New Ways to Understand the Impact of Stock-outs (Julie Holland Mortimer and Christopher T. Conlon)

1. New Ways to Understand the Impact of Stock-outs(Julie Holland Mortimer and Christopher T. Conlon) Presented by: Julie Holland Mortimer Associate Professor Economics Department Harvard University

2. Introduction: Economists have devoted a lot of energy to understanding demand in different industries. We can tell you all kinds of things about aggregate demand, especially for manufacturing. For example: we were very good at predicting which cars people would switch to when GM discontinued the Oldsmobile brand, using only data on national market shares. But believe it or not, we almost never pay attention to availability.

3. Why has availability been ignored? Previous work focused on manufacturing, not retail or distribution. Data on availability was pretty scarce until relatively recently. Many people thought of availability as a small "friction" but not a main focus of firms. When does "full availability" fail? Stock-outs Capacity constraints Search costs Awareness

4. What goes wrong by ignoring availability? An example: We stock 5 units of product A and 10 units of product B each week. Product A sells out immediately. Consumers that arrive looking for product A choose B instead, and B sells 10 units by the end of the week. When we look at aggregate sales we get the story wrong. B appears to be more popular, but the product that truly sells is A. So sales data don't reflect true demand patterns. This gives two kinds of errors: Censoring: We only observe demand up to capacity. This understates demand for the stocked-out product. Forced Substitution: We observe sales for products that represent consumers' second choices as they substitute away from missing goods. This overstates demand for remaining products.

5. Bad demand estimates lead to bad predictions about profitability. Some consumers may walk away when a product is stocked-out. Many others may simply purchase a different product. Effect on profitability depends on this behavior, and on the margins of the different products. The effects might be different in the short-run than in the long-run.

6. Research goals: Incorporate availability in demand estimation, and estimate its impact for firms. Test the impact of stock-outs in the field. Quantify the impact of stock-outs on profitability. Develop a method to handle stock-outs, even with incomplete data reporting. Quantify the size of the error from ignoring the problem. Future goal: How can firms use wireless data to optimize restocking visits? (We need to understand the impact of stock-outs first.) Why vending? (I started in video rentals....) Great data for identifying stock-out events. Some aspects of supply are relatively straightforward (e.g., pricing). Feasible laboratory for field experiments. Other settings: retail, perishable and seasonal goods, sporting/entertainment events, airlines, etc.

7. Outline: Describe field experiments on availability Results from field experiments Impact of stock outs on profitability in experiments Preliminary model of consumer decisions Results from the model Comparison of the model results to the field experiments Implications for vending operators and lessons for economists

8. Description of Field Experiments

9. Field experiments implemented by Mark Vend Company in Chicago, Illinois. A total of 62 machines in office buildings in downtown Chicago Spread across 5 sites/locations “White collar” customer base Fairly stable demand patterns over time at these sites/machines Experimental design: 8 experiments were run (2 were repeated for accuracy) 6 experiments stocked out a single product 2 experiments stocked out two products simultaneously

10. The 8 experiments: Snickers Zoo Animal Crackers Dorito Nacho Cheetos Chocolate Chip Famous Amos M&M Peanut Dorito Nacho and Cheetos Snickers and M&M Peanut

11. More on the experimental design: For each run, we removed the focal product(s) for about 2.5 weeks Each machine is visited about 3 times during the experiment Data were collected from January, 2006 – February, 2009 Experimental dates range from June 2007 to September 2008 Experiments were run during the months of May – October (one in February, 2008) Data detail: DEX data collected at each service visit for each product/machine Few stock-outs occur outside of the experiments

12. Small products are consolidated into “generic” category products for reporting. Example:

13. Finding a baseline for comparison: We compare each visit during the experimental periods to several “control visits.” The control visits come from the same set of machines at other times. We “match” each experimental visit to four control visits of similar length (adjusting for weekends). In order to find a good “match” we use control visits with similar rates of sales for “non-substitutes.” Example of how we make a match: The focal product is Snickers, so all Snickers bars are removed during the experiment. To make a “match” we look at the rate of sales of salty snack products. We find visits during the control period in which sales of Doritos, Ruffles, and Cheetos are the same as the sales of Doritos, Ruffles, and Cheetos during the experimental period.

14. Calculating the effect of a stockout: A stockout changes the rate of sales of substitute products. Compare rates during the experimental periods to rates during control periods. There is one thing we can’t observe directly (at least without a video camera and approval from the Government): consumers “walking away” But we can estimate this from the change in rate of total vends. Economists call this the “outside good.”

15. An example of the data, from Experiment 1:

16. Identifying the best substitutes: There is always noise in sales rates, so we look at products with a statistically significant increase in sales. Call these products “substitutes.” Identifying “walkers”: The experiments show no change in total vends, implying no “walkers”. We calculate the impact of the stock-out under two scenarios: Assume there really are no walkers (May be OK in the short-run). Assume that some percentage of people walk away when their favorite product is stocked-out. Estimate the percentage that walk away for each product from a model of consumer choice, which examines how total vends fall when a product is not carried in a machine.

17. Modeling “walkers”: Use the maximum rate of sales at any machine for a visit. This allows for slower sales rates at Christmas, for example. Assumes that no machine beats your busiest machine. Under this assumption, model consumer choice during control periods to see how total sales respond when various products are not stocked. (This includes responses from machines that have different facings, in addition to stock-out events.) Results (for these machines/locations): Consumers of salty snacks rarely walk away (1%). Consumers of cookies walk away more often (11-12%). Consumers of chocolate bars walk away most often (20%). Alternatives: See how your own machines respond and use that to deflate. More data collection (use pressure pads or video cameras). Best method may vary based on machine location (public, office, school).

