Understanding Entropy Generation during the
Execution of Business Process Instantiations:
An Illustration from Cost Accounting
Peter De Bruyn, Philip Huysmans, Herwig Mannaert and Jan Verelst
University of Antwerp
Faculty of Applied Economics
Department of Management Information Systems
Normalized Systems Institute (NSI)
3rd Enterprise Engineering Working Conference (EEWC 2013)
Luxembourg, 14 May 2013
Outline
• Introduction
• Theoretical framework: Entropy
• Entropy generation within a business process context
-
the business process run-time instantiation space
defining microstates and macrostates
possible (cost) information aggregation dimensions
understanding business process entropy generation by
(cost) information aggregation
- towards controlling business process entropy
• Impact on (cost) accounting information systems in
practice
• Limitations and conclusions
1
Outline
• Introduction
• Theoretical framework: Entropy
• Entropy generation within a business process context
-
the business process run-time instantiation space
defining microstates and macrostates
possible (cost) information aggregation dimensions
understanding business process entropy generation by
(cost) information aggregation
- towards controlling business process entropy
• Impact on (cost) accounting information systems in
practice
• Limitations and conclusions
2
Introduction
• Appropriate business decisions require accurate
information of the organization (e.g., to diagnose and trace
problematic situations)
-
much of this data is delivered by the execution of business processes
e.g., cost information as collected by cost accounting
however, often gathered at different aggregation levels and therefore
complex to understand or insufficient to decide
• Sound theoretical basis regarding the structuring of
information from executed business processes seems lacking
-
e.g., theoretical basis for cost accounting frameworks?
• This paper: leveraging the concept of entropy to study
complexity at the business process level
-
what does this imply for – for instance – cost accounting?
requires run-time perspective
3
Outline
• Introduction
• Theoretical framework: Entropy
• Entropy generation within a business process context
-
the business process run-time instantiation space
defining microstates and macrostates
possible (cost) information aggregation dimensions
understanding business process entropy generation by
(cost) information aggregation
- towards controlling business process entropy
• Impact on (cost) accounting information systems in
practice
• Limitations and conclusions
4
Theoretical framework: Entropy
• Statistical thermodynamics
- entropy proportional to number of microstates
consistent with one macrostate (i.e.,
multiplicity )
• microstate = the whole of microscopic properties of the
particles of a system
• macrostate = the whole of externally observable and
measurable properties of a system
5
Head
Tail
1
2
3
4
Macrostate
8 tail, 0 head
5
6
7
8
Microstate
1
Multiplicity  1
6
Head
Tail
1
2
3
4
Macrostate
7 tail, 1 head
5
6
7
8
Microstate
8
Multiplicity  8
7
Head
Tail
1
2
3
4
Macrostate
4 tail, 4 head
5
6
7
8
Microstate
70
Multiplicity  70
8
Outline
• Introduction
• Theoretical framework: Entropy
• Entropy generation within a business process context
-
the business process run-time instantiation space
defining microstates and macrostates
possible (cost) information aggregation dimensions
understanding business process entropy generation by
(cost) information aggregation
- towards controlling business process entropy
• Impact on (cost) accounting information systems in
practice
• Limitations and conclusions
9
Entropy generation and aggregation dimensions
in a business process context:
a general business process BP1
𝐵𝑃1 = 𝑡1 , 𝑡2 , 𝑡3 , 𝑡4 , 𝑡5
11
The run-time
instantiation space
task instantiation: 𝑡𝑘,𝑚
business process instantation: 𝐵𝑃𝑖,𝑗
𝐵𝑃1,1 = 𝑡1,1 , 𝑡2,1 , 𝑡3,1 , 𝑡5,1
𝐵𝑃2,1 = 𝑡1,2 , 𝑡2,2 , 𝑡4,1 , 𝑡5,2
𝐵𝑃3,1 = 𝑡1,3 , 𝑡2,3 , 𝑡3,2 , 𝑡5,3
12
Interpreting macrostates and
microstates
• Here: particles are tasks or “information units”
• Hence:
- microstate = the union of the values of the properties
(e.g., costs) for each individual information unit (i.e., task
instantiation): 𝐶 𝑡𝑘,𝑚 𝑘,𝑚
- macrostate = the aggregated information available for
the observer, generally entailing unrecoverable loss of
information
• How easily can we solve typical management
questions?
