Course Name: Logistics and Supply Chain Management

Assignment Title: Sport Obermeyer, Ltd.

Submitted by:

Group Member Name PG IDLakshmi Dharmarajan 61510280Pankhuri Chakraverti 61510524Piyush 61510241Ashutosh Walia 61510682

IntroductionSport Obermeyer is a fashion skiwear manufacturer which provided strong new designs every year.

Obermeyer had great designs, but the ultimate success depended on the prediction of market demand for

styles and colors. It is facing the newsvendor problem.

Challenges1. Committing to production quantities for each item with minimum information. This depends on a

combination of analysis, experience, intuition and speculation. The commitment had to be made

despite the fact that market response data for the previous year was not available. Any delays in

the ordering will result in negative impact on the supply and ultimately the customers.

2. Accurate forecast of demand was becoming increasing difficult over the years due to greater

product variety and higher market competition. Higher forecasts meant selling the excess

production at high discounts. Lower forecasts will result in lost sales in best sellers.

3. Allocation of production between factories in Hong Kong and China. Though labor cost was low

in China, but quality and reliability of operations was questionable. China required larger

minimum order quantity than Hong Kong. In addition, there were quota restrictions for goods

produced in China by the US government.

Production processBoth qualitative and quantitative analyses are used to decide the order quantity and location of production

for various styles:

1. Marginal contribution is used to decide the order quantities and the plant where production will

happen, which in turn is determined is by minimum order quantity and costs.

2. In addition, there are qualitative and business risks associated with production in China:

a. Though China provides extremely cheap labor and thus contributes to cost benefits, there

are no guarantees about quality and reliability of Chinese operations systems. This adds

to production and inventory risks.

b. There is quota on the amount of Chinese goods that will be imported in the United States.

This leads to the risk that produced goods will not be sold in the US market.

c. The uncertainty in the trade relationship between US and China results in business risk of

products produced in the Chinese plant not being sold in the US.

3. Forecast: Current European demand predicts the US market since the US market lags behind by

one year. Accuracy in demand forecast is necessary to maximize profits.

Analysis and Methodology The order size of an item can be increased as long as its marginal contribution is more than any other

product. The initial production commitment by Obermeyer is 10,000. To calculate the optimal order

quantity, we use Large Marginal Contribution (LMC). We set initial quantity for all the products as zero.

After computing the marginal contribution of all the items as shown in the Exhibit 1, we select the item

with the largest positive marginal contribution. Then increase the quantity of Electra Parka till it matches

the next highest contribution margin of the item i.e Daphne parka. Then follow the process until sum of

quantity of all the items is equal to 10,000. So in case of no minimum order quantity the quantity for all

the items has been shown in the Exhibit 1.

Order during initial phase without minimum order quantity: In case Sports Obermeyer orders 10,000

from Hong Kong, the capacity constraint is 600 and 1200 for China.

CASE 1 : The 1st order quantity is after the Las Vegas show(80%) followed by the 2nd order

quantity(20%) from the same plant location. For the 1st order quantity, the order has been catered as per

three prevailing conditions (1) Order < 600 to be served from the Hong Kong plant with a tradeoff

between serving the customer (responsiveness) and cost of overstocking (2) Orders between 600 and 1200

to be served by the China plant (3) Orders above 1200 to be served by the China plant.

Measure of Risk : We have used the concept of Coefficient of Variability (COV) to identify and quantify

risk with the assumption that styles with COV > 0.257 are high risk and COV > 0.257 are low risk. High

risk style lines are ordered 20% in the 1st order with an order revision ahead. Also, with higher variability

and higher risk it is safe to order from locations with low minimum order quantity requirement.

CASE 2 : The 1st order quantity has been catered to when near to the 600 / 1200 minimum order quantity

with Hong Kong and China plant respectively. We again use LMC and compute the quantities for all the

items.

RECOMMENDATIONS

Obermeyer should try to focus on improving its operational efficiencies and accuracy of demand forecasts. This can be achieved by the following

1. Improvement in demand forecast to reduce variability : Presently average of all the individual

lines are being taken into account, to reduce the variability it is recommended that depending on

past historic data of forecasting, weights should be assigned to the various lines and a weighted

average calculation of demand should be used for forecasting.

2. Lead time reduction : Vertical integration with the suppliers for reducing lead time both on RM

and FG side so that the utilization is increased by reducing the waiting time.

3. Increase in Production capacity : More subcontractors can be used to increase the production

capacity. Also the number of hours could be increased in the China plant.

4. Usage of “Griege Fabric System” in place so that we can integrate the concept of last moment

customization in order to decrease the set up time.

5. Long Term Changes : Improve the production efficiency specially in the China plant by

integrating principles of six sigma and JIT and by providing training to the unskilled labours.

Exhibit 1

Exhibit 2

Exhibit 3

Exhibit 4

Capacity Left

Order Quantity (Recommended)Gail Isis Entice Assault Teri Electra Stephanie Seduced Anita Daphne

10,000 0 0 0 0 0 0 0 0 0 0-70 600 600 630 1227 600 1200 600 1853 1560 1200-726 600 600 728 1298 600 1200 600 2163.67 1735.8 1200-3,053 813.6 600 1086 2020 600 1720 600 3213.33 1200 1200104 600 600 600 1200 600 1200 600 1200 2095.8 1200

Exhibit 5

Exhibit 6

Exhibit 7

CASE 1 :

Basis - Concept - 1

STYLE μ1st Order 2nd Order

1st Order Revised

2nd Order Revised Location sigma

Gail 1017 515 502 600 600 H 388Isis 1042 510 532 600 600 H 646Entice 1358 630 728 630 728 H 496Assault 2525 1227 1298 1227 1298 C 680Teri 1100 585 515 600 600 H 762Electra 2150 1175 975 1200 1200 C 808Stephanie 1113 625 487.5 600 600 H 1048Seduced 4017 1853 2163.666667 1853 2163.666667 C 1112Anita 3296 1560 1735.833333 1560 1735.833333 C 2094Daphne 2383 1320 1063.333333 1200 1200 C 1394

TOTAL 20000 10070 10725.5

CASE 2 :

Basis - Concept-2STYLE mu COV 1st

Order1st Order Revised

2nd Order 2nd Order Revised

Location TOTAL

Gail 1017 0.19076 813.6 813.6 203.4 600 H 1413.6Isis 1042 0.30998 208.4 600 442 600 H 1200Entice 1358 0.18262 1086.4 1086.4 271.6 600 H 1686.4Assault 2525 0.13465 2020 2020 505 1200 C 3220Teri 1100 0.34636 220 600 500 600 H 1200Electra 2150 0.18791 1720 1720 430 1200 C 2920Stephanie 1112.5 0.47101 222.5 600 512.5 600 H 1200Seduced 4016.7 0.13842 3213.333 3213.33 803.33667 1200 C 4413.33Anita 3295.8 0.31767 659.1667 1200 2095.8333 2095.83 C 3295.83Daphne 2383.3 0.29245 476.6667 1200 1183.3333 1200 C 2400TOTAL 10640.07 13053.33 6947.0033 9895.83 22949.16

Exhibit 8

Exhibit 9

Exhibit 10

Exhibit 11

Exhibit 12