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Capacity Utilization Rate Definition
The capacity utilization rate is a metric that measures the actual economic output that a firm or an economy realizes in relation to how factors of economic output are put to use. The capacity utilization rate reflects the proportion at which the levels of economic output are used.
In another language, a capacity utilization rate examines how well a nation or a firm utilizes its productive capacity. Hence, if there is a slack in the utilization of output capacity at a specific time, it is reflected through the capacity utilization rate.
The formula for calculating capacity utilization rate is:
(Actual Output / Potential Output ) x 100
A Little More on What is the Capacity Utilization Ratio
The capacity utilization rate measures the rate at which a country or firm utilized its levels of economic output, this is reflected by the proportion of economic output that a country actualizes over a period of time. When the capacity utilization rate is below 100%, it means the firm or country has not exhausted all its installed productive capacity. Firms or economies in this category can harness the unused part of their production capacity to achieve a greater economic output.
The capacity utilization rate is an essential metric that helps to relate economic output with the productive capacity of a nation.
Corporate Capacity Utilization Rates
Oftentimes, the capacity utilization rate is applied to goods that can be quantified, mostly physical goods that can be numbered. The efficiency of a company’s operating system is reflected through the capacity utilization rate. This ratio is helpful in determining the installed productive capacity of a firm or country and how effectively they have been used to realize economic output. Also, increase in unit costs can be identified through the capacity utilization rate.
Historical Capacity Utilization Rates
The capacity utilization rate has been put to use in many occasions. Data and results of capacity utilization in the United States are published by the Federal Reserve. The Federal Reserve also gathers historical data which helps in relating past events pertaining to economic output to recent happenings. Data on capacity utilization published by the Federal Reserve dated as far back as 1960. This was when the highest capacity utilization rate was recorded, at that time, the economic output achieved was up to 90%.
The greatest decline in the utilization rate was recorded in 1882 and 2009 respectively, this was when the Federal Reserve recorded a 70.9% and 66.7%, accordingly.
Effects of Low Capacity Utilization
According to the data gathered as published by the Federal Reserve, it is not only possible to achieve a high capacity utilization rate, a low capacity utilization rate can be realized. Capacity utilization is an essential economic indicator that draws the attention of policymakers and other stakeholders. When the utilization rate of a nation is low, it will have some potential effects on the nation and this is a major concern for policymakers.
Common examples of countries that were affected by low capacity utilization are France and Spain in 2015 and 2016. There was a high degree of slack noticed in these European economies as a result of low capacity utilization.
References for Capacity Usage Rate
Academic Research for Capacity Usage Rate
Productivity change, capacity utilization, and the sources of efficiency growth, Hulten, C. R. (1986). Journal of econometrics, 33(1-2), 31-50. This research indicates that we can decompose the actual short-run average cost into 2 terms: the Berndt-Fuss (BF) capacity utilization effect and the multi-factor productivity growth rate. We can carry out this decomposition using quantities and prices without recurring to the econometric assessment of the production function parameters. Another result examines the relationship between the actual short-run cost per unit and actual and false productivity residual which is multi-factory or by erroneously supposing that all factors are totally flexible. The results are that the growth rate of the real and false short-run residual is the same and it differs by the BF capacity utilization effect.
Capacity utilization measures: underlying economic theory and an alternative approach, Berndt, E. R., & Morrison, C. J. (1981). The American Economic Review, 71(2), 48-52. This paper focuses on the need to re-examine the Capacity Utilization (CU) measurement on the basis of the framework of a firm’s economic theory to estimate the impacts of changes in PE on it. The CU and capacity output is short-run notions. They are conditional on the quasi-fixed inputs’ stock of a firm. The authors illustrate the concept with the help of an example in which the variables are a production function, output flow, variable inputs vector, vector of service flows. The authors characterize the optimization issue as the increasing variable profits depending on output price and variable input price.
