Transfer Pricing & Inventory: Throughput vs. Traditional Accounting

The British Accounting Review 54 (2022) 101001
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The British Accounting Review
journal homepage: www.elsevier.com/locate/bar
The impacts of inventory in transfer pricing and net income:
Differences between traditional accounting and throughput
accounting
Gustavo da Silva Stefano*, Tiago dos Santos Antunes, Daniel Pacheco Lacerda,
Maria Isabel Wolf Motta Morandi, Fabio Sartori Piran*
~o Leopoldo, Brazil
Research Group on Modeling for Learning e GMAP | UNISINOS, Universidade do Vale do Rio dos Sinos e UNISINOS, Sa
a r t i c l e i n f o
a b s t r a c t
Article history:
Received 21 November 2019
Received in revised form 26 March 2021
Accepted 31 March 2021
This research proposes the Theory of Constraints (TOC) throughput accounting (TA) as an
alternative management control mechanism in an international transfer pricing setting.
We compare TA with the traditional accounting method and demonstrate that the traditional method underestimate factors as demand variation and inventories, which affects
decisions, such as moving production to an offshore plant. A detailed system dynamics
model is built to simulate the production process in an offshore supply chain to compare
the methods. The study aims to fill a gap in the management accounting studies and
contribute to the understanding of international transfer pricing and their management
controls, exploring more than just the tax savings, which are usually considered isolated
from operational factors for supply chain (SC) offshoring decisions. Furthermore, we
conduct a brief literature review, present the model and discuss the results. It has been
observed that inventory levels are an important part of accounting, offshored supply
chains, and transfer pricing. Traditional cost and accounting methods favour higher inventory levels, and they can overestimate net income results up to 70% e especially in
higher demand variation scenarios e when compared to the throughput accounting.
© 2021 British Accounting Association. Published by Elsevier Ltd. All rights reserved.
Keywords:
Transfer pricing
Theory of constraints
International supply chain
Offshoring
1. Introduction
Multinational supply chains make use of offshoring for cost-reducing reasons like tax avoidance (Joseph et al., 2017) and
cutting operational costs (Drtina & Correa, 2011). Consequently, the transfer of goods within these supply chains has become a
topic of interest. Accordingly, multinational enterprises (MNEs) utilize the transfer price to sell goods from one Supply Chain
Unit to another in the same company. Thus, transfer prices can be defined as the pricing of intercompany transactions that
occurs within its own subsidiaries (Feinschreiber, 2004).
Although widely discussed, transfer pricing has become a concern for multinational companies. In a report from Ernst &
Young (2016), p. 75% of the companies studied claimed that their major priority about transfer pricing was related to tax risk
management, while 72% assumed that transfer pricing has been a core focus of controversy within organisations. This
* Corresponding authors. Production and Systems Engineering Graduate Program, Universidade do Vale do Rio dos Sinos, 950, Unisinos Avenue, 93022~o Leopoldo, RS, Brazil.
750, Sa
E-mail addresses: [email protected] (G. da Silva Stefano), [email protected] (F.S. Piran).
https://doi.org/10.1016/j.bar.2021.101001
0890-8389/© 2021 British Accounting Association. Published by Elsevier Ltd. All rights reserved.
G. da Silva Stefano, T.S. Antunes, D.P. Lacerda et al.
The British Accounting Review 54 (2022) 101001
controversy might be related to the fact that companies still consider isolated benefits, commonly only tax avoidance or
production and operational costs (Joseph et al., 2017), demonstrating a gap in accounting and supply chain management
perspectives as stated by Ramos (2004). This gap is concerning, since building a strong foundation of sound accounting
techniques is key for supply chain collaboration (Vann, 2016). To Borkowski (2002) transfer prices present challenges related
to tax compliance, supply chain management, and the location of manufacturing facilities, i.e. offshoring. More specifically,
there is still a gap between international tax objectives and the operational decisions of management control in MNEs, as
noted in studies such as the ones by Baldenius et al. (2004), Hyde and Choe (2005), and Narayanan and Smith (2000).
Tax optimisation motives and the usage of generally accepted accounting principles (GAAP) e absorption or full costing
methods e are often considered unfavourable for the optimisation of operational aspects regarding transfer prices such as
sourcing, pricing, and inventory decisions. In those scenarios, the utilisation of variable costing methods can provide better
guidance to managers and are seen as a valid option for management control (Eccles & White, 1988; Horngren et al., 2004).
Management accounting and control studies, however, have given little attention to transfer pricing in international settings
(Cools & Slagmulder, 2009). From this perspective, concerned with the negative impact of inventory reduction demonstrated
in GAAP, the Theory of Constraints (TOC) proposes the throughput accounting (TA) as an alternative (Budd, 2010; Hilmola & Li,
2016). In TA, throughput is defined as revenue minus all the variable costs e i.e. manufacturing, general, selling and
administrative (Brignall, 1997; Budd, 2010). TA can be similar to variable costing, following the same concepts of it, but
differing in that it can recognise labour as a fixed cost (Boyd & Cox, 2002; Budd, 2010). Since transfer pricing occurs in international transactions where longer delivery times are expected, and, consequently, higher inventory levels occur, a
comparative study between the GAAP and throughput accounting concerning the impacts of transfer pricing in offshore
supply chains is important. However, as pointed out by Budd (2013), there is a lack of TOC literature in accounting and finance.
So far, the TOC literature has considered only local or domestic transfer prices (Ronen & Pass, 2008). An analysis considering
the international transfer price setting, as proposed in this paper, has not been identified in the TOC literature as well.
In a broader sense, with an accounting perspective in a supply chain context, the relationship between these two topics is
also explored. More specifically, this work aims to fill the gap in management accounting and control studies proposing
throughout accounting as an alternative and demonstrating the importance of inventory considerations in an international
transfer pricing (TP) setting. We demonstrate that by conducting a preliminary comparative study of the GAAP and
throughput accounting, which could lead to better understanding of the impacts of offshoring operations and international
transfer pricing on supply chains, based on the TOC perspective. Therefore, we analyse the importance of inventory levels in a
transfer pricing setting and the impacts on the Net Income After Tax using and comparing GAAP and throughput accounting.
To do so, a basic transfer pricing example was created, and a sensitivity analysis was conducted through simulation in a
system dynamics model. We demonstrate with our analysis that inventories affect the optimal transfer price, that the
traditional cost and accounting methods favour higher inventory levels and that it can overestimate net income results e
especially in higher demand variation scenarios e when compared to the throughput accounting.
