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Vol. 30, No. 1
Spring 2018
pp. 143–167
American Accounting Association
DOI: 10.2308/jmar-51686
A Comparison of Activity-Based Costing and Time-Driven
Activity-Based Costing
Sophie Hoozée
Ghent University
Stephen C. Hansen
Naval Postgraduate School
ABSTRACT: The relationship between activity-based costing (ABC) and time-driven activity-based costing
(TDABC) has not been systematically investigated. We compare the two systems analytically and via a numerical
experiment. Our analytical comparison generates formulas that describe how each system maps resources to
activities and finally to products. We demonstrate that ABC aggregates resource-to-activity information by resources
(columns), while TDABC selects partitions of activity-by-resource information. Our numerical experiment shows that
TDABC is more accurate than ABC when traceability of resources to activities is high and activity traceability to
products is low, while ABC is more accurate when activities are more traceable to products, irrespective of the level
of resource traceability to activities. Finally, we examine the impact of hybridizing an ABC (TDABC) system with
TDABC (ABC). We find that adding one ABC element into a TDABC system usually improves accuracy. However,
adding one TDABC element into an ABC system usually substantially degrades accuracy.
Keywords: costing system design; costing accuracy; costing error; analytical modeling; numerical experiment.
Data Availability: The simulated datasets are available from the first author on request.
ctivity-based costing (ABC) is an important and widespread full costing system in accounting (e.g., Innes and
Mitchell 1995; Malmi 1999; Jones and Dugdale 2002). Practitioners (e.g., Demeere, Stouthuysen, and Roodhooft
2009; Öker and Adigüzel 2010), however, have complained that ABC is not suitable for complex activities and is too
costly to update in dynamic environments. Kaplan and Anderson (2004, 2007) developed time-driven activity-based costing
(TDABC) to try and solve these problems. TDABC uses a time equation to allocate resource costs directly to products, rather
than ABC’s two-stage method of assigning resource costs to activities and then allocating activity costs to products. While the
relationship between ABC and TDABC has been examined conceptually (Balakrishnan, Labro, and Sivaramakrishnan 2012a,
2012b), the deeper, calculation-based issues have not been addressed. We examine three related questions: When do ABC and
TDABC systems generate the same product costs? What settings is ABC (TDABC) best suited for? What is the effect of
hybridizing the systems, that is, incorporating ABC (TDABC) logic into a TDABC (ABC) system?
Our TDABC approach is more general than found in prior work. Practitioners have typically presented results for only one
time equation, or they have created a timetable for each resource. Our TDABC analysis is more general. We allow multiple
resource costs to be combined into a resource cost pool, create a time equation for each resource cost pool, and use
heterogeneous cost rates for each time equation. In addition, each time equation can contain multiple subtasks, although the
same subtasks are used in all equations.
We appreciate the insightful comments from Thomas Albright, Ramji Balakrishnan, Werner Bruggeman, John Christensen, Patricia Everaert, Maurice
Gosselin, Robert Kaplan, Eva Labro, Karen Sedatole, Alexandra Van den Abbeele, Mario Vanhoucke, Lea Vermeire, and Marc Wouters. We also
acknowledge the comments from participants at the 2014 AAA Management Accounting Section Research and Case Conference, 2010 Conference on
New Directions in Management Accounting, 2011 EAA Annual Congress, 2011 GLOBAL Management Accounting Research Symposium, and 2010
Manufacturing Accounting Research Conference, as well as workshops at Erasmus University Rotterdam, Ghent University, IÉSEG School of
Management, SKEMA Business School, and Trento University. Finally, special thanks are due to Stefan Creemers for invaluable programming advice.
Editor’s note: Accepted by Dennis Campbell, under the Senior Editorship of Ranjani Krishnan.
Submitted: August 2014
Accepted: November 2016
Published Online: February 2017
Hoozée and Hansen
Product costing systems are essential ingredients in many management processes: price setting, budgeting, and planning, to name
a few. Firms calculate product costs based on imperfect and incomplete information about resource costs and consumption patterns
because it is not possible for a firm to calculate error-free product costs (e.g., Hwang, Evans, and Hegde 1993; Datar and Gupta 1994).
