J. of Acc. Ed. 28 (2010) 85–102

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Teaching and Educational note

The causes of misapplied capacity related manufacturingcosts and corresponding reporting implications: Aconceptual perspective

Kenneth Snead ⇑, David Stott 1, Andy Garcia 2

Department of Accounting & MIS, College of Business, Bowling Green State University, Bowling Green, OH 43403-0001, USA

a r t i c l e i n f o a b s t r a c t

Article history:Available online 8 March 2011

Keywords:Fixed manufacturing overheadCapacity costs

0748-5751/$ - see front matter � 2011 Elsevier Ltdoi:10.1016/j.jaccedu.2011.02.001

⇑ Corresponding author. Tel.: +1 419 372 8160.E-mail addresses: ksnead@bgsu.edu (K. Snead),

1 Tel.: +1 419 372 2709.2 Tel.: +1 419 372 7812.

To meet external financial reporting requirements, fixed (i.e.,capacity related) manufacturing overhead costs are typicallyapplied to inventory via the use of a predetermined overheadapplication rate. However, textbooks do not consider all appropri-ate conceptual issues regarding the setting of the overhead appli-cation rate nor how these issues influence the causes ofmisapplied capacity costs (under/over-applied fixed manufactur-ing overhead) typically reported as the Production Volume Vari-ance. Specifically, discussion is lacking related to thosemisapplied capacity costs potentially caused variously by the pres-ence of capacity that is not explicitly planned to be used, capacitythat is currently unused but in the longer-term is planned to beused (due to anticipated growth), and capacity that is currentlyunused but in the shorter term is planned to be used due to season-ality. Determining if any of these three causes are contributing tomisapplied capacity costs is critical, as there are important mana-gerial accounting and financial accounting reporting implicationsassociated with each. And while the relevant literature to be dis-cussed offers support for these causal constructs, this paperextends this literature by developing a parsimonious and concep-tually-based approach to permit a simultaneous partitioning ofmisapplied capacity costs into these causal categories. Further, thispaper will identify the important conceptual differences amongthese three causes, how these differences warrant uniqueapproaches for the managerial and financial reporting of informa-tion related to capacity costs and utilization, and needed changes

d. All rights reserved.

dstott@bgsu.edu (D. Stott), garcia@bgsu.edu (A. Garcia).

3 For this reason, these manufacturing support cos4 The authors performed a review of a dozen man

from 1979 through 2011 (nine were dated post-200potential causes of the Production Volume Varianexception of one, all simply recommended eitherexception is found in the text of Blocher, Stout and CProduction Volume Variance as well as the need for

86 K. Snead et al. / J. of Acc. Ed. 28 (2010) 85–102

to Generally Accepted Accounting Principles to facilitate moreappropriate financial reporting in this area.

� 2011 Elsevier Ltd. All rights reserved.

1. Introduction

In manufacturing contexts, fixed manufacturing overhead (FMO) costs are considered the costs ofproviding production capacity (e.g., Balachandran, Balakrishnan, & Sivaramakrishnan, 1997).3 Further,misapplied capacity costs are the dollars of under/over-applied FMO resulting from the difference be-tween a given period’s actual FMO cost incurred and the FMO dollars applied/attached to productionactivity during the same period. Understanding the underlying causes of these misapplied capacity costsis important for the effective management of production capacity. Capacity management has been de-scribed as one of the most challenging areas confronting management (McNair, 1994), prompting spe-cific exhortations to provide more detailed reporting for management related to the cost of both usedand unused capacity (Buchheit, 2003; Euske & Vercio, 2007; McNair & Vangermeersch, 1998). Unfortu-nately, traditional managerial accounting product-costing procedures have not been responsive to theseexhortations.

Given the ‘‘indivisible’’ nature of capacity costs, the requirement of Generally Accepted AccountingPrinciples (GAAP) (see FASB ASC 330-10-30, 2010) to attach these costs to products presents the inter-esting conceptual issue of selection of the level of base, or capacity level, to be used to determine therate at which these costs will be applied to products. Accordingly, textbooks offer for considerationvarious levels of capacity, ranging from measures of theoretical capacity to expected annual capacity.Upon selection, the level is typically used to compute the annual predetermined FMO rate found innormal and standard costing environments. However, textbooks fall short of considering all appropri-ate conceptual issues when discussing issues related to the reporting of the misapplied FMO, or capac-ity costs, which inevitably results from the choice of the capacity level used to establish thepredetermined (fixed) overhead application rate. In particular, discussion is lacking that simulta-neously considers the implications of these various capacity-level measures for identification of thepotential causes of the misapplied capacity costs (e.g., permanently idle capacity, temporarily idlecapacity related to inter-year growth, temporarily idle capacity related to intra-year seasonality) typ-ically reported in a single summary measure referred to as the Production Volume Variance (or theDenominator Activity Variance).4 Accurate identification of all causes is vital, as there are importantinternal and external reporting implications associated with each cause (Dilton-Hill & Glad, 1994).Therefore, the purpose of this paper is to provide a more complete conceptual discussion of the issuespertinent to the causes of misapplied capacity costs, and the managerial accounting and financial report-ing implications associated with these causes.

This discussion is organized as follows. First, we review the literature supporting both the motiva-tion of, and concepts discussed in, the paper. Then, concepts relevant for that portion of the FMO raterelated to the capacity cost component are discussed. This is followed by a presentation of the impli-cations of various capacity levels for identifying causes of intra-year misapplied capacity costs due toseasonality and idle capacity; consideration is then given for the potential for estimation error to alsobe a source of these misapplied capacity costs. The discussion then broadens to consider these sameissues in a longer-term, multiple-year context, involving increasing annual demand growth. An illus-trative example is then presented and is followed by an examination of the implications of thesecauses of misapplied capacity costs for both managerial accounting and financial reporting.

ts are sometimes referred to as capacity-related costs or simply capacity costs.agerial accounting and cost accounting textbooks ranging in publication dates

0). With the exception of one, none provided an in-depth discussion of thece beyond the obvious/generic presence of idle capacity. Further, with thea ‘‘write-off’’ or proration approach for disposing of these variances. Theokins (2010) which notes both the potential for seasonality to be causing thea ‘‘holding account’’ for that portion of the variance that is seasonally related.

K. Snead et al. / J. of Acc. Ed. 28 (2010) 85–102 87

2. Supporting literature

Evidence of the perceived importance of the management of capacity costs and associated issuespresented in this paper is provided by the abundance of articles/works appearing in a variety of pub-lication venues, ranging from practitioner-oriented, to academic-oriented, in both ‘‘accounting’’ and‘‘non-accounting’’ journals. While capacity costs can include both manufacturing and non-manufac-turing costs, the focus of both the literature cited below and this paper’s analysis is on the former. Arti-cles/works of relevance for this paper relate to the need for managing capacity likely to be idle forcertain periods within the year due to the presence of seasonal demand, capacity likely to be idlefor certain periods in the longer-term due to the presence of anticipated growth, and capacity havingthe potential to remain permanently idle.

With respect to seasonal demand, Ketzenberg, Metters, and Semple (2006) and Ludwig, Treitz,Rentz, and Geldermann (2009) develop heuristics for balancing the opposing costs of carrying inven-tory versus idle capacity in seasonal markets. Deng and Yano (2006) use analytics to develop optimalapproaches for joint production and pricing decisions in the presence of seasonal demand, capacityconstraints, and setup costs. Instead of relying on inventory buildups, several articles (e.g., Alp &Tan, 2008; Tan & Alp, 2009) use mathematical modeling to develop optimal policies related to theuse of a temporary workforce and overtime to create ‘‘flexible capacity’’ to be used in combinationwith ‘‘permanent capacity’’ as way to deal with seasonal demand. Similarly, Corominas, Lusa, and Pas-tor (2007), and Lusa, Corominas, and Pastor (2008), employ mathematical modeling to investigate thepotential for irregularly distributing company workers’ working hours over the year (‘‘annualizingworking hours’’) as another way to create flexibility for managing seasonal demand. And, the needto consider the prospect for seasonality when planning for demand is discussed in Bower (2010).