Investment, capacity utilization, and the real business cycle, Greenwood, J., Hercowitz, Z., & Huffman, G. W. (1988). The American Economic Review, 402-417. This study takes important factors of the business cycle into consideration, such as capacity utilization and investment. The analysis is based on the Keynes’ view which states that shocks to the investment marginal efficiency are significant for business fluctuations but come with the neoclassical structure with endogenous capacity utilization. If there is a rise in the efficiency of newly-produced products, it indices the new capital formation, increased depreciation of old capital and intensive utilization. The quantitative and theoretical analysis shows that the transmission and shock mechanisms may be significant components of business cycles.
Measuring and assessing capacity utilization in the manufacturing sectors of nine OECD countries, Berndt, E. R., & Hesse, D. M. (1986). European Economic Review, 30(5), 961-989. This paper follows the economic model of cost and production introduced by Klein, Cassels and Hickman. It states that the economic capacity output is the output at which short-run and long-run total cost curves are, on average, tangent to one another. The authors compute rates of CU (Capacity Utilization) as a ratio of actual output to capacity output. They identify and compute a translog variable cost function, to implement this concept empirically for the manufacturing industries of 9 OECD countries. The findings are that by the end of the sample collected from the history of the early 1980s, there exists chronic and pervasive excess capacity.
Capacity utilization, Corrado, C., & Mattey, J. (1997). Journal of Economic Perspectives, 11(1), 151-167. This paper examines the way, the Federal Reserve (FR) measures Capacity Utilization (CU) and explains the reason for CU considered as a useful indicator of business cycle fluctuations and inflationary pressures. The author also elaborates what is the reason that the economic developments have not substantially influenced the indicator value of CU, for example, the technological change, a shift in the workforce share to service-providing industries and enhanced international trade. The author presents a micro-theoretic definition of CU. Finally, he reviews the evidence on the microeconomics structural interpretations plausibility of the relationship between price changes and CU.
On the measurement of capacity utilization, Nelson, R. A. (1989). The Journal of Industrial Economics, 273-286. We can define the CU (Capacity Utilization) as a ratio of actual output to the one corresponding to (1) the least point on the SRATC curve (2) the tangency point between the SRATC and LRATC curves. However, practically, we mostly measure CU as a ratio of actual output to the highest potential output having consistency with the given capital stock. This research shows how we can compute the CU’s theoretical measures. The author evaluates the correlation between its 3 measures and its McGraw-Hill estimates with the help of data collected from a sample of United States privately-owned e-utilities from 1961 to 1983.
Scale economies, capacity utilization, and school costs: A comparative analysis of secondary and elementary schools, Riew, J. (1986). Journal of Education Finance, 11(4), 433-446. This research seeks a cost function to check, to what extent, the utilization and scale rate in school operation affect school costs. The variables include service quality, enrollment or service quantity degree of school CU (Capacity Utilization), environmental conditions influencing input requirements and input factors prices. The author makes a comparative analysis of the elementary and secondary schools and finds the scale economies, school costs and Capacity Utilization.
Primal and dual capacity utilization: an application to productivity measurement in the US automobile industry, Morrison, C. J. (1985). Journal of Business & Economic Statistics, 3(4), 312-324. The measures of Capacity Utilization This paper defines and represents the measures of Capacity Utilization (CU), i.e. dual cost and primal output. The author formalizes these CU measures in a monopolistic firm’s dynamic model. In order to characterize the measures of CU, he illustrates the concept with the help of a model, estimated for the automobile industry of the United States from 1959 to 1980. Then, he constructs dual and primal indexes of CU. Finally, he carries out the implementation of these indexes to the measures of Adjustment-of-Productivity for disequilibrium using the measure of dual cost.
The role of capacity utilization in long-period analysis, Amadeo, E. J. (1986). Political Economy, 2(2), 147-160. This paper is an extended version of previous research named ‘Steady-State Model of Capacity Utilization’ (SSMCU) where companies fix a target degree of CU (Capacity Utilization) to achieve with the help of their investment decisions based on the related expected events. The author demonstrates that while expectations of investors are validated by present experience in Steady-State Equilibrium, normal utilization degrees may persistently diverge. The author argues that this solution can be obtained if some expectations formation rules are introduced which were not on the previous version of this model. The new model contains 4 equations by which the author makes the long period analysis of the CU.