The main contribution of the paper is to demonstrate that TA can be used as an alternative to GAAP accounting for
management accounting and control, addressing serious disadvantages and concerns presented in absorption costing (GAAP)
methods. Thus, we show that it can support supply chain decision-making regarding the offshoring of operations and that
inventory levels play an important role in such settings. As, in those scenarios, the suggested usual performance metric is
profit and/or return on investment of company divisions (Smolarski et al., 2019), the traditional accounting method can be
misleading to managers, as they tend to overestimate profits based on inventory levels. The throughput accounting tries, in
this case, to diminish this problem by being more restrictive regarding the gains derived from inventories, as it accounts only
variable costs in its value.
The remainder of this paper is structured as follows: the next section provides a brief literature review on throughput
accounting and transfer pricing. Section 3 covers the research methodology. Section 4 presents the model. Section 5 presents
the results and the discussion, and Section 6 concludes the study.
2. Literature review
This section aims to provide a basic understanding of the concepts used in our research. We will first present the systematic literature review (SLR) conducted for our study, which is followed by the background literature on throughput accounting and on the relevant transfer pricing problems.
The systematic literature review search was conducted in the Scopus and Web of Science (WOS) databases using the
defined keywords and search terms shown in Appendix A and delimited by publications from the last ten years. As no relevant
important papers were found in 2020, we decided to set the time frame from 2009 to 2019. We also limited our search by the
relevant important subjects, documents written in English and considered only articles or proceedings papers. This initial
search resulted in 205 documents, and, by excluding the duplicates, 156 documents remained. All of these had their titles and
abstracts analysed, and only 28 papers were selected for the final analysis. After the complete reading of these, 3 of them were
removed for not being relevant, which resulted in 25 papers for the literature review. Additionally, we also searched the
British Accounting Review database and found 13 documents. However, from the analysis of their titles and abstracts, none of
these documents were found to be relevant to offshoring scenarios or the international transfer pricing setting.
From the documents analysed, two streams of journals were found. One of them was more related to SCM e mixing
operations research (OR) and operations management (OM) e and the other was more linked to accounting. The publications
2
G. da Silva Stefano, T.S. Antunes, D.P. Lacerda et al.
The British Accounting Review 54 (2022) 101001
seemed to be spread among different journals, as, out of the 25 documents and 19 different journals, only 4 journals had 2 or
more publications, as can be seen in Table 1.
The journals were also classified in their most related research stream, these being either accounting or SCM and operations (OM/OR). From Table 1, for example, the first two journals were classified as SCM and operations, while the next two
were classified as accounting. A total of 9 documents were related to accounting journals, while the remaining 16 were related
to supply chain and operations. It is possible to note that a significant part (36%) of the publications come from accounting
research, whereas most publications (64%) are related to operations management and operational research.
Most of the literature, both in accounting and SCM, consider inventories as just a variable in a complex system and not a
key aspect of it, such as seen in Rossing et al. (2017) and Perron et al. (2010). In the SCM literature, inventory in global
€la
€ et al. (2014) demonstrate through a case-study that a company
offshored supply chains has received more attention. Seppa
division in one country is more profitable than the others due to its domestic production and thus smaller inventory and
shipping costs. Similarly, Stentoft et al. (2018) mentioned high inventory levels as a decisive motivation for backshoring e the
reverse strategy of offshoring, i.e. moving back to the domestic country. Blackburn (2012), however, demonstrates that
inventory-related costs are insensitive to the increased lead-time derived from offshoring when measured as a percentage of
unit cost.
Although accounting research has not given proper attention to inventories in a global supply chain setting with international transfer pricing, it has received attention in other cases. Pong and Mitchell (2006) found that inventories are a key
aspect in profit measurement, and that some companies in the UK could have their reported profitability changed drastically,
depending on the method used for inventory valuation. Comparing full cost and variable costing, the authors suggest more
studies exploring such comparisons. More recently, the authors found that inventory control improvement became an
operational focus of managers, and, consequently, inventory days have been decreasing over time (Pong & Mitchell, 2012).
Thus, we show through a simple model that these inventories can have a huge impact on the profits of a global supply chain.
We continue by providing a brief literature background on throughput accounting and transfer pricing.
2.1. Throughput accounting
The Theory of Constraints (TOC) was defined by its founder as a general approach to managing an organization (Goldratt,
1988). In the TOC, three paradigms are used as its primary guiding principles: logistics, global performance measures and the
thinking process (Tulasi & Rao, 2012). The context of global performance measures is where we identify the Throughput
Accounting (TA). According to Tulasi and Rao (2012), as the goal of an organization is to make money now and in the future,
unlike traditional accounting, the TOC global performance measures propose a set of indicators to focus on achieving the goal.
The throughput accounting method originated in the 1990s, and was improved as a managerial decision-making tool by
Goldratt (Hilmola & Gupta, 2015; Ronen & Pass, 2008; Sulaiman & Mitchell, 2005). The focus of throughput accounting is how
to use the capacity constraint to generate, as much as possible, throughput dollars (Bragg, 2011). The TOC pursues three basic
goals: increased throughput, decreased inventory and decreased operating expenses. Compared to the generally accepted
accounting principles (GAAP), throughput accounting reflects the same behaviour regarding increases in throughput
(contribution margin in GAAP) and decreases in operating expenses (fixed costs in GAAP). Inventory decreases, however,
reflect unfavourable figures on GAAP statements due to reductions in assets and operating income (Budd, 2010). Table 2
exemplifies and compares GAAP and the throughput accounting statements.
Basically, throughput can be defined as revenue minus all the variable costs e i.e. manufacturing, general, selling and
administrative (Brignall, 1997; Budd, 2010). Noreen et al. (1995) stated that, if necessary, in order to simplify the throughput
accounting, only direct material should be considered as variable costs. The authors claimed that the assumption is coherent,
as most of the labour time, for instance, cannot be considered variable, since wages are not related to units produced or sold,
and adjustments or cuts in the workforce cannot be directly related to production or sales levels. In the example demonstrated in Table 2, labour is considered as fixed cost, whereas other than direct material some manufacturing overhead is
identified as variable as well. Corbett (1999) claimed that the TA is a more modern method than traditional accounting to
assess throughput, direct labour and overheads. Dugdale and Jones (1998), however, stated that TA techniques had already
been presented in previous accounting theory and that its contribution lies in the call for change of the accounting mindset.