Examples of works dealing with the issues related to anticipated growth implications for capacitymanagement include Dilton-Hill and Glad (1994) who explore this area via the scenario of launching anew product with plans for capacity use to increase over time as market share increases. Further,Banker, Hwang, and Mishra (2002) mathematically investigate, in their ‘‘Proposition 4,’’ the benefitsof carrying idle capacity in a period preceding expected demand growth and appropriate productcharges for capacity costs. An additional example is found in Ryan (2004) whose mathematical mod-eling of capacity expansion, in the presence of demand growth and lead times, finds that high ex-pected demand growth will motivate larger and earlier capacity expansions. Further, Bower (2010)notes the importance of incorporating growth trends into demand plans developed by management.

And as to the potential for some measure of capacity to be permanently idle, Zhang, Fu, and Zhu(2008) demonstrate the importance of assigning ‘‘protective capacity’’ to machines in the semi-con-ductor manufacturing industry as a buffer against machine utilization variability caused by lack ofin-process or materials inventory, wait times, etc. In a similar vein, Hutchinson and Liao (2009) citethe advantages of carrying ‘‘capacity buffers’’ as opposed to ‘‘inventory buffers’’ in stochastic environ-ments, while Leitch (2001) demonstrates, via simulation techniques, the benefits of carrying excesscapacity in the areas of inventory cost reduction and contributions to increased throughput. In a re-lated study, Göx (2002) develops a capacity planning and product-pricing model that simultaneouslyconsiders demand uncertainty and both ‘‘soft’’ and ‘‘hard’’ capacity constraints. ‘‘Soft’’ constraints maybe relaxed with incurrence of a penalty cost while ‘‘hard’’ constraints are incapable of being relaxed,and thus have the potential for related capacity to be permanently idle.

The above-identified concepts of temporarily idle capacity due to short-term seasonality and/oranticipated longer-term growth, as well as potentially permanently idle capacity, can be associatedwith categories of unused capacity presented in more comprehensive works related to capacity man-agement. Specifically, the Consortium for Advanced Management-International (CAM-I) capacitymodel (1996) and the capacity management monograph by McNair and Vangermeersch (1998) iden-tify constructs for unused capacity that can be directly mapped onto those presented in this paper. TheCAM-I model’s capacity categories of ‘‘idle, off limits’’ and ‘‘idle, not marketable’’ and McNair and Van-germeersch’s (1998) capacity-deployment category of ‘‘excess capacity’’ are consistent with this pa-per’s ‘‘permanently idle capacity’’ construct. Further, the CAM-I model’s ‘‘idle, marketable’’ capacityand McNair and Vangermeersch’s (1998) ‘‘planned idle capacity’’ are consistent with the notions of

88 K. Snead et al. / J. of Acc. Ed. 28 (2010) 85–102

both temporarily idle capacity sources caused by seasonality and growth presented in this paper. It isnoted that several works underpinned by the CAM-I model have emerged. Examples include casestudies by Buttross, Buddenbohm, and Swenson (2000) and Muras and Rodriguez (2003) which applyCAM-I’s capacity cost portioning notions to jet propulsion and lubricants manufacturing entities,respectively. In addition, Sopariwala (2006) provides a hypothetical example of how CAM-I capac-ity-reporting notions can be implemented across various levels and resource types within a manufac-turing content. Euske and Vercio (2007) augment the cost and process relationship portrayed by theactivity-based costing cross diagram with CAM-I capacity-cost categorizations to assist managementin balancing the demand for, and supply of, capacity.

All of the notions espoused by these works are motivated by the desire for management to make‘‘optimal’’ decisions in the areas of capacity acquisition, capacity deployment, and capacity divest-ment. Conspicuously absent in this stream of literature are the managerial accounting implicationsof the capacity-management topic. In fact, Balachandran et al. (1997) conclude their article withthe exhortation, ‘‘Finally, field-based research that examines actual capacity planning practices andthe role of management accounting in capacity planning would be a useful next step’’ (p. 616). Sim-ilarly, Buchheit (2003) concludes his article with the observation that providing management withmore detailed reports related to the costs of unused capacity may mitigate the dysfunctional reactionsof managers to traditionally reported capacity-utilization information he observed in his study. Exhor-tations for more detailed reporting for managers in this area also come from Euske and Vercio (2007).

The few articles that directly address this managerial accounting component often do so by offeringimprovements to the way capacity-utilization information is reported to management. For example,Balakrishnan and Sprinkle (2002) develop a modified version of profit variance analysis that incorpo-rates the impact of capacity cost absorption from planned changes in inventory levels. Sopariwala(2003) extends notions presented in the Horngren, Foster, and Datar (2000) textbook to incorporatethe marginal impact of growth on capacity cost variance analysis when that growth has motivatedan expansion of capacity. Kren (2008) discusses behavioral implications of various methods of servicedepartment allocations containing capacity costs, while Parkinson (2009) discusses the interrelation-ship between capacity cost variances and the computation of sales volume variances.

While all of the above-referenced works support both the importance of capacity-cost manage-ment as well as the potential causes of misapplied capacity costs (e.g., CAM-I, 1996; McNair & Van-germeersch, 1998), the primary contribution of this paper is found in its development of aparsimonious and conceptual algebraic approach to operationalize many of the capacity constructspresented in these works. Specifically, this approach simultaneously partitions periodic misappliedcapacity costs into the potential causal categories of ‘‘temporarily idle due to either seasonalityand/or growth,’’ ‘‘permanently idle,’’ and/or ‘‘estimation error.’’ This is accomplished by the mergingof capacity-deployment notions with common measures for capacity that should be readily availableto management (viz., practical capacity, normal capacity, expected annual capacity usage, and actualcapacity used). Further, this paper considers the potential for some of these causes of misappliedcapacity costs to offset in both the short-term due to seasonality, and in the long-term due to demandgrowth. This potential offsetting creates some interesting and challenging implications for managerialaccounting and financial reporting, which are also discussed in this paper and are thought to beresponsive to exhortations for more detailed reporting of capacity deployment for both used and un-used capacity (e.g., Buchheit, 2003; Euske & Vercio, 2007). Finally, the parsimonious nature of the ap-proach employed by this paper is considered a significant contribution over other works. For example,the CAM-I model, one of the models discussed in the McNair and Vangermeersch (1998) monograph,was originally developed for the semi-conductor industry and requires that a significant amount ofdetailed data be obtained via sophisticated information system capability. In contrast, this paper’smore parsimonious model should make it more easily adaptable to a variety of industries.

3. Rate of capacity-cost assignment

Establishing the rate of capacity-cost assignment (FMO rate) requires the selection of a measure ofcapacity to serve as the rate’s ‘‘denominator level’’ (per Horngren et al., 2000). Potential capacity mea-

K. Snead et al. / J. of Acc. Ed. 28 (2010) 85–102 89

sures for this purpose, in either normal or standard costing contexts, include theoretical capacity,practical capacity, normal capacity, and expected annual/budgeted capacity (Dilton-Hill & Glad,1994). Each of these measures can be defined conceptually (see Blocher et al., 2010 for support forthese definitions).

Theoretical capacity (TC) is typically described as the maximum annual output a plant is capable ofproducing with no allowance for downtime, waste, or idle time. Practical capacity (PC) represents theestimated level at which the plant can operate efficiently and often makes reasonable allowances forinevitable disturbances that are ignored by TC. Normal capacity (NC) is often said to represent the an-nual average level of operation required to satisfy estimated demand over a particular span of time(typically 3 to 5 years), normalizing for anticipated multiple-year cyclical and trend factors. Expectedannual (budgeted) capacity (EAC) is the estimate of capacity to be used in the upcoming fiscal year.The NC and EAC measures will fluctuate, over time, with market conditions; the TC and PC measureswill also fluctuate, over time, as firms add or eliminate capacity.

