theory of constraints vs. activity-based costing

7/30/2019 Theory of Constraints vs. Activity-Based Costing

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Who Wins in a Dynamic World:Theory of Constraints vs. Activity-Based Costing?

Robin Cooper*

Goizueta Business School

Emory UniversityAtlanta, GA 30322

David BrayGoizueta Business School


Michael ParzenGoizueta Business School


Abstract

Two system-based views exist regarding managerial value chain analysis: Theory of Constraints(TOC) and Activity-Based Costing (ABC). There has been considerable debate whether TOC or

ABC is the more optimal approach for strategic planning. This study seeks to compare TOC andABC, while keeping constant the level of environmental turbulence each of the approachesencounter. With regard to organizational systems, literature regarding complex adaptivesystems supports the idea that bottom-up approaches are more resilient to volatility.Consequently, this study hypothesizes that the bottom-up ABC approach will prove more agileand less limiting than the top-down TOC approach. This study then performs twocomputational experiments. The first experiment reveals that the ABC approach generated

more PROFIT than the TOC approach, while the TOC produced a larger amount of REVENUE,for all instances of the simulation. The second experiment reveals that a hybrid TOC+ABCapproach is the most optimal in the midst of environmental turbulence out of four possibilities.This hybrid TOC+ABC selects a first cut of orders that will generate the highest REVENUES perthe TOC approach, and then selects a second cut of orders that will have the lowest COSTS andthus the highest PROFIT per the ABC approach. These results challenge the establishedliterature espousing the TOC approach alone.

* = Corresponding author, ([email protected])

Keywords: Theory of Constraints (TOC), Activity-Based Costing (ABC), value chain analysis,strategic planning, bottom-up approaches, environmental turbulence


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Who Wins in a Dynamic World:Theory of Constraints vs. Activity-Based Costing?

Robin Cooper, David Bray, and Michael Parzen

Value chain analysis serves as a powerful tool for economic planning by (1) categorizing thevalue-adding activities of an organizational system and (2) illuminating areas most likely toincrease profitable outcomes. Specifically, value chain analysis identifies the value and costdrivers for each activity performed by an organization. Such analysis then strives to maximizeaggregate value creation and minimize costs internal to an organization (Smith & Pretorius,2003).

Within the established literature, two system-based views exist regarding managerial valuechain analysis: Theory of Constraints (TOC) and Activity-Based Costing (ABC).

Proponents of the TOC worldview subscribe that every profit-making organization must have atleast one constraint preventing an organizational system from achieving a higher performancerelative to its goal. Such constraints include resource-related, market-related, and policy-relatedconstraints. According to the TOC approach, identifying and addressing these constraintsfacilitates successful management choices regarding optimization of an organizational system(Goldratt, 1990).

Contrasting with the TOC worldview, proponents of the ABC approach subscribe thatidentifying the causal relationships behind the costs of a profit-making organization facilitatescost assessment more optimally than the TOC approach. This alternative approach firstidentifies activity-based costs and then attributes such costs to product creation based upon theactivities performed during production. Consequentially, the ABC approach illuminates areas ofhigh overhead costs for management to consider and adjust (Cooper & Kaplan, 1991).

There has been considerable debate whether TOC or ABC is the more optimal approach foreconomic planning regarding order acceptance (Baxendale and Gupta, 1995; Holmen, 1995).Though TOC represents the established approach, a few empirical cases examining real-worldfirms support the ABC approach as more effective in increasing profitability and reducinginventory levels (Kirche & Srivastava, 2005). To formalize and buttress these empirical findings,this study seeks to compare TOC and ABC on a level playing field keeping constant theenvironmental turbulence each of the approaches encounter.

As a hypothesis, this study predicts that the ABC approach will demonstrate itself to be moreoptimal than the TOC approach in a dynamic world, where volume and costs vary per a set ofnormal distributions representative of empirical cases. Specifically, in a dynamic world, this

study predicts the bottom-up, activity-based attribution of costs associated with the ABCapproach will prove more agile and less limiting than the top-down constraint-identifying tactassociated with the TOC approach. With regard to organizational systems, literature regardingcomplex adaptive systems supports the idea that bottom-up approaches are more resilient to

volatility (Clippinger, 1999; Eisenhardt & Galunic, 2000). This study hopes to provide similarevidence with regard to strategic planning approaches.


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Methodology

This study developed a computational simulation to evaluate both the TOC and ABCapproaches. While unorthodox among the established literature surrounding the TOC and ABCdebate, a computational simulation was the only methodology that allowed a level playingfield to compare these two approaches. While the primary objective of this study is toinvestigate the performance of the TOC and ABC approaches, a secondary objective includesintroduction and espousal of computational simulations as beneficial methodologies for bothlong-term value chain analysis and the managerial accounting literature.

Microsoft Visual Studio .Net served as the platform of choice for the simulation, allowingcomposition of intuitive code and comments, should others wish to modify the simulation later.Further, the simulation itself included a friendly graphical interface intended to allow studentsto explore the differences between TOC and ABC interactively.

