Search results

The purpose of this study is to develop a map for the holistic business process management (BPM) diagnosis in order to guide the choice of techniques that encompass all dimensions of the business process.

The design science research method was used, with the elaboration of seven steps to project solutions to empirical problems: (1) identification of the problem, (2) awareness of the problem, (3) definition of expected results, (4) design and development, (5) demonstration, (6) evaluation of artifacts and (7) communication. These steps were organized in different analyzes: descriptive, experimental and observational. The descriptive analysis comprised steps one to three (identification of the problem, awareness of the problem, definition of expected results) and made use of the systematic literature review procedure for proposing artifacts. The experimental analysis comprised steps four to five (design and development, and demonstration), where the consultation with specialists' procedures and then the Delphi procedure for the construction of the artifacts were carried out. In the observational analysis, steps six (evaluation of artifacts), where two case studies were performed, and step seven (communication), in which the map for the holistic BPM diagnosis was presented were carried out.

The article systematizes the BPM diagnostic techniques scattered throughout the literature and relates how these techniques relate to dimensions. A map for the holistic BPM diagnosis is generated containing 21 techniques and 9 dimensions, with 45 relationships between these techniques and tools. Another aspect is that the map shows that in BPM promotion projects, techniques are not restricted to any specific phase of the life cycle.

Professionals can use the map to form a blend with selected techniques and use them for holistic BPM diagnosis according to the skills and other resources of the project team.

The map developed is innovative because it relates a set of consolidated techniques for each dimension of the process to provide the holistic diagnosis for the organization. It is important to highlight that these techniques and dimensions were scattered in the literature.

This paper focuses on organizational change through the business process management approach. While “business process modeling” permits understanding process activities and their activities with other participants, “current reality tree (CRT)” technique promotes the identification of process constraints. The purpose of this study is to compare the results from applying both diagnostic techniques, process modeling, using the business process modeling notation, and root cause analysis, using the CRT.

The comparison is made using a pre-experiment in which two teams conducted diagnoses concomitantly in the information technology management (ITM) process of one unit of the biggest and prestigious higher education institution (HEI) in Brazil.

The modeling technique and the CRT should be considered complementary techniques, since applying one does not diminish or exclude the importance of using the other. Results were compared analyzing which dimensions of the process each technique highlighted: strategy, organization, activity/information and resources.

A possible limitation of this research is that the experiment was conducted in a single process and the result cannot be generalized to other processes.

It may be noted that the main contribution of this study is the presentation of the steps of two techniques for process diagnosis. It is expected that with the reports on diagnoses outcomes, team's assessment and the perception of the managers presented here other improvement teams may use the results of this research as an inspiration to perform process diagnosis, and as basis for decision making to define which technique to use according to the specific needs of process improvement.

The paper stands out the comparison of the technique application's outcomes. This study offers valuable insights to the organizations that are interested in restructuring their processes. It delineates many important benefits of such a diagnosis techniques. It also identifies possible pitfalls and recommends guidelines for the successful conduction of process diagnoses initiatives.

Driven by the development in sensing techniques and information and communications technology, and their applications in the manufacturing system, data-driven quality control methods play an essential role in the quality improvement of assembly products. This paper aims to review the development of data-driven modeling methods for process monitoring and fault diagnosis in multi-station assembly systems. Furthermore, the authors discuss the applications of the methods proposed and present suggestions for future studies in data mining for quality control in product assembly.

This paper provides an outline of data-driven process monitoring and fault diagnosis methods for reduction in variation. The development of statistical process monitoring techniques and diagnosis methods, such as pattern matching, estimation-based analysis and artificial intelligence-based diagnostics, is introduced.

A classification structure for data-driven process control techniques and the limitations of their applications in multi-station assembly processes are discussed. From the perspective of the engineering requirements of real, dynamic, nonlinear and uncertain assembly systems, future trends in sensing system location, data mining and data fusion techniques for variation reduction are suggested.

This paper reveals the development of process monitoring and fault diagnosis techniques, and their applications in variation reduction in multi-station assembly.

The purpose of this paper is to propose and describe a method that uses recurrent problems to increase the efficiency and effectiveness of the diagnosis of new product development (NPD) processes and supports the identification of improvement opportunities. The proposed method, called Diagile, is based on recurrent current reality trees (CRTs) and is a new way of building CRTs that includes best project management practices, and the identification and prioritization of improvement opportunities. To support the execution of the method, recurrent problems were identified and a computational tool to aid the diagnosis, a database of improvement opportunities and an automated spreadsheet to prioritize improvement projects were developed.

The proposed method was evaluated through a controlled experiment at a multinational manufacturer of office supplies.

The results achieved confirm that the use of the Diagile method increases the diagnostic efficiency and effectiveness when compared to diagnoses performed by the traditional CRT method.

