Management Accounting Determinants and Economic Consequences of Production
Decisions: Field and Archival Evidence from the US Auto Industry
ALEXANDER BRÜGGEN
Maastricht University
RANJANI KRISHNAN
Michigan State University
KAREN SEDATOLE
Michigan State University
October 2008
We are grateful to our industry research partners for providing data, as well as valuable insights
for our study. We are also grateful to PWC Autofacts for providing the sales and production data
used in the study. We appreciate the helpful comments of Mike Shields, Sally Widener, and
participants at the 2008 Global Management Accounting Research Symposium (Sydney,
Australia).
Management Accounting Determinants and Economic Consequences of Production
Decisions: Field and Archival Evidence from the US Auto Industry
Abstract
This study presents field evidence from a “Big Three” US automaker that an accounting system
that fails to separately account for excess capacity, along with the use of short-term financial
metrics for evaluating production managers encourages excess production. Field interviews
further reveal that excess production is associated with greater customer incentives necessary to
sell excess inventory. While excess production increases contribution in the short-term, we
conjecture that it also leads to increases in discretionary costs in the medium-term (e.g.,
advertising) and harms brand image in the long-term. Using archival data from the auto industry,
we empirically establish a positive link between excess capacity and excess production,
suggesting that our field evidence holds more generally. We further find that excess production
is positively associated with three tangible costs: rebate percent, advertising spend, and our
proxy for inventory build-up. Finally, we show that rebate percent, rebate penetration, and
inventory build-up are negatively associated with brand image as measured by the JD Power
APEAL index. Our combined field and archival results show a complex interplay between
production planning, performance measurement, and accounting practices within organizations,
with implications for tangible costs and the intangible asset of brand image.
Key Words: Capacity accounting, intangible assets, production decisions, auto industry
Management Accounting Determinants and Economic Consequences of Production
Decisions: Field and Archival Evidence from the US Auto Industry
“I know we can’t turn [the production strategy] around in a year or two. If we’re going
to change, that’s a long-term view, and there’s going to be a long period in there - four to
eight years probably that would be required to try to turn that around. But four to eight
years of hurt. Can we afford that?”
Big Three US auto industry manager
1. Introduction
It is well recognized in the academic and business community that accounting and
performance measurement systems have powerful influences on managerial decisions. While
extant literature in accounting has examined the effect of incentives on a variety of outcomes
such as managerial effort, earnings management, budget padding, etc., the effect of accounting
and performance measurement systems on production decisions and the impact of these
decisions on intangible assets is relatively underexplored (Ashton 2005). When managers make
production decisions, greater consideration is typically placed on the effect of those decisions on
short-term financial costs and revenues and, as a result, the long-term impact of such decisions
on intangible assets is rarely considered. One reason for this omission is that intangible assets
require a long-term decision frame, as well as the use of non-financial measures to identify and
track their values, neither of which are common features of traditional accounting systems.
In this research, we use field evidence as well as archival data from the US auto industry
and examine the effect of capacity accounting and performance measurement systems on
production decisions, and the impact of those production decisions on both tangible costs and on
an important intangible asset, i.e., brand image, that prior research (e.g., Kim et al. 2003) shows
has long-term performance consequences. In the first part of the paper, we provide field evidence
that failure to account for excess capacity, combined with a performance measurement system
that places considerable emphasis on short-term costs, leads managers to significantly
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overproduce relative to our proxy for expected demand and, in turn, to increase customer
incentives in order to sell the excess production. In the second part of the paper, we use archival
data for 132 nameplates (e.g., Chrysler Grand Cherokee, Honda Accord) for 23 automakers to
investigate the extent to which our field evidence holds more generally. Specifically, we examine
the association between excess capacity and excess production, and the effect of excess
production on tangible costs such as customer incentives, advertising, and inventory build-up,
and ultimately on the intangible cost of brand image erosion.
The US auto industry provides an excellent research setting to empirically examine the
determinants and economic effects of excess production for three reasons. First, the US auto
industry during the period of study, 2005-2006, has high levels of excess capacity arising, in
part, from increased foreign competition. Second, the industry is characterized by high fixed
costs. Third, as described in our field evidence, firms in the industry have a short-term oriented
incentive structure that focuses on improving short-term contribution margins. Because
production decisions by managers are influenced by capacity accounting and short-term
incentives as discussed below, incentives for overproducing are high in this industry.
This industry is also relevant to the research question from a managerial perspective.
Industry analysts have observed the tendency for US automakers to overproduce vehicles relative
to demand and to use liberal customer incentives to sell the excess production, as compared to
foreign automakers, notably Japanese firms, that produce to demand and make judicious use of
incentives (Boudette and White 2007). These analysts express concerns about the implications of
overproduction on long-term profitability (Ingrassia 2002). One analyst notes, “GM has had to
continue discounting and dump thousands of Chevy HHRs into rental fleets, which eroded the
margin on the car, and badly watered down its cachet” (Stoll and White 2007). Another writes,
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To return to profit in the US, GM - after years of selling cars mainly with the help of
heavy discounts - needs to build more appealing vehicles and persuade consumers to pay
full price for them. To do that, it has to stop making more vehicles than the market
demands, or risk triggering a new cycle of discounting. (Boudette and White 2007)
We first explore the role of capacity accounting and performance measurement systems
in production decisions. An important factor that influences production decisions in firms is the
treatment of capacity costs by accounting systems. Cooper and Kaplan (1992) argue that
conventional accounting systems do not adequately distinguish between the cost of resources
supplied in advance of the period (i.e., investments in production capacity), and the cost of
resources used in the production process. The difference between the cost of resources supplied
and resources used is the cost of unused capacity. Unused capacity (both desirable and
undesirable) arises for several reasons including lumpiness of fixed resource acquisitions,
demand fluctuations, and long-term contracts with employees and suppliers. By using budgeted
production volume as the denominator for computing cost driver rates, traditional accounting
systems confound the cost of unused capacity with the cost of resources used. As a result, cost
driver rates and computed product costs vary over time with budgeted volume. Moreover, these
variations in budgeted volume are caused by demand fluctuations that arise from factors outside
the control of the manager. If performance measurement and control systems do not recognize
the variance in cost caused by fluctuations in the denominator volume, distorted managerial
incentives arise (Fry, Steele, and Saladin 1995). To absorb the period overhead costs, managers
have incentives to increase production (even in excess of market demand), as long as short-run
contribution margins are positive (Balakrishnan and Sprinkle 2002). While this may be the
optimal short-term economic decision, such overproduction can give rise to a significant increase
in discretionary costs in the medium term (e.g., increased advertising) and harm brand image in
the long-term.
5
In the first part of this paper, we use field evidence from a “Big Three” US automaker to
show that a failure to account for excess capacity is associated with overproduction relative to
“free” (i.e., non-incentivized) market demand. The excess capacity in this firm is a result of
potentially suboptimal capacity investment decisions as well as a decline in the market share of
US automakers relative to foreign automakers, notably Japanese firms. As such, the excess
capacity is outside the control of the firm’s middle and lower level managers and should be
excluded when unit costs are computed for performance measurement and evaluation purposes.
However, our field interviews reveal the presence of an accounting system that focuses on the
absorption of all costs, but fails to separately account for excess capacity costs. In addition, a
substantial portion of the overhead costs (the numerator) are committed due to long-term supply
contracts. When production volume (the denominator) is higher, unit costs are lower. The firm
has a balanced scorecard for production managers as well as executives, which places
considerable emphasis on short-term costs and profits. Our conversations with the managers at
this firm indicate that the combination of improper accounting for capacity combined with a
performance measurement system that focuses on short-term costs and profits are important
drivers of excess production decisions at this firm.
We next turn to the negative consequence of overproduction. For example, when firms
overproduce, significant costs related to storage, obsolescence, and damage of unsold inventory
can accrue. Another negative consequence that has been identified by the business press is that to
sell the excess production, firms have to increase the extent of incentives provided to customers.
This has the potential to reduce the firm’s brand image if customers perceive the discounted
brand to be inferior. Although the goal of overproduction is to increase revenues and improve the
short-term financial situation of the firm, the increased long-term costs of brand image erosion
6
can be significant. Conventional wisdom suggests that overproduction and providing customer
incentives has negative brand image effects for firms engaging in such practices (Styhre and
Kohn 2006), and previous research shows that prices function as a signal of quality to consumers
(Kalita, Jagpal and Lehmann 2004). Higher customer incentives can therefore be considered as a
signal of lower quality with potentially negative consequences for the brand image of a firm.
However, prior academic literature has not empirically examined whether excess production
influences brand image. The effect of excess production on short-term revenues is more readily
apparent than its effect on deterioration in brand image, because most traditional accounting
systems are not designed to enable managers to assess or identify changes in brand image.
