Data, information and knowledge management in the field of smart business
Mehri Chehrehpak; Abbas Tolouei Ashlaghi; Kamran Mohammadkhani
Abstract
Effective knowledge-based processes are essential for companies operating in the information technology industry. These
Effective knowledge-based processes are essential for companies operating in the information technology industry. These processes rely on the expertise of skilled workers and play ...
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Effective knowledge-based processes are essential for companies operating in the information technology industry. These
Effective knowledge-based processes are essential for companies operating in the information technology industry. These processes rely on the expertise of skilled workers and play a crucial role in the value chain of such organizations. Decision-making is a critical element of knowledge-based processes, highlighting the need to identify decision rules and models accurately. In this paper, we examine the process of identifying and deciding on proposed ideas in the software industry, analyzing decision logs from a leading software company. The Rough sets theory and fast Reduction algorithm are employed to provide a step-by-step approach to data analysis and decision mining. The algorithm identifies vital features used in decision-making and presents the decision model as if-then rules, utilizing existing equivalence rules between data. The results demonstrate that this model can significantly reduce the direct involvement of decision-makers and the duration of the decision-making process. In today's competitive landscape, effective knowledge-intensive processes are fundamental for companies in the information technology (IT) industry. These processes are highly dependent on the expertise of skilled professionals and are integral to value creation across various organizational fronts. Decision-making—considered a cornerstone of knowledge-intensive processes—underscores the necessity of accurately identifying decision rules and models. This paper focuses on the methods of identifying and evaluating proposed ideas within the software industry, specifically analyzing decision logs from a leading software company. By employing the Rough Set Theory along with the Fast Reduction Algorithm, we provide a detailed methodological framework for data analysis and decision mining. This structured algorithm identifies critical features relevant to decision-making and presents the resulting decision model in the form of if-then rules, which are derived from pre-existing equivalence relations among data. Our results illustrate that the implemented model can significantly lessen the direct involvement of decision-makers as well as the time taken in the decision-making process, highlighting a potential path for enhancing operational efficiency in IT firms.
Introduction
The field of information technology is constantly evolving, marked by rapid developments and intense competition. To navigate this landscape successfully, organizations must rely on effective knowledge-based processes that are essential for sustaining competitive advantages. These processes hinge on the expertise of skilled workers who play a pivotal role in various stages of product development and innovation.
This paper aims to illuminate the decision-making facets of knowledge-intensive processes in the context of new idea generation within software companies. By scrutinizing decision logs from a prominent software firm, we aspire to discern decision rules and models that could significantly optimize decision-making efficiencies, ultimately positively impacting innovation outcomes.
Research Questions
This research is driven by several key inquiries aimed at uncovering various dimensions of decision-making in IT innovation processes:
What methods can be employed to identify decision points in the innovation processes of IT companies?This question targets the analytical techniques used to pinpoint where crucial decisions occur during the innovation lifecycle.
How can critical decision-making features be identified within these organizations, and what are the characteristics of these features?Identifying these features assists in understanding what influences decisions, including both internal and external factors.
In what ways can structured procedures be developed to expedite and improve the decision-making processes in IT innovation?This question seeks to establish procedural guidelines that can streamline decision-making, allowing companies to react swiftly to new information and emerging market trends.
Literature Review
The importance of Business Process Management (BPM) and decision mining in enhancing organizational efficiency is well documented in the literature. Earlier studies have primarily focused on implementing process mining techniques across various sectors, including healthcare and manufacturing, to improve overall decision-making efficiency. However, there exists a relative scarcity of research that specifically addresses decision mining in the context of IT innovation processes.
This study builds on existing frameworks, particularly leveraging the Rough Set Theory and the Fast Reduction Algorithm. These methodologies facilitate a thorough analysis of decision-making features, enabling the development of a tailored decision model for the software industry. By filling this notable gap, our research generates insights that can be applied to enhance decision-making within knowledge-intensive sectors.
Methodology
This research employs a comprehensive case study methodology, focusing on a well-established Iranian IT firm with over 25 years of industry experience. Our approach is structured into several key phases:
Identifying Decision Points: We apply a four-stage model, as outlined by Bazhenova and Weske (2016), to systematically pinpoint decision-making instances throughout the innovation process.
