Price Variance – Definition

Cite this article as:"Price Variance – Definition," in The Business Professor, updated September 20, 2019, last accessed June 5, 2020, https://thebusinessprofessor.com/lesson/price-variance-definition/.

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Price Variance Definition

Price Variance is the difference in the actual amount for which an item is purchased and the standard price of the item. It refers to the actual cost of an item minus its standard cost.

The Formula for calculating price variance is:

Price variance = (actual price – standard price) x actual quantity

If the actual cost of an item is higher than the standard price, the price variance would be positive. If the actual price is lower than the standard price, the price variance will be negative.

A Little More on What is the Price Variance

Price variance is an important factor that is considered when planning the annual budget of a company. Oftentime, there is a variance between the actual price paid for an item and the standard price that the company is meant to pay. When budgeting, the management team often assign standard price to items.

A modification in the number of goods to be purchased can cause price variance. When a price variance is positive, it means the actual price paid for the item purchased is more than its standard price and this is unfavorable for the company. There are also occasions where the actual price paid will be lower than the standard price of an item.

Analysis

Material usage variance reflects how materials or items purchased are put to use. The utilization of materials determine where there would be a favorable material usage or an unfavorable one.

References for Price Variance

https://accounting-simplified.com/management/variance-analysis/material/usage.html

https://www.investopedia.com/ask/answers/052215/what-price-variance-cost-accounting.asp

http://www.businessdictionary.com/definition/capacity-usage-variance.html

Academic Research for Capacity Usage Variance

Activity-based systems: Measuring the costs of resource usage, Cooper, R., & Kaplan, R. S. (1992). Accounting horizons, 6(3), 1-13. This article explores the use and design of emerging systems known as ABC (Activity-Based Cost). The conventional cost systems use allocation bases that are volume driven, e.g. sales dollars, direct labour dollars and machine dollars to allocate organizational expenditures to customers and products individually. But mostly, their resource demands are not proportional to the unit’s volume sold or produced. Hence, traditional systems don’t accurately calculate the resources costs to deliver and sell the products to customers. Firms, inclusive of those having good conventional systems, have introduced cost systems based on activities. This is to directly associate the cost of organizational activities to the customers and products for which they perform these activities.

Productivity measurement and management accounting, Banker, R. D., Datar, S. M., & Kaplan, R. S. (1989). Journal of Accounting, Auditing & Finance, 4(4), 528-554. This paper describes the importance of productivity measurement for the United States industry. Most of the researchers and the economists emphasized the productivity measurement on the national level. If a company fails to achieve its targets or becomes inefficient, then the accounting system will report several unfavourable usage variances. The authors present an empirical analysis of the management accounting and the standard cost accounting systems.

Flexible budgeting and variance analysis in an activity-based costing environment, Mak, Y. T., & Roush, M. L. (1994). Accounting Horizons, 8(2), 93. This research has been carried out to discuss the costing environment based on the activity. The authors present the variance analysis and bring the flexible budgeting under consideration for a better understanding of the costing environment.

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 compute accurate costs with the help of activity-based costings, such as costs/available seat KMs, operating costs for all airlines and flights and per available Ton KMs. It also specifies items of the main activity and drivers of every flight and airline. Moreover, it describes a case study to elaborate on the estimation of marketing variance, expected idle passenger capacity and production variance in the airplane sector. This information is useful while the lease or buying an airplane under the idle capacity conditions.

Information distortion in a supply chain: The bullwhip effect, Lee, H. L., Padmanabhan, V., & Whang, S. (1997). Management science, 43(4), 546-558. Each firm, in a supply chain, orders from its quick upstream member. Inbound orders act as valuable input. This research claims that the data transferred as orders are likely to be distorted and can mislead the upstream members in their production and inventory decisions. Particularly, the orders variance can be larger than the sales variance and the distortion enhances as one moves upstream. This phenomenon is called ‘Bullwhip Effect’. The author analyzes 4 means of the Bullwhip Effect, i.e. rational game, price variations, demand signal processing and order batching. He also discusses the actions taken to lessen the detrimental effect of his distortion.

