Actual Business Process Research Articles

Integrated detection and localization of concept drifts in process mining with batch and stream trace clustering support

Process mining can help organizations by extracting knowledge from event logs. However, process mining techniques often assume business processes are stationary, while actual business processes are constantly subject to change because of the complexity of organizations and their external environment. Thus, addressing process changes over time – known as concept drifts – allows for a better understanding of process behavior and can provide a competitive edge for organizations, especially in an online data stream scenario. Current approaches to handling process concept drift focus primarily on detecting and locating concept drifts, often through an integrated, albeit offline, approach. However, part of these integrated approaches rely on complex data structures related to tree-based process models, usually discovered through algorithms whose results are influenced by specific heuristic rules. Moreover, most of the proposed approaches have not been tested on public true concept drift-labeled event logs commonly used as benchmark, making comparative analysis difficult. In this article, we propose an online approach to detect and localize concept drifts in an integrated way using batch and stream trace clustering support. In our approach, cluster models provide input information for both concept drift detection and localization methods. Each cluster abstracts a behavior profile underlying the process and reveals descriptive information about the discovered concept drifts. Experiments with benchmark synthetic event logs with different control-flow changes, as well as with real-world event logs, showed that our approach, when relying on the same clustering model, is competitive in relation to baselines concept drift detection method. In addition, the experiment showed our approach is able to correctly locate the concept drifts detected and allows the analysis of such concept drifts through different process behavior profiles.

МОДЕЛЮВАННЯ БІЗНЕС-ПРОЦЕСІВ ДЛЯ СТВОРЕННЯ ІНФОРМАЦІЙНОЇ СИСТЕМИ УПРАВЛІННЯ ЯКІСТЮ ПІДПРИЄМСТВА БІОЕКОНОМІКИ

The article contains the results of research on modeling business processes for a bioeconomy enterprise. Modeling procedures are related to the distribution of management processes by scale and taking into account the peculiarities of production and management of the enterprise. The modeling procedure itself is necessary to determine each business process, its owners, results, the need to control and save information about its execution. Business processes of the bioeconomy can be divided into mandatory in accordance with the legal framework and reporting of all enterprises, production and management, taking into account the profile of the development of the enterprise. Modeling allows you to determine all production and information flows, control indicators. The results of modeling are a clear visualization, a description of the process. The documentation contains the sequence of all procedures, their provision and control. The proposed concept combines mandatory procedures for accounting for emissions, the implementation of environmental legislation and the introduction of a full production cycle at a bioeconomy enterprise. The adapted concept of mirrors for modeling the actual business processes of creating a balanced system of indicators for the enterprise involves the introduction of new sections, in the general projection Production. When introducing new management technologies, in particular, lean production, it is envisaged to make changes to the company's development strategy, implement procedures for restructuring production processes and retrain staff. An analysis of the life cycle of a bioeconomy enterprise shows that each of its phases requires a reassessment of strategic indicators and business processes. This approach allows you to combine the development strategy of the bioeconomy enterprise and its business processes into one whole. Known modeling techniques and tools based on information technologies can be used for modeling, analysis and optimization, provided that adapted model templates are formed for each specific enterprise. Among the known concepts of the use of business processes, the most relevant are the use of a quality management system and the formation of three mirror models of production and management processes, taking into account the features of environmental legislation and bioeconomy production processes. The proposed concept of modeling business processes can be the basis for making changes to the quality system and management of a bioeconomy enterprise.

Analysis of Value Chain Model of Sheep-Fattening Business in Langkat District

Aims: To increase the beef cattle business by minimizing business production costs.
 Study Design: This research begins with preliminary studies such as conducting literature studies, problem identification, needs analysis, and system identification. Then conduct field observations and expert interviews. Best practice value chain analysis consists of several steps, which are both descriptive and analytical. Value chain analysis describes the overall market in which a particular value chain operates.
 Place and Duration of Study: The research was conducted from November 2022-January 2023 in Sei Bingai District, Langkat Regency.
 Methodology: The method used in this study is value chain analysis through mapping Hayami's value chain and added value.
 Results: The value chain model for fattening sheep in the research area can be applied to improve performance through efficiency, integration of actors, and distribution channels to deliver to consumers. Based on data processing and analysis results, it is known that the problem of sheep value chain management is a constraint on resource input in the form of a shortage of sheep supply, so Langkat Regency still depends on supplies from other regions.
 Conclusion: Mapping a value chain model based on actual business processes requires designing a supply chain solution to address critical aspects that hinder the smooth management of the sheep value chain.

