A gap between Business Process Intelligence and redesign process

Sohail, Abid; Dominic, P. D. D.

doi:10.1109/iccisci.2012.6297227

Cited by 11 publications

(4 citation statements)

References 34 publications

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“…There have been four approaches towards the utilization of BI concepts for business process based operational analytics. The first approach occurs in three variations in the form of (i) using BI concepts for dynamic process performance evaluation [8,22,24,40,44,45,47], (ii) analyze event logs to improve the quality of business processes [2,3,14], and (iii) monitor process instances to inform users about unusual or undesired situations [17]. These variations are either short on implementation or apply BI analytics to individual business processes for discrete performance assessment associated with business process activities; but there is no emphasis on concepts like performance inferencing across business processes.…”

Section: Related Workmentioning

confidence: 99%

Operational Intelligence Performance Inferencing Across Business Processes

Kaula¹

2020

IJDMS

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Business process intelligence improves operational efficiency that is essential for achieving business objectives, besides facilitating competitive advantage. As an organization is a collection of business processes, operations in one business process do influence or have relationship with other business processes. Consequently, from an operational intelligence standpoint, insights from one business process may have their genesis or implications in the performance of some other business process. This paper outlines a framework to sequence insights in the form of performance inferencing across multiple business processes. The framework logically sequences insights across business processes in the form of business rules. The paper illustrates the concepts through a prototype that is implemented in Oracle's PL/SQL language.

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Section: Related Workmentioning

confidence: 99%

Operational Intelligence Performance Inferencing Across Business Processes

Kaula¹

2020

IJDMS

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“…There have been three approaches towards the utilization of BI concepts for business process based operational analytics. The first approach occurs in three variations in the form of (i) using BI concepts for dynamic process performance evaluation [6,8,18,35,36], (ii) analyze event logs to improve the quality of business processes [2,3,14], and (iii) monitor process instances to inform users about unusual or undesired situations [17]. These variations are either short on implementation or apply BI analytics to individual business processes for discrete performance assessment associated with business process activities; but, do not emphasize concepts similar to affinity cluster.…”

Section: Related Workmentioning

confidence: 99%

Affinity Clusters for Business Process Intelligence

Kaula¹

2018

IJDMS

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Business process intelligence improves operational efficiency that is essential for achieving business objectives, besides facilitating competitive advantage. As an organization is a collection of business processes, enhancing business process performance on a continual basis is essential for organizational success. This paper outlines the concept of affinity clusters that can influence process performance with respect to some success criteria. Affinity refers to the percentage of time certain dimensional factors occur together with respect to some success measure. The paper illustrates the concepts through an adaptation of Oracle E-Business Suite Lead to Forecast business process that is implemented in Oracle's PL/SQL language.

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“…For instance in-efficient flow in product delivery can influence the whole sales process. It is therefore of utmost importance to develop a systematic methodology for business process analysis, that serves as a basis for identification of opportunities for process improvement [2].…”

Section: Introductionmentioning

confidence: 99%

A process warehouse based resource performance evaluation method for business processes improvement

Sohail

Dominic

Shahzad

2014

2014 International Conference on Computer and Information Sciences (ICCOINS)

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Existing studies on business process improvement (BPI) have widely focused on development of separate techniques for resource management and structural redesign of processes. In this study, we contend that the relationship of resource with task is not sufficiently understood. Particularly, business intelligence based approaches to BPI have not adequately explored this relationship. To understand this, we classify resources into human and non-human resources, because the two types of resources behave differently leaving different impact on process performance. Subsequently, a set of relationships between human resources, non-human resources and tasks (named suitability, preference and competence) are presented. However, only human resource's relationship with non-human resource/s (named preference) is presented in detail, as a resource preference model. The model bundled with the presented preference evaluation method guides users for evaluation of resources' preference. The applicability of the method is illustrated through a healthcare case study.

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A gap between Business Process Intelligence and redesign process

Cited by 11 publications

References 34 publications

Operational Intelligence Performance Inferencing Across Business Processes

Operational Intelligence Performance Inferencing Across Business Processes

Affinity Clusters for Business Process Intelligence

A process warehouse based resource performance evaluation method for business processes improvement

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