Sport Obermeyer Case Prof Mellie Pullman

Objectives Supply Chain Choices & Operations Strategy Product Category challenges Operational changes that reduce costs of mismatched supply and demand Coordination Issues in a global supply chain

Type of Product Typical Operational & Supply Chain Strategies Cost Quality Time (delivery, lead time, etc) Flexibility (multiple choices, customization) Sustainability Sport Obermeyer ?

Challenge of delivering on the strategy?

Challenges of matching supply to demand Supply Side Demand Side

Costs & Risks of Over-stock versus Under-stock

China Colorado US Retailer November Pre year November March August Design clothes Make forecasts Order textiles & styles November Take Orders Make Fabric Assemble Clothes March Make orders to Sport O. Las Vegas show August Deliver to Colorado Warehouse Distribute to retailers September Retail Season February

Two Order Periods How are they different?

Risk-Based Production Sequencing Strategy New Info. Lead Time to Store Material Lead time Speculative Production Capacity Reactive Production Capacity

Planning Approach How many of each style to product? When to produce each style?

Buying Committee Forecasts Ave Forecast Stand Dev 2 x Stand Style Price Laura Carolyn Greg Wendy Tom Wally Job Market Director CS Mgr Product-ion mgr Product-ion coord Sales Rep VP Gail $110 900 1000 1300 800 1200 1017 194 388 Isis $ 99 700 1600 950 1042 323 646 Entice $ 80 1500 1550 1350 1358 248 496 Assault $ 90 2500 1900 2700 2450 2800 2525 340 680 Teri $123 1100 1850 381 762 Electra $173 1800 2000 2150 404 807 Stephani $133 600 2125 1113 524 1048 Seduced $ 73 4600 4300 3900 4000 3000 4017 556 Anita $ 93 4400 3300 3500 4200 2875 3296 1047 2094 Daphne $148 1700 2600 2300 2383 697 1394 Totals 20000 Standard Deviation of demand= 2x Standard Deviation Forecast

Team Break out 1 Using the available data, assess the risk of each suit and come up with a system to determine: How many of each to style to produce When to produce each style Where to make it

Low Risk Styles We under-produce during initial production so we want: Least expensive products Low demand uncertainty Highest expected demand

Standard Normal Distribution - produce m-zs

Production Strategy A Account for production minimum If we assume same wholesale price, we want to produce the mean of a style’s forecast minus the same number of standard deviations of that forecast i.e., mi-ksi (k is same for all). Approach: produce up to the same demand percentile (k) for all suits. Sum (m-ks)each style = 10,000 (meet production minimum) Determine k for all styles

Solve for k with total close to 10000 (k=1.06)

But what about the batch size minimums? Large production minimums force us to make either many parkas of a given style or none. How do we consider the batch size minimums for the second order cycle?

Strategy B: Categories for Risk Assessment m= minimum order quantity (600 here) SAFE: Styles where demand is more than 2X the minimum order quantity (we’ll have a second order commitment) SOS: Sort of Safe=expected demand is less than minimum order quantity. “If we make ‘em at all, make ‘em first” (have to make minimum) RISKY: demand is between C1 & C2.

Compute risk for each style Rank styles by risk Approach Compute risk for each style Rank styles by risk Figure out the amount of non-risk suits to produce in the first run

Assign Risk

Modified Approach Determine how many styles to make to give total first period production quantity. Assess each case by determining the optimal quantities for non-risk suits using Production Quantity = Max(600, mi-600-k*si) Same approach as before (determine the appropriate k so that lot size <10,000)

Example: Production Quantity = Max(600, mi-600-k*si) ; k =.33

Should we make more suits? Production minimum order is 10,000? Pros? Cons?

Sport Obermeyer Savings from using this risk adjustment Model’s Decisions Sport O Decisions Total Production (units) 124,805 121,432 Over-production (units) 22,036 25,094 Under-production (units) 792 7493 Over-production (% of sales) 1.3% 1.73% Under-production .18% 1.56% Total Cost (% of sales) 1.48% 3.30%

Team Breakout 2 What supply chain & operations changes can be implemented to reduce stock-outs and mark-downs? Design, production, forecasting, etc.? Specific: How are you going to do it, Actions?

Operational Changes to Reduce Markdown and Stock-out Costs Reducing minimum production lot-size constraints How ?

Effect of Minimum Order Quantity on Cost

Capacity Changes Increase reactive production capacity How? Pros and cons? Increase total capacity How? Pros and Cons?

Stock-out & Mark-down Costs as a Function of Reactive Capacity

Lead Times Decrease raw material and/or manufacturing lead times Which ones? How?

Lead Times Reduce “findings” leads times (labels, button, zippers) inventory more findings standardize findings between product groups more commonality reduced zipper variety 5 fold.

Where does it make sense to inventory product?

Obtain market information earlier

Accurate Response Program Using buying committee to develop probabilistic forecast of demand and variance (fashion risk) Assess overage and underage costs to develop relative costs of stocking too little or too much Use Model to determine appropriate initial production quantities (low risk first) “Read” early demand indicators Update demand forecast Determine final production quantities