Analyzing Decision Logs: In this phase, we extract and scrutinize decision logs to identify critical features that influence decision-making. This analysis involves various statistical and data mining methods to validate findings.
Utilizing Rough Set Theory and Fast Reduction Algorithm: Following feature extraction, we employ Rough Set Theory alongside the Fast Reduction Algorithm to develop a robust decision model. This model is articulated through if-then rules that encapsulate significant decision-making aspects.
Evaluating Model Effectiveness: To ascertain the model's effectiveness, we conduct an extensive analysis of the product development process within the company, assessing how well the model predicts decision outcomes.
Results
The results of implementing the proposed decision model revealed several significant features critical to decision-making processes:
Idea Relevance: The relationship of the proposed idea to existing business operations emerged as a crucial factor.
Idea Source: Determining whether the idea originated from internal staff or external consultants significantly influenced the decision-making progression.
Anticipated Customer Acceptance: Factors related to customer acceptance and assessments of the competitive landscape were also primary considerations in the decision-making process.
The model showcased a remarkable 91.5% accuracy rate in predicting decision outcomes based on the identified features, illustrating its effectiveness. More importantly, the implementation resulted in a pronounced reduction in the direct involvement of decision-makers and a considerable decrease in the duration required for decision-making processes.
Conclusion
The research findings underscore the potential of applying Rough Set Theory along with decision mining techniques to significantly enhance the efficiency of decision-making in IT innovation processes. By systematically identifying and modeling essential decision features, organizations can streamline operations, minimize redundant tasks, and improve the overall effectiveness of their innovation strategies.
This study contributes to the growing body of knowledge on decision mining in the software industry, offering a structured approach that can be adapted to various knowledge-intensive environments. Looking ahead, further research is needed to explore the adaptability of this model in larger organizations and diverse contexts, further expanding its applicability within the broader IT landscape.
The implications of this research extend beyond the immediate findings, suggesting that strategic implementation of structured decision-making models can enhance operational efficiency across various sectors. Future studies could investigate the scalability of these models in larger organizations and their applicability in other innovation-driven industries.
Keywords: Process Mining, Decision Mining, Rough Set Theory, Knowledge-Intensive Process, Information Technology.
Data, information and knowledge management in the field of smart business
Ehsan allah Khoshkhoy Nilash; Mansour Esmaeilpour; Behrooz Bayat; Alireza Isfandyari Moghaddam; Erfan Hassannayebi
Abstract
Banks have complex and long-term processes for facilities, including many stages, control points and approvals. Continuous analysis of such processes is increasingly important for continuous improvement and gaining knowledge from them. The main goal of the present research is to provide a comprehensive ...
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Banks have complex and long-term processes for facilities, including many stages, control points and approvals. Continuous analysis of such processes is increasingly important for continuous improvement and gaining knowledge from them. The main goal of the present research is to provide a comprehensive methodological framework based on process mining and data mining regarding the analysis of fixed capital facility processes. The method used in the present research is derived from the techniques of process mining and data mining based on the event log of the facility system, an active bank in Iran. This method includes nine phases of initiation, preparation, inspection, exploration and analysis, evaluation, multi-dimensional analysis, prediction, review of results and improvement. Among the results of the present research is the extraction of the real process model, identification of bottlenecks, frequent activities in a case and all cases and process variant. In addition to this identification of branches and people with the most important roles and based on data features in reducing the time of payment of facilities, the analysis of the process from dimensions such as the province was one of the other findings. One of the initiatives of the present research was the use of data mining to predict the payment time of facilities. In the comparison of various methods, the decision tree algorithm had the best performance with 72% accuracy. In addition to identifying deviations, based on the creation of event log and its analysis, the improved process of extracting which showed a 67% improvement.
Introduction
Today's businesses benefit from a number of processes in order to earn more income and better services (Dakich et al., 2018). They are looking for processes that have better and more successful performance in order to achieve organizational goals and optimal use of resources in the operational environment (Van Der Aalst, 2016). Therefore, continuous analysis of processes for continuous improvement in organizations is very important.