As a first step, researchers have focused on how the product costing errors are generated. We follow prior work (Labro and Vanhoucke
2007, 2008; Balakrishnan, Hansen, and Labro 2011) and examine how the use of heuristically designed full costing systems affects
the accuracy of reported product costs, but not how these product costs affect particular decisions such as setting prices.
At their heart, all costing systems are based upon a simple concept. Tracking all resources and all activities is too expensive, so
every costing system aggregates information. All full costing systems have the following generic structure. In the production system,
resources are used to generate inputs (activities or subtasks), and the inputs (activities or subtasks) are used to create products or serve
customers. The costs of the resources (overhead costs) are assigned to activities or subtasks, which, in turn, are allocated to cost
objects (e.g., products or services).1 Although the underlying accounting information is the same, ABC and TDABC aggregate
information in different ways and create qualitatively distinct costing systems. ABC generates costing systems with two explicit
stages. In Stage I, resource costs are combined into resource cost pools, which are then assigned to activities. In Stage II, activity
costs are collected into activity cost pools and then allocated to cost objects. TDABC generates costing systems with one composite
stage. The heart of TDABC is the time equation, which directly allocates the costs from resource cost pools to cost objects but allows
them to consume resources in different proportions depending on their specific characteristics (Kaplan and Anderson 2004, 2007).
Given the disparity in how costs flow through the systems, it is not clear how the ultimate product costs for ABC and
TDABC are related. We first compare the two systems analytically. We create a formula for each system showing how resource
costs are allocated to products. Comparing these allocation formulas shows that the systems aggregate the resource-to-activity
information in different ways. ABC aggregates information in the resource-to-activity matrix by resource columns. A set of
resources forms a resource cost pool, and then the costs in the resource cost pool are assigned to activities by one resource cost
driver (i.e., one resource column). In contrast, TDABC selects activity-by-resource partitions from the resource-to-activity matrix
and calculates cost rates for each partition. Because aggregation occurs along fundamentally different dimensions (columns versus
partitions), ABC and TDABC are analytically non-comparable and generate different product costs in almost all circumstances.
The second part of our paper takes a complementary approach to differentiate between the two systems. We run a numerical
experiment with an embedded simulation to generate numerical outcomes(product costs) that can be compared. We compare the global
accuracy of the two systems using a common metric, the product costing errors for each system. The advantage of using a numerical
experiment is that we can control the permutations and investigate what parameters generate greater or smaller product costing error.
Our numerical experiment focuses on systematically manipulating two different elements. Given the different information
aggregation of the resource-to-activity mapping, we vary all elements of the Stage I resource-to-activity mapping. Second,
given the importance of aggregation to understanding the results, we vary the parameters that determine the number of cost
pools in each system (i.e., the amount of costing system aggregation).2 We ultimately generate product costs for the ABC and
TDABC systems and compare them to the noise-free benchmark costs using the Euclidean distance metric.
We find that while many environmental parameters have opposite effects, both TDABC and ABC have qualitatively identical
responses to two important system properties: traceability of resources, measured as the percentage of zeros in the resource-toactivity matrix, and traceability of activities, measured as the percentage of zeros in the activity-to-product matrix. Although both
systems have qualitatively identical responses to these properties, the magnitude of the responses varies dramatically across systems.
We then compare the accuracy of the two systems to understand the circumstances where each performs best. We find that
TDABC performs better than ABC when resources are more traceable to activities, while ABC performs better than TDABC
when activities are more traceable to products. When both types of traceability are high, ABC performs best.
Figure 1 ties our comparisons back to specific industries.3 Neither ABC nor TDABC are likely to perform well when there
is low resource or activity traceability (industrial bread manufacturer). TDABC is likely to do well when there is high resource
In line with previous research (e.g., Labro and Vanhoucke 2007), we assume that direct costs are measured without error for every product, and hence
exclude them from the analysis.
