Rethinking BPM in Cognitive World

This report discusses the impact of cognitive computing on business process management and explores the capabilities and advancements of cognitive BPM. It also presents a framework for implementing cognitive BPM.

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RETHINKING BPM IN COGNITIVE WORLD
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Table of Contents
Introduction....................................................................................................................2
Discussion......................................................................................................................2
Cognitive computing and business process management..........................................2
Capabilities of cognitive computing in business process management.....................3
Advances of BPM with the Human centric processes...............................................4
Evolvement of BPM platforms..................................................................................5
Framework for cognitive BPM..................................................................................6
Conclusion......................................................................................................................7
References......................................................................................................................8

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Introduction
This report aims to discuss rethinking BPM in cognitive world using relevant articles
and other materials. A brief discussion of how cognitive computing is helping in transforming
the business process management is presented in the report. Finally, this report settles with an
suitable conclusion for the report.
The assurance that is provided by the cognitive computing in the present world is to
primarily transform the corporate data and the problem answering. Cognitive computing
intends to convey the continuous learning and autonomous reasoning by considering the
natural interaction of the machines and humans and the contextual insights (Hull and Nezhad
2016).
Discussion
Cognitive computing and business process management
In the present world, significant importance has been given to the Cognitive
Computing in the businesses and in several industries. Cognitive Computing generates the
insights that are context aware from the unstructured and structured data by leveraging the
continuous learning and the autonomous reasoning on the basis of the expanding of the base
of knowledge and by building on latest developments on data, computer science and
cognitive. The area of BPM comprises of the topics of context awareness, automation of the
unstructured tasks and flexibility and it is commonly believed that the features of cognitive
computing have immense impact on the transformation of the BPM and the practices in
future (Nezhad and Akkiraju 2014). For the effective lifecyle of BPM that is based on the
paradigm and it is essential to understand the potential of the cognitive computing in context
of BPM. The new lifecycle of BPM integrated with cognitive computing would support the
processes that ranges from the extensively standardised processes of routine for lower
predictable processes of ad-hoc. The aspect of cognitive BPM includes the aspects of BPM
where the cognitive computing provides the innovative opportunities either with the
alteration of the methods how the processing of data is done, the presentation of the data and
the designing of the processes is done (Roeglinger et al. 2017).
As an instance, it can be considered that the cognitive management of data would fuel
the automation of process with the integration of machine learning, providing the businesses
with the potential of massive returns in the area of the SMAC (social, mobile, analytics,

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cloud), the processes that are smarter are more than any tool for the planning and execution;
they are the intelligence engine that provides decision making with cognitive intelligence that
is based on the operational insights in the context (Kannengiesser et al. 2014). With the
combination of the business process with machine learning, the cognitive solutions are
obtained that has the ability of enhancing the experiences of customers. The solutions has the
ability of comparing the data that is generated by the activities of business with the data from
several other sources, providing the dynamic and the decision making that is sensitive to the
context. In any process of predictive maintenance, the algorithms of machine learning are
applicable for the data of sensor for identifying the situations that indicates whether the
breakdown of any machine is imminent (Hull 2017). The cognitive process of business would
ensure the insights from several devices undergoes efficient processing and that the field
service team is completely utilised in most suitable way. These insights would help in the
improvement of the optimisation of the processes by the automatic releasing of any remote
patch for any device or by offering guidance to the customers with steps of self-service.
Capabilities of cognitive computing in business process management
The bifurcation of the capabilities of cognitive computing can be done by:
Enhancement of the optimisation and decision making: The introduction of cognitive
computing provides the processes of businesses to undertake decisions on the behalf of
humans on the basis of the rich experience and the huge amount of unstructured data
(Lapouchnian et al. 2017). While dealing with huge unstructured information, the cognitive
systems has the ability of injecting the insights based on intelligence in the processes of
decision making. With the exploitation of the capabilities of adaptive and predictive decision,
value is added to the preventive decision making. The cognitive interactions improves the
communication channels by supporting the new devices and channels. For example, the
individuals can be guided by the system through a conversation or process and then
effectively leverage the channel preferences of the person. Afterwards, the interaction is
done, the results can be communicated for an orderly and clear post-analysis (Motahari and
Shwartz 2017).
Advancement in the intelligent automation: The cognitive process has the capability
of processing the interactions among the humans over several preferred channels of
communication. Exploiting the ML and AL, the systems can effectively capture the insights
and then codify the specifications of process and then reveal the new opportunities of

