Focusing Business Improvements Using Process Mining Based Influence Analysis

Lehto, Teemu; Hinkka, Markku; Hollmén, Jaakko

doi:10.1007/978-3-319-45468-9_11

Cited by 14 publications

(16 citation statements)

References 10 publications

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“…For this purpose, the list of the most relevant features and the extent of their contribution should somehow be returned. One example of this kind of root cause analysis technique is influence analysis described in [19].…”

Section: Arxiv:171002823v2 [Cslg] 17 May 2018mentioning

confidence: 99%

Structural Feature Selection for Event Logs

Hinkka

Lehto

Heljanko

et al. 2018

Business Process Management Workshops

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We consider the problem of classifying business process instances based on structural features derived from event logs. The main motivation is to provide machine learning based techniques with quick response times for interactive computer assisted root cause analysis. In particular, we create structural features from process mining such as activity and transition occurrence counts, and ordering of activities to be evaluated as potential features for classification. We show that adding such structural features increases the amount of information thus potentially increasing classification accuracy. However, there is an inherent trade-off as using too many features leads to too long run-times for machine learning classification models. One way to improve the machine learning algorithms' run-time is to only select a small number of features by a feature selection algorithm. However, the run-time required by the feature selection algorithm must also be taken into account. Also, the classification accuracy should not suffer too much from the feature selection. The main contributions of this paper are as follows: First, we propose and compare nine different feature selection algorithms by means of an experimental setup comparing their classification accuracy and achievable response times. Second, we discuss the potential use of feature selection results for computer assisted root cause analysis as well as the properties of different types of structural features in the context of feature selection.

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Section: Arxiv:171002823v2 [Cslg] 17 May 2018mentioning

confidence: 99%

Structural Feature Selection for Event Logs

Hinkka

Lehto

Heljanko

et al. 2018

Business Process Management Workshops

Self Cite

View full text Add to dashboard Cite

show abstract

“…Finally, BPI is an important domain of process management. Prior research identifies critical success factors of BPI such as product/process modularity (Wang et al , 2014), information systems/technology integration (Bhatt, 2000; Trkman, 2010; Zelt et al , 2018), knowledge sharing (Oyemomi et al , 2016) and data-driven process mining (Lehto et al , 2016), among others. Such studies explore the antecedents of BPI from the perspective of internal management (Raucci et al , 2020), but tend to ignore the key role of process participants, that is, the people.…”

Section: Discussionmentioning

confidence: 99%

Improving business processes or human resources? The performance implications and contingencies

Wang

et al. 2021

IMDS

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PurposeThe purpose of this study is to examine both the separate effects and the interaction effects of business process improvement (BPI) and human resource enhancement (HRE) on operational performance in manufacturing firms and to examine the moderating effect of product life cycle (PLC).Design/methodology/approachThe conceptual model was based on the resource-based view, the dynamic capabilities perspective and contingency theory. Using survey data from 215 manufacturing companies and structural equation modeling, the hypothesized relationships were tested.FindingsThe empirical findings suggest that the performance implications of HRE and the interaction of HRE and BPI are contingent on PLC. HRE and its interaction with BPI have stronger effects on operational performance in the maturity stage than in the growth stage. The effect of BPI on operational performance is equally important in both stages of PLC.Practical implicationsThe results of this empirical study can help to guide manufacturing firms to enhance their performance through improvements in their business processes and human resources at the different life cycle stages of their products. First, firms should upgrade both their processes and people to keep pace with the changing business environment and to sustain their competitive advantage. Second, the performance implications of improving processes and people are contingent on PLC, which suggests that managers should emphasize different things according to their roles.Originality/valueA simultaneous examination of the performance implications of HRE and BPI practices and their contingencies reveals the roles that people and processes play in achieving organizational objectives. The results contribute to our understanding of how firms leverage their resource allocation between processes and people at different stages of PLC to effectively improve their performance.

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“…The testing framework was built on the test system using the Python programming language. The actual recurrent neural networks were built using Lasagne 11 library that works on top of Theano 12 . Theano was configured to use GPU via CUDA for expression evaluation.…”

Section: Methodsmentioning

confidence: 99%

Exploiting Event Log Event Attributes in RNN Based Prediction

Hinkka

Lehto

Heljanko

2019

Communications in Computer and Information Science

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In predictive process analytics, current and historical process data in event logs is used to predict the future, e.g., to predict the next activity or how long a process will still require to complete. Recurrent neural networks (RNN) and its subclasses have been demonstrated to be well suited for creating prediction models. Thus far, event attributes have not been fully utilized in these models. The biggest challenge in exploiting them in prediction models is the potentially large amount of event attributes and attribute values. We present a novel clustering technique that allows for trade-offs between prediction accuracy and the time needed for model training and prediction. As an additional finding, we also find that this clustering method combined with having raw event attribute values in some cases provides even better prediction accuracy at the cost of additional time required for training and prediction.

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Focusing Business Improvements Using Process Mining Based Influence Analysis

Cited by 14 publications

References 10 publications

Structural Feature Selection for Event Logs

Structural Feature Selection for Event Logs

Improving business processes or human resources? The performance implications and contingencies

Exploiting Event Log Event Attributes in RNN Based Prediction

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