Considering that the processes of providing facilities, especially fixed capital, are very effective in the creation and development of industrial, mineral and tourism units, having knowledge of them is of increasing importance. One of the efficient and effective methods for analyzing and improving business processes is process mining. With the help of its various concepts and techniques this method provides useful knowledge for the detailed examination of processes and how they are realized.
On the other hand, the efficient method of data mining, which provides the possibility of extracting knowledge from historical and predictive data (Basha, 2017), can be combined with the process mining method. With the investigations carried out, the methodological framework in order to provide process-centric and data-centric analysis, including the discovery of the real process model of facility payment, performance analysis of such processes, analysis of process varints, multi-dimensional process-centric analysis, payment time prediction, recommendations for improvement and process improvement based on event log simulation is not presented. Also, due to the novelty of the process mining method, the purpose of this research is to provide a comprehensive methodological framework using these techniques, concepts and tools of process mining in combination with data mining methods regarding the analysis of business processes with the study of fixed capital facilities processes.
Research Question(s)
How to provide a methodological framework for the analysis of fixed capital processes by using the techniques and concepts of process analysis and data mining methods?
Literature Review
In Table No. 1, a number of related studies are compared with each other.
Table 1. Summary of the research conducted
Research
Business
Components used
Event log
Miners
(Urrea-Contreras et al., 2017)
SME organizations
Event Log extraction, discovery, conformance checking, extend model, and return integrated model
software development system (JIRA)
inductive
(EL KODSSI & Sbai, 2024)
Smart environments
Data selection, data transformation, generation of event log, discovery, enhancement
Unstructured sensor generated data
MDA and machine learning
(Rashed et al., 2023)
hospital
Preprocessing, model discovery and analysis
Heart surgery unit in a hospital in Egypt
heuristic, inductive, ILP and ETM
(Erdogan & Tarhan, 2022)
Emergency
Determining goals, extracting event log, pre-processing, applying multi-perspective process mining, analysis, recommendation for improvement and evaluation of results.
Emergency system log
fuzzy
(Pan & Zhang, 2021)
Construction project
Event log generation and preparation, discovery and validation
Example of a construction project
Fuzzy and inductive
(Lorenz et al., 2021)
Production business
Mapping, analysis and improvement
Production business event log
fuzzy
(Augusto et al., 202)
Healthcare trends
Planning, data extraction, data processing and evaluation
Patients in Victoria, Australia
fuzzy
(Pang et al., 2021)
Acute care and treatment processes
Coding and categorizing activities, extracting and filtering event log, discovering and improving the process model and performance analysis
Stroke care process
IDHM miner, alpha, fuzzy and heuristic
(Ramos et al., 2021)
ERP configuration, intelligent agriculture and computer configuration
Extract configuration event log, control and clean data based on feature model, build data clusters and discover related workflow.
Greed, hierarchy and genetics
A number of studies are not comprehensive in using the concepts of data mining and process mining. Some of them lack features such as multidimensional process centric analysis, event log simulation for improvement, evaluation of results with field specialists and so on. Comparing the studies, each of these cases can be expressed as a research gap. It is also necessary to consider all the components and phases as a methodological framework as another research gap.
Methodology
The method used in the present research is based on the techniques, concepts and methods of the process mining in its manifest (Will van der Alast et al., 2011). In this research, the event log of the fixed capital facility system of one of the active banks in Iran has been used. The proposed framework includes nine phases of initialization, preparation, inspection, analysis, evaluation, process centric analysis, prediction, transfer results and finally improvement. Figure 1 depicts the mentioned methodological framework.
Figure 1. The mentioned methodological framework
Results
Process models were discovered based on alpha, alpha++, heuristic, genetic, fuzzy and inductive techniques. By comparing inductive and fuzzy model, fuzzy model is very effective due to less edge filter and coverage of all activities. Process bottlenecks, people and branches with the most important roles were identified.
The heuristic algorithm with a value of 0.833 had the best performance in the average values of the quality indicators of the process model. In Figure 2, the mentioned methods are compared.