The second part of this paper takes an important step in this direction. Using archival data
from the US auto industry, we first examine the effect of excess capacity on excess production,
and the result of excess production on discretionary tangible costs that are easily identifiable by
the accounting system such as customer incentives (e.g., rebates), advertising spend, and
inventory buildup. Second, we examine the effect of customer incentives, advertising, and
inventory build-up on the intangible cost of brand image erosion. We conduct empirical analysis
at the level of the product nameplate for the US auto industry. Our empirical results indicate that
excess capacity is a significant driver of excess production. Every percentage point of excess
capacity is associated with a 0.49 percentage point increase in excess production, i.e., production
above year-ahead estimated production. Taken together with our insights from the field, these
results indicate that traditional accounting and performance measurement systems encourage
excess production in the presence of excess capacity. Third, we find a positive association
between excess production and customer incentives and conclude that firms are indeed using
incentives to dispose excess production (as opposed to using customer incentives as an overall
7
sales strategy).1 Fourth, excess production is associated with an increase in advertising spend and
our proxy for inventory build-up. Finally, we find that higher customer incentives are negatively
associated with our measure of brand image, the JD Power APEAL Index. Every additional one
percent of rebate is associated with a two point decline in the APEAL index; a one percent
increase in rebate penetration is associated with a 0.2 point decline in the index. Inventory buildup is also negatively associated with brand image. These archival results confirm our field
evidence that suggests managers focus on the short-term contribution margin benefits of
increased production. They fail to incorporate into the production decision (i) the resulting
increase in tangible costs necessary to dispose of the excess inventory (which are captured in the
accounting system but likely not until a period subsequent to the production decision), and (ii)
the potential harmful effects on brand image (not captured in the accounting system).
This study is important because it shows that the cost-benefit tradeoffs of production
decisions may be suboptimal if firms use a short-term accounting approach rather than a longterm approach that considers intangible costs. In addition, it responds to recent calls (e.g.,
Ashton 2005) for the identification and testing of value-based financial and non-financial
measures that can be used by managers within and outside the firm for identifying, measuring,
creating, and monitoring intangible assets. Specifically, an important implication of this study is
that the inclusion of (likely non-financial) measures of intangible assets such as brand image into
the incentive structure may improve production decisions.
Finally, there is a significant body of accounting research related to ex ante capacity
planning decisions (e.g., Balachandran et al. 1997; Balakrishnan and Sivaramakrishnan 2001,
1
If incentives were a part of an intentional sales strategy, then we would not expect to find an association between
excess production and sales incentives. Rather, an incentive sales strategy would suggest that all products –
regardless of the level of excess production – would have high incentives.
8
2002; Banker and Hughes 1994; Buchheit 2003).2 There are also studies examining the costs
associated with “congestion” (e.g., Banker et al. 1988; Balakrishnan and Soderstrom 2000), and
the ex post capacity decisions that may lead to cost “stickiness” (Anderson et al. 2002;
Balakrishnan et al. 2004; Anderson and Lanen 2007). Our study contributes to this literature by
examining, not the capacity decision itself, but the subsequent production decisions that are
made as the result decisions leading to excess capacity and in the context of firms’ cost
accounting and performance measurement systems.
The reminder of the paper is organized as follows. Section 2 discusses the theory and
research questions, and Section 3 describes the research setting. We present the field evidence in
Section 4 and the archival analysis in Section 5. Section 6 concludes.
2. Theory and Research Questions
Most firms build capacity in excess of the amount required to meet current production.
Excess capacity is useful when demand is uncertain (Banker, Datar, and Kekre 1988), or
unavoidable when capacity decisions are lumpy. However, most firms do not view large amounts
of excess capacity as desirable (Varian 1984; Buchheit 2003). Because excess capacity results in
higher reported fixed (or period) costs per unit in traditional absorption-oriented financial and
managerial accounting systems, when firms have excess capacity, managers have an incentive to
produce in excess of demand. Facing potential inventory buildup, they then identify mechanisms
to dispose of the excess production, such as by offering sales discounts to customers. In the short
run, as long as revenues exceed the full absorption cost of production, this strategy improves
financial performance in firms whose accounting systems do not separately identify the cost of
excess capacity. However, prices serve as a signal of quality and hence over a longer time
2
See also Kouvelis (2005) and Van Mieghem (2003) for reviews of capacity planning research in the operations
literature.
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period, extensive use of sales incentives can have strong negative signals to customers,
especially in settings where customers have incomplete information about products (Balachander
and Srinivasan 1994; Kalita, Jagpal and Lehmann (2004). Therefore sales incentives can have
negative effects on brand image in the long run, and to the extent that these unaccounted for
marginal intangible costs of brand erosion result in future realized accounting costs or future
decrease in revenues, firm performance will suffer.
While prior accounting research provides empirical support for a relation between
customer-related intangible assets and financial outcomes (e.g., Banker et al. 2000; Behn and
Riley 1999; Ittner and Larcker 1998; Dikolli et al. 2007) and between customer-related
intangible assets and market value (Barth, Clement, Foster, and Kasznik 1998; Gupta, Lehmann,
and Stuart 2003; Rajgopal, Venkatachalam, and Kotha 2003), extant research has not explored
the influence of production decisions on the value of customer-related intangible assets, nor has
it examined the accounting and performance measurement determinants of those production
decisions.
We first consider how accounting systems and, in particular, the accounting for capacity
costs, can affect production decisions. Accounting systems in most manufacturing firms are
based on absorption costing in which fixed manufacturing overhead is allocated to product costs
(i.e., product costs fully “absorb” all costs of production, including fixed costs). One limitation
of managerial accounting systems in many firms is that they do not separately identify the costs
of excess capacity, and hence excess capacity results in a higher fixed or period cost per unit of
produced output (Cooper and Kaplan 1992).
Accounting theory suggests that allocations should be based on practical capacity-that is,
the capacity of production with allowance for downtime due to, e.g., maintenance. This ensures
10
that excess capacity costs are not charged to current production (Cooper and Kaplan 1992),
thereby preserving the accuracy of product costs and presumably improving pricing, production,
and capacity investment decisions. Consistent with this, Buchheit (2003) examines the effect of
explicit capacity reports on decision-making and experimentally shows that with decreasing
demand, separately identifying capacity costs makes the effect of excess capacity on costs more
salient and can contribute to improved decision-making.
Statement of Financial Accounting Standards (FAS) 151, “Accounting Costs,” appears to
be consistent with accounting theory. Effective in fiscal years beginning after June 15, 2005,
FAS 151 (Cairns 2005) “requires that allocation of fixed production overheads to the costs of
conversion be based on the normal capacity of the production facilities.” The statement further
directs that “abnormal” excess capacity should be charged to the current period. However, the
definition of “normal capacity” remains vague, leading to a disconnection between what
accounting theory prescribes, and what is observed in practice. According to FAS 151:
Normal capacity refers to a range of production levels. Normal capacity is the production
expected to be achieved over a number of periods or seasons under normal
circumstances, taking into account the loss of capacity resulting from planned
maintenance. Some variation in production levels from period to period is expected and
establishes the range of normal capacity. The range of normal capacity will vary based
on business- and industry-specific factors. Judgment is required to determine when a
production level is abnormally low (that is, outside the range of expected variation in
production). … The actual level of production may be used if it approximates normal
capacity. (emphasis added)
These guidelines provide enough leeway for a range of denominators to be used in the
computation of fixed overhead allocation rates. It is this leeway that provides an opportunity for
firms to lower per unit costs with increased production and thereby improve short-term financial
performance. In sum, increased budgeted production decreases unit cost because fixed costs are
spread over a larger number of units. This provides a primary motivation for managers to make
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excess production decisions.
Performance measurement systems in firms also contribute to managers’ incentives to
engage in excess production. Most firms have performance measurement systems which
evaluate managers based on cost reduction relative to a budget. These budgetary goals typically
use expected production as the denominator, instead of practical capacity as prescribed by
accounting theory. This provides incentives to managers to increase production even in excess of
expected demand, as long as the variable accounting cost of production is lower than the
expected marginal accounting revenue.3
Taken together, a combination of accounting system shortcomings and a short-term
decision orientation induced by performance measurement systems can lead to a persistent
tendency among firms to increase production volume to cover fixed costs and lower total unit
costs. While excess production may yield short-term revenue gains, it has several disadvantages.
First, the company has to generate demand for the excess production by increasing advertising
and by offering customer incentives that can be of a sizable magnitude. Second, excess
production is associated with additional costs related to storage, obsolescence, and warranty
claims, and these costs are incurred with a lag and possibly even at a different point in the value
chain. Finally, and importantly for our research, provision of incentives may lead to an erosion of
brand image (e.g., as reflected in lower unit residual values for all products produced by the
firm) in the long-run. Any such damage to the brand image is likely to be reflected in lower
customer satisfaction and loyalty that may even have negative spillover effects on the success of
3
Misperceptions regarding scale economics may also drive the mentality to overproduce. Prior research indicates
that most firms overestimate the economies obtained from scale because scale economies tend to be very visible and
salient (Hambrick 1983). For example, within the relevant capacity range, if a firm is operating below capacity, it
can increase output without the need to increase its plant and equipment cost, or with the same level of personnel.
