Process improvement : the creation and evaluation of process alternatives

Companies continuously strive to improve their processes to increase productivity and delivered quality against lower costs. With Business Process Redesign (BPR) projects such improvement goals can be achieved. BPR involves the restructuring of business processes, stimulated by the application of information technology. Although BPR is widely applied in industry, a systematic approach that helps a team in deriving designs for better performing business processes is lacking. The approach for Process Improvement by Creating and Evaluating process alternatives (in short: the PrICE approach) is developed to describe and support the concrete steps that will lead a design team from the as-is process to the to-be process. The starting point for the PrICE approach is a model of an existing process. The as-is model contains tasks and their execution ordering, data elements that are created and used, resources and their allocation and performance information. First, redesign opportunities are identified in the process model. A redesign opportunity leads to a combination of a certain redesign operation and a process part on which this operation can be applied. The PrICE approach consists of four steps. The first step of the PrICE approach describes the selection of redesign operations. Eight redesign operations have been developed, each supporting a particular type of redesign creation. We introduce two possible means to select applicable operations: process measures and process mining. Process measures provide a global view on the characteristics of the process and their values may reveal weaknesses in the process. The idea of process mining is to discover, monitor and improve business processes by extracting knowledge from event logs. Process mining results point out what type of changes may be beneficial. Moreover, bottlenecks, i.e., parts of the process that need improvement, are detected. The second step of the approach is the selection of process parts. In this step we focus on the selection of a process part in such a way that the application of a redesign operation results in a correct process model. The third step of the approach concentrates on the creation of process alternatives. An application of a redesign operation to a selected process part results in an alternative process model. An overview of the created alternatives is provided with the process alternatives tree. The root node of the tree is the original model and the other nodes represent the created alternatives. Each of the nodes may serve as a starting point for the creation of another alternative. In the last step of the approach, the performance of the process alternatives is evaluated with simulation. Simulation provides quantitative estimates for the performance, e.g., on time or costs, of a process model. By comparing the simulation results of the models in an alternatives tree, a quantitatively supported choice for the best alternative model, the to-be process, can be made. The PrICE approach is supported with the PrICE tool kit. The tool support is implemented as part of the Process Mining (ProM) framework. The tool kit supports the application of the various steps of the approach. The first two steps are supported with the process mining techniques that are available in ProM. The main features of the PrICE tool kit are the user guidance in the selection of process parts, the creation of process alternatives, the construction of the process alternatives tree and the evaluation of the alternatives with simulation. After the selection of a redesign operation, a process part for redesign is selected by the user by clicking on the tasks in the process model. Colors are used to guide the user and show which tasks may be added to the current selection to form a process part. This way, it is ensured that the input for the creation of a process alternative is such that a correct alternative model can be created. After the creation of an alternative model, the process alternatives tree is updated with a new node representing this alternative. Each node in the alternatives tree can be selected as starting point for the creation of another process alternative. With regard to the evaluation of the alternatives, one can select a subset of nodes for simulation or simulate the complete tree. A simulation study is performed in batch, i.e., all selected models are simulated without user interaction. Afterwards, the simulation results are displayed on the tree nodes. In addition, colors are used to guide the user in finding the best performing alternatives. The developed tool support demonstrates the feasibility of our ideas. This feasibility is also illustrated with several applications of the tool kit to real life processes. Apart from the development of the PrICE approach and tool kit, the thesis includes several other contributions. A contribution is the creation of correct process models. We refer to a process model as correct if the workflow structure is sound and if the data distribution is correct. A correct data distribution is an assignment of the data elements to the tasks in the process in such a way that the data elements necessary for the execution of a task have been written when the task becomes enabled. Requirements on the workflow structure and data distribution are set on the selection of process parts and the creation of alternatives to ensure the construction of correct process alternatives. Another contribution is the overview of the created process alternatives with the process alternatives tree. An alternative model may be created from the original model (the root node) or from one of the alternative models (any other node). The alternatives tree is also used as input for the evaluation of the performance of the alternatives and to provide an overview of the simulation results. A final contribution is the enhancement of the practical use of simulation for process redesign. On the one hand, the automation of the simulation study reduces the necessary time investment because intermediate input from the user is not required. On the other hand, we present a simulation plan that facilitates the understanding of the various aspects that should be addressed in a simulation study

