Presentation Outline. Direct to Consumer (DTC)

Transcription

1 Presentation Outline CAFE & Pricing Dynamic Pricing for Manufacturing Deterministic Model Computational Analysis Extensions & Issues Direct to Consumer (DTC) Consumers Manufacturer Distribution Center Retailers Dynamic Pricing and Manufacturing 1

2 Dynamic Pricing & E-tailingE E-tailing environment one motivation for shift to a DTC model Enables collection of demand information Many pricing models available Permits price flexibility e.g., lower menu costs Pricing Constant pricing or flat rate Differential pricing Price differences based on customer type Price differences based on service level Dynamic pricing Auctions Price fluctuates with demand or supply, particularly over time Dynamic Pricing and Manufacturing 2

3 Dynamic Pricing Definition Increased interest from manufacturers Ford Motor Company Dell Computers Industrial Partner Manufacturing vs. Retailing Relationship to Revenue Management Examples Fashion Retailers Boise Cascade Office Products Kay 1998 Amazon.com or Dell Computers Agrawal & Kambil (2000) Dynamic Pricing and Manufacturing 3

4 Revenue Management Allocating the right type of capacity to the right kind of customer at the right price so as to maximize revenue or yield Traditional Industries: Airlines Hotels Rental Car Agencies Retail Industry FOR EXAMPLE McGill, J. and G. van Ryzin (1999), Revenue Management: Research Overview and Prospects. Transportation Science, 33, 2, pp Traditional Requirements Perishable inventory Limited capacity Ability to segment markets Product sold in advance Fluctuating demand FOR EXAMPLE Weatherford, L. and S. Bodily (1992), A Taxonomy and Research O verview of Perishable-Asset Revenue Management: Yield Management, Overbooking, and Pricing. Operations Research 40, 5, pp Dynamic Pricing and Manufacturing 4

5 Dynamic Pricing in Manufacturing Non-perishable inventory Production schedule needs to be determined Production has capacity limitations Demand and prices over time are bi-directional FOR EXAMPLE Federgruen, A. and A. Heching (1999), Combined Pricing and Inventory Control under Uncertainty. Operations Research, 47, 3, pp Stochastic demand with backlogging Why price? Dynamic Pricing and Manufacturing 5

6 Benefits & Reasons Supply Chain efficiencies Coordination among elements Variability Information from electronic channels There s No Such Thing as a Free Lunch Recent Example Dell Computers Market segmentation and price differentiation Dynamic prices on products, even daily Supplier prices fluctuate Advance ordering or staggered delivery dates IBM and Compaq considering similar strategies Dynamic Pricing and Manufacturing 6

7 Challenges Determining relationship between price and demand Forecasting demand over time Finding market segmentations Determining the best prices Coordinating prices with other decisions such as production The Pricing Problem Examine dynamic pricing in manufacturing Include non-perishable inventory Determine production decisions Limit production by the available capacity Model lost sales Coordinate pricing and production decisions Dynamic Pricing and Manufacturing 7

8 The Pricing Problem Make to order product Prices go up or down Non-negotiable price & delivery time with no customer segmentation Compare dynamic pricing to static pricing in same environment The Pricing Problem Objective: Maximize Profit Revenue based on actual sales & price Inventory holding cost Production cost per item Constrained by: Production capacity limits Inventory must be carried from period to period Produce enough to cover units sold Product must be produced and sold in whole units Dynamic Pricing and Manufacturing 8

9 Model Assumptions Multiple Periods Deterministic Demand Concave Revenue Function in each period Example: Linear Demand Curves Periodically varying parameters: Demand Curve Capacity Holding Cost Production Cost Revenue Curve Revenue curve incorporates lost sales or limits on demand and remains concave with respect to satisfied demand Customer Demand Revenue Price Satisfied Demand Dynamic Pricing and Manufacturing 9

10 The Deterministic Pricing Problem Max F U (D) = S 1<t<T (R tu (D t ) - h t I t - k t X t ) Subject to: (1) Beginning Inventory: I 0 = 0 (2) Inventory Balance: I t = I t-1 + X t - D t, t = 1,2,,T (3) Production Capacity: X t Q t, t = 1,2,,T (4) Integrality: I t, X t, D t, integer 0, t = 1,2,,T At each period t, D t is the satisfied demand (sales) X t is the units of product produced I t is the end of period inventory Theoretical Results Theorem 1: If the revenue functions R are concave in the pricing problem, then the greedy algorithm (MAA) generates an optimal solution. The Greedy Algorithm Step 0: Set Solution = 0 demand in each period Step 1: Choose a period to increase demand by one unit so that the contribution to profit is maximized and feasibility is maintained. Step 2: If no such period exists, stop. Dynamic Pricing and Manufacturing 10