The simulation included eight normally distributed random variables, each representing someaspect of a value chain associated with creation of a product. These random variables included:

Totally variable cost of a unit TVC (mean, stddev)Labor content of a unit LAB (mean, stddev)Batch costs with an order BOE (mean, stddev)Product costs with an order POE (mean, stddev)Order size as an integer OS (mean, stddev)Run minutes on a bottleneck per unit RM (mean, stddev)Setup minutes on a bottleneck per run of an order SM (mean, stddev)Selling price per unit SP (mean, stddev)

Figure 1: Normally distributed random variables included in the simulation

These normally distributed, random variables defined the orders received by a firm. For theeight normally distributed random variables, the simulation utilized means and standarddeviations based upon empirical cases. The simulation allowed for two modes: (1)independence of all random variables or (2) correlations between TVC, SP, LAB, and RM, in anattempt to increase fidelity of the simulation. That said, tests revealed no difference in theresults of the simulation between the two modes.

The simulation itself approximated a hypothetical world in which a firm received more ordersthan it could possibly produce. A firm then had to select which orders would be the mostprofitable to accept, given its internal limitations. A single instance of the simulation simulateda firm making its long-term decisions based on the TOC approach first, and then based on the

ABC approach second, for the same set of possible orders. These two long-term decisions wereindependent. Given their theoretical differences, the TOC and ABC approaches should suggest

accepting different orders, resulting in different REVENUE, COSTS, and PROFIT for eachinstance of a firm.

For either approach, a firm could only select orders up to its production bottleneck. A firmrecorded a profit equal to its overall REVENUE minus COSTS for all the orders it accepted, for aspecific approach. For the basis of comparison, an approach (either TOC or ABC) demonstrateditself as the more optimal approach if and only if it made a larger PROFIT than the alternativeapproach.


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The simulation allowed the user to specify four additional variables, to include:

Number of orders generated per run N (integer)Maximum number of runs per order NR (integer)Approximate percentage of orders accepted per instance K (percentage)Number of instances for the simulation I (integer)

Figure 2: Four additional variables include with the simulation

Some orders included multiple runs to complete, representing instances where a first run couldnot complete a larger order. A linear relationship, between the size of an order and themaximum number of runs, determined the number of runs required for each order.

Additionally, the approximate percentage of orders accepted per instance of the simulationserved to calculate the BOTTLENECK CAPACITY (BC) for a firm. A firm had the same

bottleneck capacity regardless of whether it employed the TOC or ABC approach, defined by:

BC =K * N * (RMmean * OSmean +NR * SMmean))

A simulated firm selected orders based on the TOC approach via a ranking function, which

ranked orders from high to low based on their attractiveness to a firm. For a specific order, theTOC ranking function was:

TOCRANKING =(OS * (SP - TVC) / ((SM * NRorder) +(RM * OS)))

A simulated firm also selected orders based on the ABC approach via a ranking function,similarly ranking attractive orders from high to low. The ABC ranking function was:

ABCRANKING =((((SP - (TVC +LAB)) * OS) - (BOE * NRorder) - (POE)) / OS)

A firm accepted orders based on their attractiveness, with the most attractive orders acceptedfirst. A firm could not exceed its BOTTLENECK CAPACITY (BC). If a firm could not accept an

order because BOTTLENECK CAPACITY (BC) already was full, it moved on to the nextattractive order considering all orders. The firm then processed the accepted orders, allowingcalculation of REVENUE, COSTS, and PROFIT for a specific approach.

Experiment 1: TOC vs. ABC

Having verified the formulae and code employed, this study ran the simulation for 250independent instances and outputted results for both the TOC and ABC approaches. The resultsare clear: for all of the 250 instances of the simulation consisting of 500 orders each time, the

ABC approach generated more PROFIT than the TOC for all 250 instances. An additional 10repeat simulations confirmed this finding:ABC always generated more PROFIT than theTOC approach did in a dynamic world.

That said, in every instance, TOC produced a larger amount of REVENUE than the ABCapproach did. This finding is consistent with established literature that the TOC approachidentifies and addresses system constraints, maximizing REVENUE received. What this studydemonstrates is that TOC does indeed maximize REVENUE, but at the expense of alsoincreasing COSTS, such that PROFIT is no longer larger than the orders the ABC approach

would have accepted if faced with the same choices. The ABC approach results in less


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REVENUE, but also smaller COSTS and consequentially larger PROFIT than the TOCapproach, every time.

The fundamental assumptions about the way that operating expenses (OE) behave over timerepresent the primary difference between the TOC and ABC approaches. The TOC approachtreats OE as fixed while the ABC approach treats OE as variable. This study reveals a key findingfor the two approaches: OE vary by a greater amount than revenue. An ABC firm, with lowerOE, will be more profitable compared to a TOC firm earning higher revenue. That said, if the

world were static and OE were held constant (at the level required to support a TOC firm), thenthe TOC approach would be the dominant approach indeed, earning a higher profit than an

ABC firm as a result of its higher revenue.