Capacity utilization under increasing returns to scale, Wen, Y. (1998). Journal of Economic theory, 81(1), 7-36. The Benhabib-Farmer Model (BFM) was objected on the ground of its empirical relevance as a potential account of real business cycle fluctuations. This research overcomes the objection of BFM. This is credited to the return-to-scale and elasticity effects of capital utilization. There is a close relationship of these effects with the empirical puzzles that capital plays an important role in explaining output’s cyclical movements and that the computed labour elasticity is larger compared to labour’s share. Because of these effects, persistent fluctuations and multiple equilibria easily occur for externalities in a growth model sufficiently mild so that the demand curve of aggregate labour is downward sloping.
The impact of product variety on automobile assembly operations: Empirical evidence and simulation analysis, Fisher, M. L., & Ittner, C. D. (1999). Management science, 45(6), 771-786. This paper examines the effect of product variety on the performance of an automobile assembly plant with the help of data collected from GM’s Wilmington Delaware Plant. The authors provide evidence on the production losses magnitude that is variety-related, the mechanisms by which variety affects performance and the impacts of labour staffing and option bundling policies on the product variety cost. Day-to-Day greater variability in the content of option/car has a fairly adverse effect on total labour hours/car produced, assembly-line downtime, overhead hours/car produced, inventory levels and major rework and minor repair but has no considerable short-run effect on total direct labour hours.
Rationing capacity between two product classes, Balakrishnan, N., Sridharan, V., & Patterson, J. W. (1996). Decision Sciences, 27(2), 185-214. This paper goes through the capacitor allocation issue which make-to-order manufacturing units face coming across expected demand excessive of available capacity. Particularly, the authors emphasize on manufacturing short-life-cycle of a firm or seasonal products, for example, fashion apparel. They devise a policy of capital allocation which enables such firms to differentiate between 2 product classes(one generating higher gains contribution/unit of its allocated capacity than the other). Consequently, there is selective orders rejection for a class with a lower unit contribution. The authors investigate capacity rationing effectiveness in a large array of conditions. The findings are that capacity rationing has a great role in raising total gain.
MRP with uncertainty: a review and some extensions, Murthy, D. N. P., & Ma, L. (1991). International Journal of Production Economics, 25(1-3), 51-64. MRP (Material Requirement Planning) is a system model used for planning in production processes. Several types of uncertainty influence the production process. The economists have advocated various approaches for Material Requirement Planning with uncertainty. This research has been carried out to review and discuss the present research of economics researchers on MRP with uncertainty because of quality variations in the process of production. The authors present their own approach keeping in view the previous literature and propose an extended model with examples.
A system dynamics model for dynamic capacity planning of remanufacturing in closed-loop supply chains, Vlachos, D., Georgiadis, P., & Iakovou, E. (2007). Computers & Operations Research, 34(2), 367-394. This paper reviews the development of policies related to efficient capacity planning in reverse supply chains for remanufacturing facilities considering economic as well as environmental problems, for example, the take-back obligation, the legislation imposes and the Green Image (GI) Effect on customers demand. The authors analyze the generic system behaviour using a simulation model on the basis of principles stated in the system dynamics methodology. This model uses an experimental tool to examine alternative policies of long-time capacity planning with the help of the total profit of the supply chain as a measure of the effectiveness of the policy. Further, the authors illustrate the implementation of the developed approach.
Performance impacts of information technology: Is actual usage the missing link?, Devaraj, S., & Kohli, R. (2003). Management science, 49(3), 273-289. The driver of Information Technology effect is not to invest in the technology but to actually use the technology. The authors test this proposition in a healthcare system’s longitudinal setting containing 8 hospitals. They analyze the monthly data for 3 years on different non-financial and financial hospitals performance measures and the use of technology. The findings are that the use of technology is positively associated with measures of quality and hospitals revenue. The authors triangulate the analysis using 3 technology usage measures. The actual usage can be the main variable in highlighting the effect of technology on performance. The omission of this variable can be a missing link in Information Technology Payoff analysis.