Pil and Holweg (2003) note that in most industries executives have a “minimum efficient scale” mindset and assume
that scale of production can act as a barrier to entry. This mindset provides additional incentives to overproduce.
12
other products of a firm. Ultimately, brand image erosion results in a reduction in long-term firm
value.
The relationships that we examine in this research are shown in Figure 1. Our primary
research questions are as follows. First, do firm-level factors such as accounting for excess
capacity (Figure 1, Link 1) and performance measurement systems that provide incentives for
short term cost reduction (Figure 1, Link 2) influence the relation between excess capacity and
increased production (Figure 1, Link 3)? Second, does increased production influence tangible
costs such as customer incentives (Figure 1, Link 4), advertising spend (Figure 1, Link 5), and
excess inventory cost (Figure 1, Link 6)? Finally, what is the effect of these tangible costs on the
intangible cost of brand image erosion (Figure 1, Links 7, 8, and 9)? We present field evidence
from a Big Three US automaker to provide insights regarding Figure 1, links 1-6. We test links 3
through 9 using archival data from the US auto industry.
3. Research Setting
We examine our research questions in the context of the US auto industry during the
period 2005 to 2006. The auto industry provides an attractive context to examine the
determinants and consequences of excess production for at least three reasons. First, the auto
industry has been undergoing significant changes in recent years. Some of these changes include
substantial increase in international competition, increase in customer information because of the
internet, rapidly changing technologies in safety as well as style features, and the increase in
customer segmentation and niche markets such as customers with a preference for hybrid
vehicles (Power Report, January 2003). Market share of US companies has been steadily eroding
and the Big Three automakers (GM, Ford, and Chrysler) have been losing market share to
Toyota, Nissan, and Honda (see Figure 2). Reasons for the decline in the competitiveness of US
13
automakers include the superior reputation of Japanese cars in both design quality and style, and
the ability of Japanese automakers to build higher brand loyalty (Regassa and Ahmedian 2007;
Train and Winston 2007). As a result of the decrease in market share, the US auto industry has
been plagued with excess capacity, estimated at about 20 percent (Carson 2003).
Second, US automakers have a cost structure that is highly leveraged (i.e., a greater
proportion of fixed costs relative to variable costs). A significant fixed cost burden for Big Three
US firms (and to a lesser extent foreign automakers producing in the US) accrues from health
care and pension costs of retired employees, as well as committed contracts with labor. For
example, healthcare related costs account for approximately $1,500 for every vehicle produced
by General Motors (Murray 2005) and every employee supports two-and-a-half pensioners
(Klier 2004).
Finally, incentive structures in the auto industry encourage excess production. Most US
automakers have performance measurement systems that place considerable emphasis on short
term cost reduction and managerial compensation is based on short-term accounting performance
measures (Tay 2007), which exacerbates the incentives to overproduce. In addition, automakers
recognize revenue when the product is shipped to the dealer, rather than when the product is sold
to the final customer. Hence, in the short term, excess production allows a firm to report greater
revenues as well as lower unit costs, and thereby increase short-term income.
As anecdotal evidence of these production practices, in October 2006 Chrysler executives
revealed to analysts and investors that the company had been producing far in excess of demand,
just to fill capacity (Henry 2007). Auto analysts had already noticed in the summer of 2006 that
over 100,000 Chrysler vehicles were idling in the Detroit area (Maynard 2006). Similarly, during
December 2006, GM had more than one million vehicles in stock in the US. In 2006, GM had
14
about 41,000 vehicles for every one percent market share in the US; whereas Toyota had only
16,000 vehicles of inventory per percent of its market share (Boudette and White 2007).
Moreover, the business press has noted the correlation between overproduction and provision of
incentives (Maynard 2006). A Wall Street Journal article notes: “Detroit automakers still tend to
push sales using big discounts, a tactic that undermines both brand image and the resale value
that customers get when they trade in or sell their car” (Boudette and White 2007).
4. Field Evidence on the Determinants and Consequences of Excess Production
4.1 Interview Protocol
To obtain detailed insights into the organizational dynamics that encourage excess
production, we conducted field interviews at one of the Big Three automakers. Such information
cannot be obtained from secondary sources and hence field-based research provides an
opportunity to study these questions in greater detail. Our objective was to discern the role of
capacity accounting and performance measurement systems in encouraging excess production in
the presence of excess capacity. We further sought to get managers’ perceptions regarding the
consequences of any such excess production. We describe the insights from our field interviews
below.
Interviews were facilitated by the strategy group within the organization. The interviews
were conducted over a two-day period in late 2006 with ten managers having titles of directors of
strategy, HR, finance, production planning, costing, market research, sales, transportation, and
dealer management. Each interview lasted approximately one-an-one-half hours. Although a
basic interview protocol was followed, the interviews were primarily open-ended to allow
interviewees to provide unique perspectives on the issue of overproduction by the firm.
Following these interviews, we held weekly phone meetings with two key contacts from the
15
strategy group from January, 2007 through June, 2007. These meetings were used to clarify
insights from the interviews and to facilitate data collection and interpretation. In the following
subsection, we describe results from interviews with key decision-makers in the firm regarding
production planning, performance measurement, and accounting practices.
4.2 Field Evidence on the Determinants of Excess Production
Our interviews revealed that the accounting system encourages an excessive focus on
short-term financial performance and hampers long-term strategic thinking. The firm uses a
standard absorption costing system. Labor rates are obtained from the industrial engineering
department, and the manufacturing finance department provides the overhead burden rate. The
manager of product costing said that the overhead burden rate is based on estimated overhead
cost divided by expected plant volume. When we inquired whether excess capacity costs were
separately identified, the product manager responded that “excess capacity costs are not
separated.”
The failure of the accounting system to separate excess capacity costs results in
variability in costs arising from fluctuations in production that may or may not reflect demand
fluctuations. Because the responsibility for meeting unit cost targets is assigned to plant
managers, these managers have an incentive to increase production to lower unit costs. In 2006,
the company was operating at only 56% of its installed capacity and about 50% of total
manufacturing costs were fixed. Plant managers admitted that the high fixed costs and low
capacity utilization encouraged them to “build more to reduce unit costs.”
Lack of accounting for excess capacity has a salient effect early in the production
planning process, when the firm is in the process of forecasting demand. The production
planning department uses the projections of “free demand” (i.e., demand absent any customer
16
incentives) generated by the economics department as the starting point for determining
production quantity. It then consults with marketing/sales to obtain an estimate of sales quantity.
Based on these two numbers, if the production quantity is “not good enough,” (i.e., production is
inadequate to absorb the costs to obtain the targeted cost per unit), the production planning
managers explore options such as offering additional features, or tweaking the price to increase
demand. Based on the production planning department’s estimates of the potential increase in
demand that can be generated via these changes, a new free demand estimate is generated. Note,
however, that although these are referred to as free demand numbers, they are not free in the
sense that they have already been inflated. Although these final demand estimates are already
optimistic, a combined decision is made to produce “a little more – to fill capacity.” The
manager of strategy remarked “basically we talk ourselves into overproduction.” Thus, our
interviews suggest that failure to account for excess capacity contributes to the relation between
excess capacity and excess production (Figure 1, Link 1).
The performance measurement system at this firm exacerbates the tendency for excess
production. The firm uses a balanced scorecard to evaluate and reward the performance of
managers at corporate, divisional, and plant levels. The performance measures that are used to
evaluate manufacturing include: fixed cost, program spending, material cost, plant cost, variable
cost, and cost per vehicle. As a result, there is an incentive to overproduce to improve cost per
vehicle and justify the expenditures on fixed cost, program spending, and plant cost. All these
costs have a fixed component that decline on a per-unit basis when production volume (the
denominator) increases. As a result, even though production managers are aware that the demand
estimates are optimistic, the performance measurement system discourages reducing production
quantity if demand is lower than expected, because then unit costs increase, and as one manager
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remarked “profit targets would not be met.”
Indeed, a factor that further encourages excess production is that upper-level managers at
the firm are held strictly accountable for short-term profit targets. As a result, as long as there
was a non-zero short-term contribution margin per unit, excess production increased short-term
financial performance. The excessive focus on short-term financial performance was apparent in
many interviews. The manager of production planning remarked:
The issue is that when the executive committee approves those volumes, they have been
overly optimistic—extremely overly optimistic. And this is where the truth comes out.
And again, this is the crux of the problem—in order to make the money—the profit
targets—you have to build more units. So, even though the [marketing department
managers] come back and say, “Listen, we really can’t sell that many units,” they are
told: “You have to sell more units, because otherwise we can’t hit the profit number.”
