Probabilistic time‐driven activity‐based costing

• Author: Marianela De Batista,Marisa A. Sánchez
• Published date: 01 October 2020
• DOI: http://doi.org/10.1002/jcaf.22468
• Date: 01 October 2020

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Probabilistic time-driven activity-based costing

Marisa A. Sánchez | Marianela De Batista

Departmento de Ciencias de la

Administración, Universidad Nacional del

Sur, Bahía Blanca, Argentina

Correspondence

Marisa A. Sánchez, Departmento de

Ciencias de la Administración,

Universidad Nacional del Sur, Campus

Altos de Palihue, Bahía Blanca,

Argentina.

Email: mas@uns.edu.ar

Funding information

Secretaria de Ciencia y Tecnología de la

Universidad Nacional del Sur, Grant/

Award Number: 24/C055

Abstract

This article proposes a Probabilistic Time-driven Activity-based costing model.

Time consumption is represented using probabilistic variables and Monte

Carlo simulation technique is used to forecast total cost. The simulation exper-

iments provide data to estimate a confidence interval for the total cost of a cost

object. Hence, simulation can easily address variations in time forecasts and

provide a more realistic characterization of total cost. The definition of the

number of resources (e.g., employees) as decision variables allows analyzing

alternatives and answer “what-if questions”. The proposal would be very use-

ful for an operation manager in analyzing uncertain scenarios and providing

information indicating under what conditions different cost estimates are

feasible.

KEYWORDS

accounting, cost management, Monte Carlo simulation, probabilistic model, time-driven activity-

based costing

1|INTRODUCTION

The sources of company disruptions include economy-

wide shocks, natural calamities, human-caused disasters,

and technological breakthroughs. While we can expect

most of these events to have a low probability of occur-

rence, the digital transformation has impact on compa-

nies through all sectors of the industry. The impact

affects both operations management and the definition of

new business models. For example, products enabled by

the Internet of Things can monitor users’utilization and

satisfaction and represent an opportunity for manufac-

turers to create new business models that change the

focus from independent product offers to services

(Porter & Heppelmann, 2014). The data, connectivity,

and analytics available through smart, connected prod-

ucts are expanding the traditional role of the service func-

tion and creating new offerings (Porter & Heppelmann,

2014, 2015). At the same time, solution operation and

maintenance extends after release. During design and

manufacturing of traditional devices, the need of

resources tend to decline as the project progress. How-

ever, IoT solutions may require additional resources

after release. Cost estimation for post-release activities is

not easy because activities will be accomplished in the

long-term. Costing techniques assuming data can be

accurately defined are challenged. As observed by Fisher

and Krumwiede (2015) having timely, relevant cost

information is essential for profitability analysis and

strategic planning. There are many costing systems and

methods. In this work, we consider Time-driven

Activity-based costing (TDABC; Kaplan & Anderson,

2007) and we propose a probabilistic TDABC model

where time consumption is represented using probabi-

listic variables. The proposal would be very useful for

operational managers in analyzing uncertain scenarios

and providing information indicating under what condi-

tions different cost estimates are feasible. First, we pro-

vide a brief introduction of TDABC, its benefits, and

shortcomings. Second, we present the probabilistic

TDABC and describe an illustrative example. Finally,

conclusions unf old.

Received: 13 April 2020 Revised: 20 July 2020 Accepted: 24 July 2020

DOI: 10.1002/jcaf.22468

J Corp Acct Fin. 2020;31:73–81. wileyonlinelibrary.com/journal/jcaf © 2020 Wiley Periodicals LLC 73