Nevertheless, the main distinctions between TA and GAAP are: throughput in GAAP is the total production of a company,
while in TA it is the rate at which the system generates money through sales; in TA fixed costs should not be allocated to
product units; direct labour is a variable expense in GAAP, while in TA it is a fixed cost, at least in the short and medium terms;
traditionally, inventory is treated as an asset and finished and unfinished goods increase its value, in TA and TOC though they
Table 1
SLR results by Journal. Prepared by the authors.
Journal
Documents
European Journal of Operational Research (EJOR)
International Journal of Production Economics (IJPE)
Management Accounting Research (MAR)
Journal of Accounting and Organizational Change (JAOC)
Other journals
4
2
2
2
15
3
G. da Silva Stefano, T.S. Antunes, D.P. Lacerda et al.
The British Accounting Review 54 (2022) 101001
Table 2
Traditional and throughput accounting comparison. Adapted from Budd (2013).
Traditional Income Statement
Revenues (15,000 units @ $ 20)
Cost of goods sold
Beginning finished goods
Direct materials used
Direct labour (all variable)
Variable mfg. overhead
Fixed mfg. overhead
Total cost of goods mfgd.
Ending finished goodsa
Total cost of goods sold
Gross margin
Selling and administrative expenses
Variable
Fixed
$300,000
$0
($40,000)
($25,000)
($20,000)
($80,000)
($165,000)
$41,250
($123,750)
$176,250
($105,000)
($30,000)
($75,000)
Net operating income
$71,250
Throughput Income Statement
Revenues (15,000 units @ $ 20)
Variable costs
Beginning finished goods
Direct materials
Variable mfg. overhead
Var. cost of goods mfgd.
Ending finished goodsb
Sum of var. costs and goods
Variable sell. and admin.
Total variable costs
Throughput
Fixed costs
Manufacturing
Labour
Selling and admin.
Total fixed costs
Net operating income
a
b
$300,000
$0
($40,000)
($20,000)
($60,000)
$15,000
($45,000)
($30,000)
($75,000)
$225,000
($80,000)
($25,000)
($75,000)
($180,000)
$45,000
5000 units @ $ 8.25 (variable and fixed manufacturing costs).
5000 units @ $ 3.00 (only variable manufacturing costs).
are regarded as raw-material and not all the processing costs are accounted for work-in-progress or goods (Corbett, 1999;
Naor et al., 2013). In this sense, in TA as inventory costs do not carry fixed costs as GAAP does, inventory values are smaller,
reducing the benefits of holding inventory, as can be seen in Table 2.
With regards to the application of TA with TP, Ronen and Pass (2008) proposed a structured methodology based on the
throughput accounting, the global decision-making (GDM). The GDM is composed of three steps: 1) Make a global economic
decision based on the CEO perspective; 2) Consider strategic factors; and 3) Change local performance measures. The
throughput accounting is used in the first step, and, according to the authors, such a decision should achieve an optimal
contribution to the organization’s objective and reflect the expectations of the CEO. The utilisation of TA should allow a
company to make optimal choices globally instead of locally. They cited a brief example of a company that had the option of
purchasing a service for a project from their own company or from a supplier at a lower price. In such a case, most project
managers would prefer the local optimal decision of buying at the lower price, even though it leads to a loss for the organisation. Then, the authors affirmed that the GDM methodology can be helpful in pricing decisions, definition of bid prices,
and determination of transfer prices. However, the example given is brief and used in a simple domestic transfer price situation. To the best knowledge of the authors, no work has made use of TA and a complete, detailed assessment in an international TP setting.
According to Jones and Dugdale (1998), TOC and the TA provides a coherent, comprehensive and articulated solution to
substitute traditional management approaches. Throughput accounting offers a different vision suitable for a manufacturing
environment that requires a paradigm change for accounting and accountants, focusing and reinforcing attention (Corbett,
1999; Dugdale & Jones, 1998). Although throughput accounting has received attention in accounting research (Gupta &
Boyd, 2008), more recently, it has received less publications (Hilmola & Gupta, 2015), leaving a gap in the research on the
theme.
2.2. Transfer pricing for management control
Gao and Zhao (2015) defined transfer pricing as the pricing of an intermediate good or service that is transferred between
divisions of the same multinational company. Transfer pricing is a complex subject, as many variables impact and are
4
G. da Silva Stefano, T.S. Antunes, D.P. Lacerda et al.
The British Accounting Review 54 (2022) 101001
impacted by decisions related to it; to cite a few: centralisation or decentralisation of supply chain decision-making (Wang
et al., 2016); uncertainty of demand (Zhang et al., 2016); logistics and transportation costs (Miller & De Maria, 2008; Vidal &
Goetschalckx, 2001); the defined transfer pricing methodology (Huh & Park, 2013); inventory holding (Susarla & Karimi,
2012); production capacity (Hasani et al., 2014); the arm’s length principle (Matsui, 2011); among many others.
Transfer prices are a key strategic aspect of determining the location of manufacturing or shared service facilities for MNEs
(Borkowski, 2002; Kim et al., 2018). MNEs can set their transfer prices strategically to increase results, determining how the
€ ffler, 2019). In this sense, tax authorities set
income of the agents is divided among the countries for income tax purposes (Lo
rules on transfer pricing methods to control tax avoidance and ensure compliance (Rossing, 2013). However, the TP concepts
related to the tax compliance issue cannot be separated from its managerial and economic aspects (Baldenius et al., 2004;
Hyde & Choe, 2005), as they also affect the incentives of their divisions, based on their own performance (Cools et al., 2008).
Thus, TP decisions increase in complexity if they need to consider both taxation compliance and management control di€ ffler, 2019).
mensions (Lo
Although transfer prices are regulated by governments following the arm’s length principle defined by the Organisation
for Economic Co-operation and Development (OECD) (Hammami & Frein, 2014a), the transfer price can be optimised to
increase net profits through tax avoidance and reduce operational expenses (Joseph et al., 2017), and improve other performance measures. The arm’s length principle provides guidance for the national tax authorities, providing proper taxation
among countries, but it is not necessarily applicable to managerial uses of TP. However, in reality, as found by Joseph et al.
