Sport Obermeyer Case John H Vande Vate Spring 2006 1 1 Issues Question What are the issues driving this case How to measure demand uncertainty from disparate forecasts How to allocate production between the factories in Hong Kong and China How much of each product to make in each factory 2 2 Describe the Challenge Long lead times It s November 92 and the company is starting to make firm commitments for it s 93 94 season Little or no feedback from market First real signal at Vegas trade show in March Inaccurate forecasts Deep discounts Lost sales 3 3 Production Options Hong Kong Mainland Guangdong Lo Village More expensive Smaller lot sizes Faster More flexible Cheaper Larger lot sizes Slower Less flexible 4 4 The Product 5 Genders Price Type of skier Fashion quotient Example Adult man Fred conservative basic Rex rich latest fabrics and technologies Beige hard core mountaineer no nonsense Klausie showy latest fashions 5 5 The Product Gender Styles Colors Sizes Total Number of SKU s 800 6 6 Service Deliver matching collections simultaneously Deliver early in the season 7 7 The Process Design February 92 Prototypes July 92 Final Designs September 92 Sample Production Fabric Component orders 50 Cut Sew begins February 93 Las Vegas show March 93 80 of orders SO places final orders with OL OL places orders for components Alpine Subcons Cut Sew Transport to Seattle June July Retailers want full delivery prior to start of season early September 93 Replenishment orders from Retailers Quotas 8 8 Quotas Force delivery earlier in the season Last man loses 9 9 The Critical Path of the SC Contract for Greige Production Plans set Dying and printing YKK Zippers 10 10 Driving Issues Question What are the issues driving this case How to measure demand uncertainty from disparate forecasts How to allocate production between the factories in Hong Kong and China How much of each product to make in each factory How are these questions related 11 11 Production Planning Example Rococo Parka Wholesale price 112 50 Average profit 24 112 50 27 Average loss 8 112 50 9 12 12 Sample Problem Style Price Laura Carolyn Gail 110 00 900 1 000 Isis 99 00 800 700 Entice 80 00 1 200 1 600 Assault 90 00 2 500 1 900 Teri 123 00 800 900 Electra 173 00 2 500 1 900 Stephanie 133 00 600 900 Seduced 73 00 4 600 4 300 Anita 93 00 4 400 3 300 Daphne 148 00 1 700 3 500 Total 20 000 20 000 Individual Forecasts Greg Wendy Tom Wally Average Std Dev 2X Std Dev 900 1 300 800 1 200 1 017 194 388 1 000 1 600 950 1 200 1 042 323 646 1 500 1 550 950 1 350 1 358 248 496 2 700 2 450 2 800 2 800 2 525 340 680 1 000 1 100 950 1 850 1 100 381 762 1 900 2 800 1 800 2 000 2 150 404 807 1 000 1 100 950 2 125 1 113 524 1 048 3 900 4 000 4 300 3 000 4 017 556 1 113 3 500 1 500 4 200 2 875 3 296 1047 2 094 2 600 2 600 2 300 1 600 2 383 697 1 394 20 000 20 000 20 000 20 000 20 000 Cut and Sew Capacity 3000 Units month 7 month period First Phase Commitment 10 000 units Second Phase Commitment 10 000 units 13 13 Recall the Newsvendor Ignoring all other constraints recommended target stock out probability is 1 Profit Profit Risk 8 24 8 25 14 14 Ignoring Constraints Style Gail Isis Entice Assault Teri Electra Stephanie Seduced Anita Daphne Mean Std Dev Recommended Order Quantity 1 017 388 1 278 1 042 646 1 478 1 358 496 1 693 2 525 680 2 984 1 100 762 1 614 2 150 807 2 695 1 113 1048 1 819 4 017 1113 4 767 3 296 2094 4 708 2 383 1394 3 323 26 359 Note This suggests over buying Everyone has a 25 chance of stockout Everyone orders Mean 0 6745 P 75 from 24 24 08 Probability of being less than Mean 0 6745 is 0 75 15 15 Constraints Make at least 10 000 units in initial phase Minimum Order Quantities 16 16 Objective for the first 10K First Order criteria Return on Investment Expected Profit Invested Capital Second Order criteria Standard Deviation in Return Worry about First Order first 17 17 First Order Objective Maximize Expected Profit Invested Capital Can we exceed return Is L Max Expected Profit Invested Capital 0 18 18 First Order Objective Initially Ignore the prices we pay Treat every unit as though it costs Sport Obermeyer 1 Maximize Expected Profit Number of Units Produced Can we achieve return L Max Expected Profit Qi 0 19 19 Solving for Qi For fixed how to solve L Maximize Expected Profit Qi Qi Error here let p be the s t Qi 0 wholesale price Note it is separable separate decision each Q Profit 0 24 p Exactly the same thinking Risk 0 08 p Last item 0 08p P 0 24p 0 24p Profit Profit Probability Demand exceeds Q 0 75 32p Risk Loss Probability Demand falls below Q Set P Profit Profit Risk 0 75 Profit Risk 20 20 Solving for Qi Last item Profit Profit Probability Demand exceeds Q Risk Risk Probability Demand falls below Q Also pay for each item Error This was omitted It is not needed later when we calculate cost as for 53 4 Wholesale Profit 1 P example Risk P price because it factors out Profit Profit Risk P of everything Balance the two sides So P Profit Profit Risk In our case Profit 24 Risk 8 so P 75 32 Wholesale Price How does the order quantity Q change with 21 21 Q as a function of 1400 1200 Doh As we demand a higher return we can accept 800 less and less risk that the item won t sell So 600 We make less and less 1000 Q 400 200 0 3 2 7 12 17 22 27 22 22 Let s Try It Style Gail Isis Entice Assault Teri Electra Stephanie Seduced Anita Daphne Mean Std Dev 1 017 388 1 042 646 1 358 496 2 525 680 1 100 762 2 150 807 1 113 1048 4 017 1113 3 296 2094 2 383 1394 Wholesale Price Recommended Order Quantity 1 278 110 00 1 478 99 00 1 693 80 00 2 984 90 00 1 614 123 00 2 695 173 00 1 819 133 00 4 767 73 00 4 708 93 00 3 323 148 00 26 359 Adding the Wholesale price brings returns in line with expectations if we can make 26 40 24 of 110 on a 1 investment that s a 2640 return Order Quantity at Return 749 1778 1474 471 568 1767 697 2005 658 0 1148 1938 10 000 Min Order Quantities 23 23 And Minimum Order Quantities Maximize …