Figure 2. Comparison of miners
Analyzing the impact of data features with a target throughput time of 271 days, according to the dimensions of the Civil Partnership Bases contract, Riyal Civil Partnership Contracts and SME customers had the greatest impact in reducing the process throughput time.
The J48 decision tree algorithm had the best performance with 72% accuracy compared to all the data mining methods used.
Figure 3. Results of data mining analysis with J48 algorithm
203 records were used to simulate new event data. The results of the analysis showed a 67% improvement.
Keywords: Fixed capital processes, methodological framework, event log, process mining, data mining.
Data science, intelligence and future analysis
Elmira Darzi; Mehrdad Agha Mohammad Ali Kermani; Mostafa Jafari
Abstract
Due to their temporary nature and precise time and cost planning, project organizations are more involved in the relationship between data and operational processes, which requires the correctness of the actual processes of the organization. On the other hand, one of the essential issues for managing ...
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Due to their temporary nature and precise time and cost planning, project organizations are more involved in the relationship between data and operational processes, which requires the correctness of the actual processes of the organization. On the other hand, one of the essential issues for managing project-oriented organizations is its business process management, but due to the dynamic behavior and complexity of the nature of a project-oriented organization, identifying the processes through the traditional modeling of business process management is not reliable. The emerging solution to this problem is called "process mining." The paper introduces a framework that employs accurate process identification to measure the performance of business units relative to reality. This comprehensive framework undertakes the prerequisite steps of identification, including monitoring and cleaning the process-aware information systems' data to discover the process's current state and examine it from different perspectives based on the selected process. The primary purpose of this paper is to develop a framework for improving the P2P process in Chavosh Rah Company through process mining. The paper presents a framework to enhance the P2P process in project-oriented organization by implementing and extracting knowledge from the process, discovering unexpected and hidden relationships, and finding bottlenecks by employing process mining.
Introduction
Today, organizations must identify and manage their current processes for an effective approach. Workflow management systems are used to support business processes. Although current workflow management systems support the design, configuration, execution, and control of the processes under their control, there are deficiencies in the troubleshooting phase. Process mining is used to fill these gaps. Process mining is a bridge between data science and process science. The main aspects of process mining are the "discovery, monitoring, and improvement of real processes by extracting knowledge from event information" that is accessible in today's systems.
By evaluating real behaviors, process analysis provides a realistic view of operational processes, which is useful and important in developing support systems or redesigning previous processes. The purpose of process mining is to extract non-obvious and practical information related to processes from the event graph. The event log is actually the recorded data related to the events of the execution of a business process in an organization. One of the most important characteristics of an event diagram is that it is formed based on the events that happen. This means that regardless of how an organization's business process is planned or designed, the event graph contains data on how the process is implemented in reality.
Applications of process mining have been covering articles in the fields of health, information technology, finance, education, government affairs, energy, agriculture, logistics, public relations, media, and tourism. The purchase request process with the process analysis approach in the project organization is the innovation center of this article because no research has been done in line with this point of view. Of course, this article is a scientific and practical project. Naturally, the analyzes and results are based on the real data of each organization, which is usually different from other organizations, but by doing such a project, the obtained results can be generalized for organizations that have similar performance.
After the preparation of the event diagram, it is possible to define the APQC-approved relevant indicators in parallel with the start of the process analysis and analyze the organization from the perspective of these indicators. Then, with the help of interviews with the organization's experts who are involved in the purchasing process, improvement suggestions are collected and announced to the organization's management unit. The case study in this article is about the purchasing process of a contracting company. Chavosh Rah Bana Company was established in order to implement infrastructure projects in the fields of road construction, construction, and facilities. Shopping in Chavosh Rah Bana company includes the steps of registering a request, checking the request, checking the warehouse by the warehouse of the available goods, requesting a non-existent purchase, asking the price by the procurement unit, management approval, choosing the payment method and issuing a valid check or purchase, and finally registering a debt or registration It is creditable.
Research Question(s)
In this article, the following questions are raised, which we will try to answer by advancing the goals of the article had:
1) Does the mining process have a direct impact on the purchase request process?
2) Is time optimization effective in planning based on process analysis?
3) Is there a logical and acceptable answer in planning based on the use of real data? Will we reach the mining process?
