BCO5501:Business Process Engineering - Business Process Management - Assessment Answer

Business Process Engineering

Case Scenario

From the Proceedings of the 11th through 13th International Conference on Business Process Management (BPM) (weblinks are available on VU Collaborate), select and review one paper from one of these Proceedings. The paper you select must be directly relevant to business process engineering or business process management. You must identify at least 3 other relevant articles and use them to support your review.

Assignment Task

i) Identifies the paper under review

ii) States the purpose of the student essay

iii) Is refined and clearly stated

iv) Provides an overview of the papers being discussed Body of paper has appropriate content

i) Contains appropriate material

ii) Presents a well-structured discussion

iii) Uses material from other authors

iv) Summarises and discusses the content of the article(s)

v) Identifies and discusses the article’s conclusions

Introduction

A Review of the Article ‘Towards the Next Generation Intelligent BPM- in the Era of Big Data’ By Xiang Gao

Big data refers to the data, which exceeds the processing capability of conventional database technologies that it requires alternative ways of processing it. Big data has become feasible as a cost effective approach of taming the cost, the volume, and variability of huge data. Big data is revolutionising business processes and its continued use is also resulting in the emergence of a new paradigm of data-intensive computing in business, and the way business research is carried out. The availability of huge amounts of data together with many advanced tools of data analysis and data visualisation offers a completely new way of comprehending business processes.

In the article, ‘towards the next generation intelligent BPM- in the Era of Big dataXiang Gao (2013) explores the defining characteristics of Business Process Management (BPM) in an industrial perspective. This paper explores the author’s insights into the application of BPM approaches including the evolution of BPM, and the challenges of implementing BPM in an organisation. The authors’ proposal of a big data perspective based on intelligent BPM and highlights of the opportunities of application of BPM to an organisation are reviewed. A comparison of Xiang Gao’s perspective about big data and BPM will also be compared with the views of other authors on the subject.

Xiang GAO’s article starts by highlighting the development of BPM. According to Gao (2013), BPM is an all-inclusive management technique, which enhances efficiency of an organisation and effectiveness, which also aims at increasing flexibility, innovation, and it’s Amalgamation with technology. BPM approaches utilises the available data to make inferences and decisions that affect business processes. Nowadays, businesses are faced with the huge data they need to interpret and make decisions. Big data analytics is therefore, becoming more important in making business decisions and in making the available data actionable. BPM has provided an impetus to businesses through the integration of analytical technologies into the business processes. BPM System is a word, which indicates the development of traditional BPM Systems. BPM entails the integration of mobile devices, social media, and big data analytics into organisations business support systems (Motahari-Nezhad, Recker, & Weidlich, 2015).

Intelligent BPM has enabled businesses to make business processes more effective through the provision of real-time situational awareness of their business processes and the ability to tailor the business processes to the existing situations appropriately (Motahari-Nezhad, Recker, & Weidlich, 2015). Thus, BPM is the next stage of evolution of BPM because it meets the need for business processes agility, leverages the superior accessibility of information within an organisation and outside the business into the decision-making process of an organisation. BPM enhances the collaborations and interactions among businesses. Intelligent BPM inherits all the features of traditional business process management, but it is more complemented with advanced technologies. BPM encompasses traditional business process management, external data advanced analytics, and cloud platforms (Gao, 2013; Motahari-Nezhad, Recker, & Weidlich, 2015).

The major difference between intelligent BPM and Business Process Management is that Intelligent BPM has more capability for advanced data analytics; it is automatic, adaptive, and agile. Intelligence business process management enhances the actionable process of decision-making through embedding real-time intelligence into the business processes. The decisions are made within the context of the processes, and business date. Thus, intelligent business processes also ensures immediacy in the making of decisions to ensure the decision-making at the appropriate times. According to Motahari-Nezhad, Recker, & Weidlich (2015), the decision-making process of BPM is also actionable and ensures that fast and efficient corrective action is taken due to real-time decision-making approaches. The decision management services are also incorporated into the structured and non-structured processes to avail the most appropriate action suitable for particular contexts. Due to this incorporation, intelligence business process management avails agility and enhances business performance (Gao, 2013).

Big data has brought unimaginable impetus and vitality to business process management. Big data has also provided a novel stage for study and growth grounded on big data frameworks. In embracing the idea of BPM, the following perspectives must be taken into consideration.

Sparsely versus redundancy: Large amounts of big data usually exhibit redundancy rather than sparsity. The use of traditional sets of data and artificial intelligence algorithms usually exhibit sparsity on large sets of data due to high dimensionality.

Population versus Sample: It is important to pay attention to the unprecedented challenges when population-based analysis is involved. In big data, the emphasis is based on population rather than the sample since the collection of large amounts of data is now possible. Analysis based on samples is done to infer the total behaviour of a population from a sample.

Network versus individual in the scrutiny of a huge diversity of multifaceted methods, the observation reflects mainly decentralised links and individual data sets that can be consolidated into a network. Big data is usually associated with complex data networks so that it offers a fresh perspective that is rapidly developing into new discipline. As such, big data also has some subtle influences on business process research and applications.

Causality versus correlation: Correlation plays a very vital function compared to causality in the research of big data. Correlation is also more important in big data in the contexts where the clustering grounded methods must be taken into account However, correlation and causality are important in the BPM field. The technique of Mining is grounded on the inference made from the logging events. On the contrary, the analysis of similarity and clustering grounded methods usually entail the consideration of correlation.

