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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.
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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 ?
Supply Side Demand Side Challenges of matching supply to demand
Over-stock Under-stock Costs & Risks of Over-stock versus Under-stock
Colorado US Retailer China November Pre year Design clothes Make forecasts Order textiles & styles November Take Orders Make Fabric Assemble Clothes Make orders to Sport O. March 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 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
Production Strategy AAccount 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
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.
Approach • Compute risk for each style • Rank styles by risk • Figure out the amount of non-risk suits to produce in the first run
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?
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 ?
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.
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