Sports Obermeyer Case Study week 1 Assignment PDF

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A descriptive paper on the Sport Obermeyer lack of meeting customer demand because of forecasting issues. the case study breaks down the questions into short answer give the reader the necessary tools to solve the problems....

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Sport Obermeyer

Santos J. Soto School of Business, Liberty University

Author Note Santos J. Soto I have no known conflict of interest to disclose. Correspondence concerning this article should be addressed to Santos J. Soto Email: [email protected]

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Understanding the importance of forecasting is an essential tool for the success of a company. Inaccurate forecasting can be any company's worst nightmare if not done correctly. Forecasting goods in real-time (daily and weekly) and their impact on the supply chain has become critical management and policy-making requirement (Nikolopoulos et al., 2021). Sport Obermeyer, a merchandising and skiwear company, had a problem when it came to making a product decision. Deciding which style to make could be better if done in a market that is more accepting of said style. The question for Sport Obermeyer owner Walley to ask is, which style should be made in Hong Kong and which will be made in China? Selecting the style will need careful consideration and thorough analysis to ensure an improved operation when selecting a location of which style will to which country. In addition to the analysis, the company will have to develop a forecasting method. An update to the inaccurate forecast system they have currently will help the company better identify the customers' needs and improve the excess merchandise. The excess merchandise that Sport Obermeyer has been sold at a deep discount, which resulted in a substantial loss of profit for the company. If Sport Obermeyer wants to be successful, it will need to determine its production plan. Through this case study, Sport Obermeyer will identify its production plan during the initial phase, measure the risk of ordering policy, recommend an operational change, identify differences between Hong Kong and China, and lastly, look into a long- or short-term sourcing for both countries. Recommendation of Style Based on the information given in exhibit 10 of the case study, a recommendation will be made to Walley on what style he should make during the initial phase of the production. To

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understand the optimal order quantity for Sports Obermeyer, the method that should be used is the new vendor model (NVM). According to Simchi-Levi et al. (2021), this model is named after a newspaper vendor who encountered a problem where he had to decide how many papers to order to satisfy that day's demand. The NVM tackles the multi-item inventory optimization problem with a capacity restriction, and estimates demand using deep learning, random forest, and time-series methods (Punia et al., 2020). Using this model, Wally can make the necessary recommendation of what style should be made during the initial production phase. In the case of Wally, recommending the number of units per style during the initial production would follow the model. To compute the optimal order quantity for this question, Wally needs to find the production order for each style, knowing that the total for everyone is 10,000 units. To get that number, Wally will need to find each inventory cost "c" unit and then determine how much it can be sold for price "p." If the cost for one of the parkas in exhibit 10, e.g., the Gali, was a wholesale price of $110 and the company were expected to make a 24% profit on the parka the gain would be $26.40, and its expected loss for each park left would have been 8% of the $110 would equal $8.80. to figure the optimal order for the Gali, take the cost of selling $110 minus the cost of production $83.60. using the formula =NORMINV (0.836,1200,194) would give an optimal order of the Gali of EOQ 1389.761156 or 1400. Measure of Risk A couple of risk measures associated with the ordering policy can be considered in this situation. According to Simchi-Levi et al. (2021), the first one that could be used is the standard of deviation, which is a measure of the demand that tends very around the average. They also identified the Coefficient of Variation (CV), "which is the standard deviation to the average demand" (p.59). CV merely evaluates the variability relative to the average demand, enabling

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them to compare the product at different demand levels. In this case, the CV would be the best measure of risk in the ordering policy. Because the CV is the most used method of measuring relative dispersion, it has the most application in many areas, especially in economic and finance (Arachchige et al., 2020). The CV will tell Wally the difference in the mean data for each parka, providing him and the organization with a more realistic picture of demand for that season. In the Las Vegas trade show, the company had secured 80% of its annual volume with retailers. With the retailer placing an order, Sports Obermeyer could easily forecast the demand with greater accuracy. Using the CV can calculate the different parkas with the highest risk and ensure that it falls within the forecasted demand. The company would need to ensure that future orders should be saved because of the high amount of deviation. This can help if the demand is to rise in the future. The company would have a safety cushion to be able to meet future demand. The Methodology Looking at the production between China and Hong Kong, it is easily identifiable that Hong Kong production was much faster than China. The minimal production for parkas by China is 1200 units, while Hong Kong is 600 units. Therefore, if Wally analyzes this situation, we would have to look at it with a different economic lot size model (ELS). According to Simchi-Levi et al. (2021), Ford W. Harris created the standard economic lot size model in 1915, a simple model that demonstrates the trade-offs between ordering and storage expenses. Looking at the question that was asked to Wally, ELS would be the best method to use as it will help develop effective policies ensuring inventory demands are met. According to Gharaei et al. (2020), ELS problems have gotten much attention since they are the foundation for pushing the boundaries of knowledge in the supply chain (SC). ELS issues may be successfully managed by increasing cooperation among SC levels. They are adjusting the EOQ for each parka made in