Design and Development of Integrated Platform for Automobile Sales Model under the Background of Big Data

Based on the integration of the big data background and the exploration of related technologies in the enterprise service bus, this article focuses on the management strategy of auto parts, carries out theoretical analysis on parts classification, parts demand forecast, parts inventory control, and combines the characteristics of the auto parts industry. Research. At the same time, combined with the demand analysis of the actual business process of the car dealer, the car dealer business system based on the SOA integrated platform is designed. In the system design stage, the current more popular model-view-controller (MVC) three-tier design pattern is adopted to better focus on the hierarchical development of each level.

IMPROVING THE FISHERY BUSINESS PERMIT ISSUANCE USING BUSINESS PROCESS IMPROVEMENT

This study aims to analyse the actual business process applied in the Capital Investment Agency and One-Stop Licensing Centre of Aceh Province, mainly on issuing a fishery business permit. The institution has established 240 working hours (equivalent to 14,400 minutes) to issue the permit based on the SOP (standard operating procedures). However, a field observation showed that the actual process time exceed up to 600 hours (equivalent to 37,104 minutes). To investigate the gap and analyse the business process activities, this study employs Business Process Improvement (BPI) which consists of phase 1: organizing for improvement, phase 2: understanding the process, and phase 3: streamlining. This study occupies Bizagi Modeler to build and simulate a systematic business process model according to Business Process Modelling and Notation (BPMN). Based on the simulation process, the actual business process resulted 110 activities with total time 37,104 minutes, while the redesign model resulted 72 activities with total time 197.25 working hours (equivalent to 11,835 minutes). The redesign model further measured using devil’s quadrangle and obtained 68.11% efficiency. The efficiency aligns with better flexibility and service quality improvement.

A Model Based Framework for IoT-Aware Business Process Management

IoT-aware Business Processes (BPs) that exchange data with Internet of Things (IoT) devices, briefly referred to as IoT-aware BPs, are gaining momentum in the BPM field. Introducing IoT technologies from the early stages of the BP development process requires dealing with the complexity and heterogeneity of such technologies at design and analysis time. This paper analyzes widely used IoT frameworks and ontologies to introduce a BPMN extension that improves the expressiveness of relevant BP modeling notations and allows an appropriate representation of IoT devices from both an architectural and a behavioral perspective. In the BP management field, the use of simulation-based approaches is recognized as an effective technology for analyzing BPs. Simulation models need to be parameterized according to relevant properties of the process under study. Unfortunately, such parameters may change during the process operational life, thus making the simulation model invalid with respect to the actual process behavior. To ease the analysis of IoT-aware BPs, this paper introduces a model-driven method for the automated development of digital twins of actual business processes. The proposed method also exploits data retrieved by IoT sensors to automatically reconfigure the simulation model, to make the digital twin continuously coherent and compliant with its actual counterpart.

An Abstraction-Based Approach for Privacy-Aware Federated Process Mining

Process awareness is an essential success factor in any type of business. Process mining uses event data to discover and analyze actual business processes. Although process mining is growing fast and it has already become the basis for a plethora of commercial tools, research has not yet sufficiently addressed the privacy concerns in this discipline. Most of the contributions made to privacy-preserving process mining consider an intra-organizational setting, where a single organization wants to safely publish its event data so that process mining experts can analyze the data and provide insights. However, in real-life settings, organizations need to collaborate for performing their processes, e.g., a supply chain process may involve many organizations. Therefore, event data and processes are often distributed over several partner organizations, yet organizations hesitate to share their data due to privacy and confidentiality concerns. In this paper, we introduce an abstraction-based approach to support privacy-aware process mining in inter-organizational settings. We implement our approach and demonstrate its effectiveness using real-life event logs.

Distributed Supervision Model for Enterprise Data Asset Trading Based on Blockchain Multi-Channel in Industry Alliance.

