6.6 Additional Behavioral Aspects of Decision Making
6.6.3 Order Adaptation and Demand Chasing
As we do not have independent demand over time, decision makers observing demand realizations should adapt the forecast in order to account for autocorrelated time series. Based on the above forecasting (Sect.6.3), we can analyze the normative effect of demand realizations on the production decisions of the decision maker.
Comparing order quantities between weeks, we can derive Theorem6.2regarding rational changes in order quantities based on demand realizations.
Theorem 6.2 (Order Adaptation) Orders are adapted as
qi;tD qi;t qi;t1D C z; (6.23) where
i;t D i;t i;t1; and i;tD i;t i;t1:
In Theorem6.2, z D Fi1.˛i/ denotes the z-value of the normal distribution for the optimal product-specific target service level from Eq. (6.10). As a result, we see that the decision maker might react to demand realizations based on the forecast updating, as we do not have independent demand over time.
As a first analysis on reactions to demand realizations Fig.6.5shows the changes in order quantities between weeks. Adjustment towards demand realizations might be a good strategy according to forecast updating.
If the decision maker is subject to demand chasing, he over-adapts his order quantities. Therefore, we have to take optimal order adjustments into account. To test the demand chasing effect for our data we conduct a regression for Eq. (6.24).
Formally, we estimate
qt;p;s D a0qt;p;s CX
˛
dt;p;s qt;p;s
C t;p;s C usp (6.24) using a fixed-effect regression, controlling for each store-product combination s p.
To calculate the order quantity qt;p;s for each product p at time t in store s and its
, we use zpcorresponding to the optimal target service level according to the best-fitting Model 2. ˛kis the chasing factor for the lag of k days. We model the demand chasing effect over the last D 6 working-days.
We use the same approach as in Sect.6.5.1, namely Model 2. Table6.5shows the estimation results.
Fig. 6.5 Order adjustments
0 .2 .4 .6 .8
Percentage of all decisions
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17* 18* 19* 20* 21* 22* 23*
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17* 18* 19* 20* 21* 22* 23*
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17* 18* 19* 20* 21* 22* 23*
By Products Repeat choice
.1 .2 .3 .4 .5
Percentage of all decisions
By Products
Adjustment toward prior demand
0 .05 .1 .15 .2 .25
Percentage of all decisions
By Products
Adjustment away from prior demand
98 6 Empirical Newsvendor Decisions Under a Service Level Contract
Parameter Opt M2 Baker Baker M2
qt;p 0 1
.d q/t6 0.189 0.695 0.480
Ind. stockoutt6 (0.001) (0.002) (0.002)
0.265 0.406 0.274
Log-likelihood (0.007) (0.013) (0.016)
506,803 727,842 732,136
Note that
dt;p qt;p
0, as we have censored demand data. Therefore, we additionally test the effect of a stockout indicator which is 1 for a stockout and zero otherwise. Our model generally also enables a normative increasing of order quantities, e.g., in cases of stockouts, qt;pis most likely greater than zero, as we have an average SL 70 % > 50 % and the updated forecast tends to increase in stockout situations and so most likely qt;p > 0.
We analyzed different models to investigate the effect of demand realizations.
As a baseline we regress the optimal order adaptations using the stockouts and the leftovers of the previous week. As we use these factors to calculate the optimal order quantities, this yields a simplified model. We see that the decision maker should adopt his order quantities downwards in case of left-over inventory (0:189) and upwards in cases of stockouts (C0:265). Model Opt M2 shows the overall effect of inventory and stockouts on order quantities. Using a0 D 0 in Model Baker, we see that the decision maker decreases the order quantity of a product by 0:695 units for each item left over in the previous week and increases his order quantity if a product is out-of-stock in the previous week (C0:406). Comparing this to our optimal baseline model we see that the decision maker adapts stronger than expected in both cases. Model Baker M2 analyzed this in detail and tests whether these adaptations are rational, setting a0 D 1. We also tested the other lags (1–5) which partly have a significant impact, but the size of these effects is negligible.
We see that ˛6 is significantly different from 0 for all models and we conclude that the decision maker is actually chasing demand with respect to the same weekday of the previous week. This chasing is not based on rational changes (Model Baker M2) and we conclude that the decision maker is chasing demand.
In a recent study Lau and Bearden (2013) analyze which method to use in order to test demand chasing. They show that several methods are prone to false detection of demand chasing, showing demand chasing where there is actually no chasing. They show that a simple correlation analysis is not prone to this failure.
Therefore, we also conduct a correlation analysis between last period demands and recent order quantities. In order to account for the autocorrelated demand series we do not compare the correlation coefficients against 0, but against the demand correlation. Table6.6shows the results of this analysis. We find that the correlation between last period demand and recent orders are significantly higher than the
Table 6.6 Correlation analysis to detect demand chasing
Product .dt; dt6/ .qt; dt6/
1 0.55 0.75
2 0.51 0.71
3 0.48 0.67
4 0.41 0.66
5 0.32 0.54
6 0.32 0.56
7 0.50 0.74
8 0.58 0.76
9 0.61 0.77
10 0.55 0.71
11 0.62 0.73
12 0.47 0.68
13 0.56 0.74
14 0.29 0.69
15 0.66 0.75
16 0.30 0.52
17 0.63 0.75
18 0.53 0.70
19 0.53 0.72
20 0.45 0.61
21 0.73 0.83
22 0.53 0.81
23 0.47 0.70
demand correlation (paired t -test, p < 0:0001). This also shows the demand chasing behavior of our empirical decision maker.
As a third test of demand chasing we analyze the correlation of last period’s inventory and the change of order quantities between periods (Rudi and Drake 2014). Figure 6.6shows the changes of empirical and optimal orders and sales depending on inventories of the last week. We see a strong positive correlation between inventories of the last week and the changes between weeks . D 0:63/.
Opposed to that optimal changes have a significant lower correlation . D 0:32/
and even slightly negative for sales . D 0:19/.
Our results show that demand chasing is not only a laboratory artifact, but also occurs in the real world. But the chasing is asymmetric, as the decision maker reacts more strongly on inventory than on stockouts.
100 6 Empirical Newsvendor Decisions Under a Service Level Contract
Delta order empirical Delta order opmal Delta sales
L6(d-q) 0
-50 -50 L6(d-q) 0 -50 L6(d-q) 0
50
0
-50
Fig. 6.6 Changes in quantities compared to inventories