Sport Obermeyer Case Study

3144 Words 13 Pages
Register to read the introduction… Sport Obermeyer invested 1 million dollars in the facility in China and the main question that arises is why Sport Obermeyer invested in a new facility and could Sport Obermeyer benefit from moving all its production to China. If the figures are interpreted in the South African currency, production of 20 000 units in China can save R163 200 than in Hong as can be mathematically interpreted as (81.92- 60.80)*20 000 and which could equal R163 200. This is due to the fact that labour in China is exploited and relatively cheap while compared to Hong Kong. According to Simchi-Levi (2008) Sport Obermeyer directly owns the Chinese facility which in essence could offer flexibility in terms of production be it that the demand rises than expected or drops. Based on the findings of the case it could be noted that the Chinese workforce is sufficient, but productivity and skills are not up to standards while compared to Hong Kong. Based on the findings by Simchi-Levi (2008) it could noted that the workforce that Hong Kong has is highly skilled and productive and can produce more goods in a short period of time while compared to China. Even though this is the case with Hong Kong it still faces some draw backs as there is insufficient workforce thus meaning that the labour …show more content…
These can be lead times, demand signal processing, order batcing, price fluctuations and shortages. All the variations lead to extensive inventory due to the need for large safety stocks, unacceptable service levels and product obsolescence.
According to Simchi-levi et al (2002), when it is difficult to match demand and supply, it leads to long lead times caused by the bullwhip effect. There is a relationship that exists between bullwhip effect and lead time, for long lead times, a small change in the estimate of demand variability implies a significant change in stock, leading to significant changes in order quantities which lead to a bullwhip effect.
According to the Chandra (2002), the time period of forecasting for seasonal products depends on certain characteristics. For most seasonal products the life cycle stage is very short, there is high variability and very long replenishment time. Seasonal demand variations include innovative and fashion products with a short life cycle. Huge variations and fluctuations for seasonal products bring the risk of obsolete inventory, lost sales, poor service level which leads to products being sold at discounts (Chandra,

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