Following Balakrishnan et al. (2011), we use the correlation-based big pool heuristic to generate our costing systems. We form resource (activity or
subtask) cost pools by combining into a pool those resources (activities or subtasks) of which the consumption pattern has a correlation with a nucleus
resource (activity or subtask) above a threshold. We form a miscellaneous cost pool of the remaining resources (activity costs or subtask costs) when
the value of the leftovers falls below a threshold. The cost driver for each cost pool is the allocation base of the nucleus resource (activity or subtask).
Specifics about each cited industry follow. Industrial bread manufacturing has high-speed production involving comparatively few resources (flour,
yeast, heat, wrappers) and comparatively few products (white or wheat bread). Given the common nature of the inputs and the production process, there
is low traceability at each stage. A specialty ice cream manufacturer may use many different types of ingredients (vanilla, blueberries, walnuts) that
vary across orders, so the resource traceability is high. However, the ice cream production process combines all the ingredients in a large vat and
freezes them, so activity traceability is comparatively low. Airplane maintenance involves several standard resources (hangar space, repairperson time),
so it has low resource traceability. However, each airplane may require a unique set of repairs and tests, so it has high activity traceability. Finally,
specialty chemical manufacturing involves generating novel chemicals using unique processes. Both resources and activities are comparatively unique
and traceability is high at both stages.
Journal of Management Accounting Research
Volume 30, Number 1, 2018
A Comparison of Activity-Based Costing and Time-Driven Activity-Based Costing
Industry Examples of High and Low Traceability
traceability to activities, but moderate or low activity traceability to products (specialty ice cream manufacturer). ABC will do
well when there is high activity traceability to products and either low (airplane maintenance) or high resource traceability to
activities (specialty chemical manufacturer).
Our comparisons give the impression that ABC and TDABC are polar opposites and cannot be combined. Our basic
analyses cleanly separate the two approaches to provide sharper results. The final section of our paper hybridizes ABC
(TDABC) systems by adding an element of TDABC (ABC) logic. We find that there is an asymmetry in the effects. Adding
one ABC element into a TDABC system on average improves accuracy, while adding one TDABC element into an ABC
system on average decreases accuracy. We now turn to the literature review.
Kaplan and Anderson (2004, 2007) developed TDABC as a method to simplify the calculations needed to generate
product costs and thereby allow firms to be more dynamic in changing their costs. While they describe their new approach, their
numerical examples only show that ABC and TDABC can generate different outcomes.
Hoozée, Vermeire, and Bruggeman (2012) specifically analyzed the impact of refinement on the accuracy of time equation
outcomes. Their mathematical model provides some novel insights into the balancing of errors when searching for an optimal
refinement level. For example, when building a time equation, it is recommended to first add subtasks with high mean time
consumption and high variance (originating from high structural variance in the time driver volumes from one transaction to
another). However, the paper does not provide a direct comparison to ABC.
Labro and Vanhoucke (2007) investigated interactions among errors in ABC systems and found that the impact of
parameters affecting the activity-to-product mapping on overall accuracy is stronger than the impact of parameters affecting the
resource-to-activity mapping. In a follow-up study, Labro and Vanhoucke (2008) showed that refinement of ABC systems pays
off most when resource cost pools are very different in terms of size and when there are large differences in the proportional
resource usage at each pool. Based upon these results, our simulation adjusts the variation in the total driver consumption as
well as the dispersion in the driver consumption for each individual element in the resource-to-activity matrix.
Balakrishnan et al. (2012a, 2012b) created a framework to compare and contrast four costing systems (classic product
costing, ABC, TDABC, and resource consumption accounting). Their conceptual framework identified differences in the
selection of cost drivers, different approaches to unused capacity, and different approaches to identifying variable and fixed
resource costs as differentiating the systems. Our numerical experiment is not as general as their conceptual framework. In
particular, we do not directly incorporate unused capacity or model variable/fixed resource costs.