4) Which is the most common path in the process?
5) In what order are the items (cases) distributed in the process?
6) How much do the cases conform to the process model? What problems are there?
7) What is the average/minimum/maximum operation time of the process?
8) Which of the tasks takes more time?
9) How are the cases actually implemented?
Literature Review
In the field of the purchasing process, two articles were studied, which are related to 2019 and 2018. The first article with the topic "Using process mining to find the main factors of delay in the internal purchasing process" was prepared by Virginia Eitzel Contras, Jesus Andres Portillo, and Fernando Gonzalez. In this article, the internal purchasing process of Quintal company was investigated. The software used in this article is Fluxicon Disco software. In this article, 608 cases (9199 events) were analyzed. The purpose of this paper was to increase the efficiency of Quintal's internal purchasing department through recommendations based on the analysis of their process reports.
The second paper "Process Mining Analysis of Purchasing Process in a Heavy Manufacturing Industry" was prepared by Chiwon Chu and Hind Rebigid. In this article, the purchasing process in a marine and ship parts manufacturing company in Korea was investigated. The software used in this article is Fluxicon Disco software. In this article, 663 cases (9829 events) were analyzed. This article identified the activities in which the process consumes a lot of time and also rework occurs in them.
In the review article on the application of process mining by Dakik et al., a review of the researches conducted on the subject of the applications of process mining until 2018 was done and the result was that the main use of process mining was in the fields of health, information technology, finance, production and It is education.
In 2018, Baykazoglu et al. published an article entitled "An approach based on process analysis to evaluate students' performance in computer tests". In this article, by tracing the logs of the students' journeys on the computer, the process of answering them has been discovered and analyzed.
The first study that used process mining to explore and analyze an inter-organizational process was conducted by VanderAalst in 2000. During this research, workflows between different organizations were modeled and analyzed. After that, an article on supply chain processes in the field of discovery of distribution processes in the supply chain was done by Maroster et al. in 2003.
In 2009, Garek et al. analyzed the RFID-oriented supply chain process. In this supply chain, the position of each item is tracked by its special code, and this makes it possible to get the most out of the mining process.
In 2014, Bernardi et al. discovered inter-organizational business rules through the data available in cloud data and by process mining. In 2014, Klaze et al. presented research on the integration of the event diagram of several different organizations to start process analysis.
Many researches have been conducted on the application of process mining for the three main actions of discovery, compliance review, and improvement. The literature review of this section includes all the books and articles published in the journal and some theses that have accurately used the words process analysis and performance or efficiency in their title. The first time that process mining has been introduced as a performance measurement methodology, Park et al. compared 19 block production processes in a Korean shipbuilding company by DEA. The main contribution and goal of their research is the development of one of the DEA models, and they used automatic process analysis results only to measure the 5 performance indicators they considered. The review goes under these subheadings.
In 2015, a part of Leer et al.'s book was published in Germany called Process Performance Evaluation. In this section, the process performance evaluation procedure is described as a part of the BPM cycle by introducing the generalities of process analysis and DEA along with an application example. Then in the same year in 2016, in his senior thesis at the University of Eindhoven in the Netherlands, van den Ing measured the performance of different paths of purchase-to-payment process in an organization.
Many articles have been published in the field of health in this regard. In 2019, Rojas et al. analyzed the performance of emergency room departments to help decision-makers improve the quality of medical center services. Also, using a case study of process mining, by extracting data from a hospital information system, Bettinni et al. The performance of this system was evaluated using the time indicators available in the process analysis tool. In 2020, Anastasia Pika and colleagues studied process mining to protect the privacy of people's information recorded in healthcare and analyzed data privacy and application requirements for healthcare process data.
In the field of the food industry, in 2021, Mathew Mastella investigated the process of mining in this industry. Also, in 2020, Peyman Badakhshan and his colleagues investigated the purchase order process with the help of mining in the paint industry.