Reaction to these capacity measures often results in TC being eliminated from consideration givenits unrealistic assumption of a perfect operating environment, although some argue its use would becongruent with measurement systems facilitating continuous improvement (e.g., McNair, 1994). Forthe purposes of this discussion, TC is no longer considered given its utopian assumptions. Manyauthors suggest that PC be used for computing the FMO rate in order to avoid impounding idle capac-ity costs into the rate of application and all associated pitfalls discussed in the literature (e.g., Blocheret al., 2010; Proposition 4 advanced by Banker et al., 2002; DeBruine & Sopariwala, 1994; and, thedeath spiral discussion of Cooper & Kaplan, 1988). Of course, using PC to prevent idle capacity costsfrom attaching to the product means these same costs are isolated as misapplied FMO. It is PC’s real-istic estimate of the production capacity made available by FMO costs (whether used or idle) thatmakes it the most conceptually appealing measure for the application of capacity costs (Atkinson,Kaplan, Matsumura, & Young, 2007; Blocher et al., 2010). Accordingly, for internal reporting purposes,the remainder of this discussion will presume PC will be used for this component of the FMO rate; thisrate will hereafter be referred to as ‘‘FMO/PC.’’5

It is noted that even if PC is the capacity measure chosen to apply capacity costs to products, allthree capacity measures (PC, NC, EAC) need to be simultaneously considered in order to accuratelyidentify the causes for misapplied capacity costs. Correct causal identification is important for manag-ers to receive accurate information congruent with their need to effectively manage capacity costs,and for the appropriate financial accounting treatment of the misapplied capacity cost of any givenaccounting period. Accordingly, the next two sections of this paper will discuss how these measurescan be used to isolate/partition misapplied capacity costs by unique cause. Initially, this discussionwill be presented in a single/interim-year (four-quarter) time frame. The discussion will then expandto a multi-year (4-year) time span.

4. Causes of single/interim-year misapplied capacity costs

To facilitate the discussion in this area, figures are presented to provide a visual comparison of thelevels of different capacity measures across a four-quarter time period. The height of the figures rep-resents the level of cost driver associated with each capacity measure, while the horizontal axis iden-tifies each quarter.6 The initial figure presented, Fig. 1, contains quarterly levels of cost driver for threedifferent capacity measures: practical capacity (PC); expected quarterly capacity (EQC); and expected an-nual capacity (EAC).

The PC measure has been previously defined. Expected quarterly capacity (EQC) represents thequarterly expected or budgeted level of cost-driver utilization and is intentionally varied in our

5 For external reporting purposes, GAAP requires that NC be used for determining the FMO rate. The implications of thisrequirement will be discussed in the ‘‘Financial Accounting Reporting Implications’’ section of this paper.

6 A cost driver is typically defined as an activity that causes costs to be incurred. Cost drivers, once identified, are used tocompute MO rates in order to apply both variable MO and FMO ultimately to the product. It is the application of FMO, or capacitycost, that is relevant to this paper. Further, the measures of capacity discussed in this paper are typically stated in units of the costdriver employed for MO rate determination (e.g., machine hours, direct labor hours).

EQCEAC

Q1 Q2 Q3 Q4

Cos

t-Driv

er L

evel PC

Fig. 1. Comparison of quarterly cost-driver capacity levels (intra-year seasonality assumed). Legend: PC = practical capacity,EAC = expected annual capacity, EQC = expected quarterly capacity.

90 K. Snead et al. / J. of Acc. Ed. 28 (2010) 85–102

example across all four quarters to mimic seasonal variation in demand. Expected annual capacity(EAC) represents the annualized (mean) of the four EQC levels. The capacity measure levels conspic-uously absent from this graph are normal capacity (NC) and a level for the actual capacity of costdriver used (AC). Because it is a multi-year ‘‘averaging/normalizing’’ measure of expected capacityuse, NC is not germane to the single-year discussion. Accordingly, NC will be considered later inthis paper when we expand the context to a multiple-year time frame. AC is also ignored as theinitial discussion will assume no cost-driver estimation error as well as no FMO cost estimation er-ror.7 Thus, EQC is presumed to equal AC for each of the four quarters, controlling for the influences onmisapplied FMO stemming from estimation errors for the cost driver. Cost-driver estimation error (i.e.,differences between EQC and AC) will also be considered later in the paper.

As expected, Fig. 1 depicts that both PC and EAC remain constant across all four quarters, while EQCfluctuates from quarter to quarter, due to the seasonality assumed in our example. The implicationsfor quarterly misapplied capacity costs depicted by the scenario in Fig. 1, under the assumption thatPC is used to apply capacity costs to products, are discussed below.

4.1. Single-year time frame: PC used for FMO rate, no cost-driver estimation error

The following equation depicts quarterly misapplied capacity costs (QMCC):

7 ThiFMO co

QMCC ¼ ðFMO=PCÞðPC—EQCÞ ð1Þ

As computed with this approach, QMCC is driven by the discrepancy between PC and EQC. How-ever, as Fig. 1 reveals, this discrepancy has two fundamentally distinct causes: idle capacity (repre-sented by PC–EAC) and seasonality (represented by EAC–EQC). Accordingly, QMCC can bepartitioned as follows:

QMCC ¼ ðFMO=PCÞ½ðPC—EACÞ þ ðEAC—EQCÞ� ð2Þ

The first term in the bracket represents the portion of misapplied capacity cost attributed to idlecapacity, while the second term captures the portion caused by seasonality. Idle capacity is bestrepresented by (PC–EAC), as opposed to the entire difference (PC–EQC), since idle capacity is a sep-arate construct from seasonality. As previously noted, this distinction is supported by the categoriesof unused capacity presented in both the CAM-I capacity model (1996) and McNair and

s assumption could easily be relaxed without affecting the conclusions of this paper, as any estimation error occurring forsts would be isolated as an FMO ‘‘spending variance’’ for the period.

PC

AC

EQCEAC

Q1 Q2 Q3 Q4

Cos

t-Driv

er L

evel

Fig. 2. Comparison of quarterly cost-driver capacity levels (Fig. 1 augmented with actual capacity usage). Legend: PC = practicalcapacity, EAC = expected annual capacity, EQC = expected quarterly capacity, AC = actual capacity.

K. Snead et al. / J. of Acc. Ed. 28 (2010) 85–102 91

Vangermeersch (1998). Further, distinguishing those misapplied costs attributed to idle capacityfrom those attributed to seasonality is important, as it will be shown that the seasonally-causedmisapplied capacity cost for all four quarters necessarily sums to zero.8

This partitioning of QMCC between the two causes of idle capacity and seasonality permits the pos-sibility of more informed responses from both internal and external reporting perspectives. Internally,more detailed information regarding the causes of misapplied capacity costs should facilitate manage-ment attempts to more effectively manage capacity resources. From an external reporting perspective,a more accurate understanding of the causes of misapplied capacity costs should allow for a more con-ceptually sound financial reporting of capacity costs. These issues are discussed in later sections of thispaper.

4.2. Single-year time frame: PC used for FMO rate, allow for cost-driver estimation error

To this point, the potential for the estimation error of the cost driver volume has been ignored.Now, the potential for, and implications of, estimation error for interim-year misapplied capacity costwill be considered. This will be accomplished by modifying Fig. 1 to include an additional measure ofcost-driver activity representing actual quarterly capacity used (AC). This revised figure is presented inFig. 2.