11 Presentation Outline CAFE & Pricing Dynamic Pricing for Manufacturing Deterministic Model Computational Analysis Extensions & Issues Computational Analysis Goal: Develop managerial insights about dynamic pricing in manufacturing Performance measures: Profit and sources of profit Variability in sales Variability in price Frequency of price changes Dynamic Pricing and Manufacturing 11

12 Computational Details Demand curve represents a typical mid-size car Linear demand curves from an industrial partner 12 periods 3 levels of demand variability Includes production and holding cost Capacity = 75% of optimal uncapacitated demand 5 Demand Scenarios Demand Scenarios Seasonality 1 (SEAS1) Low in winter, high in late spring (Car) Seasonality 2 (SEAS2) High in winter, low in late spring (Ski Apparel) Increasing Mean (INCMEAN) Word of mouth or learning effect (Musical CD) Decreasing Mean (DECMEAN) Sales decrease over time (Computer) Sawtooth (SAW) Dynamic Pricing and Manufacturing 12

13 Demand Scenarios Demand Scenarios Optimal Uncapacitated Demand Time (periods) Seas1 Seas2 IncMean DecMean Saw Profit Potential Define profit potential to be Profit Potential = Profit with Dynamic Prices - 1 Profit with Static Price Profit potential is the percentage of profit to be gained from dynamic prices Also called Percent Benefit Dynamic Pricing and Manufacturing 13

14 Profit Potential by Scenario Profit potential depends on type of demand variability as well as amount of variability Profit Potential of Dynamic Pricing over Fixed Pricing Profit Potential 8% 7% 6% 5% 4% 3% 2% 1% 0% Seas1 Seas2 IncMean DecMean Sawtooth Demand Scenarios low var med var high var Sources of Profit Potential Bars above the X-axis indicate favorable contribution to dynamic pricing profit potential Sources of profit potential vary Sources of Profit Potential as a Percentage of Total Contribution 100% Contribution (%) 80% 60% 40% 20% 0% -20% -40% Production Cost Inventory Cost Revenue change due to sales volume Revenue change due to price -60% Seas1 Seas2 IncMean DecMean Sawtooth Average Demand Scenarios Dynamic Pricing and Manufacturing 14

15 Sources of Profit Potential Main source of profit potential may vary, but commonalities exist among the scenarios Common Trends: Inventory cost decreases Sales volume increases Price decreases Production cost increases Sales Variability: Static Pricing Variability of sales under optimal static pricing can be significant. Sales under Fixed Pricing Policy Sales Seas1 Seas2 IncMean DecMean Sawtooth Time (Periods) Dynamic Pricing and Manufacturing 15

16 Sales Variability: Dynamic Pricing Variability of sales under optimal dynamic pricing is more moderate. Sales under Dynamic Pricing Policy Sales Seas1 Seas2 IncMean DecMean Sawtooth Time (Periods) Implementation of Dynamic Pricing How much variability is there in price? How many prices in a horizon are needed to obtain significant profit benefit? 12 periods analyzed Considered 1, 2, 3, 4, 6, and 12 prices during the horizon or 0, 1, 2, 3, 5, and 11 price changes Dynamic Pricing and Manufacturing 16

17 Price Variability under Dynamic Pricing Compared to the optimal static price, dynamic prices vary by as much as 10% over time. Variability of Optimal Prices under Dynamic Pricing Policy 110% 105% Dynamic Price as % of Optimal Fixed Price 100% 95% 90% 85% 80% 75% 70% Time (periods) Seas1 Seas2 IncMean DecMean Sawtooth Number of Prices Usually 1 price every 3 periods gives 80% of the potential profit increase Less is sometimes more Percentage of Potential Profit Increase due to Number of Price Changes 100% % of Potential Profit Increase 80% 60% 40% 20% 0% Number of Price Changes in 12 periods Seas1 Seas2 IncMean DecMean Sawtooth Dynamic Pricing and Manufacturing 17