The PrICE tool kit: tool support for process improvement

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The creation of process redesigns by selecting, transforming and replacing process parts

For companies to sustain competitive advantages, it is required to redesign and improve business processes continuously by monitoring and analyzing process enactment results. Furthermore, organizational structures must be redesigned according to the changes in business processes. However, there are few scientific approaches to redesigning organizational structures. This paper presents a method for deriving and analyzing organizational relations from process models using social network analysis. Process models contain information on who performs which processes or activities, along with the assignment of organizational units such as departments and roles to related activities. To derive social relations among organizational units from process models, three types of metrics are formally defined: transfer of work metrics, subcontracting metrics, and cooperation metrics. By applying these metrics, various relations among organizational units can be derived and analyzed, which can suggest how organizational structure must be redesigned. To verify the method, the proposed metrics are applied to standard process models of the semiconductor and electronic industry in Korea

BPR best practices for the healthcare domain

Healthcare providers are under pressure to work more efficiently and in a more patient-focused way. One possible way to achieve this is to launch Business Process Redesign (BPR) initiatives, which focus on changing the structure of the involved processes and using IT as an enabler for such changes. In this paper, we argue that a list of historically successful improvement tactics, the BPR best practices, are a highly suitable ingredient for such efforts in the healthcare domain. Our assessment is based on the analysis of 14 case studies. The insights obtained by the analysis also led to an extension of the original set of best practices

Response shift after coronary revascularization

Purpose The aims of this study were to investigate (1) the extent to which response shift occurs among patients with coronary artery disease (CAD) after coronary revascularization, (2) whether the assessment of changes in health-related quality of life (HRQoL), controlled for response shift, yield more valid estimates of changes in HRQoL, as indicated by stronger associations with criterion measures of change, than without controlling for response shift, and (3) if occurrences of response shift are related to patient characteristics. Methods Patients with CAD completed the SF-36 and the Seattle Angina Questionnaire (SAQ7) at baseline and 3 months after coronary revascularization. Sociodemographic, clinical and psychosocial variables were measured with the patient version of the New York Heart Association-class, Subjective Significance Questionnaire, Reconstruction of Life Events Questionnaire (RE-LIFE), and HEXACO personality inventory. Oort's Structural Equation Modeling (SEM) approach was used to investigate response shift. Results 191 patient completed questionnaires at baseline and at 3 months after treatment. The SF-36 showed recalibration and reprioritization response shift and the SAQ7 reconceptualization response shift. Controlling for these response shift effects did not result in more valid estimates of change. One significant association was found between reprioritization response shift and complete integration of having CAD into their life story, as indicated by the RE-LIFE. Conclusion Results indicate response shift in HRQoL following coronary revascularization. While we did not find an impact of response shift on the estimates of change, the SEM approach provides a more comprehensive insight into the different types of change in HRQoL following coronary revascularization.Biological, physical and clinical aspects of cancer treatment with ionising radiatio

Diversity among Bi-ethnic students and differences in educational outcomes and social functioning

The number of bi-ethnic children is increasing. The focus of this study is on bi-ethnic students in the Netherlands with one parent with an ethnic majority background and one parent with an ethnic minority background. Most studies that have investigated educational outcomes and social functioning in school for bi-ethnic students have not focused on the diversity within this group. In this study, we described the demographic, social and cultural diversity among bi-ethnic students and examined whether, in particular, ethnic background and gender of the migrant parent were related to the educational outcomes and social functioning of bi-ethnic students. Data on a total of 653 sixth grade bi-ethnic students (age 11–12) in primary education of the national Dutch cohort study (COOL5−18) were used in this study. To analyse the relationship between the ethnic background and gender of the migrant parent and the educational outcomes and social functioning among bi-ethnic students, multivariate multilevel analyses were performed. The research findings indicate that bi-ethnic students differ demographically, socially and culturally in a manner dependent on ethnic background and gender of the migrant parent. We also found that the ethnic background and the gender of the migrant parent were related to cognitive outcomes, social-emotional functioning and citizenship competences. When trying to understand and support bi-ethnic students, we must consider the diversity among them