Balakrishnan and Sivaramakrishnan (2014) compared the accuracy of two variants of ABC and TDABC systems. More
specifically, they compared aggregated ABC systems for which the number of activities is equal to the number of time
equations in TDABC to ‘‘hybrid’’ TDABC systems in which the times needed by different resources to execute the subtasks in
a process vary in direct proportion to the times required by a base resource. They found that the hybrid TDABC system resulted
in more accurate cost estimates than the aggregated ABC system.
Balakrishnan et al. (2011; hereafter, BHL) is the most important prior paper for our work. They incorporated simulations to
compare different heuristics used to generate cost pools and select allocation bases. BHL assumed a particularly simple
resource-to-activity mapping (no resource splitting across multiple activity cost pools), but also contained a complex activityto-product mapping. Given the importance of the resource-to-activity mapping for the comparison of ABC and TDABC, in our
Journal of Management Accounting Research
Volume 30, Number 1, 2018
Hoozée and Hansen
Operating Expenses
Wages East (R1)
Wages West (R2)
Wages Central (R3)
Depreciation East (R4 )
Depreciation West (R5 )
Lighting and Heating East (R6 )
Lighting and Heating West (R7 )
Lighting and Heating Central (R8)
Total East (R1 þ R4 þ R6 )
Total West (R2 þ R5 þ R7 )
Total Central (R3 þ R8)
simulation, we make both our resource-to-activity and activity-to-product mappings as complex as their activity-to-product
mapping. BHL also found that the percentage of zeros in the activity-to-product matrix (the traceability of activities to
products) was an important source of error. Greater traceability drove finer costing systems, that is, systems with more cost
pools. Our numerical experiment manipulates the number of zeros in the activity-to-product matrix (activity traceability to
products), but also varies the number of zeros in the resource-to-activity matrix (resource traceability to activities).
We clarify the differences between ABC and TDABC in several steps. The differences are highlighted by an example that
shows the different ways in which the two costing systems are obtained from a common set of benchmark information. Each
system’s example is then generalized into an allocation formula. Once the examples and formulas are created for both systems,
we compare them.
Common Benchmark Information for Both Examples
In order to simplify the presentation, we focus on an example setting with no measurement error4 and no excess capacity.
Logistics, Inc. has three warehouses: Warehouse East, Warehouse West, and Warehouse Central. In the benchmark system,
each warehouse is a separate resource cost pool. Table 1 provides the company’s operating expenses (resource costs) for each
In each warehouse, the process of picking a delivery consists of three subtasks:
T1: loading boxes on the forklift truck; driver X1: number of boxes.
T2: wrapping up pallets in plastic film; driver X2: number of pallets.
T3: driving to the appropriate storage rack; driver X3: number of order lines.
Table 2 provides information about the subtask times and driver volumes for each warehouse, while Table 3 contains the
customer records, which provide information about each customer’s (cost object’s) use of driver volumes for each warehouse.
The customer records are the finest level of information available. We will show that each costing system uses the customer
record information in distinct ways.
Table 4 calculates the benchmark costs. Panel A depicts the benchmark resource-to-activity matrix. The assigned costs are
copied from Table 2 (last column). Table 4, Panel B shows the calculations of the subtask cost rates. Combining these rates
with the driver volumes from Table 3 gives the benchmark costs allocated to each customer in Table 4, Panel C. It should be
In both our analytical model and our simulation there is no measurement error. Because different measurement errors combined with different
calculation methods could provide a confounding factor in interpreting our results, we remove measurement error entirely from our analysis. Our
simulation levels the playing field between ABC and TDABC by constructing both systems using the same error-free data. Differences between the
costs are due to aggregation error and specification error.
Journal of Management Accounting Research
Volume 30, Number 1, 2018
A Comparison of Activity-Based Costing and Time-Driven Activity-Based Costing
Subtask Times and Driver Volumes
Panel A: Warehouse East
Unit Time
to Do Subtask
Warehouse East
Driver Volume
Warehouse East
Subtask Time
Warehouse East
of Time
Spent on
Warehouse East
1 minute per box
12 minutes per pallet
2 minutes per order line
15,800 boxes
2,150 pallets
7,600 order lines
15,800 minutes
25,800 minutes
15,200 minut …
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