Compared with traditional physical commodities, data are intangible and easy to leak, and the related trading process has problems, such as complex participating roles, lengthy information flow, poor supervisory coverage and difficult information traceability. To handle these problems, we construct a distributed supervision model for data trading based on blockchain, and conduct multi-party hierarchical and multi-dimensional supervision of the whole process of data trading through collaborative supervision before the event, at present and after the event. First, the characteristics of information flow in the data trading process are analyzed, and the main subject and key supervision information in the data trading process are sorted out and refined. Secondly, combined with the actual business process of data trading supervision, a multi-channel structure of distributed supervision is proposed by adopting an access–verification–traceability strategy. Finally, under the logical framework of the supervision model, the on-chain hierarchical structure and the data hybrid storage method of “on-chain + off-chain” are designed, and multi-supervisor-oriented hierarchical supervision and post-event traceability are realized through smart contracts. The results show that the constructed blockchain-based distributed supervision model of data trading can effectively isolate and protect sensitive and private information between data trading, so as to realize the whole process, multi-subject and differentiated supervision of key information of data trading, and provide an effective and feasible method for the controllable and safe supervision of data trading.

A SOFTWARE SOLUTION FOR QUALITY ASSESSMENT OF BUSINESS PROCESS MODELS

One of the primary tools of the BPM (Business Process Management) paradigm is business process modeling. Business scenarios can be presented in the form of graphical models that can be easily understood by both Information Technology (IT) professionals and non-IT professionals – business analysts, software customers, department heads, top managers, and other stakeholders interested in business process improvement – using business process modeling. Small and large businesses can capture their operations in the form of graphic diagrams using business process modeling as the fundamental tool of the BPM approach, which can then be brainstormed by business analysts to uncover ways to optimize organizational workflows. Typically, business process improvement is accomplished by the automation of operations that have been recognized as “bottlenecks” following analysis. However, analyzing a business process model is only viable if it is clear and correct in terms of compliance with both the notation used and the actual business process it reflects. Therefore, this work examines the structural measures of the BPMN (Business Process Model and Notation) business process model. It is assumed that business process models that violate business process modeling rules are neither understandable nor suitable for further work with them, which can also lead to various errors occurring during the stage of business process analysis, as well as the stage of its improvement and implementation of proposed changes, i.e., during development, testing, and maintenance of distinct software components, information system modules, and so on. The object of this work is a process of quality assessment of business process models created using the BPMN notation. The subject of this work is a software solution for the quality assessment of business process models. The goal of this work is the evaluation of a probability of errors in business process models to improve their quality.

An Exploratory Study on the Design and Management Model of Traditional Chinese Medicine Quality Safety Traceability System Based on Blockchain Technology

Aiming at the problems of long life cycle complex roles of participants, diverse risk factors, poor supervision coverage and difficult information traceability of TCM (Traditional Chinese Medicine) supply chain, this paper constructs a TCM quality safety traceability system based on blockchain technology by analyzing the business process and supervision characteristics of TCM supply chain. Blockchain technology is a new application mode of computer technology such as distributed data storage, peer-to-peer transmission, consensus mechanism, encryption algorithm, and other computer technologies. It has the characteristics of “decentralization, nontampering, transparent and open, and data traceability.” The function of nontampering, hash function, and time stamp can effectively solve the traceability problem of TCM supply chain. Through the analysis of the blockchain TCM traceability principle and network structure, combined with the actual business process of TCM traceability, this paper designs the blockchain TCM quality safety traceability system, analyzes its overall structure, data storage mode, digital signature, consensus algorithm, and main functions, and preliminarily constructs a new model of TCM quality safety traceability management based on blockchain technology. Using blockchain technology to realize the quality safety traceability of TCM, we can complete the omnidirectional, multiangle, and wide coverage of the data and information in whole supply chain of TCM breeding and seedling raising, planting, innovation and research and development, processing of TCM pieces, circulation of TCM, and consumption, so as to realize that the main body of responsibility of TCM can be put on record, the production records can be queried, product flow can be traced, quality safety can be forewarned, main responsibility can be identified, regulatory information can be shared, and product source can be traced.