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automation that would be leveraged by using the robotics process automation for augmenting
and then replicating the human intelligence. When it has been joined with the business
processes with the application programming interface, the existing assets could be available
to the partners of business, and supports the new business models that shifts the step cycle
from the define-execute-analyse-improve to the learn-plan-act.
Advances of BPM with the Human centric processes
The conventional management of business processes, the information systems and the
workflow management systems that are process aware supports the automation of the well-
specified, repetitive, and the business processes that are hugely automatable extensively.
There has been significant work for helping the tractability of the business processes along
with supporting the work that are human-centric in the context of applications of case
management, the processes that are artefact centric and the analytics of process and the
predictive monitoring (Visi, Schramm and Miranda 2014). The focus of these efforts are on
the utilisation of the information for execution of process for adapting or altering the
execution course. One more classification of the prevailing work emphases on the analysis of
the past execution of the data of process for recommending the future course of the actions
for innovative cases of execution. Moreover, the information from any larger context, which
is the container for process execution and the analytics uses this data has been less discovered
for helping the automation of the processes that are human centric and not solely in adapting
the execution but for adapting the model of process with creation of various variations of the
model of process for various contexts, where the performance is well (Fleischmann, Schmidt
and Stary 2015).
The challenges that are connected with the support of the processes that are human-
centric in any enterprise is analysis of the data for enabling innovative processes. The huge
shift in this aspect is the refinement on the methods how the defining, refining, enacting and
adapting of the processes is done as due to the data analytics along with the data coming from
the process perspective and all the external channels like the devices, enterprise repositories,
social media and the internet (Nezhad, Hull and Bentallah 2018). The data from these sectors
are based on describing the cognitive system of business process management where the
results of analytics enables the sensing, analysing, and learning for the process management
systems and due to this automatically and constantly adapting to the system for reflecting the
situation and the context where is it running and then achieve the awareness of situation,
adaptiveness and self-learning.

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Evolvement of BPM platforms
The major two limitations of the present suite of BPM technologies to support the
human centric are: limitation in the context understanding that are data driven for driving the
decisions of process and exploiting the analytics result for adapting the process execution and
the process model for accommodating the continuous data and the updates of process
decision (Lapouchnian, Babar and Yu 2017).
Definition and adaptation of the analytics driving process model: It has been observed
that the frequently, the modelling of process cannot be done in details. Based on the process
model of the library of the available actions, reference, significant actions and the decision on
the dynamic composition of actions of the process is motivated by the outcomes of analytics.
Support for adapting the process enactment: when the availability of new information
occurs, the new results of analytics also becomes accessible in various courses of the
prescriptive, predictive, discovery, and the descriptive analytics (Slominski and Muthusamy
2017). Specifically, the analytics with predictive method needs to be stretched regarding the
processes of identifying what the steps of process and the resources might be required as
result of the specific predictions that are focussed on business. For example, on the basis of
such predictions, for suggesting the creation of the alternative paths in any process to various
customers set for maximising the outcome of business.
The requirement of flexibility might comprise of addressing of these requirements
even though in any new approach that is driven by analytics:
1. Ability of starting from any reference process and the activities, and then compose
process in any approach that is data driven
2. Ability of adapting with the process of reference and then create the extra
annotations for showing the business performance of the actions in the automated
approach
3. Distinctive other activities that are human centric, ability of adapting to the step of
process and then re-execute any step in the manner of aware with context (Kharb
2018).
4. Ability of making significant reasoning and then provide the answers to the
questions regarding the probability scenarios regarding the process on the basis of
the analytics