(2017), companies usually do not calculate their tax savings versus the supply chain costs related to the tax strategy, and
most firms still focus on tax compliance issues (Klassen et al., 2017). Still, tax compliance has a great impact on the performance of divisions and their management control systems (Cools et al., 2008; Cools & Slagmulder, 2009). Thus, in the model
that considers an international transfer pricing setting with the relevant taxes, we study how management control and tax
dimensions co-exist and affect the overall system by comparing both traditional accounting and the TA.
Being a complex matter, transfer pricing has generated many complex model variations to optimize supply chain profits.
These models vary according to the supply chain models and other variables, and use techniques like linear programming,
heuristics (De Matta & Miller, 2015; Goetschalckx et al., 2002) and game-theoretical approaches (Clempner & Poznyak, 2018;
Rosenthal, 2008). However, other studies have been conducted outside the profit optimisation problem. Kumar and Sosnoski
(2011) researched the SME environment, analysing the impacts of not complying with transfer price regulations, and of
proposing a decision framework to select the transfer pricing method; Matsui (2011) studied the impact of arm’s length
regulation of consumer welfare in the trading countries, multinational firm profits and total world economic welfare; Joseph
et al. (2017) conducted an empirical research to collect evidence on how tax strategies may affect supply chain decision€la
€ et al. (2014) affirmed that managers are
making and strategies. In a case study on global supply chains and TP, Seppa
well aware that inventory and logistics costs significantly impact the overall supply chain profitability. However, according to
the authors, generally those costs have been neglected or included in other residual costs.
3. Methodology
Bertrand and Fransoo (2002) mention two types of quantitative models: axiomatic and empirical. Empirical models focus
on fitting the model according to observations and reality, while axiomatic models aim to achieve solutions within a specific
model and ascertain that the solutions obtained give insights into the problem structure. Additionally, they also differentiate
normative and descriptive research. Normative research is concerned with developing policies, strategies and actions to
achieve better results than those found in the current literature; finding an optimal solution to a new problem or comparatively analysing different strategies based on a specific problem. Descriptive research, on the other hand, is concerned with
analysis of the model, seeking understanding and explanation of its characteristics. Therefore, we define our research as
axiomatic descriptive.
Bertrand and Fransoo (2002) affirmed that, in axiomatic descriptive research, the researcher uses a conceptual model e
usually taken from the literature e to conduct analyses that provide insights into the behaviour of this model. Therefore, the
authors proposed a research methodology that would guide this research, containing a conceptualisation of the problem,
creation of the model, the results, analysis of the results, and the insights generated by the model. Thus, the paper begins with
the definition of the problem, structured by a literature review already presented in the Introduction and the Literature
Review sections. In this section, we present the methodology applied, followed by an explanation on the System Dynamics
methodology. Next, the model is constructed using an example from the literature. Then the validation is conducted by
comparing the results of the model to the figures presented in the original example. The model construction, model solution
and validation are all detailed in the next section. Finally, we implement the proposed TA techniques for the result analysis by
conducting a descriptive analysis composed of multiple sensitivity analyses e which are fully described in Section 5. Having
defined the methodology used, in the next section, we continue the work by presenting the model construction.
Within the international setting of transfer prices, axiomatic research is commonly used in transfer price and accounting
studies. Empirical research proves difficult as the required data sensitive for organisations is scarce (Cools & Slagmulder,
2009). Examples of this can be found in Baldenius et al. (2004), Hyde and Cloe (Hyde & Choe, 2005), and Narayanan and
Smith (2000). Such research explores idealised models to generate knowledge about transfer prices, management control
systems and their impacts on the model and system in an exploratory way. Next, continuing the method explanation, the
System Dynamics technique is presented.
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The British Accounting Review 54 (2022) 101001
3.1. System dynamics
Created by Jay Forrester in the 1950s (Law, 2014), system dynamics is a set of tools and a simulation approach originally
designed to be used in the industrial sector (Mansilha et al., 2019; Pidd, 2003). Pidd (2003) defined two modes of operation in
the system dynamics (SD) approach. The basic one provides a way of visualising human systems by focusing on the
importance of some particular structural features, such as feedback control. Instead, the second operation mode uses the
same structural features as in the basic one to develop a computer-based simulation model of the systems, and uses quantitative data.
System dynamics tends to look at systems from a high-level view, and is used to take decisions that are more strategic
(Law, 2014; Martins et al., 2020). However, the (SD) modelling can be used for any dynamic system, at any time and on any
spatial scale (Sterman, 2000). According to Sterman (2000), SD is concerned with the behaviour of complex systems, and
requires more than just technical tools to create mathematical models. As Law (2014) specified, (SD) has three key components; Fig. 1 presents a basic SD model and its components, which are described next:
a) Stock: defined as a resource accumulation, and is represented by a rectangle;
b) Flow: a stream of a specific resource that enters or leaves the inventory. It is represented by double-line arrows with a
valve in the middle;
c) Converter: used to represent parts at the boundary of the system, i.e. parts whose values are not determined by the
behaviour of the system itself. It is represented by a circle;
d) Information link: used to transfer information about an inventory or a variable to a flow. Here it is represented by a
curved arrow.
However, within the supply chain context, other techniques could be reasonable options too, such as Discrete-EventSimulation (DES), Agent-Based Simulation (ABS) or optimisation models. According to Sweetser (1999), DES is best suited
to providing detailed analysis of systems composed of linear processes, and is used when the goal is a statistically valid
estimation of system performance. Also, SD is best used when the problem is associated with a continuous process in which
feedback loops are frequent in the system and impact its own behaviour (Morecroft & Robinson, 2005; Sweetser, 1999; Tako &
Robinson, 2012). However, there is an overlap between these two techniques, and many problems could be modelled with
either approach and still present very similar results (Morecroft & Robinson, 2005; Sweetser, 1999). Agent-based modelling
simulates autonomous entities that act on behalf of real-world actors, dynamically supporting the decision-making process,
as well as taking into consideration knowledge of their own environments (Christos et al., 2016). In other words, the agentbased approach models the complexity arising from individual actions and interactions that occur in the real world (Siebers
et al., 2010).
In turn, analytical approaches, such as optimisation or mathematical models, provide good results in supply chains that are
well-defined, possess few decision variables and restrictive assumptions (Chen & Paulraj, 2004). However, Ge et al. (2015)
claim that analytical representations of problems may not represent a realistic perspective in cases within complex dynamic systems. Chan and Chan (2010) suggest simulation instead of analytical models when the behaviour and the dynamics
Fig. 1. Basic SD model. Adapted from Law (2014).