18 Number of Prices Number of prices needed varies depending on the pattern of variability The potential profit benefit varies depending on the pattern of variability Increase in Profit due to Dynamic Pricing Policies 6.0% Profit Increase over Fixed 5.0% 4.0% 3.0% 2.0% 1.0% 0.0% Number of Price Changes in 12 periods Seas1 Seas2 IncMean DecMean Sawtooth Summary of Insights Dynamic pricing may be used to increase profit or decrease demand variability The potential benefits of dynamic pricing depend on the type of demand variability of the product Significant profit potential may be attained with less than full dynamic pricing Dynamic Pricing and Manufacturing 18

19 Other Potential Impacts Reduction of variability in sales or production schedule Increase in average sales Reduction in average (or weighted average) price Reduction of inventory Multiple Products Multiple products share common production capacity No demand diversions among the different products Demand diversions allow for the price changes in one product to influence customers to divert from or to other products Corollary: If the revenue function associated with each product is concave, than the greedy algorithm generates the optimal prices. Dynamic Pricing and Manufacturing 19

20 Multiple Products 12 period horizon, with production and holding costs Demand curves based on typical products Demand Scenarios: Seasonality (car): low demand at beginning, increases in middle, decreases at end of horizon Decreasing Mean (laptop): demand steadily decreases from beginning to end of horizon Each product experiences the same seasonality effect Profit Potential with Multiple Products The percentage of profit potential often decreases as the number of products increases Profit Potential with Multiple Products 6.0% 5.0% Profit Potential 4.0% 3.0% 2.0% Seasonality (car) Seasonality (car), high var DecMean (laptop) DecMean (rev. portfolio) 1.0% 0.0% Number of Products Dynamic Pricing and Manufacturing 20

21 Toolbox of Options Systems have many tools to control profit Mix of products in the portfolio Inventory levels Price changes to match demand and supply Presentation Outline CAFE & Pricing Dynamic Pricing for Manufacturing Deterministic Model Computational Analysis Extensions & Issues Dynamic Pricing and Manufacturing 21

22 The Deterministic Pricing Problem Max F U (D) = S 1<t<T (R tu (D t ) - h t I t - k t X t ) Subject to: (1) Beginning Inventory: I 0 = 0 (2) Inventory Balance: I t = I t-1 + X t - D t, t = 1,2,,T (3) Production Capacity: X t Q t, t = 1,2,,T (4) Integrality: I t, X t, D t, integer 0, t = 1,2,,T At each period t, D t is the satisfied demand (sales) X t is the units of product produced I t is the end of period inventory Extensions Production set-up cost Multiple products with no diversions Multiple classes differentiated by leadtime Limited scenarios with multiple products and multiple parts Dynamic Pricing and Manufacturing 22

23 The Deterministic Pricing Problem Max F U (D) = S 1<t<T (R tu (D t ) - h t I t - k t X t ) Subject to: (1) Beginning Inventory: I 0 = 0 (2) Inventory Balance: I t = I t-1 + X t - D t, t = 1,2,,T (3) Production Capacity: X t Q t, t = 1,2,,T (4) Integrality: I t, X t, D t, integer 0, t = 1,2,,T At each period t, D t is the satisfied demand (sales) X t is the units of product produced I t is the end of period inventory Critical Issues Dynamic Pricing and Manufacturing 23

24 Critical Issues Stochastic demand Customers strategic behavior Competition Interaction among products Integrating components and configurations Learning of demand Special Interest: Automobiles In 1999, 40% of new buyers used Internet for shopping Several options for web purchasing e.g., Autobytel.com, Carsdirect.com Automobile manufacturers are founding e-divisions and e-initiatives e.g., e-gm Dynamic Pricing and Manufacturing 24

25 Special Interest: Automobiles Price changes exist, but prices almost exclusively decrease on a given product Price changes effected through rebates or dealer-customer negotiations To implement dynamic pricing in e-tailing, manufacturers and dealers would need to coordinate OEM/Retailer Models Dealer network Dealer network Dealer network Customers Dealers only Dealers only + Internet Customers Hybrid Customers Direct Physical delivery Sales/pricing to end consumer Dynamic Pricing and Manufacturing 25

26 Hybrid Models Strategic customers may break the system Certain models seem unstable Are there system policies that will make the models workable? OEM forces retail price within bounds Pricing and Customization Customers desire customization Having a few number of standard configurations is cheaper for production How can price be used to steer customers? How can price be used to reflect differences in available components? Dynamic Pricing and Manufacturing 26

27 Conclusions Dynamic pricing is useful but not in all situations It impacts profit as well as the system efficiency Pricing is one tool in the kit to match supply and demand and control profit Significant research needs to be done to increase realism Dynamic Pricing and Manufacturing 27