Experiment 2: TOC, ABC, ABC+TOC, vs. TOC+ABC

This study then performed a second experiment, based on the first set of results. If the TOCapproach was optimal in selecting orders that maximized REVENUE, and the ABC approach

was optimal in selecting orders that resulting in smaller COSTS, would a hybrid of the twoapproaches be better than just the ABC approach alone?

The revised simulation now included the variable D, which represented the coefficient for thefirst approximate cut-off point of orders to accept. An ABC+TOC firm accepted a cut of ordersequal to this coefficient (D) times its bottleneck (BC) via the ABC ranking mechanism first, andthen accepted a second cut of orders from this set equal to its bottleneck (BC) alone via the TOCranking mechanism second.

Inversely, a TOC+ABC firm accepted a cut of orders equal to this coefficient (D) times itsbottleneck (BC) via the TOC ranking mechanism first, and then accepted a second cut of ordersfrom this set equal to its bottleneck (BC) alone via the ABC ranking mechanism second. Normal,non-hybrid ABC and TOC firms accepted orders equal to the bottleneck (BC) only, per their

respective approaches.

Again, this study ran the simulation for 250 independent instances consisting of 500 possibleorders each time. This time, the results demonstrated that for 115 instances, the hybridTOC+ABC approach was more optimal than any other approach. For the remaining 135instances, the ABC and hybrid TOC+ABC approaches tied, meaning they selected the sameorders exactly. An additional 10 repeat simulations resulted in a mean of 114.4 instances and astandard deviation of 3.307 where the TOC+ABC approach was more optimal than any otherapproach.

# 01 02 03 04 05 06 07 08 09 10 TOC+ABC

wins

115 114 110 118 119 116 112 114 117 109 114.4 3.306559

TOC+ABC andABC tie

135 136 140 132 131 134 138 136 133 141 135.6 3.306559

Figure 3: of different approaches for the same level of environmental turbulence


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Implications

For organizations seeking to thrive in increasing environmental turbulence, where volume andcosts frequently fluctuate globally, the first experiment demonstrates that ABC issuperior to TOC. The second experiment demonstrates that a hybrid TOC+ABC

approach is the most optimal strategy to adopt for long-term value chains.For volatile markets, both experiments challenge the established literature espousing the TOCapproach alone, yet this study also attempts to reconcile the debate between TOC vs. ABC byrecognizing the value of incorporating elements of TOC into a hybrid approach.

In a dynamic world, the second experiment demonstrates that the optimal features of the TOCapproach aid by selecting a first cut of orders that will generate the highest REVENUES.Following this first cut with a second cut employing the ABC approach, consequentially selectsfor orders that have the lowest COSTS and thus the highest PROFIT. This hybrid TOC+ABCsolution wins or ties as the most optimal approach every time when compared to the ABC, TOC,or ABC + TOC approach.

For firms to recognize the value of either the ABC or the hybrid TOC+ABC approaches in themidst of environmental turbulence, they will need to identify their activity-based costs, and then

be able to attribute such costs to product creation. Per the ABC approach, such firms also willneed to be more agile at either adopting or ceasing activities dependent upon their PROFIT vs.associated COSTS. In a sense, the global economy is already progressing down this path,particularly with firms becoming less vertically structured and instead more market-based.

Deeper ramifications of this study, as aforementioned, involve espousal of computationalsimulations as beneficial methodologies for both long-term value chain analysis and themanagerial accounting literature. Given that these fields are predominantly quantitative innature, it would seem both natural and rational for these fields to look toward computational

simulations as a methodology allowing objective evaluation of both existing and new theories.

References

Baxendale, S. and Gupta, M. Aligning TOC and ABC for Silkscreen Printing. Management Accounting, 79,(1995).

Clippinger, J . (ed). The Biology of Business: Decoding the Natural Laws of Enterprise. J ossey-Bass, SanFrancisco, CA (1999).

Cooper, R. and Kaplan, R. Profit Priorities from ABC.Harvard Business Review, 69, 3, (1991).

Eisenhardt, K. and Galunic, D. Coevolving: At Last, A Way to Make Synergies Work. Harvard BusinessReview, 78, 1, (2000).

Goldratt, E. What Is This Thing Called Theory of Constraints and How Should It Be Implemented? North RiverPress, Great Barrington, MA (1990).

Holmen, J . ABC vs. TOC: Its a Matter of Time. Management Accounting, 76, (1995).

Kirche, E. and Srivastava, R. An ABC-Based Cost Model with Inventory and Order Level Costs: A Comparisonwith TOC. International Journal of Production Research, 43, 8, (2005).

Smith, M. and Pretorius, P. Application of the TOC Thinking Processes to Challenging Assumptions of Profitand Cost Centre Performance Measurement. International Journal of Production Research, 41, 4, (2003).

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