# OPSM 501: Operations Management

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OPSM 501: Operations Management
Koç University Graduate School of Business MBA Program OPSM 501: Operations Management Week 11: The Newsvendor Problem-ways to avoid mismatch Zeynep Aksin

Hammer 3/2 timeline and economics
Each suit sells for p = \$180 TEC charges c = \$110 per suit Discounted suits sell for v = \$90 The “too much/too little problem”: Order too much and inventory is left over at the end of the season Order too little and sales are lost. Marketing’s forecast for sales is 3200 units.

The demand-supply mismatch cost
Definition – the demand supply mismatch cost includes the cost of left over inventory (the “too much” cost) plus the opportunity cost of lost sales (the “too little” cost): The maximum profit is the profit without any mismatch costs, i.e., every unit is sold and there are no lost sales: The mismatch cost can also be evaluated with Mismatch cost = Maximum profit – Expected profit

Revisit Example 3: Manufacturing cost=60TL,
Selling price=80TL, Discounted price (at the end of the season)=50TL Market research gave the following probability distribution for demand. Find the optimal q, expected number of units sold for this orders size, and expected profit, for this order size. Demand Probability P(D<=n-1) 0.1 0.3 0.5 0.7 0.8 0.9 Cu=20 Co=10 P(D<=n-1)<=20/30=0.66 <=0.66 q=800 For q=800: E(units sold)=710 E(profit)=13,300 Max profit=20*770=15400

When is the mismatch cost high?
Hammer 3/2’s mismatch cost as a percentage of the maximum profit is \$31,680/\$223,440 = 14.2% Mismatch cost as a percent of the maximum profit increases as … (1) the coefficient of variability of demand increases (2) the critical ratio decreases

Options to reduce the mismatch cost
Make to order Reactive Capacity Unlimited Limited

Make-to-Stock Model Suppliers Configuration Assembly

Assemble-to-Order Model
Suppliers Configuration Assembly

Unlimited, but expensive reactive capacity
TEC charges a premium of 20% per unit (\$132 vs. \$110) in the second order. There are no restrictions imposed on the 2nd order quantity. O’Neill forecast of total season sales is nearly perfect after observing initial season sales. How many units should O’Neill order in October? 12-9

Revisit Example 2: Finding Cu and Co
A textile company in UK orders coats from China. They buy a coat from 250€ and sell for 325€. If they cannot sell a coat in winter, they sell it at a discount price of 225€. When the demand is more than what they have in stock, they have an option of having emergency delivery of coats from Ireland, at a price of 290. The demand for winter has a normal distribution with mean 32,500 and std dev 6750. How much should they order from China??

Example 2: Finding Cu and Co
A textile company in UK orders coats from China. They buy a coat from 250€ and sell for 325€. If they cannot sell a coat in winter, they sell it at a discount price of 225€. When the demand is more than what they have in stock, they have an option of having emergency delivery of coats from Ireland, at a price of 290. The demand for winter has a normal distribution with mean 32,500 and std dev 6750. How much should they order from China?? Cu=75-35=40 Co=25 F(z)=40/(40+25)=40/65=0.61z=0.28  q= *6750=34390

Apply Newsvendor logic even with a 2nd order option
The “too much cost” remains the same: Co = c – v = 110 – 90 =20. The “too little cost” changes: If the 1st order is too low, we cover the difference with the 2nd order. Hence, the 2nd order option prevents lost sales. So the cost of ordering too little per unit is no longer the gross margin, it is the premium we pay for units in the 2nd order. Cu = 132 – 110 = 22 Critical ratio: Corresponding z-statistic F(0.05)=0.5199, F(0.06)=0.5239, so z = 0.06.

Profit improvement due to the 2nd order option
With a single ordering opportunity: Optimal order quantity = 4101 units Expected profit = \$191,760 Mismatch cost as % of revenue = 4.9% The maximum profit is unchanged = \$223,440 With a second order option: Optimal order quantity = 3263 units Reduction in mismatch cost = 38% (19,774 vs 31,680) Mismatch cost as % of revenue = 3.1%

Limited reactive capacity
Units in the 2nd order are no more expensive than in the 1st order But there is limited capacity for a 2nd order

Sample of wetsuits 1st order must be at least 10,200 suits so that there is enough capacity for the 2nd order. Also a minimum order quantity-order once What should we produce in the 1st order?

Profit and mismatch with only 1 ordering opportunity
Use the Newsvendor model to evaluate the optimal order quantity, expected profit, maximum profit and mismatch cost A suits produced in the 1st order earns the Newsvendor profit but a suit produced in the 2nd order earns the maximum profit. 12-16

Produce “safer” products early, produce “risky” products with reactive capacity
Sort items by their mismatch cost to order quantity ratio. Fill the 1st order up to the minimum quantity (10,200) with the items that have the lowest mismatch – quantity ratio The mismatch cost is reduced by 66%! 12-17

Push-Pull Supply Chains
The Supply Chain Time Line Push-Pull Boundary PUSH STRATEGY PULL STRATEGY Customers Suppliers Low Uncertainty High Uncertainty

A shift from a Push System... Production decisions are based on forecast …to a Push-Pull System Parts inventory is replenished based on forecasts Assembly is based on accurate customer demand

Demand Forecast The three principles of all forecasting techniques:
Forecasts are always wrong The longer the forecast horizon the worst is the forecast Aggregate forecasts are more accurate The Risk Pooling Concept

Business models in the Book Industry
From Push Systems... Barnes and Noble ...To Pull Systems Amazon.com, And, finally to Push-Pull Systems Amazon.com, 1999-present Around 40 warehouses

Business models in the Grocery Industry
From Push Systems... Supermarket supply chain ...To Pull Systems Peapod, Stock outs 8% to 10% And, finally to Push-Pull Systems Peapod, 1999-present Dedicated warehouses Stock outs less than 2%

Locating the Push-Pull Boundary

Organizational Skills Needed
Raw Material Customers Push Pull High Uncertainty Short Cycle Times Service Level Responsiveness Low Uncertainty Long Lead Times Cost Minimization Resource Allocation

O’Neill: quick response (reactive capacity)
Low Risk: Push High Risk: Push-Pull Speculative Production capacity Reactive Production capacity Initial forecast Later orders

Announcement Read the HP case for next week
We will analyze it in-class Bring your laptops!