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5. Ability of processing the unstructured information that is available for the
personal communication, news feeds and interaction and all across several devices
and channels for supporting the process automation
6. Integration of artefact and the process management, as the management of the
processes and the artefacts are done separately using various systems. The BPM
that are driven by analytics requires a holistic view of the method of relevancy of
the data to the execution of the process (Teniente and Weidlich 2018).
7. Revisiting the major definitions and notions of process.
The present workflow systems considers the instance of task that is closed once and
the marking of the task has been done. Moreover, commonly, the task might not be executed
and it can be the subject to various revisions prior the completion of the process. It is
commonly believed that the field of BPM would undergo major transformations in future and
it would be called cognitive BPM. The advancement of the platforms and big data tools and
the growth of the platforms of cognitive computing like the IBM Watson made the possibility
of analysing the huge quantities of data in the immediate moment for providing the
perceptions at a magnitude and scale that was not discovered before (Marrella and Mecella
2017). As it was noted from the experiences from the sales domain of IT services, these
domains would have to deal with huge amount of data that is unstructured for understanding
the updates of the process over various channels for dynamically adapting with the processes
of business.
Framework for the cognitive BPM
This part discusses the theoretical structure for gaining knowledge about the crucial
methods that the cognitive computing would make over the BPM in future. The four pillars
for the development of cognitive BPM are:
1. Cognitive decision support: Several processes in the present times ranging from
unstructured to structured depends on the efforts of humans to execute the
decisions that are dependent on the deep understanding and with the orientation of
huge unstructured data volumes. The quantity and the breadth of these decisions
would be increased by cognitive computing.
2. Cognitive interaction: The advances in the multi-modal interaction among the
humans and machines and in the cognitive computing provides a huge opportunity

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for improving dramatically these interactions by supporting the new interaction
devices and channels.
3. Cognitive process learning: Across complete spectrum from the unstructured to
structured processes, the capturing and codifying of the specifications of the
process would be ensured by the cognitive computing for enabling increased
automation while holding all the mandatory flexibility (Bhilegaonkar 2016).
4. Cognitive process enablement: The vision that is mostly connected with the
process enablement using cognitive computing is to allow various kind of process
support of business and it helps is employing the user in control.
Conclusion
Therefore, it can be concluded that the introduction of the cognitive computing in the
business process management would help in enhancing the operations of business. The
assurance that is provided by the cognitive computing in the present world is to primarily
change the corporate information and the problem solving. With the combination of the
business process with machine learning, the cognitive solutions are obtained that has the
ability of enhancing the experiences of customers. The cognitive process of business would
ensure the insights from several devices undergoes efficient processing and that the field
service team is completely utilised in most suitable way. The conventional management of
business processes, the information systems and the workflow management systems that are
process aware excels at supporting the automation of the repetitive, well-specified, and the
business processes that are hugely automatable.

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References
Roeglinger, M., Seyfried, J., Stelzl, S. and zur Muehlen, M., 2017, September. Cognitive
Computing: What’s in for Business Process Management? An Exploration of Use Case Ideas.
In International Conference on Business Process Management (pp. 419-428). Springer,
Cham.
Kannengiesser, U., Radmayr, M., Heininger, R. and Meyer, N., 2014, August. Generating
subject-oriented process models from ad-hoc interactions of cognitive agents. In Proceedings
of the 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and
Intelligent Agent Technologies (IAT)-Volume 03 (pp. 440-446). IEEE Computer Society.
Lapouchnian, A., Babar, Z., Yu, E., Chan, A. and Carbajales, S., 2017, November. Designing
Process Architectures for User Engagement with Enterprise Cognitive Systems. In IFIP
Working Conference on The Practice of Enterprise Modeling(pp. 141-155). Springer, Cham.
Fleischmann, A., Schmidt, W. and Stary, C. eds., 2015. S-BPM in the wild: Practical value
creation. Springer.
Teniente, E. and Weidlich, M. eds., 2018. Business Process Management Workshops: BPM
2017 International Workshops, Barcelona, Spain, September 10-11, 2017, Revised
Papers(Vol. 308). Springer.
Bhilegaonkar, A., 2016. Machine learning and cognitive computing: a proposed framework
to navigate the opportunities (Doctoral dissertation, Massachusetts Institute of Technology).
Visi, F., Schramm, R. and Miranda, E., 2014, June. Gesture in performance with traditional
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Hull, R. and Nezhad, H.R.M., 2016, September. Rethinking BPM in a cognitive world:
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Motahari Nezhad, H.R. and Shwartz, L., 2017, January. Towards open smart services
platform. In Proceedings of the 50th Hawaii International Conference on System Sciences.
Hull, R., 2017, June. Blockchain: Distributed Event-based Processing in a Data-Centric
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Marrella, A. and Mecella, M., 2017, September. Cognitive Business Process Management for
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Management (pp. 429-439). Springer, Cham.
Lapouchnian, A., Babar, Z. and Yu, E., 2017, November. Designing user engagement for
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Nezhad, H.R.M., Hull, R. and Bentallah, B., 2018, January. Introduction to the 1st
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