# Sport Obermeyer Case Analysis Essay Sample

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There were two main driving issues behind our analysis of this Sport Obermeyer case: the measurement and understanding of demand from uncertain and disparate forecasts, and the allocation of production between factories in Hong Kong and Mainland China (Lo Village, Guangdong). The main challenges facing the company were long lead times, little to no feedback from the market before the first production decision (the first real demand signal is at the Las Vegas trade show in March) and inaccurate forecasts along with the lost profits that can result.

The first part of our analysis involved deriving an order policy from the forecasts provided in the sample problem. We solved this problem using simplifying assumptions and then relaxing some of those assumptions. Our initial assumption was that there was no minimum order quantity. We decided that risk would be minimized by producing the smallest allowable amount during the first production run due to the lack of information. Thus, we calibrated our order quantity formulas to sum to 10,000 units.

We wanted to use a formula that took into account the average forecast as well as the standard deviation – in other words, we wanted to account for both the expected demand and the uncertainty. We began with the formula Q = Average Forecast – 2* Standard Deviation of the forecasts, since twice the standard deviation was said to approximate the standard deviation of the actual sales. Since this number did not sum to 10,000, we multiplied the standard deviation by a scaling factor, k, and solved for order quantity 10,000 units across all designs. We found k = 1.0607, which gives a quantity of 10,000 with no minimum order quantity.

Next, we had to modify this order policy because designs Stephanie, Isis, and Teri had initial orders below the minimum order (for Hong Kong) of 600. Since we could not compare profitably analyses without using the wholesale prices (which we were explicitly told to ignore in the problem), we “rounded up or down” depending on whether the order quantity without the minimum was 2/3rds of the minimum or not and adjusted our k value so that the quantities summed to 10,000 units. Thus, we removed those three and calibrated again, and found that k = .9675. So we are exposed to slightly higher risk due to the other designs having to be ordered in larger quantities, but no design is being ordered in a quantity above 75% of the average forecast or higher than the lowest committee estimate, and so risk is still relatively low. We trust our committee to be at least fairly accurate in their predictions, especially since Wally took steps to gather many separate estimates from a panel experts in a manner that prevented groupthink. The appendix shows the resulting orders.

Finally, when considering ordering from China instead of Hong Kong, the higher minimum order of 1200 units becomes a major obstacle. As the appendix shows, Gail cannot now be ordered, and Daphne and Entice must be increased to 1200 to meet the minimum order. When factoring these new constraints in, we find k = 0.9345. The forecast is now much more risky; for Entice, the order quantity is now greater than the minimum estimate, in addition to being very close to the average forecast. All orders that are not constrained are now significantly closer than one standard deviation from the mean, increasing the chances that we will over order. We are forced to consider the uncertainty less in our predictions and are more controlled simply by the number of units that we think we need.

Although we did not relax the assumption that all prices were the same, relaxing this assumption would have made this problem into a complex optimization problem and the optimal minimum order quantities would have been slightly different. We compared our results with the quantitative analysis of this case done by an Industrial Engineering professor at Georgia Tech using constrained optimization and lagrangian multipliers and found that our results were quite similar.

The risk for these orders can be measured using statistics based on the expected standard deviation of demand. Since the expected standard deviation is twice the standard deviation of the forecast samples, we can find the proportion of the time that the actual value of demand will be within a certain range. Because of our k value, our orders are all around 1 standard deviation below the mean, meaning that about 12% of the actual observations will fall below these orders. In that case, Sport Obermeyer would lose money based on the extra units. If demand falls less than 600 units above the initial order, we have a similar problem in the second ordering stage – Obermeyer cannot effectively adjust its order size to fit the reevaluated demand. Based on the cost of each item, which we ignored in our analysis, we could calculate the expected over-ordering from each item and find the ideal level, similar to the outside analysis that we found.

The second main issue to consider is whether to source in Hong Kong or China. Sourcing in Hong Kong provides Wally more flexibility because its minimum order quantity is 600 units compared to 1,200 units in China. The ability to order fewer units allows Wally to order units in a less risky manner. In either case Wally orders combinations of products so that he minimizes the chance he’ll order too much of any one product. Lower minimum order quantities allow Wally to order lower amounts of more products. Since he believes he will sell many of each product, enough to cover the minimum order cost, Wally can lower his order amount’s distance from its expected value (reducing the chance he’ll order too much).

This difference in minimum lot sizes cannot be attributed to mechanical advantages because the factories in the two areas use similar equipment (as described in the case). The workers in Hong Kong are better trained than those in China, and in particular they are better cross-trained. This most likely explains why a typical production line in Hong Kong can have fewer workers and thus produces fewer units per day. However, the average Hong Kong worker is more productive (more units produced per day) but the average Chinese production line produces more units because it contains more workers. This suggests why the minimum lot size is greater in the mainland factory.