BUSINESS PROCESS MODELING TO IMPROVE THE ENTERPRISE MANAGEMENT SYSTEM

The article contains the results of research on the feasibility of modeling business processes. The modeling procedure itself is necessary to determine the level of efficiency of each business process, identify duplication of operations, a clear understanding of the roles of each participant and owner of the process. Business processes can be divided into production and management. Coverage of the quality system of the enterprise of all areas of activity involves modeling and analysis of each business process at all stages of the life cycle of the enterprise. The availability of various information systems for modeling, optimization and participation in enterprise management facilitates the work of analysts, but requires the definition of uniform rules and a clear reflection of information flows in modeling. The results of modeling are a clear visualization, a description of the process. The documentation contains the sequence of all procedures, their provision and control. Models allow you to detect redundancies and duplication, as well as clearly defined moments of beginning, end, transition from one process to another. The proposed concept combines a strategy based on a balanced system and detail of each process. The implementation of this concept is individual for each individual enterprise. The relevance of research is also confirmed by the need to form management hierarchical links between enterprise development strategy and business processes models of management, production and provision of services, management interaction and information flows. Among many concepts of using business process models, the most developed is the concept of business process management, which is based on the use of BPM-systems in the enterprise. This approach involves the formation of business process models in accordance with the process approach, their definition, design, documentation, measurement to achieve target results in accordance with the strategic objectives of the enterprise. The disadvantages of this approach are the difficulty of adaptation and the insufficient level of use of all digitalization tools in enterprises. The concept of mirrors involves the use of at least three mirrors for modeling – the actual business processes and their analysis, ideal processes in accordance with the strategy and quality system, optimized processes in accordance with the capabilities of the enterprise at the moment. The most adapted combination of strategic and process management is to create a balanced system of indicators in the main areas of enterprise development. For example, the following projections can be defined as: Production (production business processes), Personnel (personnel and corporate culture management processes), Finance (financial management processes), Orders (order management processes). For each of these projections will be formed special relevant indicators. The level of business processes is clearly distributed in a hierarchy – from strategy to customer and, according to the characteristics of production or services varies at each level. This analysis allows you to form each level of monitoring and control of the business process in order to optimize it. In addition, the introduction of new management and information technologies will make changes from strategic goals to each individual business process. For example, with the introduction of lean production, it is planned to make changes in the development strategy of the enterprise, in particular the restructuring of production processes and retraining of personnel. The result is the optimization of business processes and improving the efficiency of production resources. Such a restructuring project provides for variable modeling of business processes taking into account the qualifications of staff. The resulting models are the basis for determining the baseline that will be implemented and implemented. Monitoring, evaluation of business processes and projections is carried out periodically. Business process modeling, analysis and optimization are well-known procedures for process management, which is actively used for manufacturing enterprises. Analysis of the life cycle of the enterprise shows that each of its phases requires a reassessment of strategic indicators and business processes. This approach allows you to combine the strategy of enterprise development and its business processes. Known modeling techniques and tools based on information technology can be used for modeling, analysis and optimization, provided the formation of adapted model templates for each individual enterprise. Among the known concepts of using business processes, the most relevant are the use of quality management system and the formation of three mirror models of production and management processes The proposed concept can be implemented using a system of balanced scores and a variety of information technology tools, provided clear algorithms for the interaction of management and information system of the enterprise.

A Formal Model for the Business Innovation Case Description

The purpose of this paper is to formalize the plural business innovation cases to compare each other. In case method learning, class discussions are based on cases that summarize actual business processes. This paper presents a model to re-description formally business innovation cases written in natural language. The model we named Managerial Decision-making Description Model (MDDM). MDDM consists of less than ten kinds of symbolic components and a simple syntax, for ease of writing and reading. In MDDM we define the structure of a business as layered relationship between objectives and resources. Then MDDM illustrates business innovation as a transition in the objectives-resources relationship and expresses the role of decision making in that transition. By formally describing business innovation cases in this way, it is possible to visually compare the characteristics of individual innovation processes. The model also allows us to compare the understandings between a facilitator and learners for the same case in a case study classroom. This formal description can be applied to the outcomes of organizational simulations and business games as well as to actual business innovations. In this paper, we introduce MDDM and its examples of cases descriptions derived from an actual business case and an organizational simulation.