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The British Accounting Review 54 (2022) 101001
of the supply chain are the focus. As there is no interest in individual actions and interactions within the proposed system,
and, given the exploratory nature of the research, which aims to understand the behaviour of the system through two
different accounting methods, the authors decided to use the SD approach.
Although SD is presented as a reasonable approach to simulate enterprise systems, the authors found no previous work
using this approach to deal with the aforementioned problem. Similarly, throughput accounting has only been applied to
domestic transfer price scenarios, and no studies were found dealing with international transfer prices. In the next section, we
aim to demonstrate the creation of an SD model.
4. Model presentation
This section covers the model construction, explaining its logic and detailing the sectors within it, followed by an
explanation of the process used to validate it.
4.1. Model construction
The authors found it difficult to find actual empirical data to create the model, mainly because these kinds of data are
strictly confidential for companies, as they concern pricing methodologies and tax information. We also acknowledge the TP
setting complexity (Cecchini et al., 2013; Smolarski et al., 2019), and thus we chose to take a simpler approach to the problem
using a more didactic model. Therefore, we derived data from the basic example provided by Vidal and Goetschalckx (2001).
In the example, the authors used a company comprising Unit A and Unit B to demonstrate a simple income statement and the
transfer pricing impacts on it. Unit A is located in a country with lower tax rates, and transfers materials to Unit B. They also
noted that the transfer price that maximised net profit after tax had an optimal value of around $12 per unit. We present this
example in Table 3.
In the example given, we included inventory. Logically, inventory levels may be higher in international supply chains when
compared with those in domestic supply chains. For that matter, we took the table above and added inventory levels to both
subsidiaries (Units) A and B, which is demonstrated in Table 4. Table 4 data also represents the initial data (DT ¼ 1.00) of the
model.
Table 3
Income statement with transfer prices. Adapted from Vidal and Goetschalckx (2001).
SubsidiaryA
SubsidiaryB
Group Total
400,000
400,000
(140,000)
Detail
Transfer price @ $11/unit, sales @ $20/unit and20,000 units sold
Sales
220.000
Variable costs
(140,000)
Procurement costs
Import Duties (12%)
Fixed Costs
(20,000)
Net Income Before Tax (NIBT)
60,000
Tax rate
34%
Tax payable
(20,400)
Net Income After Tax (NIAT)
39,600
(220,000)
(26,400)
(120,000)
33,600
50%
(16,800)
16,800
Transfer price @ $12/unit, sales @ $20/unit and20,000 units sold
Sales
240,000
Variable costs
(140,000)
Procurement costs
Import Duties (12%)
Fixed Costs
(20,000)
Net Income Before Tax (NIBT)
80,000
Tax rate
34%
Tax payable
(27,200)
Net Income After Tax (NIAT)
52,800
400,000
(240,000)
(28,800)
(120,000)
11,200
50%
(5,600)
5,600
Transfer price @ $13/unit, sales @ $20/unit and20,000 units sold
Sales
260,000
Variable costs
(140,000)
Procurement costs
Import Duties (12%)
Fixed Costs
20,000
Net Income Before Tax (NIBT)
100,000
Tax rate
34%
Tax payable
(34,000)
Net Income After Tax (NIAT)
66,000
400,000
(260,000)
(31,200)
(120,000)
(11,200)
50%
(11,200)
7
(26,400)
(140,000)
93,600
(37,200)
56,400
400,000
(140,000)
(28,800)
(140,000)
91,200
(32,800)
58,400
400,000
(140,000)
(31,200)
(140,000)
88,800
(34,000)
54,800
G. da Silva Stefano, T.S. Antunes, D.P. Lacerda et al.
The British Accounting Review 54 (2022) 101001
Table 4
Income statement with inventory levels. Prepared by the authors.
Transfer price @ $11/unit.3.000 units of inventory inA and2,000 units of inventory inB. 25,000 units produced in A and 22,000 units bought by B
Sales
Variable costsa
Procurement costs
Import Duties (12%)
Fixed Costs
Total cost of goods mfgd
Inventoryb
Cost of goods sold
NIBT
Tax rate
Tax payable
NIAT
a
b
c
d
SubsidiaryA
SubsidiaryB
Group Total
242,000
(175,000)
400,000
400,000
(175,000)
(242,000)
(29,040)
(120,000)
(391,040)
35,549d
(355,491)
44,509
50%
(22,255)
22,255
(20,000)
(195,000)
23,400c
(171,600)
70,400
34%
(23,936)
46,464
Variable cost of 7 $/unit * 25,000 units.
(Total cost of goods mfgd./units produced or bought) x units in inventory.
(195,000/25,000 units) x 3000 units ¼ 23,400.
(391,040/22,000 units) x 2000 units ¼ 35,549.
Fig. 2. Production sector of the model. Prepared by the authors.
8
(29,040)
(140,000)
(586,040)
58,949
(527,091)
114,909
e
(46,191)
68,719
G. da Silva Stefano, T.S. Antunes, D.P. Lacerda et al.
The British Accounting Review 54 (2022) 101001
For the model construction, we added safety stock levels to both Units A and B so that we could have units stocked in the
inventories from the beginning of the simulation. Also, we built our model simulation to run on a monthly time frame. In our
model, Unit A supplied Unit B once a month, according to the quantity ordered from Unit A. However, we considered a delay in
the flow of the units bought in order to simulate the time to produce and deliver the goods. The demand was directly linked to
sales, and was considered known, normally distributed with an average of 20,000 units, a standard deviation of 1000 units
and a starting value of 20,000 units. Safety stocks serving as protection were set at 3000 and 2000 units in Unit A and B,
respectively. The model was constructed in Stella version 9.0 software from Isee systems. The production sector of the model
is presented in Fig. 2.
From the production model, we derived the sectors for traditional accounting and throughput accounting. As our model
used a monthly time frame, we needed to “store” the net monthly incomes before tax (NIBT), so that, after 12 months, we
“paid” the proper taxes. Additionally, we used flows for both taxes and NIBT to keep track of them. This simulated a real
company where the taxes were provisioned to a specific account to be paid at the end of the fiscal year. We used flows to
determine actual values being accounted and variables to find specific metrics. Fig. 3 presents the traditional accounting
sector of the model.
The throughput accounting sector uses the same logic as the model above, although the net income after tax (NIAT) uses
the taxes paid from the traditional accounting sector, as governments regulate taxes through traditional income statements.
All the equations, formulas and starting values of the model are presented in Appendix A. Fig. 4 presents the throughput
accounting sector of the model.