The process of OA Process consolidation procedure starts with mining processes that utilise particular business logic. It utilises algorithms like improved Alpha ++, process modelling language like BPMN 2.0, and implementation using tailor-made tools. The fragmentation and reuse utilise algorithms such as RPST BPMN 2 process modelling language and implementations using a tailor-made tool. Similarity and clustering algorithms used in big data are clone detections SSDT- Matrix based behaviour similarity and implementation using a tailor-made tool. The eternal tools used are Figtree and BPCD.

Merging in big data uses algorithms based on SPL, the processing language is standard BPMN, and implementation is carried out using a tailor-made tool. Differentiation in big data based on a change in operations utilises standard BPMN 2.0 Process modelling language and a tailor-made tool for implementations. In Big data ontology, based rule modelling uses BPMN 2.0 process modelling language and implementations are done using a tailor-made tool and the use of external tools such as protégé 4.1. The proposed analytic models for social media for use with the SNA platform of China mobile based on frameworks such as No SQL databases, Hadoop and graph are propagation tracing, group and user portrait and TDT (Gao, 2013; Motahari-Nezhad, Recker, & Weidlich, 2015).

Gao (2013) also proposes a framework for China mobile cloud benchmarking framework that is based on open source benchmark and Aloysius software. The author concludes that the best way of dealing with the challenge of adopting BPM is through multidisciplinary collaboration through distributed cloud storage, semantic web, machine learning, data visualisation and social network analysis. In developing and commercialisation of the platforms, it is apparent to create cloud enabled intelligent business process systems and avail demand analytical service. It is also important to integrate many advanced technologies and tools into the process engines and accelerate the evolution to IPPM. An open research and development ecosystem is also important for open source tools and prototypes for technology incubation and innovation. Big data therefore offers the benefit of collecting and analysing intelligence from datasets and interpreting the information for the benefit of an organisation (Gao, 2013).

BPM is also used in enhancing the mining of data from E-commerce websites. Poggiet al. (2013) developed a business process insight platform that is a shared procedure intelligent system, which executes the detection of joined processes, which also has some methods for data mining that are appropriate for websites. Poggiet al. (2013), explains that most businesses utilise simple analytic tools for website analysis to make decisions about their marketing operations and making planned assessments of the business. The simple analytic tools have the limitation of not providing the necessary views of the customer and critical paths. The details include general site statistics geo-location and page views. The use of these simple web analytics is associated with low conversion rates of customers buying the product (; Motahari-Nezhad, Recker, & Weidlich, 2015).

The authors developed an intelligent business management tool set that encompasses the use of web navigation as an unstructured BPM processes, and the methodology of transforming web visits into some tasks for BPM tools. The toolset also included three different techniques in mining and the incorporation of customer views in the process models. The tool also incorporated the use of knowledge-based miners in the production of web logs. The tool allowed a better mining of customers through saturating and clustering. Clustering allowed all data miners to produce results. The tool also allowed the initialisation of searches using predefined process models and the filtering of non-critical events. The application of the intelligent business process tool also allowed the customer conversion rate to increase from 2 percent to 46 percent (Poggi et al., 2013; Motahari-Nezhad, Recker, & Weidlich, 2015). Therefore, it is possible to apply the intelligent business process techniques to E-commerce logs to enhance the processes of mining data from the websites and attracting more customers. The process of web analytics can also be enhanced through some simple process aware analytics. The application of intelligence business process management techniques is in E-commerce logs is a cost effective method of enhancing the understanding of websites to improve user satisfaction and sales.

The use of intelligent business process tools is also increasingly being used in the improvement of scheduled business processes. Traditional methods of the performance analysis are bound by many limitations like the lack of direct techniques that make use of the available data. The techniques of mining data for performance analysis have many limitations like focussing on resource perspectives to answer the performance query while also ignoring some other underlying processes like the flow-control perspective. The current methods of performance analysis do not also consider queuing semantics and the process perspectives. According to Motahari-Nezhad, Recker, and Weidlich (2015), the use of an intelligent BPM in performance analysis provides an efficient, flexible, and precise technique of data motivated inquiry of performance of the planned processes. The intelligent business process tool bridges the gap that exists between queue mining and petri net simulation models. This tool utilises execution logs and schedules of the existing processes to construct a novel QCSPN that is very communicative and comprises of stochastic times, queues, and scheduling processes (Senderovich et al., 2015).

The proposed toll encompasses semantics, statistical methods, and queuing semantics. This approach entails a combination of the techniques from queuing theory, queue-enabling coloured stochastic petri nets and coloured Petri-nets to enhance computational efficiency the folding operations are defined to assign the initial QCSPN archetypal to the Queuing systems formalism. This approach when executed and analysed using physical data enhanced the accuracy of the results obtained (Buyya, Calheiros, & Dastjerdi, 2016). The use of this approach brought together the process mining techniques with high computational cost and effective queuing concept grounded techniques that do not consider the perspectives of the procedure. The utilization of this technique means that it is possible to enhance the performance analysis using better analytical techniques (Senderovich et al., 2015).

Therefore, it is apparent that big data analytics and BPM is becoming very important to businesses in influencing decision-making and enhancing business processes. BPM is also applicable to different types of businesses. The core attributes like real-time business analytics empowers businesses in their decision-making processes and improves the processes of identifying important correlations in data. The ability of BPM systems to analyse huge volumes of data for the evaluating unexpected patterns and insights is very essential for businesses’ in responding to changing business landscapes. Intelligent business systems, therefore, provide enhanced visibility and flexible processes in the management of huge data