Estimation error is operationalized in Fig. 2 by the quarterly differences between EQC and AC. Theimplications for QMCC depicted now by the scenario in Fig. 2, again assuming that PC is used to applycapacity costs to products, is discussed below.9

Given the pattern of relationships among the capacity measures in Fig. 2, which now include actualcapacity use (AC), the following equation can be used to depict QMCC:

8 As n(2). Thuexpressdistribu

9 Forquarter

QMCC ¼ ðFMO=PCÞðPC—ACÞ ð3Þ

Eq. (3) shows that the interim-year misapplied capacity cost is driven by the discrepancy betweenPC and AC. However, as Fig. 2 implies, this discrepancy now has three root causes: idle capacity (rep-resented by PC–EAC), seasonality (represented by EAC–EQC), and estimation error (represented byEQC–AC). Accordingly, QMCC can be partitioned as follows:

QMCC ¼ ðFMO=PCÞ½ðPC—EACÞ þ ðEAC—EQCÞ þ ðEQC—ACÞ� ð4Þ

oted, the QMCC due to seasonality for any given quarter ‘‘i’’ is computed via the (FMO/PC) [(EACi � EQCi)] component of Eq.s, the expression:

Pi¼4i¼1

FMOPC

� �½EACi � EQCi� represents the QMCC accumulated over four quarters due to seasonality. This

ion will necessarily sum to zero since, by definition,Pi¼4

i¼1EACi ¼Pi¼4

i¼1EQCi given that EQC is simply a quarter-by-quartertion of EAC.simplicity of illustration, the early-quarter cost-driver estimation errors are assumed not to prompt revision of the later-estimates for EQC.

92 K. Snead et al. / J. of Acc. Ed. 28 (2010) 85–102

As previously stated, the first and second terms in the brackets represent the portion of misappliedcapacity cost attributed to idle capacity and seasonality, respectively. The addition of the third term isnow necessary as it isolates that portion of QMCC caused by estimation error. The managerial andfinancial reporting implications of this more refined partitioning of QMCC are discussed in later sec-tions of this paper. The next section will expand the discussion to a multiple-year context, permittingconsideration of expected annually increasing use of capacity due to anticipated demand growth.

5. Causes of misapplied capacity cost in a multiple-year setting

Earlier, the NC measure of volume was not considered in the discussion because most textbooksdefine it as an average over several upcoming years’ expected use of capacity (for example, see Atkin-son et al., 2007; Blocher et al., 2010). Thus, ignoring NC was appropriate given that the focus of dis-cussion to this point related to interim-year misapplied capacity cost. Now, this denominatorvolume level is necessarily considered in an expansion of previously discussed concepts to a multi-ple-year setting. However, this expansion results in salient discussion points only if one considers thatexpected capacity usage will change over the multi-year time frame.

Accordingly, consider a situation involving the intentional acquisition of idle capacity where it isassumed that expected capacity use will steadily increase over the next 4 years.10 To provide an initialbasis for discussion, Fig. 2 is modified to include a 4-year time frame, with each year’s quarterly mea-sures also incorporated. As mentioned, this multi-year expansion requires incorporating the capacitymeasure NC into the analysis. In this context, NC represents the normalized annual and quarterly averageof the expected capacity use over the next 4 years, and is distinguished from the previous capacity mea-sure, EAC; NC normalizes for anticipated inter-year growth over a projected 4-year period, while EACannualizes or normalizes for anticipated intra-year seasonality over four quarters. The multi-year mod-ification of Fig. 2 is presented in Fig. 3.

Note that, given the assumption that FMO costs remain constant across all quarters and years, PCmust also remain constant. Note further that the EQC for common quarters increases over the 4-yearperiod given the inherent assumption of annually increasing capacity utilization, with the third quar-ter of year four reaching PC. Also observe that NC (by definition) is a constant across all 4 years, whileEAC (by definition) is constant only within each year. Accordingly, each year’s EAC fluctuates cyclicallyabout the 4-year NC constant, while each quarter’s EQC fluctuates seasonally about the annual EACconstant. Estimation error continues to be operationalized by the quarterly differences betweenEQC and AC.

Since using NC to determine the FMO rate would impound idle capacity costs into the rate (aswould the use of EAC, as previously noted), this expanded discussion will continue to use PC for theFMO rate. Thus, consideration of this multi-year time frame results in no change in either the annualFMO rate of FMO/PC or the total quarterly misapplied capacity costs represented by (FMO/PC) (PC–AC). However, as implied by Fig. 3, the presence of the NC construct does allow for a further partition-ing of the idle capacity component of QMCC, as Eq. (5) below demonstrates:

10 Thepenaltycreationplans foassume

QMCC ¼ ðFMO=PCÞ½ðPC—NCÞ þ ðNC—EACÞ þ ðEAC—EQCÞ þ ðEQC—ACÞ� ð5Þ

As previously stated, the last two terms in the bracketed expression represent the portion of mis-applied capacity costs attributed to seasonality and estimation error, respectively. However, that por-tion of misapplied capacity costs attributed to idle capacity in Eq. (4) has now been furtherpartitioned into two sub-components, (PC–NC) and (NC–EAC). The first component will be a constantacross all quarters and years given that the PC and NC measures are both constants. In effect, this nor-malized component of idle capacity cost represents the evenly distributed or smoothed portion ofcapacity cost that, in total over all 4 years, is expected to be unused and is therefore considered per-

decision to acquire idle capacity is economically rational as it is sensitive to the presence of the capacity augmentationincorporated in the analytic literature (e.g., Göx, 2002), and is operationally rational for a variety of reasons, includingof backup facilities, creating the flexibility to meet customers’ emergency demands, or launching a new product with

r capacity use to increase over time as market share increases (Dilton-Hill & Glad, 1994). It is this latter scenario that isd in Section 5 of the paper.

PC

AC

EQC

NC

EAC

Y1Q1 Y1Q2 Y1Q3 Y1Q4 Y2Q1 Y2Q2 Y2Q3 Y2Q4 Y3Q1 Y3Q2 Y3Q3 Y3Q4 Y4Q1 Y4Q2 Y4Q3 Y4Q4

Cos

t-Driv

er L

evel

Year/Quarter

Fig. 3. Comparison of multiple-year quarterly cost-driver capacity levels (inter-year growth with intra-year seasonalityassumed). Legend: PC = practical capacity, NC = normal capacity, EAC = expected annual capacity, EQC = expected quarterlycapacity, AC = actual capacity.

K. Snead et al. / J. of Acc. Ed. 28 (2010) 85–102 93

manently idle capacity.11 The second component, NC–EAC, is constant for all quarters within a givenyear. However, NC–EAC decreases from year to year due to presumed increasing capacity use over timeas a result of assumed growth, and even takes on a negative value in the last 2 years. Accordingly, thisportion cannot accurately be labeled as a permanently idle capacity cost, since it merely represents unre-covered capacity cost in the first 2 years (where EAC < NC), and surplus capacity cost recovered in the last2 years (where EAC > NC). Thus, it is labeled as temporarily idle capacity due to anticipated growth, as itssum will accumulate to zero across all 4 years. As previously noted, the general concept of the above de-scribed sub-components of idle capacity are supported by McNair and Vangermeersch (1998) major cat-egories of capacity deployment as well as the constructs for idle capacity discussed in CAM-I model(1996).12

In an effort to reduce the abstract nature of the discussion thus far, a numeric example has beendeveloped, which is underpinned by the assumptions contained in Fig. 3. This example will be pre-sented and related to the concepts previously discussed and will serve as a springboard for discussionof the managerial accounting and financial reporting implications of these concepts.

6. Illustrative example

The following quarterly, 4-year, illustrative example for a manufacturing firm incorporatesassumptions considered throughout the paper. Specifically, it is assumed that one cost driver (ma-chine hours, MH) is employed with PC used for the FMO rate determination. Further, MH is seasonally

11 While the phrase ‘‘permanently idle capacity’’ may seem a bit counterintuitive, this label is necessary to distinguish capacityacquired that is never expected to be used (permanently idle) from capacity acquired that is expected to be idle in early years butultimately needed in later years to support expected demand growth (temporarily idle). This distinction will be elaborated uponlater in the paper.