After presenting the model, we conducted some validations to ensure that the proposed model worked as expected.
4.2. Model validation
To validate the model, we used a simple Excel spreadsheet, as demonstrated in Table 4. We ran the model with the initial
transfer price of $11/unit e just as proposed by Vidal and Goetschalckx (2001), and then we increased and decreased this
value to guarantee that the NIAT figures for both GAAP and throughput accounting were correct. The TP changes were to $2/
unit and $25/unit, respectively. Table 5 presents the throughput accounting values with TP $11/unit.
Fig. 3. Traditional accounting sector of the model. Prepared by the authors.
9
G. da Silva Stefano, T.S. Antunes, D.P. Lacerda et al.
The British Accounting Review 54 (2022) 101001
Fig. 4. Throughput accounting sector of the model. Prepared by the authors.
Table 5
Throughput accounting for model validation.
Transfer price @ $ 11/unit. 3,000 units of inventory in A and 2,000 units of inventory in B.
Sales
Variable costs
Procurement costs
Import Duties (12%)
Variable cost of goods mfgd
Inventory
Variable costs of goods sold
Throughput
Fixed Costs
Net Operating Income
Tax payable
NIAT
SubsidiaryA
SubsidiaryB
Group Total
242,000
(175,000)
400,000
400,000
(175,000)
(242,000)
(29,040)
(271,040)
24,640
(246,400)
153,600
(120,000)
33,600
(22,255)
11,345
(175,000)
21,000
(154,000)
88,000
(20,000)
68,000
(23,936)
44,064
(29,040)
(446,040)
45,640
(400,400)
241,600
(140,000)
101,600
(46,191)
55,409
Then, to validate the model, we compared the values found on the spreadsheets and the ones found in the model. For
validation purposes only, the model was set to run from time 1 to 2, with DT (interval between calculations) as 1. For the
validation, we used the following flows: Monthly NIAT A, Monthly NIAT B, Monthly NIAT A TA, and Monthly NIAT B TA (Table
6). It was expected that any differences or errors of any other variables would be reflected in these four.
After this, we validated the NIBT inventories and the annual NIAT flow. Since we had planned to run the model in months,
we needed to simulate the model for at least 13 months. Since we were stocking NIBT and the simulation took 1 unit of time to
increase or decrease the inventories, the total yearly NIAT would be known only at the time of unit 13. This is considered as the
time taken by a company to close its financial year. It will know its yearly results in January e considering that the fiscal year
runs from January to December. The NIAT and tax paid flows are logically set to work only after every 12 months of simulation.
Therefore, it is important to note that the NIBT inventories start again only in the second month. In the 13th month of the
10
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The British Accounting Review 54 (2022) 101001
Table 6
Expected results and model results. Prepared by the authors.
Expected value (Excel)
Model results
TP ($/unit)
NIATA
NIATB
NIATA TA
NIATB TA
NIATA
NIATB
NIATA TA
NIATB TA
2.00
11.00
25.00
127,600.00
46,464.00
249,744.00
123,054.55
22,254.55
269,090.91
130,000.00
44,064.00
247,344.00
112,145.45
11,345.45
280,000,00
127,600.00
46,464.00
249,744.00
123,054.55
22,254.55
269,090.91
130,000.00
44,064.00
247,344.00
112,145.45
11,345.45
280,000.00
Table 7
Timing validation example. Prepared by the authors.
Month
Time
Net income A
NIBT A
NIAT A
Net income A TA
NIBT A TA
NIAT A TA
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
70,400
130,059
82,465
83,134
83,503
85,094
76,497
72,886
79,694
87,934
92,289
93,003
82,046
70,485
73,448
e
70,400
200,459
282,924
366,058
449,562
534,655
611,153
684,039
763,733
851,667
943,957
1,036,960
82,046
152,531
e
e
e
e
e
e
e
e
e
e
e
e
684,394
e
e
68,000
123,000
79,404
80,100
80,484
82,136
73,172
69,376
76,516
85,080
89,580
90,316
78,968
66,840
69,968
e
68,000
191,000
270,404
350,504
430,988
513,124
586,296
655,672
732,188
817,268
906,848
997,164
78,968
145,808
e
e
e
e
e
e
e
e
e
e
e
e
644,598
e
e
From the validation, we proceeded to conducting and demonstrating the results, covered in the next section.
simulation, the value will be the aggregate value of NIBT of the previous 12 months, in other words, the complete year. To
exemplify, Table 7 provides the figures from the simulation from time 1 to 15 with transfer prices returned to $11/unit. The
blue fields register the relation of the figures. The same validation was performed for Unit B.
5. Results and discussion
To make a full analytical comparison of the company, we added one more sector to the model. Basically, we used two
variables to report the aggregate net income after tax in Unit A and Unit B. Fig. 5 demonstrates these sectors.
We first made a simulation to see the results of the net income after tax with the inventory costs and compared the GAAP
accounting method and that of throughput accounting. To do so, we used the Stella sensitivity analysis functionality. We made
a scatter chart comparing the total net income after tax with the transfer price. Fig. 6 shows the comparison between the
GAAP on the left and the throughput accounting on the right. 8 sensitivity runs were performed with transfer prices ranging
from $11/unit (run 1) to $18/unit (run 8), with incremental increases set at $1/unit for each run. Each point of the figure
represents a different run.
It is possible to notice that the optimum transfer price changed. As seen in Table 3, the optimal price was around $12/unit
while, in our example, the inventories changed the optimal transfer price to approximately $14/unit with both methods. We
can also note that the throughput accounting demonstrates smaller net incomes after tax, mainly because GAAP accounting
considers inventories positively. The NIAT difference between GAAP and throughput accounting is always constant and does
not depend on the transfer price value. Therefore, the throughput accounting always accounts $186,135.08 less net income
before tax than GAAP accounting, as long as the inventory values do not change. Thus, the transfer price is the same for both
GAAP and TA, but it is impacted and increases as inventory levels increase.
We ran another sensitivity analysis. However, this time, we varied the inventory values. We set both safety stock variables
to range from 0 (run 1) to 5000 (run 5) units, with incremental increases set at 1250 units in each run. It is important to note
that, as we increase safety stock, the overall inventory level of the system also increases, as the model produces more in order
to maintain the minimum level of inventory required, i.e. safety stocks. Fig. 7 demonstrates the charts of total net income after
tax versus the safety stock variation, with each point representing a different run of the sensitivity analysis.