Operating costs and capacity in the airline industry, Tsai, W. H., & Kuo, L. (2004). Journal of air transport management, 10(4), 269-275. This paper explains how to estimate accurate cost, which includes the individual flights and airplanes operating costs, the cost/available seat KMs and per available ton KMs with the help of activity-based costing. It allows specifies the key activity drivers and items of each flight and airplane. Moreover, using a case study, it explains how to calculate the marketing variance, production variance and estimate capacity of the idle passenger in the airplane sector. This information is useful when the lease or buying an airplane under idle capacity conditions.
Transport user charges and cost recovery, De Palma, A., & Lindsey, R. (2007). Research in Transportation Economics, 19, 29-57. As the Celebrated Cost Recovery (CCR) approach says, the extent of cost recovery from optimal user charges is subject to the degree of scale economies in user costs, operating costs and infrastructure construction costs. This paper presents an approach and reviews its different generalizations. Then, it provides statistical evidence by transport mode on the extent of scale diseconomies or economies in infrastructure and usage and deficits or the predicted surplus with efficient investment and pricing. It also brings some practical challenges under discussion in translating the theorem of cost recovery into policy.
On the efficiency of cost-based decision rules for capacity planning, Balachandran, B. V., Balakrishnan, R., & Sivaramakrishnan, K. (1997). Accounting Review, 599-619. The latest studies on activity-based product costing states that it is economically well-grounded to use product costs for making decisions of capacity planning and long-run product. The capacity resources imply soft constraints (we can increase the capacity in a short time based on ‘as-needed’). However, hard constraints are also implied by several capacity resources (we cannot change the capacity in a short time once installed). This paper finds the economic loss from the use of product cost data for capacity planning in case of hard constraints. The authors also evaluate 2 other rules of capacity planning. The bottleneck planning model solutions dominate the product cost-based solution.
A dual-cost heuristic for the capacitated lot sizing problem, Trigeiro, W. W. (1987). IIE transactions, 19(1), 67-72. This article evaluates the mathematical programming technique of accounting the capacity cost for the multi-item, deterministic single operation lot sizing issue. With the CLSP capacity constraints eliminated with Lagrangian Relaxation, the issue decomposes into a man incapacitated single product set lot sizing issues solved using dynamic programming. The subgradient optimization updates the Lagrangian dual costs. The author constructs feasible solutions (production policies within the capacity constraints) with the help of a heuristic smoothing procedure. The dual-cost heuristic provides solutions that are, on average, better as compared to other tested algorithms (faster than a few comparable algorithms).
Does capacity utilization affect the “stickiness” of cost?, Balakrishnan, R., Petersen, M. J., & Soderstrom, N. S. (2004). Journal of Accounting, Auditing & Finance, 19(3), 283-300. This research is an extension of Anderson et al paper, published in 2003, to capture the impact of 2 factors which can moderate the response of the manager to changing activity levels. The changes magnitude may affect the response proportionality. Important transaction costs attached to changing cost levels rationally may cause the response to a great change in the activity being proportionally greater as compared to the response to a minor change in activity. Finally, the authors investigate whether the Capacity Utilization (CU) influences the cost stickiness.
Information and time-of-usage decisions in the bottleneck model with stochastic capacity and demand, Arnott, R., De Palma, A., & Lindsey, R. (1999). European Economic Review, 43(3), 525-548. This study makes an analysis of the impacts of information on time-of-use decisions and participation for a facility depending on queueing congestion when the unpredictable and predictable fluctuations are found in demand and capacity. If the elasticity demand is constant and delays cost functions schedule, the expected social surplus is higher with perfect as compared to with imperfect data. But in perfect data can decrease welfare by causing adverse changes in time-of-usage of individuals. This result has implications for the emerging technologies design, for example, ATIS (Advanced Traveller Information Systems) which convey information on usage conditions.