And so we find a way to sell more units.
Before 2003, this firm was not excessively focused on cost because it was following a
differentiation and flexibility policy. Beginning in 2004, however, this firm shifted its focus as
well as its strategy toward cost-cutting. Thus, most of the measures in the revised corporate and
divisional scorecards now focus on cost. The manager of transportation and inventory remarked
that the change in production behavior in response to the change in strategy was very evident.
For example, in 2004, the extent of inventory holding costs increased by ten times compared to
2003 and previous years. In addition, new vehicle transportation was a profit center until 2003,
but beginning in 2004 it has been treated as a cost center to deal with the excessive increase in
new vehicle transportation cost. The effect of excess production on additional transportation
costs was very visible to the managers. In 2006, the company spent $343 million in storage costs
alone. Vehicles that are sent to storage take, on average, 7.3 additional transit days (company
sources).
Thus our field evidence suggests that the performance measurement system does indeed
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contribute to excess production (Figure 1, Link 2). Managers were also aware that the excess
production decisions had a number of negative consequences such as additional quality-related
warranty costs. One manager commented “No one will argue that it [warranty cost] increases as
vehicles sit in our storage yards, collecting dust.” An internal study done in 2004 showed that
vehicles that were stored for 360 days cost the company $50 more per vehicle in warranty than
those sold within 60 days, primarily arising from body repairs, cleaning dirty interiors, and
replacing drained batteries, cracked windshields, or tires.
4.3 Field Evidence on the Consequences of Excess Production
While managers felt that there were few other options to meet the performance targets,
they also acknowledged that excess production was not desirable. The manager of one of the
manufacturing plants remarked in a presentation to other managers in December 2006:
Conservatively estimating that approximately one-fourth of the vehicles sold in 2004 sat
longer than 60 days, this translates to a $5 million loss in 2004. Knowing that we pushed
and held even more vehicles the following years, the numbers can’t be prettier for 2005
or 2006.
Managers were aware that excess production was hurting long-term revenues and costs
via its impact on residual values and its impact on customer expectations. The manager of fleets
and residual values remarked:
The residual value piece of it is a lagging cost. And it’s also a hidden cost that doesn’t
get accounted. …We go out and we talk to ALG [Automotive Lease Guide], and we tell
them what a great vehicle we have; and we tell them that we’re going to build 50,000 of
these things. And they say: “Oh, if you’re going to build 50,000 then I think your
residual is 48.” And they come back, and they look at the volumes that we’re actually
producing, and they see that we didn’t build 50,000, we built 75,000 units. Well, they
lose faith. So they say: “You know what? We thought 45 was the right number. I think,
really, 43 is the right number, because I don’t know where these guys are going to stop
[producing].” So then you’ve got to bridge between 43 and 52, instead of 45 and 52. So
that’s the direct impact [on residual values] that is sort of hidden today.
Managers also remarked that once the firm is locked into an excess production and sell-
19
via-incentive mindset, it becomes very difficult to get out of this process. The manager of
strategy remarked: “You don’t necessarily want to fall on your sword for the sake of a long-term
profit down the road, because you may not be the one that’s in the chair when those long-term
profits come to roost. So, we get into this short-term cycle.” Thus, the excess production
strategy persists despite the fact that, as one manager notes, “It degrades the product.”
We explored with the managers whether provision of incentives was tied to an overall
sales strategy, rather than a function of the production planning process described above. Data
that the company shared with us for three representative nameplates (Figure 3) reveal that a
significant proportion of greater-than-anticipated incentives were specifically linked to actual
sales lower than sales projected during the production planning process. As an example, actual
rebates per unit were 1.33 times higher than planned for Nameplate 1 as a result of actual sales
40 percent lower than planned sales. One of the managers remarked about the Figure 3 data:
As our [performance] target slips further away, our reaction is to increase incentives, thus
deteriorating the total cost of ownership …. This sales strategy is a short-term strategy
that has obviously run its course.
In 2006, a team of production managers and suppliers came to the following conclusions
that were presented to the firm’s executive committee:
Remember that the {omitted $} Billion cost that we identified is just the tip of the
iceberg. There are other costs of considerable magnitude associated with [excess
production] that are yet to be considered and that could make this a {omitted $, 10X}
Billion cost. …We’ve shown you why this model contradicts every pillar of our corporate
strategy, which defines how we should operate to be successful today and tomorrow.
…Establishing realistic sales targets will help us avoid falling into a heavy ‘push’
situation again. We need to build based on what the customer wants and the market will
take. We cannot continue to stumble over and over and not learn the lesson, and our
executives need to demand realistic targets. …we need the discipline to measure our
progress to those targets, and raise the flag when we are in trouble…We need to make
sure that decisions being made along the way, every day, by all of us, are aligned to our
corporate strategy, and judged for their short term benefits as well as their long term
effects. (emphasis added)
20
Taken together, our field interviews suggest that it is the complex interplay between
production planning, performance measurement, and accounting practices within this
organization that evolved into the currently observed practice of producing in excess of (free)
demand and then selling via costly customer incentives. While these managers intuited that this
pattern has detrimental brand image effects as well, they had no empirical evidence of this
damage, nor did they have an incentive to document this damage because the intangible costs of
brand image erosion occur with a lag and are not incorporated into the performance measurement
and accounting system. In the following section we present archival evidence that the field
observations hold more generally, and that the negative effects of excess production include, not
only the tangible costs of customer rebates, advertising, and inventory build-up, but also the
intangible cost of brand image erosion.
5. Archival Analysis of the Determinants and Consequences of Excess Production
The archival analysis uses monthly and annual data at the nameplate level for the period
2005-2006. The data used for the analysis of the association between excess capacity and excess
production, and between excess production and the tangible costs of advertising spend, customer
rebate incentives, and inventory build-up are comprised of 2,364 monthly observations. Included
in these data are 132 nameplates; 103 nameplates from the Big Three US automakers and 29
nameplates from foreign automakers (Table 1, Panel A). Table 1, Panel B provides details of the
distribution of observations by auto segment. Not surprisingly, Big Three automakers have a
disproportionate number of observations in the SUV and Van segments, relative to the foreign
automakers. Our brand image analyses use annual data and consists of 157 nameplate-year
observations. Below we define the variables used in the archival analysis (i.e., measured
variables in Figure 1) and the sources for data collection.
21
5.1 Variable Definitions and Data Sources
Excess Capacity (%): Because field interviews indicate that production scheduling commences
approximately one year ahead of production dates, we base the computation of excess capacity
on information know one year in advance of the production date. We assume that capacity
investment decisions are known well in advance and, thus, use contemporaneous (to the actual
production date) annual capacity estimates (divided by twelve to convert to monthly capacity).
We measure excess capacity in two ways: (i) the difference between monthly nameplate
production capacity and nameplate production forecasted one year prior, scaled by monthly
nameplate production capacity (Excess Capacity (%) – forecast), and (ii) the difference between
monthly nameplate production capacity and actual nameplate production in the same month of
the prior year, scaled by monthly nameplate production capacity (Excess Capacity (%) – actual).
Capacity data were computed from annual capacity estimates obtained from the Autofacts
database (PWC Automotive Institute, http://www.pwcautomotiveinstitute.com/). Forecasted
production data were acquired from Global Insight (http://www.globalinsight.com/). Actual
production data were obtained from the PWC Autofacts database.
Excess Production (%): We measure monthly excess production as actual nameplate production
minus one-year-ahead forecasted production, scaled by one-year-ahead forecasted production.
Actual production data were obtained from the PWC Autofacts database.4
Customer Incentives: We measure customer incentives in two ways. First, Rebate % is the
customer rebate as a percentage of final sales price. For example, in June 2006, the Chrysler PT
Cruiser had a list price of $29,700 and a post-rebate price of $26,813, which implies that the
discount was 10.77 percent of the final price (Saranow and Chon 2006). Second, Rebate
Penetration is the percentage of sales made at a rebate. This can range from zero percent, which
4
Ideally we would like a measure of forecasted sales demand. However, we were unable to obtain these data.
22
implies that no units are sold at a rebate, to 100 percent, which implies that all sales are rebated.
Both rebate percentage and rebate penetration data are monthly data and are obtained from the
JD Power and Associates Topline Report.
Advertising: Monthly advertising spend data were acquired from TNS Media Intelligence
(http://www.tns-mi.com/). These data are for nameplate-specific advertising across all media
forms (i.e., print, television, radio). We use both a measure of total spend (Advertising Spend),
and a measure of spend per unit sold (Advertising Spend Per Unit).
Inventory Build-up: Inventory build-up is associated with increased storage and transportation
costs. We use a measure of days sales in inventory as a proxy for inventory build-up. Days
Inventory is defined as the number of days it takes to sell (using actual subsequent sales) the
current month’s production. This variable has a minimum value of zero where zero implies that
the current month production is less than the current month sales. Monthly nameplate level sales
for all automakers were obtained from our field research partner.