Event Log Preprocessing for Process Mining: A Review

Process Mining allows organizations to obtain actual business process models from event logs (discovery), to compare the event log or the resulting process model in the discovery task with the existing reference model of the same process (conformance), and to detect issues in the executed process to improve (enhancement). An essential element in the three tasks of process mining (discovery, conformance, and enhancement) is data cleaning, used to reduce the complexity inherent to real-world event data, to be easily interpreted, manipulated, and processed in process mining tasks. Thus, new techniques and algorithms for event data preprocessing have been of interest in the research community in business process. In this paper, we conduct a systematic literature review and provide, for the first time, a survey of relevant approaches of event data preprocessing for business process mining tasks. The aim of this work is to construct a categorization of techniques or methods related to event data preprocessing and to identify relevant challenges around these techniques. We present a quantitative and qualitative analysis of the most popular techniques for event log preprocessing. We also study and present findings about how a preprocessing technique can improve a process mining task. We also discuss the emerging future challenges in the domain of data preprocessing, in the context of process mining. The results of this study reveal that the preprocessing techniques in process mining have demonstrated a high impact on the performance of the process mining tasks. The data cleaning requirements are dependent on the characteristics of the event logs (voluminous, a high variability in the set of traces size, changes in the duration of the activities. In this scenario, most of the surveyed works use more than a single preprocessing technique to improve the quality of the event log. Trace-clustering and trace/event level filtering resulted in being the most commonly used preprocessing techniques due to easy of implementation, and they adequately manage noise and incompleteness in the event logs.

Conformance Checking of Dwelling Time Using a Token-based Method

Background: Standard operating procedure (SOP) is a series of business activities to achieve organisational goals, with each activity carried to be recorded and stored in the information system together with its location (e.g., SCM, ERP, LMS, CRM). The activity is known as event data and is stored in a database known as an event log.Objective: Based on the event log, we can calculate the fitness to determine whether the business process SOP is following the actual business process.Methods: This study obtains the event log from a terminal operating system (TOS), which records the dwelling time at the container port. The conformance checking using token-based replay method calculates fitness by comparing the event log with the process model.Results: The findings using the Alpha algorithm resulted in the most traversed traces (a, b, n, o, p). The fitness calculation returns 1.0 were produced, missing, and remaining tokens are replied to each of the other traces.Conclusion: Thus, if the process mining produces a fitness of more than 0.80, this shows that the process model is following the actual business process. Keywords: Conformance Checking, Dwelling time, Event log, Fitness, Process Discovery, Process Mining

The Future of Factories: Different Trends

The technological advancements promote the rise of the fourth industrial revolution, where key terms are efficiency, innovation, and enterprises’ digitalization. Market globalization, product mass customization, and more complex products need to reflect on changing the actual design methods and developing business processes and methodologies that have to be data-driven, AI-assisted, smart, and service-oriented. Therefore, there is a great interest in experimenting with emerging technologies and evaluating how they impact the actual business processes. This paper reports a comparison among the major trends in the digitalization of a Factory of the Future, in conjunction with the two major strategic programs of Industry 4.0 and China 2025. We have focused on these two programs because we have had experience with them in the context of the FIRST H2020 project. European industrialists identify the radical change in the traditional manufacturing production process as the rise of Industry 4.0. Conversely, China mainland launched its strategic plan in China 2025 to promote smart manufacturing to digitalize traditional manufacturing processes. The main contribution of this review paper is to report about a study, conducted and part of the aforementioned FIRST project, which aimed to investigate major trends in applying for both programs in terms of technologies and their applications for the factory’s digitalization. In particular, our analysis consists of the comparison between Digital Factory, Virtual Factory, Smart Manufacturing, and Cloud Manufacturing. We analyzed their essential characteristics, the operational boundaries, the employed technologies, and the interoperability offered at each factory level for each paradigm. Based on this analysis, we report the building blocks in terms of essential technologies required to develop the next generation of a factory of the future, as well as some of the interoperability challenges at a different scale, for enabling inter-factories communications between heterogeneous entities.

Developing Real Estate Automated Valuation Models by Learning from Heterogeneous Data Sources

In this paper we propose a data acquisition methodology, and a Machine Learning solution for the partially automated evaluation of real estate properties. The novelty and importance of the approach lies in two aspects: (1) when compared to Automated Valuation Models (AVMs) as available to real estate operators, it is highly adaptive and non-parametric, and integrates diverse data sources; (2) when compared to Machine Learning literature that has addressed real estate applications, it is more directly linked to the actual business processes of appraisal companies: in this context prices that are advertised online are normally not the most relevant source of information, while an appraisal document must be proposed by an expert and approved by a validator, possibly with the help of technological tools. We describe a case study using a set of 7988 appraisal documents for residential properties in Turin, Italy. Open data were also used, including location, nearby points of interest, comparable property prices, and the Italian revenue service area code. The observed mean error as measured on an independent test set was around 21 K€, for an average property value of about 190 K€. The AVM described here can help the stakeholders in this process (experts, appraisal company) to provide a reference price to be used by the expert, to allow the appraisal company to validate their evaluations in a faster and cheaper way, to help the expert in listing a set of comparable properties, that need to be included in the appraisal document.