- situations in which low entropy occurs, seem desirable
14
Possible (cost) information aggregation
dimensions during business process instantiation
execution
• Approach in this paper = cost-accounting perspective
• In an instantiated business process, each of the steps can be
associated with some costs
• Each process owner might be interested in the components
which contribute to the costs of products or services
-
what does a product or service cost?
what does a particular part of a production process cost?
where does an extremely high cost originates from?
(cf. BPR, TQM, etcetera)
• These questions require detailed and structured information of
each of the process steps
-
however, for plenty of reasons, several aggregations of these
information units may occur in practice
16
Aggregation
dimension 1
•
Information is gathered at its
most fine grained level: 𝑡𝑘,𝑚
•
No aggregation or interaction
with any other information
units occurs
17
Aggregation
dimension 2
•
Information regarding two or
more “information units” k is
aggregated for each business
process instance j
•
e.g., initial interest in only a
few major phases of the
process
18
Aggregation
dimension 3
•
Information is aggregated
over all tasks k for each
business process instance j
•
e.g., cost-based price setting
19
Aggregation
dimension 4
•
Information units among all
instances m of a particular
task k within a business
process BPi are aggregated
•
e.g., one operator solely put
in charge of checking the
completeness of an insurance
imbursement request
20
Aggregation
dimension 5
•
Information units are
aggregated according to the
time elapsed
•
e.g., a “counter” registering
electricity consumption which
can be inspected at every
point in time t
21
Aggregation
dimension 6
•
Information units regarding
all (task) instances of the
considered business process
type become aggregated
•
e.g., only incoming and
outgoing cash flows are
deemed of interest, KPI’s
22
Overview (1)
Microstate
Macrostate
24
Overview (2)
Suppose task instance t1,1 is extremely high, how easily
can we trace this within the several aggregation
dimensions?
25
Towards controlling business process entropy by
increasing the structure of the systems
• Entropy reduction in business processes can be reduced by
strict partitioning (e.g., regarding the cost structure)
1.
2.
3.
4.
•
introducing states: “measuring points” for intermediate
registration
identifying the right concerns, here: information units
data instance traceability: which were the characteristics of
the data the business process was operating on?
task instance traceability: to which business process
(instance) does a particular task instance belong?
General guidelines, consistent with the entropy rationale
within Normalized Systems theory
-
however, more business related and even domain specific
guidelines for identifying the right concern may be necessary
28
Outline
• Introduction
• Theoretical framework: Entropy
• Entropy generation within a business process context
-
the business process run-time instantiation space
defining microstates and macrostates
possible (cost) information aggregation dimensions
understanding business process entropy generation by
(cost) information aggregation
- towards controlling business process entropy
• Impact on (cost) accounting information systems in
practice
• Limitations and conclusions
29
Impact on (cost) accounting
systems in practice (1)
• Towards explaining
criticisms on traditional
cost accounting
approaches
- suppose two products A and
B (where B has a more
complex assembly step:
task 4)
- suppose aggregation
dimension 4
- traditional cost-accounting:
attribution of costs based on
volume related measures
→ costs of product A will be
underestimated and v.v.
30
Impact on (cost) accounting
systems in practice (2)
• Activity-Based costing
as a solution?
- finer-grained way of
allocating (indirect) costs
to products
- however, tasks are
considered as “composed
of the aggregation of
units of tasks”.
• Current software
applications?
?
- case: budgeting tool at
government agency
31
Outline
• Introduction
• Theoretical framework: Entropy
• Entropy generation within a business process context
-
the business process run-time instantiation space
defining microstates and macrostates
possible (cost) information aggregation dimensions
understanding business process entropy generation by
(cost) information aggregation
- towards controlling business process entropy
• Impact on (cost) accounting information systems in
practice
• Limitations and conclusions
32
Limitations and conclusion
• Contributions:
-
entropy unambiguously defined in a business process state space
6 aggregation dimensions presented
some initial principles to reduce entropy
some initial implications for practice
• Limitations:
-
cost aspects are only one perspective to be considered
-
uniform cost assumption
simplified example (e.g., all costs around 5€)
only one business process type, extra aggregation dimensions possible
case studies needed
trade-off: benefits of low entropy vs. costs for setting up and gathering
this fine-grained information.
• other perspectives might suggest other concerns (e.g., financial reporting,
throughput optimization, etcetera)
• domain-dependent concerns?
33
Questions / remarks?
e-mail
peter.debruyn@ua.ac.be
phone
+32 3 265 40 21
office
Stadscampus – S.B. 304
Prinsstraat 13
2000, Antwerpen
34