Process improvement : the creation and evaluation of process alternatives

Companies continuously strive to improve their processes to increase productivity and delivered quality against lower costs. With Business Process Redesign (BPR) projects such improvement goals can be achieved. BPR involves the restructuring of business processes, stimulated by the application of information technology. Although BPR is widely applied in industry, a systematic approach that helps a team in deriving designs for better performing business processes is lacking. The approach for Process Improvement by Creating and Evaluating process alternatives (in short: the PrICE approach) is developed to describe and support the concrete steps that will lead a design team from the as-is process to the to-be process. The starting point for the PrICE approach is a model of an existing process. The as-is model contains tasks and their execution ordering, data elements that are created and used, resources and their allocation and performance information. First, redesign opportunities are identified in the process model. A redesign opportunity leads to a combination of a certain redesign operation and a process part on which this operation can be applied. The PrICE approach consists of four steps. The first step of the PrICE approach describes the selection of redesign operations. Eight redesign operations have been developed, each supporting a particular type of redesign creation. We introduce two possible means to select applicable operations: process measures and process mining. Process measures provide a global view on the characteristics of the process and their values may reveal weaknesses in the process. The idea of process mining is to discover, monitor and improve business processes by extracting knowledge from event logs. Process mining results point out what type of changes may be beneficial. Moreover, bottlenecks, i.e., parts of the process that need improvement, are detected. The second step of the approach is the selection of process parts. In this step we focus on the selection of a process part in such a way that the application of a redesign operation results in a correct process model. The third step of the approach concentrates on the creation of process alternatives. An application of a redesign operation to a selected process part results in an alternative process model. An overview of the created alternatives is provided with the process alternatives tree. The root node of the tree is the original model and the other nodes represent the created alternatives. Each of the nodes may serve as a starting point for the creation of another alternative. In the last step of the approach, the performance of the process alternatives is evaluated with simulation. Simulation provides quantitative estimates for the performance, e.g., on time or costs, of a process model. By comparing the simulation results of the models in an alternatives tree, a quantitatively supported choice for the best alternative model, the to-be process, can be made. The PrICE approach is supported with the PrICE tool kit. The tool support is implemented as part of the Process Mining (ProM) framework. The tool kit supports the application of the various steps of the approach. The first two steps are supported with the process mining techniques that are available in ProM. The main features of the PrICE tool kit are the user guidance in the selection of process parts, the creation of process alternatives, the construction of the process alternatives tree and the evaluation of the alternatives with simulation. After the selection of a redesign operation, a process part for redesign is selected by the user by clicking on the tasks in the process model. Colors are used to guide the user and show which tasks may be added to the current selection to form a process part. This way, it is ensured that the input for the creation of a process alternative is such that a correct alternative model can be created. After the creation of an alternative model, the process alternatives tree is updated with a new node representing this alternative. Each node in the alternatives tree can be selected as starting point for the creation of another process alternative. With regard to the evaluation of the alternatives, one can select a subset of nodes for simulation or simulate the complete tree. A simulation study is performed in batch, i.e., all selected models are simulated without user interaction. Afterwards, the simulation results are displayed on the tree nodes. In addition, colors are used to guide the user in finding the best performing alternatives. The developed tool support demonstrates the feasibility of our ideas. This feasibility is also illustrated with several applications of the tool kit to real life processes. Apart from the development of the PrICE approach and tool kit, the thesis includes several other contributions. A contribution is the creation of correct process models. We refer to a process model as correct if the workflow structure is sound and if the data distribution is correct. A correct data distribution is an assignment of the data elements to the tasks in the process in such a way that the data elements necessary for the execution of a task have been written when the task becomes enabled. Requirements on the workflow structure and data distribution are set on the selection of process parts and the creation of alternatives to ensure the construction of correct process alternatives. Another contribution is the overview of the created process alternatives with the process alternatives tree. An alternative model may be created from the original model (the root node) or from one of the alternative models (any other node). The alternatives tree is also used as input for the evaluation of the performance of the alternatives and to provide an overview of the simulation results. A final contribution is the enhancement of the practical use of simulation for process redesign. On the one hand, the automation of the simulation study reduces the necessary time investment because intermediate input from the user is not required. On the other hand, we present a simulation plan that facilitates the understanding of the various aspects that should be addressed in a simulation study