Brand Image: We measure brand image with the JD Power Automotive Performance, Execution
and Layout (APEAL) index. The APEAL Index is based on annual surveys of approximately
95,000 customers (in 2006) during the first two to six months of ownership. The APEAL survey
is a widely used image measure that rates the features that people find most appealing about their
new vehicles using 100 vehicle attributes related to the vehicle’s design, features, comfort,
driving dynamics, engine performance, safety, and fuel economy (see Appendix for item details).
Control Variables: For the monthly analysis of the associations between production capacity and
excess production, and between excess production and the tangible costs of advertising, customer
incentives (Rebate % and Rebate Penetration), and inventory buildup (proxied by Days
Inventory), we use a number of controls. These include: (i) an indicator variable identifying the
23
Big Three US automakers (Big Three Indicator), (ii) an indicator variable equal to one for any
month in which production is zero for a given nameplate (Suspended Production), (iii) a measure
of the number of plants that produces a given nameplate (Number of Plants), (iv) auto segment
indicator variables, (v) two economic indices collected from the US Bureau of Labor Statistics
(http://www.bls.gov/) (CPI Index and Gas Index), (vi) and monthly indicator variables.
In our annual tests of the associations between customer rebates, advertising, and
inventory buildup and the APEAL Index, we again include controls for US automakers,
suspended production, auto segments, and a time period indicator (in this case, an indicator for
the year 2006). In addition, we control for production quality, nameplate competition, financing
terms, and customer demographics. First, in the auto industry the JD Power and Associates
Initial Quality Survey (IQS) serves as the industry benchmark for assessing new-vehicle quality
(Selko 2006). The IQS measures quality problems experienced by owners at 90 days of
ownership. The IQS captures two categories of quality: design quality, and quality of production
(i.e., defects and malfunctions). We use the JD Power IQS PP100 (problems per 100) data to
control for product quality. Second, we include the number of nameplates as a control for
segment competition. Third, we include as controls three financing term variables, the
percentage of sales in which financing is done through the automaker (Captive), the mean down
payment (Total Down), and the mean annual percentage rate (APR) for financed vehicles.
Finally, to control for differences in customer demographics across nameplates, we include
measures of the average customer age (Avg Age) and the percent of female customers (Gender)
for a given nameplate. The finance terms and customer demographic data were collected from
the JD Power and Associates Topline Report
Table 2, Panel A provides the descriptive statistics for the monthly data and Panel B
24
contains the correlations among monthly variables. Panels C and D contain the descriptive
statistics and correlations for the annual data.
5.2 Empirical Models
Figure 1, Link 3 predicts that excess capacity is associated with excess production. We
use the following linear model with an AR(1) disturbance, clustered by nameplate for the period
January 2005 to December 2006:
Excess Production = ! + "1 [Excess Capacity] + "2 Big Three Indicator
+ "3 Suspended Production + "4 Number of Plants
+ "5-11 Segment indicators + "12 CPI Index
+ "13 Gas Index + "14-24 Month indicators + #1
(1)
where [Excess Capacity] is measured as capacity relative to either one-year-ahead forecasted
production, or actual production in the same month of the prior year. We use the following
vehicle type indicator variables: Van, Compact, Large, Luxury, Midsize, Pickup, and Sporty
(SUV indicator omitted). Because we use monthly data for the 24 months of 2005-2006, we
employ an auto-regressive model to test equation 1. Auto-regressive models control for autocorrelation as well as heteroskedasticity. We expect the coefficient on the Excess Capacity, "1, to
be positive indicating that excess capacity is associated with excess production.
We expect that excess production is associated with a number of tangible costs, including
customer rebates (Figure 1, Link 4), advertising (Figure 1, Link 5), and inventory buildup costs
(Figure 1, Link 6). We estimate the following model using monthly data at the nameplate level
for 2005-2006 to examine the association between excess production and each of three
categories of tangible costs.
[Tangible Cost] =
! + "1 Excess Production + "2 Big Three Indicator
+ "3 Suspended Production + "4 Number of Plants
+ "5-11 Segment indicators + "12 CPI Index
+ "13 Gas Index + "14-24 Month indicators + #1
(2)
25
where [Tangible Cost] is either Rebate %, Rebate Penetration, Advertising Spend, Advertising
Spend Per Unit, or Days Inventory. We expect the coefficient on Excess Production, "1, to be
positive in all the models, indicating that excess production is associated with higher rebate
levels and penetration, higher advertising spent, and greater number of days in inventory. To the
extent advertising is needed to inform the customers about the presence of incentives, we expect
advertising to be driven, in part, by increased incentives. Therefore in equation 2, when the
dependent variable is advertising, we include both Rebate % and Rebate Penetration as
additional control variables.
Next, we examine the effect of the tangible short-term costs of excess production on the
long-term intangible cost of brand image erosion. We predict that customer rebates are
negatively associated with brand image (Figure 1, Link 7) based on prior research that associates
higher prices with superior quality and discounts with inferior quality (Styhre and Kohn 2006).
We also predict that when excess production results in excess inventory, deterioration in brand
image results as customers observe inventory being held in public places (e.g., mall parking lots)
(Figure 1, Link 9). Advertising also influences brand image, but the direction of the influence is
unclear. On the one hand, advertising can increase brand image, especially when it focuses on
the firm and other strong brands. On the other hand, excessive advertising of incentives can
reduce brand image. We use the following model to test the effect of customer incentives,
advertising, and inventory buildup on brand image:
APEAL Index = ! + "1 Rebate % + "2 Rebate Penetration + "3 [Advertising]
+ "4 Days Inventory + "5 Big Three Indicator + "6 IQS
+ "7 Suspended Production + "8-13 Segment indicators
+ "14 Number of Nameplates + "15 Captive
+ "16 Total Down + "17 APR + "18 Avg Age+ "19 Gender
+ "20 Year 2006 + #1
(3)
where [Advertising] is either Advertising Spend (i.e., raw dollars), or Advertising Spend Per
26
Unit. We expect the coefficients on Rebates % and Rebate Penetration, "2 and "2, to be negative.
We expect the coefficient on Days Inventory, "4, to also be negative. We use clustered standard
errors at the nameplate level to control for multiple observations for the same nameplate.
5.2 Empirical Results
Table 3 provides the results of testing Figure 1, Link 3 (equation 1), and examines the
association between excess production levels and excess capacity. The results indicate a
significant positive coefficient on excess capacity measured using forecasted production (Model
1) and using actual production in the same month of the prior year (Model 2). A one percentage
point increase in excess capacity using forecasted (one year prior actual) production is associated
with a 0.491 (0.04) percentage point increase in excess production. Thus, these results suggest
that when firms have excess capacity, they produce in excess of one-year-ahead production
forecasts. Typically, firms make decisions to increase plant capacity when there is unmet
demand. However, the results in Table 3 suggest that excess plant capacity is associated with
excess production. This provides archival evidence consistent with the insights gleaned from the
field interviews; namely, that when accounting systems neglect to separately account for excess
capacity, and performance measurement systems place excessive focus on short-term
performance, firms increase production in order to “absorb” the costs of excess capacity.
The results in Table 3 also indicate a negative coefficient on the Suspended Production
control variable. In addition, Number of Plants is positively associated with excess production.
Thus, even controlling for excess capacity, excess production is greater for nameplates produced
at multiple plants, as compared to those produced at only one plant.
Next we examine the association between excess production on the tangible costs of
customer rebates, advertising, and inventory buildup (equation 2). The results in Table 4, Model
27
1 indicate that excess production is associated with higher rebates as a percentage of total sales
price. The results also indicate that US firms provide significantly higher rebates relative to nonUS firms. Number of Plants is significantly positively related to rebate percentage, which is
consistent with our previous results that number of plants is associated with excess production
and thereby with the provision of additional rebates. In Model 2, the coefficient on rebate
percentage is not statistically significant. Rebate penetration measures the proportion of vehicles
that are sold at a rebate, and it may be that it takes a longer time period for excess production to
manifest in the form of higher rebate penetration, although additional tests of a one month lag
failed to find a significant relation. The US indicator in Model 2 has a positive coefficient, which
indicates that a greater proportion of vehicles of US automakers are sold at a rebate as compared
to non-US automakers. In sum, these results provide partial support for Link 4 in Figure 1 that
excess production is associated with increased customer incentives.
The association between excess production and advertising is provided in Models 3 and 4
of Table 4. In both columns, excess production is positively associated with advertising spend as
predicted in Figure 1, Link 5. Rebate percent is negatively associated with advertising spend.
These results suggest that although automakers increase their advertising expenditures when they
have to create demand for excess production, the additional advertising dollars are not tied to
promotion of customer incentives and, in fact, may be in lieu of customer incentives. Finally,
Table 4, Model 5 indicates a positive association between excess production and Days Inventory,
which is consistent with Link 6 in Figure 1.
