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As a consequences of these stockouts, the company was forced to bring some of its logistics function back in-house.
If the cost of a stock-out rises, it will pay to stockpile additional inventory.
There is no chance of a midyear stock-out in any of the major agricultural commodity markets.
Again, the results for this model are distinct from those of the stockout avoidance models below in that they involve a calibrated average inventory-to-sales ratio.
To alleviate this problem, we now generalize the basic model, introducing idiosyncratic shocks across firms, in order to strengthen the stockout avoidance motive.
In our equilibrium model, stockout is associated with inventories being reduced to zero. 15.
Thus, we are led to consider a multisector generalization of the model that adds idiosyncratic shocks to strengthen the stockout avoidance motive.
The planning problem for this generalized formulation of the stockout avoidance model is listed below.
The omission of capital in our results for the generalized stockout model is potentially important.
Table 3 summarizes the baseline parameters for the generalized stockout avoidance model with two sets of intermediate goods firms.
Thus, the stockout avoidance model either cannot reproduce the measured level of inventories present in the actual economy or fails with regard to other important inventory facts.
As we noted above, even when generalized to allow for idiosyncratic risk (and absent capital accumulation), the stockout avoidance model continues to have difficulty in generating empirically viable inventory holdings.
As already noted, it is difficult to generate inventory-sales ratios close to the data in the basic stockout avoidance model, where aggregate risk motivates inventory holdings.
Although stockout avoidance has been prominent in rationalizing stocks within the recent inventory literature, the model itself has not previously seen a quantitative dynamic stochastic general equilibrium inspection.
Even with idiosyncratic risk added to strengthen it, the stockout avoidance motive is insufficient to generate stocks near the data without destroying model performance along other important margins.
It is also a strategy aimed at reducing stockout costs.
This means that shoppers will have a 42% chance of fulfilling a ten-item shopping list without encountering a stockout.
