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VRIO Analysis of Improving Strategic Execution With Machine Learning
Posted by Zachary Edwards on Mar-22-2018
The VRIO Analysis of Improving Strategic Execution With Machine Learning will look at each of its internal resources one by one to assess whether these provide sustained competitive advantage. The Improving Strategic Execution With Machine Learning 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 Improving Strategic Execution With Machine Learning VRIO Analysis shows that the financial resources of Improving Strategic Execution With Machine Learning are highly valuable as these help in investing into external opportunities that arise. These also help Improving Strategic Execution With Machine Learning in combating external threats.
- According to the VRIO Analysis of Improving Strategic Execution With Machine Learning, 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 Improving Strategic Execution With Machine Learning VRIO Analysis shows that Improving Strategic Execution With Machine Learning'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 Improving Strategic Execution With Machine Learning's products.
- According to the VRIO Analysis of Improving Strategic Execution With Machine Learning, its patents are a valuable resource as these allow the firm to sell its products without competitive interference. This results in greater revenue for Improving Strategic Execution With Machine Learning. These patents also provide Improving Strategic Execution With Machine Learning with licensing revenue when it licenses these patents out to other manufacturers.
- The Improving Strategic Execution With Machine Learning VRIO Analysis shows that Improving Strategic Execution With Machine Learning’s distribution network is a valuable resource. This helps it in reaching out to more and more customers. This ensures greater revenues for Improving Strategic Execution With Machine Learning. It also ensures that promotion activities translate into sales as the products are easily available.
- According to the VRIO Analysis of Improving Strategic Execution With Machine Learning, 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 Improving Strategic Execution With Machine Learning VRIO Analysis shows that the research and development at Improving Strategic Execution With Machine Learning 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 Improving Strategic Execution With Machine Learning. It is recommended that the research and development teams are improved, and costs are cut for these.
Rare
- The financial resources of Improving Strategic Execution With Machine Learning are found to be rare according to the VRIO Analysis of Improving Strategic Execution With Machine Learning. 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 Improving Strategic Execution With Machine Learning 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 Improving Strategic Execution With Machine Learning and inhibit competitive advantage. This means that the local food products result in competitive parity for Improving Strategic Execution With Machine Learning. As this resource is valuable, Improving Strategic Execution With Machine Learning can still make use of this resource.
- The employees of Improving Strategic Execution With Machine Learning are a rare resource as identified by the VRIO Analysis of Improving Strategic Execution With Machine Learning. 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 Improving Strategic Execution With Machine Learning are a rare resource as identified by the Improving Strategic Execution With Machine Learning VRIO Analysis. These patents are not easily available and are not possessed by competitors. This allows Improving Strategic Execution With Machine Learning to use them without interference from the competition.
- The distribution network of Improving Strategic Execution With Machine Learning is a rare resource as identified by the VRIO Analysis of Improving Strategic Execution With Machine Learning. This is because competitors would require a lot of investment and time to come up with a better distribution network than that of Improving Strategic Execution With Machine Learning. These are also possessed by very few firms in the industry.
Imitable
- The financial resources of Improving Strategic Execution With Machine Learning are costly to imitate as identified by the Improving Strategic Execution With Machine Learning 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 Improving Strategic Execution With Machine Learning. 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 Improving Strategic Execution With Machine Learning provide it with a temporary competitive advantage that competitors can too acquire in the long run.
- The employees of Improving Strategic Execution With Machine Learning are also not costly to imitate as identified by the Improving Strategic Execution With Machine Learning VRIO Analysis. This is because other firms can also train their employees to improve their skills. These companies can also hire employees from Improving Strategic Execution With Machine Learning by offering better compensation packages, work environment, benefits, growth opportunities etc. This makes the employees of Improving Strategic Execution With Machine Learning a resource that provides a temporary competitive advantage. Competition can acquire these in the future.
- The patents of Improving Strategic Execution With Machine Learning are very difficult to imitate as identified by the VRIO Analysis of Improving Strategic Execution With Machine Learning. 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 Improving Strategic Execution With Machine Learning is also very costly to imitate by competition as identified by the Improving Strategic Execution With Machine Learning VRIO Analysis. This has been developed over the years gradually by Improving Strategic Execution With Machine Learning. Competitors would have to invest a significant amount if they are to imitate a similar distribution system.
Organisation
- The financial resources of Improving Strategic Execution With Machine Learning are organised to capture value as identified by the VRIO Analysis of Improving Strategic Execution With Machine Learning. 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 Improving Strategic Execution With Machine Learning.
- The Patents of Improving Strategic Execution With Machine Learning are not well organised as identified by the Improving Strategic Execution With Machine Learning 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 Improving Strategic Execution With Machine Learning starts selling patented products before the patents expire.
- The distribution network of Improving Strategic Execution With Machine Learning is organised as identified by the VRIO Analysis of Improving Strategic Execution With Machine Learning. Improving Strategic Execution With Machine Learning 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 Improving Strategic Execution With Machine Learning.
From the VRIO Analysis of Improving Strategic Execution With Machine Learning, 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 Improving Strategic Execution With Machine Learning is a competitive disadvantage. Research and Development is also a competitive disadvantage.
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