12 The situational premise of this paper is that intentional idle capacity has been acquired based upon the expectation that it willbe increasingly utilized over the next few years as the market expands. Clearly, it is possible that firms could experience decliningcapacity utilization over time as well. If unanticipated, the proposed conceptual model would highlight this situation formanagement via the increasing ‘‘estimation error’’ component of QMCC shown in Eq. (5). Presumably, this would prompt adownward revision of NC possibly leading to a decision to divest a portion of the newly anticipated longer-term excess capacity.And if the declining capacity use is anticipated, the QMCC impact would be reported in the ‘‘permanently idle capacity’’ componentof QMCC in Eq. (5).

Table 1Illustrative example of machine hour (MH) capacity measures inter-year growth coupled with intra-year seasonality.

Year/Qtr AC EQC EAC NC PC(1) (2) (3) (4) (5)

Y1q1 8000 10,900 11,750 13,017 15,000Y1q2 12,000 12,900 11,750 13,017 15,000Y1q3 13,000 14,000 11,750 13,017 15,000Y1q4 8700 9200 11,750 13,017 15,000Total Y1 41,700 47,000 47,000 52,068 60,000

Y2q1 12,000 11,723 12,337 13,017 15,000Y2q2 13,500 13,825 12,337 13,017 15,000Y2q3 14,000 14,300 12,337 13,017 15,000Y2q4 10,000 9500 12,337 13,017 15,000Total Y2 49,500 49,348 49,348 52,068 60,000

Y3q1 10,000 12,805 13,324 13,017 15,000Y3q2 13,700 14,875 13,324 13,017 15,000Y3q3 14,900 14,700 13,324 13,017 15,000Y3q4 12,000 10,916 13,324 13,017 15,000Total Y3 50,600 53,296 53,296 52,068 60,000

Y4q1 13,100 14,598 14,657 13,017 15,000Y4q2 14,500 14,900 14,657 13,017 15,000Y4q3 15,000 15,000 14,657 13,017 15,000Y4q4 11,225 14,130 14,657 13,017 15,000Total Y4 53,825 58,628 58,628 52,068 60,000

Total Y1–Y4 195,625 208,272 208,272 208,272 240,000

AC = actual capacity used. EQC = expected quarterly capacity. EAC = expected annual capacity. NC = normal capacity.PC = practical capacity.

94 K. Snead et al. / J. of Acc. Ed. 28 (2010) 85–102

distributed within the year with quarters two and three being the high-volume quarters. Finally, it isassumed that expected capacity use is increasing over the 4-year time frame.

Table 1 incorporates a pattern for the PC, NC, EAC, and EQC volume levels for MH consistent withthe above assumptions, which were also incorporated in Fig. 3. Note that PC is 15,000 MH per quarter(60,000 per year) and NC’s normalized quarterly average is 13,017 MH per quarter (52,068 per year).Annual EAC levels reveal increasing anticipated use of MH capacity over the 4-year planning period,while quarterly EQC levels reveal an assumed seasonal pattern of MH use within the year. Also, AClevels have been added to provide the possibility for estimation errors to be considered. Finally, annualFMO costs are expected to be $4800,000 with the continued assumption of no estimation error for thiscost.

Table 2 reports the partitioning of dollars of misapplied capacity cost implied by the pattern of MHmeasures reflected in Table 1. This is accomplished using $80.00/MH as the FMO rate of application ofcapacity costs ($4800,000/60,000 MH) in conjunction with the other elements of Eq. (5). The dollars incolumn 1 represent the period’s total misapplied capacity cost, with columns 2–5 used to partitioncolumn 1 dollars into the four components of permanently idle capacity (column 2), temporarily idlecapacity due to growth (column 3), temporarily idle capacity due to seasonality (column 4), and esti-mation error (column 5). Column 4’s quarterly misapplied costs due to seasonality are offset by theend of each year given the assumptions related to the within-year seasonal distribution of MH. Sim-ilarly, column 3’s quarterly and annual misapplied capacity costs attributed to growth dissipate by theend of the 4-year time frame over which NC normalizes the increasing MH usage. Column 2’s long-term or permanently idle capacity costs accumulate at the smoothed or constant quarterly rate of$158,640, while column 5’s dollars attributed to estimation error fluctuate based upon the directionof the discrepancy between and actual and expected MH usage.

The internal and external reporting implications of the causal agents of misapplied capacity costsuggested by the paper’s conceptual discussion, and numerically illustrated by the above example,are now considered.

Table 2Illustrative example continued–misapplied capacity cost partitioning implied by Table 1(presumes a FMO rate of $80.00/machinehour using PC).

Year/Qtr PC–AC PC–NC NC–EAC EAC–EQC EQC–AC(Total) (Permanently

idle)(Temporarily idle-growth)

(Temporarily idle-seasonality)

(Estimationerror)

(1) (2) (3) (4) (5)

Y1q1 $ 560,000 $ 158,640 $ 101,360 $ 68,000 $ 232,000Y1q2 240,000 158,640 101,360 �92,000 72,000Y1q3 160,000 158,640 101,360 �180,000 80,000Y1q4 504,000 158,640 101,360 204,000 40,000Total Y1 $ 1464,000 $ 634,560 $ 405,440 $ -0- $ 424,000

Y2q1 $ 240,000 $ 158,640 $ 54,400 $ 49,120 $ �22,160Y2q2 120,000 158,640 54,400 �119,040 26,000Y2q3 80,000 158,640 54,400 �157,040 24,000Y2q4 400,000 158,640 54,400 226,960 �40,000Total Y2 $ 840,000 $ 634,560 $ 217,600 $ -0- $ �12,160

Y3q1 $ 400,000 $ 158,640 $ �24,560 $ 41,520 $ 224,400Y3q2 104,000 158,640 �24,560 �124,080 94,000Y3q3 8000 158,640 �24,560 �110,080 �16,000Y3q4 240,000 158,640 �24,560 192,640 �86,720Total Y3 $ 752,000 $ 634,560 $ �98,240 $ -0- $ 215,680

Y4q1 $ 152,000 $ 158,640 $ �131,200 $ 4720 $ 119,840Y4q2 40,000 158,640 �131,200 �19,440 32,000Y4q3 -0- 158,640 �131,200 �27,440 -0-Y4q4 302,000 158,640 �131,200 42,160 232,400Total Y4 $ 494,000 $ 634,560 $ �524,800 $ -0- $ 384,240

Total Y1–Y4

$ 3550,000 $ 2538,240 $ -0- $ -0- $ 1011,760

Positive dollars = under-applied; negative dollars = over-applied.AC = actual capacity used. EQC = expected quarterly capacity. EAC = expected annual capacity. NC = normal capacity.PC = practical capacity.