18. Where do Snickers consumers go, under the two scenarios?

19. What is the impact on the sales of Snickers substitutes?

20. The impact on profitability depends on: The number of “walkers” The margins on Snickers and all its substitutes For the two scenarios, we have the following impact: Scenario 1 (no walkers): We lose 827 sales of Snickers @ $0.24 margin (-$202.43). We gain 827 sales of substitutes @ $0.30 avg. margin ($242.57). The net effect is $40.14. Scenario 2 (20% walkers): We lose 827 sales of Snickers @ $0.24 margin (-$202.43). We gain 664 sales of substitutes @ $0.30 avg. margin ($194.67). The net effect is $-7.76.

21. In this case: The higher margin on substitutes (esp. cookie products) means that the stock-out may actually be profitable (depending on “walkers”). At least, in the short run. Longer-run effects, if clients get upset over time, aren’t captured here.

22. Results from All Experiments, No Walkers

31. Effects on Sales of Substitutes, No Walkers

40. Effects on Profitability

41. Snickers: For the two scenarios, we have the following impact: Scenario 1 (no walkers): We lose 827 sales of Snickers @ $0.24 margin (-$202.43). We gain 827 sales of substitutes @ $0.30 avg. margin ($242.57). The net effect is $40.14. Scenario 2 (20% walkers): We lose 827 sales of Snickers @ $0.24 margin (-$202.43). We gain 664 sales of substitutes @ $0.30 avg. margin ($194.67). The net effect is $-7.76.

42. Animal Crackers: For the two scenarios, we have the following impact: Scenario 1 (no walkers): We lose 383 sales of Animal Crackers @ $0.43 margin (-$166.79). We gain 383 sales of substitutes @ $0.34 avg. margin ($129.36). The net effect is -$37.43. Scenario 2 (11.5% walkers): We lose 383 sales of Animal Crackers @ $0.43 margin (-$166.79). We gain 339 sales of substitutes @ $0.34 avg. margin ($114.46). The net effect is -$52.33.

43. Dorito Nachos: For the two scenarios, we have the following impact: Scenario 1 (no walkers): We lose 451 sales of Doritos @ $0.44 margin (-$197.02). We gain 451 sales of substitutes @ $0.40 avg. margin ($179.05). The net effect is $-17.97. Scenario 2 (1% walkers): We lose 451 sales of Doritos @ $0.44 margin (-$197.02). We gain 447 sales of substitutes @ $0.40 avg. margin ($177.53). The net effect is $-19.50.

44. Cheetos: For the two scenarios, we have the following impact: Scenario 1 (no walkers): We lose 568 sales of Cheetos @ $0.44 margin (-$252.30). We gain 568 sales of substitutes @ $0.43 avg. margin ($244.29). The net effect is $-8.01. Scenario 2 (1% walkers): We lose 568 sales of Cheetos @ $0.44 margin (-$252.30). We gain 563 sales of substitutes @ $0.43 avg. margin ($242.20). The net effect is $-10.11.

45. Chocolate Chip Famous Amos: For the two scenarios, we have the following impact: Scenario 1 (no walkers): We lose 363 sales of Famous Amos @ $0.47 margin (-$170.84). We gain 363 sales of substitutes @ $0.46 avg. margin ($167.07). The net effect is $-3.76. Scenario 2 (11% walkers): We lose 363 sales of Famous Amos @ $0.47 margin (-$170.84). We gain 322 sales of substitutes @ $0.46 avg. margin ($148.07). The net effect is $-22.77.

46. M&M Peanut: For the two scenarios, we have the following impact: Scenario 1 (no walkers): We lose 517 sales of M&M Peanut @ $0.24 margin (-$124.52). We gain 517 sales of substitutes @ $0.34 avg. margin ($174.11). The net effect is $49.59. Scenario 2 (20% walkers): We lose 517 sales of M&M Peanut @ $0.24 margin (-$124.52). We gain 414 sales of substitutes @ $0.34 avg. margin ($139.61). The net effect is $15.10.

47. Dorito Nachos and Cheetos: For the two scenarios, we have the following impact: Scenario 1 (no walkers): We lose 1019 sales of Doritos and Cheetos @ $0.44 margin (-$444.48). We gain 1019 sales of substitutes @ $0.44 avg. margin ($444.48). The net effect is $0. Scenario 2 (1% walkers): We lose 1019 sales of Doritos and Cheetos @ $0.24 margin (-$444.48). We gain 1010 sales of substitutes @ $0.44 avg. margin ($440.49). The net effect is $-3.99.

48. M&M Peanut and Snickers: For the two scenarios, we have the following impact: Scenario 1 (no walkers): We lose 1344 sales of both products @ $0.24 margin (-$322.56). We gain 1344 sales of substitutes @ $0.29 avg. margin ($388.80). The net effect is $66.24 Scenario 2 (21% walkers): We lose 1344 sales of both products @ $0.24 margin (-$322.56). We gain 1057 sales of substitutes @ $0.29 avg. margin ($305.89). The net effect is $-16.67.

49. Model of Consumer Decisions

50. Model of Consumer Decisions We generally don’t get the chance to run experiments, so we do our best with “observational” data. We think of a consumer buying 1 unit. She chooses the product that makes her the happiest, given her choice set. Each product has an “average quality,” and people have “idiosyncratic” tastes for different products. There are a few common models; a simple one assumes that a consumer’s tastes for products are similar within a category.