David G. Bivin

Bunching in the production process

Abstract This paper demonstrates that the variance of flows within the production process declines as the products move downstream from raw material deliveries toward completion. Thus, the production process magnifies and transmits shocks within an industry to that industrys suppliers.

Inventories and Interest Rates: A Stage of Fabrication Approach

Maccini, Moore, and Schaller (2004, hereafter MMS) recently uncovered a long-run inverse relationship between interest rates and finished goods in a number of nondurables industries and the nondurables sector overall. Their primary innovation is a regime-switching model of the real interest rate. This paper extends that model to include work-in-process and raw material inventories as well as industries that produce to order. A cointegrating model similar to that of MMS is then estimated for all three types of inventories and all industries in both the durables and nondurables sectors. The results reinforce the MMS conclusion of a long-run inverse relationship between the real interest rate and finished goods during the 1967-1997 period. But the results for the durables sectors, work-in-process and raw materials, and the 1997-2007 period are generally much less supportive. Moreover, results for the manufacturing aggregates suggest that the overall responses are small.

A Model of the Production Lag and Work-in-Process Inventories

As a rule, models of output and inventory behavior posit a very simple and mechanical view of the production process. This paper proposes a more sophisticated model in which the production lag is a decision variable. The primary goal is to develop a model of work-in-process inventories to complement the linear-quadratic model of finished goods that is prominent in the literature. This is an important extension because the month-to-month change in work-in-process accounts for a significant portion of the total variance in total inventory investment. Stylized facts on the behavior of inventories and production are presented and it is demonstrated that these facts are well-captured by the model when production lags are allowed to be very flexible.

The influence of inventories on output and prices: A stage of fabrication approach

This paper estimates models of output, price and total inventory in various manufacturing sectors that allow inventories at different stages of fabrication to exert independent influences. The hypothesis tests generally reject the hypotheses that inventories are irrelevant or can be aggregated across stages of fabrication in the output and price models. The hypothesis that raw materials and work-in-process are irrelevant is rejected in about half of the output and price model. In two-thirds of the industries, inventory behavior is independent of the composition of stocks inherited from the previous period.

Gauging the performance of the linear-quadratic inventory model

The explanatory and predictive abilities of the infinite-horizon linear-quadratic inventory model are gauged using the flexible-accelerator model as a baseline. Tests of explanatory power for six nondurables industries indicate that the flexible accelerator has superior explanatory power for inventories and output in the majority of industries. Tests of predictive ability during the late 1990s also support the superiority of the flexible-accelerator model.

Firm performance under just-in-time and traditional proxies for profit maximization☆

Abstract When the profit function is too complex to maximize directly, firms must rely on some other performance criterion to guide work station activity. Simplification is achieved by emphasizing either flow or stock management. Flow management seeks to ensure that no work station is “starved” by available inputs. Stock management seeks to minimize inventories. This paper develops a model to assess the performance of these two approaches for a firm that can carry both input and output inventories. The model includes shocks to both demand and production and the possibility of a stockout is explicitly reflected in the optimization problem. Stock management dominates flow management when carrying costs are sufficiently large. When carrying costs are moderate, the strategy of simply ignoring shocks to demand and capacity dominates both stock and flow management. The flow-management approach is superior only when carrying costs are very small.

Accounting for output fluctuations in manufacturing

There is a substantial body of evidence to the effect that output is more volatile than sales among manufacturing industries. Numerous explanations have been advanced to account for this excess output volatility. Some examples are pro-cyclical inventory movements induced by a stockout-avoidance motive, cost and technology shocks and decreasing marginal costs. This article assesses the contribution of these different motives to output volatility for six different manufacturing industries. Linear–quadratic models are estimated for each of the industries and then dynamic simulations are employed to determine the volatility of output when one or more of the factors are removed from the model. Technology shocks provide the most significant contribution to output volatility. The stockout-avoidance motive is also important. Cost shocks provide a very small contribution and marginal production costs are increasing at the margin and thus stabilize output. It is also shown that output volatility declines when current values of sales and material costs are assumed known rather than forecasted from prior periods’ values.