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Hong Kong with the value of 600 units on all orders. Also, for China, the company would have to adjust the EOQ for every product and increase the minimum 1200 units. When looking at the exhibit, the difference between the countries is noticeable when it comes to EOQ. What are these differences that they have? The difference is very noticeable between the two countries. In Hong Kong, the workers are significantly paid more and are much more skillful than in China. They have a more effective and efficient production method, which steamrolls China's production line. China is much more likely to have its workers less motivated to achieve its goals because of their pay. That can affect the production of order quantity because of the lack of morals. This is shown in the case study when the order is much lower in Hong Kong than in China. Besides the level of risk, Sports Obermeyer should focus on its initial orders during the beginning phase because of the lower risk it faces. Once the first order is out, the company would need to recalculate its forecast to ensure the reactivate product capacity. Recommendation on Possible Risks Sport Obermeyer has had several problems affecting its company's business for some time. Companies, such as Columbia, have taken 23% of the adult market on sports jackets compared to 11% on ski wear for Sport Obermeyer (Simchi-Levi et al., 2021). These problems can stem from can come from the company's inability to forecast. Forecasting differences among different members of the management team was an obvious problem. A recommendation that can be given to the company is better training on its forecast. That said, forecasting is all wrong, so it is impossible to ensure accurate forecasting (Simchi-Levi et al., 2021). However, forecasting plays an essential role in ensuring the company can meet demands as closely as possible.

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The recommended method to use in the company can be demand forecasting. Demand forecasting assist company to identify the need of their customer by using several methods to forecast products[ CITATION Hub20 \l 1033 ]. Demanding forecast is an essential tool for many companies as the companies need to make the necessary decisions to ensure the company's success. Using this method could help Sports Obermeyer could help the forecasting committee make a better decision on the correct quantity of each style to produce, reducing over or under forecasting. Another problem with Sports Obermeyer is the confidence that it has in its China location. The company doubts the quality and reliability of the product that comes from the factory in that area. Even though just a third of the gear production comes from there, the company plans to ramp up production to 50% (Simchi-Levi et al., 2021). A recommendation for the company could be to outsource to another location that the company can have better confidence in. Looking for better quality and more reliable location could help the company gain some market share back from competition. Though the company has invested much in the location and the china location is more efficient and less costly, the company could be losing clients because they may lack confidence in the factory work ethic. Short-term and Long-term Sourcing When looking at the best sourcing to recommend for Sports Obermeyer, the one that comes to mind is responsible sourcing. According to Kleijn et al. (2019), responsible sourcing addresses sustainability risks in international supply chains, but the word is now used to refer to a wide range of sustainability goals achieved through several techniques. When acquiring products from China, several of the styles require source more to reach the required production amount; this gives priority to low-risk items. On the other hand, Hong Kong is easier to adjust to

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the demands, and their quality of work is of a higher standard; because of this, it is recommended to receive the risker items. While sourcing to China is going for the long term, Hong Kong is better for the short term. As mentioned earlier, the products out of Hong Kong are of a higher quality, are faster produced, and are more reliable than in China. Sports Obermeyer should invest in training the China factory so that their production level raise to an acceptable amount and the quality of the product produce there is of a higher standard. Doing this would reduce the amount of time of the product and increase the productivity of the organization. The recommended sourcing policy mentioned, along with the training programs that can be established in the China factory, will increase the company's cost in the short term but be beneficial in the long term because of the faster production and delivery, high quality of product the customer's satisfaction. All three will ensure the company remains in the front of the ski wear industry. Conclusion Sport Obermeyer identifies its production plan during the initial phase. It measured the risk of ordering policy by using CV. The CV is the average demand's standard deviation. It evaluates the variability relative to the average demand, enabling them to compare the product at different demand levels and helping the company calculate the item with the high risk, and ensuring that it falls within the forecasted demand. Recommends and operational change in the method used in the company. NVM is the most desirable to use because it handles the multi-item inventory optimization problem with a capacity constraint and predicts demand using deep learning, random forest, and time-series techniques.

SPORT OBERMEYER Sports Obermeyer was able to identify differences between Hong Kong and China better to understand the reason for the differences in the product and decide the best long or short-term sourcing for both countries to better the company in the long term.

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Arachchige, C. N., Prendergast, L. A., & Staudte, R. G. (2020). Robust analogs to the coefficient of variation. Journal of applied statistics, 1-23. https://doi.org/10.1080/02664763.2020.1808599 Gharaei, A., Karimi, M., & Hoseini Shekarabi, S. A. (2020). Joint Economic Lot-sizing in Multiproduct Multi-level Integrated Supply Chains: Generalized Benders Decomposition. International journal of systems science, 7(4), 309-325. http://doi.org/10.1080/23302674.2019.1585595 Huber, J., & Stuckenschmidt, H. (2020). Daily retail demand forecasting using machine learning with emphasis on calendric special days. International journal of forecasting, 36(4), 1420-1438. https://doi.org/10.1016/j.ijforecast.2020.02.005 Kleijn, R., Tukker, A., Huisman, J., & van den Brink, S. (2019). Approaches to responsible sourcing in mineral supply chains. Resources, conservation, and recycling, 145, 389-398. http://doi.org/ 10.1016/j.resconrec.2019.02.040 Nikolopoulos, K., Punia, S., Schäfers, A., Tsinopoulos, C., & Vasilakis, C. (2021). Forecasting goods in real-time (daily and weekly) and their impact on the supply chain has become a critical management and policy-making requirement. European journal of operational research, 290(1), 99-115. http://doi.org/10.1016/j.ejor.2020.08.001 Punia, S., Singh, S. P., & Madaan, J. K. (2020). From predictive to prescriptive analytics: A datadriven multi-item newsvendor model. Decision support systems, 136, 1-11. https://doi.org/10.1016/j.dss.2020.113340

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Simchi-Levi, D., Kaminsky, P., & Simchi-Levi, E. (2021). Designing and Managing the Supply Chain: Concepts, Strategies and Case Studies. New York, NY: McGraw-Hill Create. https://bookshelf.vitalsource.com/books/9781307668360...