A Convergence Model for Environment Analysis of SME (Small and Medium Enterprises) Using RPA (Robotic Process Automation)

The purpose of this study is to investigate the effectiveness of the consulting project of the convergence model for analyzing the market environment of Small and Medium Enterprises (SME) using Robotic Process Automation (RPA). The subject of the study was the semiconductor manufacturing machinery manufacturing industry (Korea Standard Industry Code (KSIC): C29271), and the market environment analysis process modeling was conducted using the RPA program. In fact, the Korean RPA program, “AutomateOne,” was used to automate market environment analysis. Currently, RPA is used as a substitute for simple, repetitive and standardized work in the financial sector, and it is used only in limited fields such as accounting duties, so studies on the applicability in other industries are insufficient. In this study, in the field of knowledge service in which consultants are mainly active, in market analysis work, scripts were created through actual business process modeling to show the possibility of application in the relevant work. The application of the consulting project was proved by the contribution of digital workers, which is a realistic measure against the reduction of the production worker population. In this study, an application case of robot process automation was studied in the knowledge service industry. In the future, research is needed to increase the efficiency of business process modeling through linking with open Python libraries and cognitive functions of machine learning or deep learning.

Business process mining from e-commerce event web logs: Conformance checking and bottleneck identification

A range of advanced methods have been formulated and utilized in the efforts of improving the business processes in many enterprises. One impacting enhancement technique is to employ process mining algorithms as modeling and analysis tools in order to provide the actual business performance by digging the event log data and finding the useful information. This paper focuses on the applications of process mining in e-commerce industry. Event log data with timestamps were retrieved and analyzed from the web databases of an e-commerce company and process mining algorithms, like inductive miner and fuzzy miner were executed for generating the actual e-commerce business processes automatically and checking their conformance with the standardized processes as well as to early detecting any bottlenecks and issues in the e-commerce processes. Several e-commerce process issues were considered, such as item procurement, product order and delivery item tracking. The process mining modeling and its statistical results indicate that process mining can provide an efficient and effective tool for modeling and analyzing the e-commerce business processes allowing for real-time process auditing and reengineering.

Agent System Mining: Vision, Benefits, and Challenges

In the Agent-Based Modeling (ABM) paradigm, an organization is a Multi-Agent System (MAS) composed of autonomous agents inducing business processes. Process Mining automates the creation, update, and analysis of explicit business process models based on event data. Process Mining techniques make simplifying assumptions about the processes discovered from data. However, actual business processes are often more complex than those restricted by Process Mining assumptions. Several Process Mining approaches relax these standard assumptions by discovering more realistic process models. These approaches can discover more realistic process models. However, these models are often difficult to visualize and, consequently, to understand. Many MASs induce processes whose behaviors become more complex with each next embraced time step, while the complexities of these MASs remain constant. Thus, the ABM paradigm can cope naturally with the increasing complexity of the discovered process models. This paper proposes Agent System Mining (ASM) and ASM Framework. ASM combines Process Mining and ABM in the Business Process Management (BPM) context to infer MAS models of operational business processes from real-world event data, while ASM Framework maps ASM activities to different phases of the MAS modeling lifecycle. The paper also discusses the benefits of using ASM and outlines challenges associated with the implementation of the ASM Framework.

Redesigning an Information Systems Course for MBA Students

Master of Business Administration (MBA) students positively evaluate an Information Systems (IS) course inclusive of a real-world business project compared to one that does not. Blooms' Digital Taxonomy was used to redesign learning concepts covered in an MBA IS course by shifting from "Remembering" to "Applying" as the best teaching method for adult business professionals. A real-world group business project included in an accelerated online MBA program improved online evaluations; integrating IS concepts embedded within actual business processes resulted in a positive perception of the Management Information Systems (MIS) course. Our findings align well with Knowles' theory of Andragogy concerning the learning motivation of adult students. MBA IS Instructors must use experiential learning methods in a problem-solving context so that students understand the "Why" of the impact of essential IS business processes. This knowledge has immediate and lasting effects on student careers.