Tools by offering better compensation packages, work environment, benefits, growth opportunities etc. This makes the employees of Sample Size and Modeling Accuracy of Decision Tree Based Data Mining Tools a resource that provides a temporary competitive advantage. Competition can acquire these in the future.
• The patents of Sample Size and Modeling Accuracy of Decision Tree Based Data Mining Tools are very difficult to imitate as identified by the VRIO Analysis of Sample Size and Modeling Accuracy of Decision Tree Based Data Mining Tools. This is because it is not legally allowed to imitate a patented product. Similar resources to be developed and getting a patent for them is also a costly process.
• The distribution network of Sample Size and Modeling Accuracy of Decision Tree Based Data Mining Tools is also very costly to imitate by competition as identified by the Sample Size and Modeling Accuracy of Decision Tree Based Data Mining Tools VRIO Analysis. This has been developed over the years gradually by Sample Size and Modeling Accuracy of Decision Tree Based Data Mining Tools. Competitors would have to invest a significant amount if they are to imitate a similar distribution system.

Organisation

• The financial resources of Sample Size and Modeling Accuracy of Decision Tree Based Data Mining Tools are organised to capture value as identified by the VRIO Analysis of Sample Size and Modeling Accuracy of Decision Tree Based Data Mining Tools. These resources are used strategically to invest in the right places; making use of opportunities and combatting threats. Therefore, these resources prove to be a source of sustained competitive advantage for Sample Size and Modeling Accuracy of Decision Tree Based Data Mining Tools.
• The Patents of Sample Size and Modeling Accuracy of Decision Tree Based Data Mining Tools are not well organised as identified by the Sample Size and Modeling Accuracy of Decision Tree Based Data Mining Tools VRIO Analysis. This means that the organisation is not using these patents to their full potential. An unused competitive advantage exists that can be changed into a sustainable competitive advantage if Sample Size and Modeling Accuracy of Decision Tree Based Data Mining Tools starts selling patented products before the patents expire.
• The distribution network of Sample Size and Modeling Accuracy of Decision Tree Based Data Mining Tools is organised as identified by the VRIO Analysis of Sample Size and Modeling Accuracy of Decision Tree Based Data Mining Tools. Sample Size and Modeling Accuracy of Decision Tree Based Data Mining Tools uses this network to reach out to its customers by ensuring that products are available on all of its outlets. Therefore, these resources prove to be a source of sustained competitive advantage for Sample Size and Modeling Accuracy of Decision Tree Based Data Mining Tools.

From the VRIO Analysis of Sample Size and Modeling Accuracy of Decision Tree Based Data Mining Tools, it was identified that the financial resources and distribution network provide a sustained competitive advantage. The patents are a source of unused competitive advantage. There exists a temporary competitive advantage for employees. There exists a competitive parity for local food products. Lastly, the cost structure of Sample Size and Modeling Accuracy of Decision Tree Based Data Mining Tools is a competitive disadvantage. Research and Development is also a competitive disadvantage.