Cost Accounting in the New Manufacturing Environment [2], Howell, R. A., & Soucy, S. R. (1987). . Strategic Finance, 69(2), 42. This article is an extension of the previous research in which the authors describe the role of cost accounting in the manufacturing environment and what changes are noticed in its new environment. The consumers and manufacturers behaviour has also changed with the creation of a new environment. The producers are better able to provide high-quality products and strive to meet up the consumers’ requirements.

Capacitated lot sizing with setup times, Trigeiro, W. W., Thomas, L. J., & McClain, J. O. (1989). Management science, 35(3), 353-366. This paper emphasizes on the impacts of setup time on lot sizing. The setting is the CLSP (Capacitated Lot Sizing Problem) containing non-stationary costs, setup times and demands. A Lagrangian relaxation enables the CLSP constraints to decompose into incapacitated single product lot sizing issue. The subgradient optimization updates the Lagrangian dual costs and the dynamic programming resolves the single-item problem. The issues with constraints of extremely tight binding capacity are too hard to solve as compared to the anticipations. The algorithm resolves issues with setup cost or setup time.

Why do we need lean accounting and how does it work?, Maskell, B. H., & Kennedy, F. A. (2007). Journal of Corporate Accounting & Finance, 18(3), 59-73. American manufacturers are thinking about how to increase productivity fast and reduce costs, create better value for the customers, enhance flexibility, raise the stock price, profits and cash flows. As those firms selecting lean principles as their fundamental business model will want to get success at every cost, this paper describes 6 reasons for the need to change the accounting methods before firms can completely realize the advantages of their lean transformation. It presents many basic methods of lean accounting and tools supporting 3 main prospects of a lean organization, continuous improvement, visual management and value stream management. The authors implement these methods successfully in several companies at different stages on the way of legal transformation.

Flexible budgeting in an activity-based costing framework, Kaplan, R. S. (1994). Accounting Horizons, 8(2), 104. This research elaborates the costing framework based on the activity. The authors present the variance analysis and bring the flexible budgeting under consideration for a better understanding of the costing environment.

Supply chain models with greenhouse gases emissions, energy usage and different coordination decisions, Bazan, E., Jaber, M. Y., & Zanoni, S. (2015). Applied Mathematical Modelling, 39(17), 5131-5151. This article proposes 2 models which use energy for production with the emissions of GHG (Greenhouse Gases) from the operations of transportation and production in a single buyer-single vendor system under the emission-taxing scheme which is multi-level. The 1st model applies the policy of a VM-CS agreement (Vendor-Managed Inventory with Consignment Stock). The authors provide numerical examples and make a comparison of 2 models to highlight the insights and managerial implications. For both models, energy usage is a major component of cost, so, mitigating it is a priority. The findings are that the VMI-CS model permits the system to work more economically.

Variance amplification and the golden ratio in production and inventory control, Disney, S. M., Towill, D. R., & Van de Velde, W. (2004). International Journal of Production Economics, 90(3), 295-309. This paper presents a model of discrete linear control theory using a replenishment rule, also known as Deziel Eilon automatic inventory, pipeline and production control mechanism, is ensured to be stable. The authors derive an analytical expression for the term Bullwhip from a z-transform model. It directly equals the general statistical measure mostly used in empirical, simulation and statistical works for quantifying the Bullwhip Effect. We can decrease it if we take the fraction of error in the actual and target inventory and orders placed or Work-in-Progress (pipeline) positions. The authors present 2 types of objective function. The first one uses the golden ratio to estimate the optimal gain while the second visualizes all possible solutions.

The measurement of capital usage using electricity consumption data for the UK, Heathfield, D. F. (1972). Journal of the Royal Statistical Society. Series A (General), 208-220. This study is based on electricity consumption data in the United Kingdom. With the help of this data, the measurement of capital usage has been performed.

Production frontiers and panel data, Schmidt, P., & Sickles, R. C. (1984). Journal of Business & Economic Statistics, 2(4), 367-374. This paper focuses on the panel data and the production function of the normal regression type presented in the production frontiers model.

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