50 words agree or disagree to each questions
Coming relatively close to estimating the demand for the product that you are releasing is crucial. Estimating the demand is vital to any business and in some cases it may be more crucial to smaller businesses however if you are dealing with a larger more internationally recognized company completely undervaluing or overvaluing the demand for a product could lead to the loss of millions of dollars for the company and investors. There will always be room for margin of error and you would think that people would be better at estimating the demand for their product correctly. It is not as simple as saying I am going to produce this amount and go from there. There are different markets and trends to consider in the different regions. This does not apply to all products but it does to a lot of them. Events going on around the world can affect the process in which companies use to product demand. COVID had this affect on NVIDIA, a computer graphics card designer.
The hype had been building for months about the new line of graphics cards (GPUs). The amazing processing power, the looks, the technology, the performance and one of the biggest things that caught a lot of people off guard was the MSRP of the new 30 series GPUs. The price of these cards dropped significantly from its predecessors which were in the thousand dollar price range. The new line MSRP started at $499, $699 and $1599. The $1599 may seem expensive and it is but that GPU is for the high end.
Like many products such as Lego and Peloton, new hobbies were picked up by the masses during the on going pandemic. People getting immersed into the world of online gaming was no different as more people spent more time at home they need more ways to occupy their time. The launch came and went with a staggering amount of people not being able to secure a card for themselves in store or online. There were many factors to this such as bot programs buying massive amounts of products to be sold later at triple the MSRP on Ebay and Amazon. But, one of the biggest blunders was on NVIDIA as they severely underestimated the popularity of PCs in today’s society and simply just did not make enough product to satisfy the masses and there is still no telling when more inventory will come on to the market to quell some of the outrageous prices third party vendors are charging.
For a business, knowing the demand of your products underpins every other decision to be made. Without accurate data on the demand at different price levels, managers will not be able to understand the elasticity of their product. Instead they will have to rely on estimation and chance when setting the selling price for that product. As we saw in last week’s lesson, missing by even a small margin on the price can lead to significant losses in profit. Managers will continue to count on their subjective analysis when establishing how much to produce of a product. Again, a small miscalculation can lead not only to lost profits, but also to lost confidence in the company if it fails to supply enough of its product to consumers. Relying on insight is considered an option for demand forecasting, though it is considerably less accurate than quantitative methods.
While quantitative methods are more accurate, they are not without their drawbacks. These analyses are often complex, which can make them both expensive and difficult to troubleshoot. Nike provides an example of a quantitative forecasting error that caused significant losses before the error was recognized. In 2001, the company implemented a new $400 million supply chain management system that would predict the demand of each product and coordinate the production and transportation of those products. The new system failed to accurately predict demand for the high-selling shoes, creating an undersupply of those shoes and an oversupply of its less popular shoes. By the third quarter of 2001, Nike reported $100 million in sales losses because of these mistakes. Not only was the supply management system expensive from the start, but its complexity prevented errors from being recognized early enough. Industry leaders that are trying to remain innovative will always have difficulty estimating demand for their products, so the existence of complex forecasting tools is to be expected. However, Nike should have taken a more calculated approach when implementing this new technology. Rather than adopting this technology for the entire company at once, Nike could have taken a step-by-step approach. While this would not have prevented the errors inherent in the tool, it would have allowed the company to catch these errors much sooner.
Lauden, J & Lauden, K. (2005). A New Supply Chain Project Has Nike Running for Its Life. London, UK: Pearson. Retrieved from: https://wps.prenhall.com/bp_laudon_essmis_6/21/5555/1422333.cw/content/index.html
Principles of Managerial Economics. (2012). Washington, D.C.: Saylor Academy. Retrieved from: https://saylordotorg.github.io/text_principles-of-managerial-economics/index.html
Samuelson W.F. & Marks S.G. (2015). Managerial Economics. Hoboken, NJ: Wiley & Sons Inc.
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