K. Snead et al. / J. of Acc. Ed. 28 (2010) 85–102 95

7. Managerial accounting implications

Although there is an expressed management need for details related to the costs of unused capacity(e.g., Buchheit, 2003; CAM-I, 1996; Euske & Vercio, 2007; McNair & Vangermeersch, 1998), these coststypically reside as misapplied FMO and are often captured and reported as a Production Volume Var-iance (or, FMO Volume Variance). That is, they are typically reported in ‘‘lump-sum’’ fashion. Forexample, Quarter 1 of Year 1 (Y1q1) would have a reported an unfavorable volume variance of$560,000, as shown in column (1). Note that this traditionally computed volume variance merely re-ports the capacity costs associated with the difference between the PC and AC volume levels.13 How-ever, as columns (2)–(5) reveal, a closer examination indicates this volume variance is made up of fourdistinct components, each with its own cause. Management awareness of all four of these components

13 The traditional form of the Production Volume Variance applies the predetermined FMO rate to the discrepancy between thelevel of cost driver serving as the denominator for the FMO rate (PC in this case) and a measure for the amount of cost driver ‘‘used’’or ‘‘allowed’’ for the period. In calculating the Production Volume Variance, normal costing employs the actual level of cost driverused during the period, while standard costing utilizes the standard level of cost driver allowed for the actual level of productionachieved in the period. For simplicity, this paper’s assumption of no estimation error (i.e., no fixed overhead spending variance –see endnote # 7) includes standards related to allowed cost driver use for production taking place in any period. Thus, normal andstandard costing systems would report identical Production Volume Variances under these assumed facts. Relaxing thisassumption to accommodate a standard costing context would simply require modifying Table 1’s cost driver levels for ‘‘AC’’ to‘‘Standard Level of Cost Driver Allowed for Actual Production.’’ This modification would then appropriately modify Table 2’sreporting of dollars of Production Volume Variance attributable to ‘‘Estimation Error.’’ No change to Table 2 would occur for any ofthe other causes of ‘‘Permanently Idle,’’ ‘‘Temporarily Idle-Growth,’’ or ‘‘Temporarily Idle-Seasonality.’’

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and underlying causes would facilitate attempts to effectively manage capacity (Dilton-Hill & Glad,1994).14

Most important, managers would be able to isolate misapplied capacity costs that are expected tobe offset or recovered in future periods from those that will not be offset. Returning to Table 2 forY1q1, this partitioning would alert managers that $169,360 (i.e., 30.2%) of the total $560,000 misap-plied capacity cost will be offset; the $68,000 (i.e., 12.1%) due to seasonality is planned to be offsetwithin the year, and the $101,360 (i.e., 18.1%) due to temporarily idle capacity related to growth isplanned to be ‘‘recovered’’ over the 4-year period. Formally reporting these misapplied capacity costsas ‘‘temporary’’ will, it is hoped, preclude managers from ‘‘panicked’’ responses potentially leading toirrational reactions in the areas of product costing/pricing, planning, decision-making, and perfor-mance evaluation. For example, managers would not be tempted to consider increasing FMO ratesand ultimately product prices, initiating capacity divesting analyses, or unfavorably evaluating theperformance of those personnel having responsibilities thought to influence capacity management.Conversely, managers need to be reminded of the extent of misapplied capacity cost that is notplanned to be offset. This is represented in the example by the $158,640 (i.e., 28.3%) of the misappliedcapacity cost that is that portion of Y1q1’s ‘‘fair share’’ allocation of capacity cost that is planned to be‘‘permanently idle’’ over the 4-year normalized time frame.

As previously mentioned, this type of partitioning of capacity is consistent with the McNair andVangermeersch (1998) categories of capacity deployment discussion as well as the capacity categoriesdescribed in CAM-I (1996). In addition, information needs to be available regarding unanticipated useor non-use of capacity and resulting impacts on capacity cost absorption. As Table 2 reports, theremaining $232,000 (i.e., 41.5%) highlighted as estimation error represents the impact on capacity costabsorption due to unanticipated non-use of capacity for Y1q1 (AC below EQC by 2900 machine hours).Since this is the only one of the four causes that is unplanned, it should be the only one used to promptvariance-investigation procedures. This will increase the likelihood that potentially costly varianceinvestigative efforts will be effective, given they will be based upon a segregated and thus ‘‘truer’’measure of the capacity cost absorption impact due to estimation error.

This need for the suggested partitioning over the traditional form of the volume variance determi-nation is best illustrated by considering Table 2 information for Quarter 3 of Year 4 (Y4q3). Note forthis period that the traditional form of the volume variance would yield a variance of zero.15 As col-umns 2–5 indicate, this is due to a coincidental netting of misapplied capacity cost from the permanentlyidle and temporarily idle capacity causes. Thus, the aggregating nature of the single volume variancemasks the individual impacts on capacity cost absorption resulting from permanently idle and temporar-ily idle capacity, even in periods such as this where there is no estimation error.

Given the general notion that measurement is a prerequisite for management, the suggested par-titioning of misapplied capacity costs for capacity management is only possible if levels for PC, NC,EAC, and EQC can be estimated (AC by definition is known). Clearly, capacity levels for EAC andEQC are available, given management’s need to develop monthly, quarterly, and annual budgets. Fur-ther, PC and NC levels are also likely available from multiple-year strategic-planning processes and/orfrom information gathered to make the original decision to acquire the capacity. Given the presence ofthese various capacity levels, the popular notion that only one of them may be used to monitor capac-ity-cost application should be abandoned. It has been argued that one of these levels, PC, is conceptu-ally superior as the denominator to determine the rate of capacity-cost application. However, it isfurther argued that PC should be simultaneously used in conjunction with other capacity levels inthe manner suggested by Eq. (5) in order to more completely identify causes for misapplied capacitycosts. This added information would seem critical to the capacity-management problem. The implica-

14 Clearly, the dollars reported as the Production Volume Variance do not represent the true economic impact associated withunderlying causes. For example, the opportunity costs of intentionally carrying idle capacity and/or the contribution marginforgone due to the inability to produce marketable product should be considered. This point is made in Blocher et al. (2010).

15 Employing Eq. (5) for the presumed capacity levels for Y4q3 results in the following calculation details for this quarter’sreported QMCC of $0: $0 = ($1200,000/15,000) [(15,000 � 13,017) + (13,017 � 14,657) + (14,657 � 15,000) + (15,000 � 15,000)]Or, $0 = ($80.00) [(1983) + (�1640) + (�343) + (0)] Or, $0 = [$158,640 � $131,200 � $27,440 + $0].

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tions for financial accounting reporting of this more complete knowledge of the causes of misappliedcapacity costs are now considered.

8. Financial accounting reporting implications

While the Financial Accounting Standards Board (FASB) GAAP specifically prescribe that capacitycosts be attached to products, it is not so specific in offering guidance as to the appropriate dispositionof the inevitable misapplied capacity costs. Accordingly, most textbooks deal with disposition issues invery vague and general ways. Discussions often combine both misapplied variable and misappliedfixed MO and simply conclude the dollars of misapplied MO should either be written off to cost ofgoods sold (CGS) or prorated to inventory and costs of goods sold accounts. [See endnote # 4] This pa-per argues that misapplied capacity costs (fixed MO) warrant separate consideration from misappliedvariable MO, that such misapplied capacity costs have multiple causes, and that appropriate disposi-tion of these dollars depends upon the cause. Recall the four potential causes of misapplied capacitycosts presented to this point (from Eq. (5)) are: permanently idle capacity; temporarily idle capacitydue to growth; temporarily idle capacity due to seasonality; and estimation error. These causes, cou-pled with basic tenets of financial accounting, need to be considered when disposing of misappliedcapacity costs for financial reporting purposes.

Unfortunately, due to GAAP requirements, the permanently idle capacity cause cannot be isolatedfor financial accounting reporting purposes. This is due to the current requirement that NC, not PC, beused as the denominator for the FMO rate for external reporting purposes (FASB Accounting StandardCodification 330-10-30, 2010; previously SFAS No. 151). Accordingly, in our example, the rate thatwould result from using NC would be $92.19/MH ($4800,000/52,068 MH). The increase in this ratefrom $80.00/MH is the simply the result of the required impounding of the $634,560 annual capacitycosts associated with permanently idle capacity (see column 2, Table 2) into the rate. Accordingly, therequired use of NC for the FMO rate will permit only three of the four components contained in Eq. (5)to be identified – temporarily idle capacity due to growth, temporarily idle capacity due to seasonality,and estimation error. This is illustrated below by amending Eq. (5) to incorporate the use of NC in therate determination.