We next examine the effects of rebates, advertising, and inventory build-up on the
intangible cost of brand image as measured by the APEAL Index. Table 5 contains the results of
testing Figure 1, Links 7-9 (equation 3). Model 1 presents the results when advertising spend is
28
included as an explanatory variable, and Model 2 presents the results with advertising spend per
unit as an explanatory variable. In both models, the coefficient on Rebate % and Rebate
Penetration are negative and statistically significant, consistent with Link 7 in Figure 1. Every
one percent increase in Rebate % is associated with about a two point decline in the APEAL
Index; a one percent increase in Rebate Penetration is associated with a 0.2 point decline in the
APEAL Index.
While there is no statistically significant association between total Advertising Spend and
brand image (Model 1), Advertising Spend Per Unit is associated with an increase in brand
image (Model 2). Days Inventory is negatively associated with the APEAL Index in both Model
1 and Model 2, indicating that brand image is harmed by inventory buildup (Link 9 in Figure 1).
In sum, our empirical analyses reveal the following. Excess capacity is associated with
excess production, even after controlling for seasonality and for differences in product
categories. This excess production is, in turn, associated with higher rebates (but not
contemporaneous rebate penetration) and with increased advertising and inventory buildup. Both
higher rebates and higher rebate penetration are associated with lower brand image, as is
increased inventory buildup. Advertising, however, is positively associated with brand image as
measured by the APEAL Index. The results are summarized in Figure 4.
6. Conclusions
Three characteristics of accounting and performance measurement systems in firms can
lead to distorted production decisions. First is the tendency of accounting systems to assign
excess capacity costs to current production, which increases unit cost when production decreases.
The second characteristic is the neglect of long-term costs, especially intangible costs by
traditional accounting systems. The third characteristic is the tendency for firms to design
29
performance measurement systems that place a high degree of emphasis on short-term costs. The
net effect is that managers benefit in the short-run by increasing production.
Excess production results in costly inventory buildup and leads firms to increase
advertising and/or to offer customer incentives in order to sell the excess inventory, all of which
can harm brand image. However, since accounting systems do not highlight the adverse impact
of poor production strategies on long-term intangible assets such as brand image, such strategies
perpetuate. This paper examines the effect of excess production on both tangible and intangible
costs, i.e., brand image erosion. We use field evidence from a major Big Three US automaker to
show that failure to account for excess capacity and a focus on short-term performance measures
encourage managers to increase production. We then examine the extent to which our field
observations generalize to the industry as a whole.
We use archival data for the US automotive industry to show an association between
excess capacity and increased production. We also empirically find that increased production is
associated with increased costs in the form of higher customer incentives (i.e., rebates), higher
advertising expenditures, and greater inventory buildup. Finally, we find that inventory buildup
and higher customer incentives in the form of both rebate percentages and rebate penetration are
associated with lower brand image. Taken together, we interpret our field and archival analysis
as evidence that the complex interplay between production planning, performance measurement,
and accounting practices in the US auto industry have evolved into observed production and
marketing practices.
Our study speaks to the importance of adequately accounting for excess capacity costs.
While accounting theorists have long promoted the use of practical production capacity as the
denominator for computing fixed cost allocation rates, it is not uncommon for firms to deviate
30
from this in practice and use expected production. The result is a distorted incentive to increase
production as a means of lowering per unit costs. Our study also responds to the growing
recognition in the academic and practitioner communities of the importance of considering the
role of intangible assets in value creation (e.g., Ashton 2005). The study is unique in that we
examine the determinants of production decisions and the extent to which production decisions
affect one important intangible asset, brand image. Firms often employ a short-term financial
mindset and fail to consider the long-term implications of production strategies on brand value.
While production in excess of demand may be desirable to increase short-term revenues and
margins, they are likely to lower revenues and margins in the long run. In many industries, like
in the auto industry, firms have access to rich sources of brand image data such as the JD Power
APEAL indices used in this study. Incorporating these indices into performance measurement
and reward systems could provide the necessary incentives to ensure that the intangible costs of
decisions not captured by the accounting system are internalized by the decision-makers within
the firm.
31
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TABLE 1
Sample description
Panel A: Automakers and nameplates
Nameplates
a
Monthly Observations
Firm
GMC
BUICK
CADILLAC
CHEVROLET
HUMMER
PONTIAC
SAAB
SATURN
FORD
LINCOLN
MAZDA a
MERCURY
CHRYSLER
DODGE
JEEP
“Big Three” US
automakers
N
6
7
9
17
2
10
1
2
17
5
3
7
5
8
4
Percent
4.6
5.3
6.8
12.9
1.5
7.6
0.8
1.5
12.9
3.8
2.3
5.3
3.8
6.1
3.0
N
117
120
136
341
30
142
10
21
316
69
60
128
109
160
83
Percent
5.0
5.1
5.8
14.4
1.3
6.0
0.4
0.9
13.4
2.9
2.5
5.4
4.6
6.8
3.5
103
78.0
1,842
77.9
HYUNDAI
ISUZU
MITSUBISHI
NISSAN
SUBARU
SUZUKI b
TOYOTA
VOLKSWAGEN
Foreign Automakers
2
1
4
8
3
1
8
2
29
1.5
0.8
3.0
6.1
2.3
0.8
6.1
1.5
22.0
29
9
79
180
40
6
153
26
522
1.2
0.4
3.3
7.6
1.7
0.3
6.5
1.1
22.1
Total
132
100.0
2,364
100.0
33% ownership as of November 2007
b
2.5% ownership by GM as of November 2007
34
Panel B: Auto segments
Foreign
Automakers
“Big Three” US
Automakers
Total
795
33.63
SUV
Frequency
Percent
Row Pct
Col Pct
130
5.50
16.35
24.90
665
28.13
83.65
36.10
Van
Frequency
Percent
Row Pct
Col Pct
44
1.86
16.18
8.43
228
9.64
83.82
12.38
272
11.51
Compact
Frequency
Percent
Row Pct
Col Pct
64
2.71
33.16
12.26
129
5.46
66.84
7.00
193
8.16
Large
Frequency
Percent
Row Pct
Col Pct
0
0.00
0.00
0.00
36
1.52
100.00
1.95
36
1.52
Luxury
Frequency
Percent
Row Pct
Col Pct
0
0.00
0.00
0.00
115
4.86
100.00
6.24
115
4.86
Midsize
Frequency
Percent
Row Pct
Col Pct
161
6.81
30.73
30.84
363
15.36
69.27
19.71
524
22.17
Pickup
Frequency
Percent
Row Pct
Col Pct
103
4.36
29.94
19.73
241
10.19
70.06
13.08
344
14.55
Sporty
Frequency
Percent
Row Pct
Col Pct
20
0.85
23.53
3.83
65
2.75
76.47
3.53
85
3.60
Frequency
Percent
522
22.08
1842
77.92
2364
100.00
Total
35
TABLE 2
Descriptive statistics
Panel A: Descriptive statistics for monthly data
Variable
Capacity
Production
Forecast (12-month ahead)
Excess Capacity (%) - forecast
N > 10%
N > 20%
Excess Capacity (%) - actual
N > 10%
N > 20%
Excess Production (%)
N > 10%
N > 20%
Rebate %
Rebate Penetration (%)
Advertising Spend (000s)
Advertising Spend Per Unit
Days Inventory
N
2,364
2,364
2,364
2,355
2,355
2,355
2,355
2,355
2,355
2,364
2,364
2,364
2,364
2,364
2,336
2,223
2,251
Mean
11,839.040
9,639.230
10,204.980
2.564
0.500
0.372
3.465
0.499
0.403
-2.397
0.306
0.220
10.305
57.230
3,378.560
0.476
32.384
Std Dev
11,960.330
10,863.480
10,760.620
54.842
0.500
0.483
101.375
0.500
0.491
89.456
0.461
0.414
5.865
21.598
5,590.930
0.867
15.376
25th Pctl
3,770.250
2,798.500
3,424.000
-10.699
0
0
-14.934
0
0
-36.503
0
0
5.778
44.475
36.850
0.008
24.168
Median
8,787.750
6,415.500
7,106.500
10.012
1
0
9.941
0
0
-9.979
0
0
9.274
61.483
973.150
0.142
32.269
75th Pctl
15,744.170
13,055.500
12,897.000
29.638
1
1
40.399
1
1
16.125
1
0
13.778
74.617
4,352.200
0.512
40.681
Control Variables
Big Three Indicator
Suspended Production
Number of Plants
SUV
Van
Compact
Large
Luxury
Midsize
Pickup
Sporty
CPI Index
Gas Index
2,364
2,364
2,364
2,364
2,364
2,364
2,364
2,364
2,364
2,364
2,364
2,364
2,364
0.779
0.071
1.282
0.336
0.115
0.082
0.015
0.049
0.222
0.146
0.036
198.369
2.487
0.415
0.257
0.719
0.473
0.319
0.274
0.122
0.215
0.415
0.353
0.186
3.877
0.341
1
0
1
0
0
0
0
0
0
0
0
1
0
1
0
0
0
0
0
0
0
0
1
0
1
1
0
0
0
0
0
0
0
194.600
2.257
198.700
2.359
201.800
2.801
36
Panel B: Pearson correlations for monthly data*
Excess
Prod
Rebate
%
Rebate
Pen
Adv
Spend
Adv
Spend
Per
Unit
1
2,364
-0.026
2,364
-0.002
2,364
0.161
2,336
0.068
2,223
1
2,364
0.190
2,364
-0.113
2,336
-0.188
2,223
1
2,364
0.050
2,336
-0.005
2,223
1
2,336
0.634
2,223
1
2,223
0.295
2,251
-0.084
2,251
-0.091
2,251
0.112
2,223
0.134
2,223
-0.027
-0.044
-0.056
0.303
2,355
2,355
2,364
2,364
Suspended Production
-0.139
-0.267
-0.239
0.151
2,355
2,355
2,364
2,364
Number of Plants
0.061
-0.087
0.082
0.062
2,355
2,355
2,364
2,364
CPI Index
-0.027
0.015
0.104
0.084
2,355
2,355
2,364
2,364
Gas Index
-0.029
0.076
0.792
-0.061
2,355
2,355
2,364
2,364
*
Correlations in bold are significant at p < .01, N in second row.