Once again, the GAAP favours the net income after tax as the levels of inventory increase. It is possible to note that the NIAT
is much higher with more inventory in GAAP than in TA. However, this time, the difference between GAAP and throughput
accounting is not constant. To better observe the behaviour of the throughput NIAT with the inventory levels, we used
sensitivity analysis, once again comparing the inventory levels and the NIAT difference between GAAP and TA. Fig. 8 presents
the simulation results. We used 10 runs varying safety stocks in A and B, with ranges set from 0 (run 1) to 9000 units (run 10)
and incremental increases set at 1000 units for each run.
11
G. da Silva Stefano, T.S. Antunes, D.P. Lacerda et al.
The British Accounting Review 54 (2022) 101001
Fig. 5. Aggregative sectors of the model. Prepared by the authors.
This time the difference between GAAP and throughput accounting seemed different. In the beginning, growth was
observed when the inventory level was low, and then it became linear in run 4 and onwards. This happened mainly due to the
variation in demand. When the safety stock was low, both Units kept less inventory. Since inventory levels were lower, all the
inventory was consumed by the demand. After run 6, the safety stock levels could be maintained, and no sales were lost. Until
run 6, we may have had a capacity constraint, for instance, while, from run 6 and onwards, the constraint became the demand.
It is possible to observe then that TA accounts NIAT in a different manner when the demand is not entirely met. Also, the TA
more closely follows the increase in inventory than GAAP, as higher inventory increases NIAT considerably less than GAAP
does. Next, we demonstrate the pattern of demand and consumption observed with two more simulations. In one, safety
stocks were set at 1000 units, while, in the other, these were set at 6000 units. Fig. 9 shows the results. The top graph (a) has
the results with inventories set at 1000 units, and the other (b) at 6000 units.
As can be seen in the chart above, when inventory levels are lower, sales are lost, and demand is not met entirely. For this
reason, the revenue is affected, which, in turn, affects the net income after tax. Thus, as we keep more inventory, the
throughput accounting becomes more “punitive” than the GAAP accounting method. Once the inventory levels are kept
constant e through safety stock e the difference between GAAP and TA stabilises as well.
12
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The British Accounting Review 54 (2022) 101001
Fig. 6. Total NIAT vs. TP. Prepared by the authors.
13
Fig. 7. Total NIAT vs. Total Inventory Levels. Prepared by the authors.
Complementing the analysis, we conducted the same simulations once again, but, this time, with an increased value for
the standard deviation of demand. Instead of 1,000, we set the normal curve standard deviation at 5000 units. The increase in
demand variation would create an example that would be a more realistic demand distribution. Fig. 10 shows the results of
these simulations.
Increasing demand variation leads to increases in the inventory levels e as a security measure e in order to meet the
required demand. In part (a) of Fig. 10, it is possible to observe that the optimal transfer price moves from around $14 to
approximately $16 for both accounting methods, once again demonstrating the effects of increasing inventory levels in the
transfer price. This is the expected result, as the variation of demand results in more inventory to meet demand. The NIAT is
also impacted by the increased variation, as can be observed in part (b) the NIAT in TA is much lower than GAAP when the
inventory is higher. As can be seen in parts (b) and (c), as the demand variation increases and, consequently, the inventory
increases, the differences between the GAAP and TA become more significant in terms of absolute values. This happens due to
the increase in inventory levels that are not positively accounted under TA. Lastly, Fig. 11 demonstrates the sales and demand
over time when safety stocks are set at a) 1000 units and b) 6000 units.
It is possible to notice that the lost sales increase, meaning that the demand is not met more frequently and by greater
differences. Even when safety stocks were set at higher levels, the demand could not be met entirely, as may be seen in the
second chart of Fig. 11. The increase in lost sales also helps understanding of the differences between TA and GAAP, as seen in
chart (c) of Fig. 10. As we prejudice revenue and do not account inventories positively, the differences increase, and their
pattern varies. We can note that TA is more “punitive” in this sense. NIAT is impacted negatively in TA if there are lost sales and
G. da Silva Stefano, T.S. Antunes, D.P. Lacerda et al.
The British Accounting Review 54 (2022) 101001
Fig. 8. NIAT difference between GAAP and TA vs. Inventory. Prepared by the authors.
the inventory surplus does not impact NIAT positively as much as it does in GAAP. In order to summarise the results and the
findings, we present Frame 1 below.
Frame 1 e Summary of the simulation results. Prepared by the authors.
Finally, to increase clarity of the findings, we present the results from the last set of simulations in Table 8. Therefore, the
values presented in the table show the NIAT with safety inventories ranging from 0 to 9000 units and demand variation set at
5000 units of standard deviation. Column ‘n’ represents the run number of the sensitivity analysis, according to the safety
inventory variation. The NIAT is shown for both accounting methods, as well as their incremental variation at each one of the
simulation runs in absolute and percentual values. The percentual difference in the NIAT is presented for the sake of comparison. Finally, the surplus inventory represents the amount of inventory accumulated at the end of the period and the lost
sales demonstrates the difference between demand and actual sales for the year.
It is possible to note that with increased variation, even with safety inventories set at 9000 to both units, the demand
cannot be entirely met, even though, as inventories increase the lost sales decrease incrementally. The difference in NIAT
between the TA and GAAP, for our example, can be as high as 70%. It increases as the model keeps surplus inventories, which
are penalised in TA. It can be pointed out that, although inventory is kept at the end of the year, sales are still lost, mainly due
to demand peaks during the period (see Fig. 10 (b)). There is a decrease in the NIAT from run 6 to run 7, mainly because the
surplus inventory does not increase the throughput e the throughput increases as lost sales decrease e sufficiently enough to
increase the NIAT and compensate the increase in inventories. Then, after run 7, we can see that as the inventories increase
and lost sales decrease linearly, the NIAT TA also improves, eventually becoming higher than the value found in run 6.