16 Notidenticacapacit

QMCC ¼ ðFMO=NCÞ½ðNC—EACÞ þ ðEAC—EQCÞ þ ðEQC—ACÞ� ð5aÞ

As previously noted, the first two bracketed terms represent the temporarily idle capacity due togrowth and seasonality causes, respectively; the last term continues to represent estimation error. Re-call that the growth and seasonality components are unique in that they both reverse (or sum to zero)over time with the only difference between these causes being the period of time in which the zeronetting occurs. Further, recall that misapplied capacity costs associated with estimated growth willnet to zero over a number of years, while seasonally-related misapplied capacity costs will net to zerowithin the current year. Continuing with the illustrative example, the dollar amounts associated withthese components are now delineated in Table 2a columns (3) and (4), respectively, while estimation-error dollar amounts appear in column 5. Note that Table 2a simply amends the contents of Table 2 byusing NC as incorporated in Eq. (5a). Further, note that the dollar amounts for these components in-crease as compared to their original amounts shown in these columns in Table 2, again, due to the in-creased rate of $92.19/MH resulting from the required use of NC rather than PC (and, assumingPC > NC).16

Since these two components of misapplied capacity costs will reverse in subsequent periods, it canbe argued that they will provide economic benefits in future periods and therefore should not be ex-pensed, but deferred until the appropriate period. Financial accounting standards have already beenestablished for an analogous situation in the area of ‘‘Accounting for Income Taxes.’’ Specifically, FASBASC 740-10-25 (2010; formerly SFAS 109) handles timing differences for transactions affecting taxableincome in periods different from the period in which the transactions enter into the determination of

e that as NC approaches PC, the pattern of misapplied capacity costs depicted in Tables 2 and 2a converge and will bel when NC = PC. This is due to converging of the FMO rates and the accompanying requirement that permanently idle

y approaches zero.

Table 2aIllustrative example continued–misapplied capacity cost partitioning implied by Table 1 (presumes a FMO rate of $92.00/machinehour* using NC as required by GAAP).

Year/Qtr NC–AC PC–NC NC–EAC EAC–EQC EQC–AC(Total) (Permanently

idle)(Temporarilyidle-growth)

(Temporarilyidle-seasonality)

(Estimationerror)

(1) (2) (3) (4) (5)

Y1q1 $ 461564 $ -0- $ 116,564 $ 78,200 $ 266,800Y1q2 93,564 -0- 116,564 �105,800 82,800Y1q3 1564 -0- 116,564 �207,000 92,000Y1q4 397,164 -0- 116,564 234,600 46,000Total Y1 $ 953,856 $ -0- $ 466,256 $ -0- $ 487,600

Y2q1 $ 93,564 $ -0- $ 62,560 $ 56,488 $ �25,484Y2q2 �44,436 -0- 62,560 �136,896 29,900Y2q3 �90,436 -0- 62,560 �180,596 27,600Y2q4 277,564 -0- 62,560 261,004 �46,000Total Y2 $ 236,256 $ -0- $ 250,240 $ -0- $ �13,984

Y3q1 $ 277,564 $ -0- $ �28,244 $ 47,748 $ 258,060Y3q2 �62,836 -0- �28,244 �142,692 108,100Y3q3 �173,236 -0- �28,244 �126,592 �18,400Y3q4 93,564 -0- �28,244 221,536 �99,728Total Y3 $ 135,056 $ -0- $ �112,976 $ -0- $ 248,032

Y4q1 $ �7636 $ -0- $ �150,880 $ 5428 $ 137,816Y4q2 �136,436 -0- �150,880 �22,356 36,800Y4q3 �182,436 -0- �150,880 �31,556 -0-Y4q4 164,864 -0- �150,880 48,484 267,260Total Y4 $ �161,644 $ -0- $ �603,520 $ -0- $ 441,876

Total Y1–Y4 $ 1163,524 $ -0- $ -0- $ -0- $1163,524

Positive dollars = under-applied; negative dollars = over-applied.AC = actual capacity used. EQC = expected quarterly capacity. EAC = expected annual capacity. NC = normal capacity.PC = practical capacity.* Note that the resulting FMO rate of $92.19 was truncated to $92.00 to avoid rounding errors.

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GAAP-based income. A ‘‘deferred capacity cost’’ asset, similar in concept to the deferred tax asset ac-count, could be used to store the seasonality component of those misapplied capacity costs expectedto be offset within the upcoming year.17 This approach is consistent with the accounting treatment fordeferred tax assets since these deferred tax assets are simply a future tax benefit that results from timingdifferences between GAAP and tax-basis accounting.

It is noted that there is potential for seasonally-caused misapplied capacity costs to become moreprominent given recent literature noting the trend of moving away from smoothing monthly produc-tion, and thus building inventory, to deal with seasonal demand. Instead, there are movements towardusing temporary workforce labor (Alp & Tan, 2008; Tan & Alp, 2009) or irregularly distributing com-pany workers’ working hours over the year (‘‘annualizing working hours’’) as another way to createflexibility for managing seasonal demand (Corominas et al., 2007; Lusa et al., 2008). Note that bothof these approaches would result in ‘‘permanent’’ capacity costs (e.g., capital investment, rents, sala-ried personnel, etc.) becoming a more significant part of misapplied capacity costs as seasonal swingsare experienced throughout the year. It is acknowledged that the recommendation for deferring theseshort-term costs is likely to be more ‘‘palatable’’ than deferring the longer-term costs, discussed next,since no ‘‘sacred’’ annual boundaries of financial reporting would be crossed.

With respect to the temporarily idle capacity cost component due to growth, it is noted that themathematical source of these longer-term idle capacity costs is simply the planned deviation of ex-

17 The use of this type of ‘‘holding’’ account to temporarily house seasonally-caused misapplied capacity costs is alsorecommended by Blocher et al. (2010).

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pected capacity use about a 4-year mean or norm, with annual usage below the norm offset over timeby annual usage above the norm. Given this, it is argued that the misapplied capacity costs associatedwith these temporary deviations be deferred until all netting occurs, since these costs are incurredwith the knowledge that they will be recovered in future periods. In fact, had the capacity neededin later years not been included in the initial capacity acquisition, the economic benefits associatedwith the expansion of the market during these later years would not have been realized. Accordingly,the costs associated with this capacity should not be expensed in the early years, but be treated as adeferral that will eventually be offset when the market and MH use sufficiently expands. Again, thiscan be accomplished with procedures similar to those specified in external reporting standardsregarding accounting for income taxes. Specifically, a ‘‘long-term deferred capacity cost’’ account, sim-ilar in concept to the ‘‘long-term deferred tax asset’’ account, could be used to store this component ofthose misapplied capacity costs expected to offset over a multi-year time span.

Support for deferring these misapplied capacity cost beyond a 1-year time frame is also found inthe FASB’s discussion of recording and reporting options for ‘‘dry-hole’’ exploration costs in the FASB’soil and gas standards. Under the full cost method of accounting for exploration costs (FASB ASC 932-360-25, 2010), financial accounting standards allow for all drilling costs to be capitalized when in-curred, regardless of whether the exploration is successful or not. These costs are subsequently amor-tized against the revenue generated from the ongoing operations of the entity. The underlying theoryis that the entity must explore in order to be successful and therefore all of the costs of explorationshould be matched with the subsequent benefits derived from that exploration. Similarly, in orderto position itself to benefit economically from expanding markets, the entity represented by this pa-per’s illustrative example must knowingly invest in capacity that, while initially not needed, is essen-tial to have in place to derive these future benefits. Accordingly, these temporarily idle capacity costsshould be matched to the benefits they made possible in future years, which can only be accomplishedby permitting their long-term deferral.18

Further, permitting this treatment for these capacity costs seems consistent with Statement ofFinancial Accounting Concept (SFAC) No. 1, which specifically states that ‘‘financial reporting shouldprovide information about the economic resources of an enterprise, the claims to those resources,and the effects of transactions, events, and circumstances that change its resources and claims to thoseresources.’’ To illustrate, consider the significant ‘‘swings’’ in reported income that would result ifdeferrals of misapplied capacity costs caused by both seasonality and growth-related temporarily idlecapacity were not permitted. Using the information contained in Table 2a for Year 1, if those tempo-rarily misapplied capacity costs due to seasonality are written off/onto the income statement, incomereported for quarters 1 and 4 would be significantly understated (by $78,200 and $234,600, respec-tively), while the income reported for quarters 2 and 3 would be significantly overstated (by$105,800 and $207,000, respectively), all of which would net to zero within the year. Further, this phe-nomenon would be perpetuated for the longer-term misapplied capacity costs caused by temporarilyidle capacity due to growth. As Table 2a further indicates, if those temporarily misapplied capacitycosts due to growth are written off/onto the income statement, income reported for years 1 and 2would be significantly understated (by $466,256 and $250,240, respectively), while the income re-ported for years 3 and 4 would be significantly overstated (by $112,976 and $603,520, respectively),again, all of which would net to zero over the 4-year time span (assuming of course that all expecta-tions for NC and EAC are realized). These potential misstatements of income, both within-year andacross-years, seem incongruent with the spirit and objective of SFAC No.1.