0.126
2,364
0.031
2,364
0.160
2,364
-0.216
2,364
-0.113
2,364
-0.130
2,336
-0.112
2,336
0.321
2,336
-0.043
2,336
-0.066
2,336
-0.131
2,223
-0.097
2,223
-0.052
2,223
-0.014
2,223
-0.061
2,223
Excess Excess
Capacity Capacity
- forecast - actual
Excess Capacity (%) 1
forecast
2,355
1
Excess Capacity (%) 0.411
actual
2,355
2,355
Excess Production (%)
0.273
0.103
2,355
2,355
Rebate %
-0.079
-0.133
2,355
2,355
Rebate Penetration (%)
-0.120
-0.074
2,355
2,355
Adv Spend
0.109
0.124
2,327
2,327
Adv Spend Per Unit
0.065
0.150
2,214
2,214
0.013
0.029
Days Inventory
2,242
Big Three Indicator
2,242
Days
Inv
Big
Three
Susp
Prod
No of
Plants
CPI
Gas
1
2,251
-0.047
2,251
-0.382
2,251
0.028
2,251
-0.020
2,251
-0.017
2,251
1
2,364
0.096
2,364
0.063
2,364
-0.026
2,364
-0.018
2,364
1
2,364
0.077
2,364
-0.002
2,364
0.033
2,364
1
2,364
-0.036
2,364
-0.024
2,364
1
2,364
0.799
2,364
1
2,364
37
Panel C: Descriptive statistics for annual data
Variable
APEAL Index
Rebate %
Rebate Penetration (%)
Advertising Spend (000s)
Advertising Spend Per Unit
Days Inventory
N
157
157
157
157
157
157
Mean
853.178
9.849
55.771
41,263.220
0.495
33.409
Std Dev
29.383
4.899
16.684
46,124.870
0.587
7.763
25th Pctl
830.030
5.915
48.633
4,989.600
0.099
29.759
Median
856.900
9.129
57.733
24,349.700
0.308
32.397
75th Pctl
870.720
12.834
67.900
61,487.300
0.647
36.807
Control Variables
Big Three Indicator
IQS (PP100)
Suspended Production
Number of Plants
SUV
Van
Compact
Large
Luxury
Midsize
Pickup
Sporty
Number of Nameplates
Finance-Captive
Finance-Total Down
Finance-APR
Demographic - Avg Age
Demographic - Gender (F)
157
157
157
157
157
157
157
157
157
157
157
157
157
157
157
157
157
157
0.764
121.174
0.580
1.268
0.331
0.108
0.108
0.000
0.051
0.236
0.121
0.045
31.682
0.536
6550.220
0.071
46.283
0.358
0.426
22.003
1.784
0.592
0.472
0.312
0.312
0.000
0.221
0.426
0.327
0.207
15.938
0.171
3245.860
0.015
5.462
0.102
1
106.94
0
1
0
0
0
0
0
0
0
0
17
0.389
4204.500
0.062
42.700
0.287
1
117.28
0
1
0
0
0
0
0
0
0
0
31
0.538
5409.500
0.070
44.743
0.360
1
134.76
0
1
1
0
0
0
0
0
0
0
49
0.679
8099.360
0.081
48.150
0.445
38
Panel D: Pearson correlations for annual data (N=157)†
APEAL Index
APEAL Rebate
Index
%
1
Rebate %
-0.408
1
Rebate Penetration (%)
-0.315
0.495
1
Adv
Spend
Days
Inv
Big
Three
IQS
Susp
Prod
No of
Plants
Advertising Spend
0.191
-0.232
-0.003
1
Advertising Spend Per Unit
0.375
-0.320
-0.037
0.525
Days Inventory
-0.077
-0.305
-0.275
0.172
0.164
1
Big Three Indicator
-0.035
0.384
0.217
-0.193
-0.202
-0.127
1
IQS (PP100)
-0.281
0.019
0.090
-0.108
0.027
0.170
0.046
1
Suspended Production
-0.150
0.297
0.152
-0.163
-0.137
-0.334
0.164
-0.015
1
Number of Plants
-0.090
0.113
0.162
0.363
-0.113
0.016
0.023
-0.238
0.216
1
Number of Nameplates
Finance-Captive
Finance-Total Down
Finance-APR
Demographic - Avg Age
Demographic - Gender (F)
†
Rebate
Pen
Adv
Spend
Per
Unit
No of
Nameplates
Total
Captive Down
APR
Avg
Age
1
0.056
0.081
0.154
0.012
0.130
-0.191
0.136
-0.033
0.180
0.020
1
-0.012
-0.156
0.052
0.198
0.159
0.015
-0.061
-0.111
-0.105
0.041
0.169
1
0.508
0.011
0.044
-0.084
0.096
-0.240
0.298
-0.232
0.080
0.088
0.183
0.187
1
-0.247
0.371
-0.010
-0.093
-0.246
-0.073
0.030
0.038
0.035
-0.039
-0.318
-0.443
-0.449
1
0.167
0.236
0.009
-0.203
-0.057
-0.238
0.377
-0.219
0.133
-0.091
-0.105
0.127
0.517
-0.276
1
-0.156
0.098
-0.059
-0.013
0.089
0.160
-0.231
0.105
0.086
-0.221
0.150
-0.092
-0.477
0.216
-0.107
Correlations in bold are significant at p < .01.
39
TABLE 3
Excess production as a function of excess capacity*
Excess Production = ! + "1 Excess Capacity + "2 Big Three Indicator + "3 Suspended Production
+ "4 Number of Plants + "5-11 Segment indicator + "12 CPI Index+ "13 Gas Index
+ "14-24 Month Indicator + #1
Dependent Variable: Excess Production (%)
Predicted Sign
Intercept
Excess Capacity (%) - forecast
+
Excess Capacity (%) - actual
+
*
(1)
-46.047
0.491***
(2)
-146.430
0.043**
Control Variables
Big Three Indicator
Suspended Production
Number of Plants
Van
Compact
Large
Luxury
Midsize
Pickup
Sporty
CPI Index
Gas Index
<monthly indicator variables omitted>
1.337
-84.938***
12.922**
-3.566
25.927**
-32.817
-19.855
1.712
-22.985**
6.996
0.198
4.340
0.119
-86.567***
17.755***
4.230
37.811***
-10.617
-9.784
7.816
-18.816
26.069
0.685
2.215
N
!2 statistic
R2 - within
R2 - between
R2 - overall
" (autocorrelation coefficient)
2,355
281.94***
12.13%
24.19%
15.26%
0.446
2,355
170.04***
8.91%
12.38%
9.38%
0.443
***, **, and * indicate statistical significance at the .01, .05, and .10 levels, respectively (1-sided p-values for
coefficients with predicted signs, 2-sided otherwise). Each model is estimated as a random-effect linear model with
an AR(1) disturbance (using STATA xtregar procedure).