From the results, we understand that inventory levels play a major role in accounting and transfer pricing. Transfer prices
present an opportunity for profit maximisation for MNEs (Cecchini et al., 2013; Hammami & Frein, 2014b); but, we state that
inventory levels should be fully understood in order to reach TP real optimisation. As much as the overestimation of inventories is an issue, it is necessary to not underestimate them as well. The definition of safety inventories, for instance,
should be considered carefully considering consumption patterns, demand variation and lead-times. Estimating inventory
levels can be a challenge due to the difficulty of achieving the optimal spot between avoiding lost sales and creating excess
inventory. Therefore, even though inventories are usually neglected by other studies (Sepp€
al€
a et al., 2014), as stated by Pong
and Mitchell (2006), their valuation can significantly impact organisations’ profitability. With this in mind, in our case, we
show that traditional accounting methods tend to overestimate the benefits e up to a 70% difference in NIAT e of inventories
that may lead to superficial analyses of increases in the net income after tax. In this sense, similarly, as briefly suggested by
Ronen and Pass (2008), we found that TA seems to be a reasonable alternative for management control, as it does not
overestimate inventory levels and focuses on the throughput (i.e. more sales).
One key point is that practitioners and/or managers make their decisions considering both their taxes and management
control objectives, as stated by Joseph et al. (2017) and Cecchini et al. (2013). In other words, would managers make the same
decisions regarding offshoring e knowing that it can lead to increased levels of inventory and longer lead-times when
14
G. da Silva Stefano, T.S. Antunes, D.P. Lacerda et al.
The British Accounting Review 54 (2022) 101001
Fig. 9. Sales and Demand vs. Time. Prepared by the Authors.
compared to onshore e if they knew that their net income after tax could be up to 70% lower than originally estimated? The
throughput accounting tries, in this case, to diminish this problem. Therefore, the TA can be used as a metric to support supply
chain decision-making regarding offshoring and transfer prices optimisation.
6. Concluding remarks
In this research, we aimed to fill a gap in management accounting and control regarding international TP settings (Cools &
Slagmulder, 2009). We propose the utilisation of throughput accounting and demonstrate the importance of inventories in
those scenarios. Our main contribution lies in the presentation of TA, from a management control perspective, as a valuable
alternative to GAAP e addressing, at the same time, some of the operational concerns from traditional accounting methods.
Additionally, we also contribute to a recent lack of research in TA (Hilmola & Gupta, 2015), as well as bridging a gap between
SCM and accounting literature (Joseph et al., 2017; Ramos, 2004). First, we conducted a literature review regarding transfer
pricing and throughput accounting. Then we created a system dynamics model to evaluate the transfer pricing and offshoring
scenario, comparing the traditional accounting methods to throughput accounting. The results demonstrated that
throughput accounting is stricter about inventory levels, which supposedly increase as supply chains are offshored.
From a practical point of view, we demonstrate that throughput accounting can support decision-makers to assess and
estimate offshoring benefits for MNEs. We showed that inventories and demand variation play an important role in such
scenarios and tend to be underestimated in traditional accounting methods, impacting both the optimal transfer price and the
net income after tax. We contribute to such studies as Joseph et al. (2017), demonstrating that, in an international TP setting,
managers have to account far more than just tax savings. Additionally, we contribute to the literature by comparing traditional accounting methods of inventories to other alternatives e in our case the TA e as pointed out by Pong and Mitchell
15
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The British Accounting Review 54 (2022) 101001
Fig. 10. Simulation with increased demand variation. Prepared by the Authors.
(2006) and demonstrating the impacts of different methods on profitability. Finally, from the SCM perspective, we propose a
€ l€
method that does not neglect inventory costs as usually happens, as claimed by Seppa
a et al. (2014), instead making it more
relevant to the respective setting. In fact, the differences between the TA and GAAP are up to 70% in NIAT, mainly due to
different accounting of inventories (which accounts positively only the variable costs of inventory) and its reliance on the
throughput.
We also acknowledge some limitations of our model. We believe that an improved model considering arm’s length
regulation and setting the transfer prices via throughput accounting with comparative analyses would be useful. Other than
this, we could also enhance the model to consider decisions for or against offshoring, adding capacity constraints to the
production units, and even adding more production units. However, many other variables would have to be added to the
16
G. da Silva Stefano, T.S. Antunes, D.P. Lacerda et al.
The British Accounting Review 54 (2022) 101001
Fig. 11. Sales and demand with increased demand variation. Prepared by the Authors.
Table 8
Simulation Results. Prepared by the authors.
n
1
2
3
4
5
6
7
8
9
10
NIAT TAn - NIAT TAn- NIAT ($)
Safety Inventory
(units)
NIAT TA
($)
1
Absolute
%
e
1000
2000
3000
4000
5000
6000
7000
8000
9000
464,829.96
547,097.66
631,287.33
704,262.62
749,974.79
751,776.63
648,205.68
703,377.86
760,428.44
817,594.45
e
82,267.70
84,189.67
72,975.29
45,712.17
1801.84
103,570.95
55,172.18
57,050.58
57,166.01
e
15.0%
13.3%
10.4%
6.1%
0.2%
16.0%
7.8%
7.5%
7.0%
677,129.85
856,228.63
1,064,239.49
1,283,978.87
1,517,351.28
1,790,306.42
2,158,414.37
2,362,570.98
2,564,751.25
2,766,785.09
NIATn - NIATn-1
Absolute
%
e
179,098.78
208,010.86
219,739.38
233,372.41
272,955.14
368,107.95
204,156.61
202,180.27
202,033.84
e
20.9%
19.5%
17.1%
15.4%
15.2%
17.1%
8.6%
7.9%
7.3%
NIAT and NIAT TA %
difference
Surplus Inventory
(units)
Lost Sales
(units)
31%
36%
41%
45%
51%
58%
70%
70%
70%
70%
e
e
e
e
e
155
2155
4155
6155
8155
26,916
23,774
20,774
17,774
14,774
11,929
10,929
9929
8929
7929
model, thereby drastically increasing model complexity. Consequently, techniques such as exploratory modelling and analysis
(EMA) would seem to be a great opportunity for future research, as EMA is useful when information exists, but does not allow
specifying a single model that describes the behaviour of the system accurately enough (Kwakkel & Pruyt, 2013). Another
interesting factor that could be added to the model would be an evaluation of profit maximisation alongside supply chain
performance; the supply chain performance indicator could be calculated through data envelopment analysis (DEA), for
instance, and the mixing of both profit and performance measures could create a robust decision-making tool.
This research has provided more insights into the transfer pricing setting and the throughput accounting methodology.
Additionally, it has provided a preliminary model to simulate the transfer pricing and offshoring context, thereby contributing
to the understanding of the defined scope.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.org/10.1016/j.bar.2021.101001.
17
G. da Silva Stefano, T.S. Antunes, D.P. Lacerda et al.
The British Accounting Review 54 (2022) 101001
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