Less straightforward is the appropriate disposition of misapplied capacity costs attributed toestimation error, represented by the (EQC–AC) term in Eq. (5a). This is the only one of the threecauses of misapplied capacity cost that cannot be predetermined given its post hoc nature. Dispo-sition of costs generated by estimation errors fall in the domain of variance analysis procedures.Typically, if significant, variances are investigated to determine their cause, which may range fromoperational problems/windfalls to unrealistic estimates/standards warranting dispositions of ‘‘writ-

18 If the anticipated increase in future demand does not materialize, the ‘‘estimation error’’ component of the reported QMCC willbe adversely impacted. This should prompt both a needed downward revision of NC along with the ‘‘writing-off’’ of all ‘‘long-termdeferred capacity costs’’ related to this presumed, but unrealized, growth.

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ing off/on’’ and proration, respectively. However, misapplied capacity costs caused by estimation er-rors differ from variances involving variable manufacturing costs, since capacity costs are fixed andunavoidable in the short term. For example, consider the dollars of misapplied capacity cost attrib-uted to estimation error in the illustrative example in column (5) of Table 2a. If positive (i.e., un-der-applied), the dollars merely represent that portion of capacity cost not applied due tounanticipated non-use of capacity. And, if negative (i.e., over-applied), the dollars merely representthe additional portion of capacity cost applied due to unanticipated use of capacity. Recall that thisexample assumes no estimation error for the $4800,000 anticipated annual FMO; accordingly therewould be no FMO spending variance.

Again, assuming that NC is being used for the FMO rate to meet external reporting requirements,the amount of capacity-cost assignment to products is ‘‘appropriate.’’ Thus, inventories and CGS ac-counts are ‘‘appropriately’’ valued. This eliminates proration as a viable alternative, leaving some sortof ‘‘write off/on’’ procedure as an option. Another perhaps more conceptually interesting approachwould be to reset EQC to AC, once AC is known. As Eq. (5a) indicates, this would force the dollars ofmisapplied FMO due to estimation error to zero, and repartition these dollars among the remainingcauses and subject them to the disposition treatment prescribed for each. In any event, knowledgeof the estimation error may warrant revision of all remaining estimates of cost driver measures forEQC, EAC, NC, and even PC, ultimately prompting modifications to future planned misapplied capacitycosts and their ultimate disposition.

In addition to this paper’s recommendation for GAAP to permit deferral of short-term and longer-term misapplied capacity costs due to seasonality and anticipated growth, respectively, it is furtherrecommended that GAAP be modified to permit the use of PC in the determination of the FMO rate.As previously noted, the forced use of NC permits capacity costs associated with capacity never in-tended to be used (i.e., permanently idle capacity) to be absorbed by the products ultimately pro-duced. This results in the financial accounting treatment of these costs to be included in the asset‘‘inventory’’ prior to sale. Interestingly, the potential for these costs to ever become a part of the val-uation for assets seems inconsistent with the tenets of SFAC No. 6, which defines assets as ‘‘probablefuture economic benefits obtained or controlled by a particular entity as a result of past transactions orevents.’’ Given the unintended use of the capacity provided by these costs, it seems that they shouldisolated and expensed in the period incurred. As previously illustrated in Eq. (5) and Table 2, this willonly be possible if PC is permitted to be used to compute the FMO rate.

It is acknowledged that these proposed modifications to GAAP may create a greater potential formanagement manipulation of income. Clearly, permitting management to defer intra-year and in-ter-year misapplied capacity costs, coupled with permitting the use of engineering-based measuresof capacity (PC) as opposed to more verifiable market-based measures of needed capacity utilization(NC) for FMO rate determination, facilitates the potential for reporting abuses in this area. That said, itis felt that the benefits of this more conceptually appropriate financial accounting treatment of mis-applied capacity-cost information warrant confronting issues related to these potential abuses.

9. Conclusion

This paper addresses a conceptual weakness common in textbook discussions related to the causesand reporting of misapplied capacity costs. It proposes that the conceptually correct management andreporting of misapplied capacity costs is not possible without first considering the potential for, andisolating the effects of, multiple causes of the misapplied dollars. Further, the paper’s discussion dem-onstrates that permanently and temporarily idle capacity due to seasonality and growth, and to esti-mation error have the potential to simultaneously impact misapplied capacity costs, with each ofthese potential causal agents having differing internal and external reporting implications for manag-ers and external stakeholders. This more refined partitioning of misapplied capacity costs stemmingfrom non-use of capacity is consistent with various constructs of idle capacity discussed in CAM-I(1996) and McNair and Vangermeersch (1998), and expands the notions espoused by these worksby noting the offsetting nature of some of these causes as well as discussing the associated managerialaccounting and financial reporting implications.

K. Snead et al. / J. of Acc. Ed. 28 (2010) 85–102 101

It is hoped that this paper will facilitate a more complete presentation of, and discussion of, theincreasingly important area of effective capacity-cost management. This more comprehensive ap-proach to the reporting, analysis, and disposition of misapplied capacity costs is necessary if signifi-cant erroneous managerial reactions to traditionally reported Production Volume Varianceinformation, and significant misstatements of those assets and expenses whose valuation includes aFMO component, are to be avoided. Further, while a ‘‘traditional’’ manufacturing context was em-ployed in this paper, the methods discussed for more effectively managing capacity costs likely gen-eralize to non-manufacturing sectors concerned with management of capacity costs, such as thehealth care industry (e.g., Gnanlet & Gilland, 2009; Kelemen, MacArthur, & Menzel, 2007) and otherareas of the service sector including banking (e.g., McDonald & Spaller, 2007), retail (e.g., Duan & Mela,2009; Xu & Leung, 2009) and travel (e.g., Wang & Chatterjee, 2009) industries. Although capacity costsfor these non-manufacturing sectors are more subtle in nature, given their period cost classificationtypically shown as ‘‘selling, general, and administrative expenses,’’ they are still significant and typi-cally represent IT investments (see Tallon, 2010; Tallon & Scannell, 2007 for a discussion of manage-ment of IT data storage costs), depreciation, rent, staff salaries, etc. Accordingly, the existence ofcapacity levels for these costs comparable with PC, NC, EAC, and EQC will permit capacity-utilizationreporting and analysis procedures similar to those discussed in this paper. Facilitating this possibilityis the capacity-measurement system proposed by Sopariwala (2006), which includes ‘‘people-based’’capacity resources that he notes would apply to both manufacturing and service firms.

Acknowledgements

The authors wish to extend their sincere appreciation to the anonymous reviewer who providedextremely thorough and helpful review comments; these comments resulted in significant improve-ments to the paper. The paper also benefitted from the comments received during its presentation atthe 2009 Ohio Regional meeting of the American Accounting Association. In addition, the authorsacknowledge the efforts of Dr. James Rebele in overseeing the review process.

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