40
TABLE 4
Tangible costs of excess production
Dependent Variable:
Predicted Sign
*
(1)
Rebate %
4.752
0.002**
Intercept
Excess Production (%)
+
Control Variables
Rebate %
Rebate Penetration
Big Three Indicator
4.354***
Suspended Production
0.056
Number of Plants
0.666*
Van
1.350
Compact
1.276
Large
9.475
Luxury
-2.001
Midsize
0.418
Pickup
0.398
Sporty
-3.448
CPI Index
0.010
Gas Index
-1.311
<monthly indicator variables omitted>
N
2,364
2
1152.22***
! statistic
R2 - within
34.77%
21.23%
R2 - between
R2 - overall
28.65%
0.674
" (autocorrelation coefficient)
(2)
Rebate
Penetration
(%)
193.572***
-0.000
(3)
Advertising
Spend
(000s)
16,693.950
6.344***
(4)
Advertising
Spend Per Unit
(000s)
1.745
0.001***
8.074***
2.043
3.799
-1.561
-7.580
2.941
-9.416
-8.281
6.388
-27.747
-0.701
-0.328
-14.765
-1.701
-1,540.848**
-661.974
2,385.046***
-1,476.592
-1,764.22*
-1,946.769
150.375
1,340.399*
-1.789
-336.529
-71.016
-357.821
-0.011*
0.000
-0.226**
-0.070
-0.025
-0.443***
-0.533***
-0.443
0.083
-0.071
-0.321**
-0.064
-0.001
-0.222*
2,364
538.00***
21.33%
24.25%
22.17%
0.666
2,336
151.20***
5.11%
29.11%
18.32%
0.459
2,223
107.26***
4.08%
15.86%
9.92%
0.464
(5)
Days Inventory
140.845***
0.042***
-0.985
-17.016***
1.533*
4.180**
3.239
0.679
-1.391
-0.372
-2.896
0.838
-0.641***
9.404***
2,251
1478.18**
40.25%
32.75%
36.27%
0.396
***, **, and * indicate statistical significance at the .01, .05, and .10 levels, respectively (1-sided p-values for coefficients with predicted signs, 2-sided
otherwise). Generalized least squares estimate of a random-effect linear model with an AR(1) disturbance (using STATA xtregar procedure).
41
TABLE 5
Intangible costs of excess production
APEAL Index =
! + "1 Rebate % + "2 Rebate Penetration + "3 Advertising + "4 Days Inventory
+ "5 Big Three Indicator + "6 IQS + "7 Suspended Production + "8-13 Segment indicators
+ "14 Number of Nameplates + "15 Captive + "16 Total Down + "17 APR + "18 Avg Age+ "19
Gender + "20 Year 2006 + #1
Predicted Sign
Intercept
Rebate %
Rebate Penetration
Advertising Spend
Advertising Spend Per Unit
Days Inventory
Control Variables
Big Three Indicator
IQS (PP100)
Suspended Production
Van
Compact
Luxury
Midsize
Pickup
Sporty
Number of Nameplates
Finance – Captive
Finance – Total Down
Finance – APR
Demographic – Avg Age
Demographic – Gender (F)
Year 2006
*
N
F-statistic
Adj-R2
?
?
-
Dependent Variable: APEAL Index
(1)
(2)
894.843***
-1.789***
-0.189*
0.000
-0.367**
912.033***
-1.609***
-0.257**
9.931***
-0.437**
5.303
-0.070***
-1.975
-12.841
-7.787
13.705
15.779
8.723
12.7872
-0.124
-26.834***
0.006***
-138.756
-0.790
62.548
4.560
7.594*
-0.115
-1.949**
-12.935
-6.754
10.004
15.236
9.411
6.162
-0.189
-28.432***
0.006***
-121.642
-0.889**
55.729**
2.850
157
13.37***
61.34%
157
14.63***
63.60%
***, **, and * indicate statistical significance at the .01, .05, and .10 levels, respectively (1-sided p-values for
coefficients with predicted signs, 2-sided otherwise). Ordinary least squares regression with errors clustered by
nameplate.
42
FIGURE 1
Conceptual model
Performance
Measurement
Incentives
Link 2
Excess
Capacity
Link 3
Link 1
Accounting for
Excess
Capacity
Advertising
Link 5
Excess
Production
Link 4
Link 6
Customer
Incentives
Inventory
Tangible Costs
Link 8
Link 7
Brand
Image
Link 9
Intangible Costs
43
FIGURE 2
Market share trends in the US auto industry
Panel A: US market share
100
90
U.S. Market Share %
80
70
60
50
40
30
20
10
0
1970
1975
1980
1985
1990
1995
2000
2005
Year
US
Japan
Europe
Panel B: US market share of major firms
50
45
U.S. Market Share %
40
35
30
25
20
15
10
5
0
1970
1975
1980
1985
1990
1995
2000
2005
Year
GM
Ford
Chrysler
Toyota
Honda
Source: Automotive News Market Data Book, (1980-2006), Train and Winston (2006)
44
FIGURE 3
Field evidence of the association between excess production and rebatesa
Sales
Nameplate 1
Nameplate 2
Nameplate 3
Planned Units (Expected Demand)
25,200
18,010
36,081
Actual Units (Actual Demand)
15,090
9,463
27,412
Variance Units
(10,110)
(8,547)
(8,66)
Variance (% of Original)
(40%)
(47%)
(24%)
Planned Rebate $ Per Unit
$1,808
$3,213
$2,630
Actual Rebate $ Per Unit
$3,010
$3,574
$2,982
+ $2,405
+ $361
+ $352
133%
11%
13%
Variance $ Per Unit
Variance (% of Planned Rebate)
a
Numbers disguised to protect the confidentiality of the research partner.
45
FIGURE 4
Empirical results
Advertising
(total adv spend,
adv spend per unit)
+
+
Excess
Capacity
(capacity less
12mo forecast
or prior year
actual)
+
Excess
+Production
+ (partial)
(excess over
12mo forecast)
+
Customer
Incentives
Brand
Image
-
(rebate %, rebate
penetration)
Inventory
Build-up
(APEAL
Index)
-
(days sales in
inventory)
Tangible Costs
Monthly Analysis
Intangible Costs
Annual Analysis
46
SOUND SYSTEM / NAVIGATION /
ENTERTAINMENT
STORAGE & SPACE
INTERIOR
EXTERIOR
APPENDIX – Dimensions of APEAL Index
Front-end styling
Side profile appearance and styling
Rear-end styling
Appearance of wheels, rims, and tires
Appearance of exterior paint
Sound of doors when closing
How well exterior and interior colors are coordinated
Attractiveness of IP/Dashboard
Look and feel of steering wheel
Ability to comfortably rest arms while driving
Interior materials convey impression of high quality
How well interior colors/materials are coordinated
Appearance/Illumination of gauges/controls
Overall interior quietness
Pleasantness of audible signals
Usefulness of courtesy lights
Attractiveness of interior lighting
Smell of vehicle interior
Ease of getting in/out of vehicle
Front seat head/leg/foot room
Rear passenger head/leg/foot room
Effectiveness of center console
Usefulness of glove box
Usefulness of FRONT cup holders
Usefulness of REAR cup holders
Usefulness of FRONT/REAR cup holders
Location/arrangement of storage spaces
Amount of trunk/cargo area space
Ease of loading/unloading trunk/cargo area
Sound clarity at high volume
Operating controls while driving
Controls convey impression of high quality
Ease to see/read audio display
Ability to play formats I want (MP3, etc)
Quality of bass (low sounds)
Gives impression of depth or "surround"
Clarity of rear seat entertainment video display
Ease of operating rear seat entertainment sys
Ease of using navigation system
Ability of navigation sys to provide desirable route
Appearance of navigation display
47
APPENDIX, cont.
FUEL
ENGINE/
ECON- VISIBILITY/ SAFETY
TRANS
OMY
DRIVING
DYNAMICS
HVAC
SEATS
Comfort of driver's seat back/lumbar support
Comfort of driver's bottom seat cushion
Driver's seat holds you in place while cornering
How easy to reach/operate seat controls
Comfort of rear (2nd row) seat
Ease of operating rear (2nd row) seats
Comfort of 3rd row seat
Ease of operating 3rd row seats
Seat belt comfort/adjustability
Styling of the seats
Material conveys impression of high quality
Ability of seat surfaces to resist soil/lint
Flexibility of seating configuration
Ability to direct airflow
Ability to maintain desired temperature
Controls convey impression of high quality
Quietness of Heater/AC fan
Ability to seal interior from outside odors
Ease of operating Heating/AC controls while driving
How well defrost/defog interior glass
Ride smoothness in normal driving
Quietness over harsh bumps
Responsiveness/effort of steering system
Braking responsiveness/effort
Handling/stability on curves/winding roads
Handling/stability in adverse conditions
Performance during rapid acceleration from stop
Sound of eng/exhaust during rapid acceleration
Passing power at highway speeds
Smoothness of gearshift operation
Forward visibility from driver's seat
Effectiveness of sun visors
Effectiveness of headlights
How well wipers/washers clear windshield
Visibility when changing lanes
Ease of judging distances when parking
Ease of seeing/reading controls/displays while driving
Usefulness of steering wheel-mounted controls
Rating of vehicle's fuel economy (mpg)
Driving range between fuel stops
Rating of Fuel Economy/Driving Range
Source: J. D. Power and Associates
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