Methodology
The main methodology proposed in this article is briefly and clearly presented in Figure 1. As can be seen, the access to the raw data available in the current software in the company is the starting point of this article. After that, the image of the event, which is considered the input of any process mining tool, should be extracted by monitoring the raw data of the systems, so that various process mining techniques can be applied to it. Discovery and analysis of the process in order to see the details of the process paths in the studied period by Behfaleb software is the next step. After preparing the event diagram, in parallel with the start of the process analysis, the relevant APQC-approved indicators can be defined and the organization can be analyzed from the perspective of these indicators. Then, with the help of interviews with the organization's experts who are involved in the purchasing process, improvement suggestions are collected and announced to the organization's management unit.
Figure 1. Methodology
Conclusion
In this article, it is focused on the application of process mining in the purchasing process of a project-oriented organization. The competitive conditions have forced contractor companies (project oriented) to manage their processes completely and to get help from strategic and operational tools to improve their performance. In this regard, the main goal of this article is to examine one of the important processes of the project-oriented company (purchasing process). For the case study, the data obtained from the purchase process of Chavosh Rah Bana's project-oriented company has been used. With the help of the obtained data, the purchase process of the company was extracted and analyzed from different perspectives. With the help of these analyses and the review of the time indicators introduced in APQC, suggestions for improvement were presented with the help of the company's expert group. Of course, these suggestions can be used in other project-oriented organizations that have a similar function to this type of organization. The suggestions are as follows:
1) Correct purchase planning
2) Having a vendor list of suppliers with relevant indicators
3) The flow of systemic thinking in the organization
4) Using people with expertise
5) Using the warning system to implement activities on time
6) periodic reporting and timely registration in the system
7) Increasing the number of personnel in the procurement unit
8) Teaching the principles and techniques of negotiation
Acknowledgments
We are very grateful to Behin Sazan Farayand Amin Knowledge Based Company, the developer of the first Iranian mining process tool (Bahfalab) for supporting this research. We also thank Mr. Engin
nization, Purchasing Process.
ehsan allah Khoshkhoy Nilash; Alireza Tamjid Yamechlo; Roya Rad
Abstract
Banks have complex, long processes and activities with many points of control and approval, especially for facility processes. The survival of these institutions, providing quality and fast services and customer satisfaction requires improvement and analysis of results after the implementation of these ...
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Banks have complex, long processes and activities with many points of control and approval, especially for facility processes. The survival of these institutions, providing quality and fast services and customer satisfaction requires improvement and analysis of results after the implementation of these processes. The main purpose of this study is to analyze the performance and improve the working capital facility processes. For this purpose, a method based on process mining and fuzzy algorithm is used. The method includes six steps: log extraction of the Bank of Industry & Mine facility system, log inspection, control flow analysis, performance analysis based on time indicator, making suggestions and reviewing the results, and finally improving the processes using simulation.The results of the present study include the discovery of a real and improved process model, the detection of bottlenecks and max repetition activities, the reduction of the mean throughput time by 23% and the number of activities by 21%, and finally the efficiency of process mining.
Maryam Tavakoli Zaniani; Mohammad Reza Gholamian
Abstract
Process discovery is a branch of process mining that by using event logs extracts the process model that describes the events’ behavior properly. Since, Heuristic process discovery algorithms are among the most significant and popular process discovery methods and due to the fact that the ...
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Process discovery is a branch of process mining that by using event logs extracts the process model that describes the events’ behavior properly. Since, Heuristic process discovery algorithms are among the most significant and popular process discovery methods and due to the fact that the quality of outputs of these algorithms is heavily dependent on the quality of extracted dependency graph, in this paper for the first time, an approach to transform the problem of dependency graph discovery to a binary programming problem has been proposed and also, an objective function is introduced that simultaneously considers fitness and precision measures of output models. The weights dedicated to each of the measures are determined by means of a user-defined threshold. The mentioned measures are the most important metrics in assessing quality of output models of process discovery algorithms. Hence, in fact this approach focuses on improving quality metrics of output models. Moreover, by means of defining suitable constrains, the proposed approach is capable of involving domain knowledge in mining procedure, as well as guiding the result through whether the models that are more likely to be sound. This is depicted in a case study of a real company that is described in this paper. In the case study, the proposed approach has been applied to marketing event log of the mentioned company by utilizing the constrains defined according to domain knowledge and structural rules of dependency graph and at the end, the results were presented.