Choosing to operate in China is much cheaper. China labor costs are $0.78 per unit and Hong Kong’s are $9.69, which is more than a 12 fold difference. Labor costs are approximately $9 less per unit than in Hong Kong. Wally has to decide if he wants to pay $9 more per unit to have the ability to reduce risk in lower minimum orders. If Wally loses 8% of the price of a product when he can’t sell it in the primary market, then he loses less than $9 for 5 of the products. Instead of losing $13 or $12 on the most expensive products, he now only loses $4 or $3. However, waiting for the trade show to gather information creates a two step ordering process. This creates more of a barrier in China. If Wally under-orders on certain designs (which he surely will because it is part of his ordering strategy) he can only buy more units during the second cycle in groups of 1200. If he revises his forecast based on demand and decides he needs to order 500 more units of Entice, he is now faced with a choice: he can order 1200 units and have 700 likely unsold, or he can lose out on 500 units of potential sales. Note that in Hong Kong, this risk is much less dire since he can order in 600 unit blocks.

Ordering in China significantly increases the risk of ordering too much of a product but it also reduces the loss due to that error. Wally should source those designs which have the highest expected demand to China because it allows him to take fullest advantage of the cheaper labor. He should be careful, though, to order at least 1200 less than he thinks he will need, and so only those designs with an expected demand of 2400 or more can be ordered in this way. On the other hand, designs which are expected to have lower demand give Wally a tough decision: he can order 0 units, as we recommended with Stephanie, and try to gauge demand after the trade show to determine whether the cheaper labor costs will make up for having to order 1200 units; he can order more than 1200 units in the first order cycle from China, in a case like Entice, and hope that his estimate is accurate, since he will not be able to go back again for 1200 units; or he can order from Hong Kong with greater flexibility. Wally must decide where to source. Sourcing in China would cover losses by decreasing to amount lost per unit and sourcing to Hong Kong covers losses by decreasing the amount of units produced beyond demand in the primary markets.

In the short-term, Wally must consider this tradeoff between the lower labor cost of the mainland site and the lower minimum lot size of the Hong Kong factory and decide how to optimally source production to maximize profitability. In the long-term, there are three elements that could decrease the minimum lot size at the Chinese factory: worker skill and cross-training, factory layout (assembly line versus a different model of production), and the nature of the cutting equipment.

We feel that broad operational changes can be very beneficial to Sport Obermeyer, both in the way that it orders its products and also about the business model it uses to respond to consumer demand. Firstly, Obermeyer should be able to cut down on its product lead time, which gives it more time to analyze data and react, by stocking raw materials instead of ordering solely based on finished products. For example, zippers, snaps, and insulation can be standardized across many designs, presumably without significant value lost to the customer, and stored for the second ordering cycle and then applied to whichever design ends up being successful. A typical production cycle takes almost 9 months to complete- from the trade show in Las Vegas to the beginning of the ski season, and cutting down on 60 or 90 day lead times on zippers might allow Sport Obermeyer to customize their replenishment holdings to make sure the popular items can be replenished in the stores.

The second operational change Obermeyer could implement is to narrow their product line and change their push-pull strategy. Currently, Sport Obermeyer is trying to respond to the demand pulls, by making a wide variety of parkas to attract every buyer. They could cut a major portion of their costs, though, if they offered only one or two parkas for each of their four “genders.” There are many reasons why this could be a successful strategy. Obermeyer’s main competitor, Columbia, targets lower-income skiers, and so the danger of popular Columbia designs stealing Obermeyer customers is low. In addition, two of the genders, Rex and Klausie, are heavily impacted in their buying decisions by status, and fewer designs would make those designs more recognizable, increasing the status associated with them. Also, it would make designs from the previous season stand out more, and thus increase the obsolescence of the product, encouraging new purchases.

Another gender, Biege, cares strictly about technical performance, thus a standardized parka with the latest technology, perhaps offered in different colors, would be enough to keep him buying Obermeyer. However, the biggest benefit to narrowing the offerings would be to cut costs. All of the designs could be ordered in much larger quantities from China instead of Hong Kong, taking advantage of the cheaper labor. Since demand for each gender would not have to be estimated and split between many designs, it could be tracked over time and a much more accurate forecast could be made. Also, the problem of running out of popular styles for replenishment would be mitigated, since a much larger portion of the stored parkas would be of any given type.

References:

D. Simchi-Levi, P. Kaminsky, E. Simchi-Levi, Designing and Managing the Supply Chain Concepts, Strategies, and Case Studies, McGraw-Hill, Boston, 2000