The validity of the method must be tested on a larger scale, since this work involved only one controlled experiment for this purpose. The experiment involved the participation of postgraduate research assistants, who cannot be considered specialists in the diagnosis of NPD. One could question whether the method will be as helpful for proficient users as well. The authors did not have proficient users available to run the experiment. However, the authors believe that such a specialist would save time in carrying out a diagnosis with Diagile, and also be more effective in validating the diagnosis. However, this assumption could not be tested here and can therefore be considered a limiting factor of the experiment. Nevertheless, the positive results of the evaluations of the companies and users of the two case studies corroborate the statement that the objective of this work was attained.

The greater efficiency and effectiveness provided by the proposed Diagile method was also evident in the identification and prioritization of improvement opportunities. The experimental group drew up a more relevant and coherent list of improvement projects than the control group, and provided documentation for these projects in the form of project charts. The authors believe these results can be of a great impact if implemented by practitioners.

This paper proposes a new way to perform diagnostic of NPD process. In particular, this process is well known to be highly strategic, nevertheless, normally excluded out of improvement initiatives because of its complexity. The diagnostic method proposed is a powerful tool to assist practitioners finding systemic improvement opportunities, expanding the assessment to all dimensions of a business process, e.g. people, technology and process activities.

Purpose – There is a paucity of research that examines how diagnostic decisions are made by psychiatrists. Moreover, previous work in the area tends to be grounded in labeling theory, which highlights the conflict-based nature of diagnosis. The goal of this research is to examine the utility and benefits of diagnosis to psychiatrists' everyday work.

Methodology – Using institutional ethnography (IE), I undertook a small-scale interview-based study that documented the diagnostic processes of three psychiatrists in Calgary, Alberta, Canada. The IE-based goals of the study were to: (1) identify what texts were employed during the diagnostic process, (2) map sequences of action and text that coordinated psychiatric decision-making, and (3) theorize the utility of diagnosis for the everyday work of psychiatrists.

Findings – The analysis demonstrates how diagnosis can be understood as a valuable work process that produces a standardized diagnostic story in order to bring an individual's experiences of distress into relation with psychiatrists' daily practices, and institutional discourses more generally.

Limitations – Although IE-based research does not depend on large sample sizes for analytic accuracy, results from the current study need to be replicated because of the limited number of interview participants and to examine whether the diagnostic process is generalizable to other settings.

Social implications – This research challenges the idea that standardization through diagnosis is a negative process and highlights the value of diagnostic decision-making in the daily work of psychiatrists.

The paper proposes an innovative systemic method helping decision-makers to control servitization transition process, through decision process risk diagnosis.

The proposed method is based on the modeling of decision processes and risk identification and analysis. This method was based on an action-research approach, in close relationship with two companies (SMEs). The paper develops the feasibility experiment at Automelec company.

The method was successfully implemented and delivered concrete diagnosis results.

The generalization of the applicability of the method needs to be tested on several different cases.

The first practical implication is related to the efficiency of the method to help decision-makers in a servitization context to limit uncertainty and get a global view of the weaknesses of their decision-making process, it raises their awareness about servitization transition for their companies. Furthermore, the method also helps to explain the strategy of a servitization transition. It enhances the level of maturity of the decision process of the company, and can be used as a training/learning tool for managers.

The results brought by the research contribute to give the decision-making boards for organization living a servitization transition and especially SMEs a better control over the servitization decision process and related risks, which will increase the economic stability of the company and its vision over long, medium and short horizons. This will bring positive impact on the overall economic and social environment and networks of the servitized SME, and enhance the confidence of coworkers, subcontractors and clients.

The first originality of the paper is related to the new way of considering risk, not only as an analysis criterion but as the central driver in steering a strategic transition for the company, such as servitization. The second originality of the study is about assessing risk occurrence over a decision-making process through decision reliability and decision confidence.

Suggests that there is justification for the use of a new methodology for process diagnosis which is simple to understand and realistic to implement. The control of quality of a process typically requires that multiple process variables be monitored simultaneously. Due to the multi‐dimensionality of the data collected, process diagnosis is complex and the data often are not efficiently integrated to capitalize on the wealth of available information. A two‐phased diagnostic approach for process diagnosis and identification of suspect causes for this multi‐dimensional problem is introduced in Krishnamurthi et al. (1993). Provides an in‐depth analysis of phase two of the statistical process control (SPC) diagnostic approach. Specifically, simulation is used to generate different cause and effect scenarios to determine the effectiveness of the SPC approach in correctly diagnosing a process disorder. The analysis utilizes analysis of variance to estimate the effect of various process variables, process steps, and associated out‐of‐control conditions on the performance of the SPC approach and its ability to diagnose correctly an out‐of‐control condition. As a result of these findings, the critical means are plotted and the findings are presented. Additionally, a comparison between the SPC approach and parsimonious covering theory (PCT) is made. Concludes that for the process scenarios considered, which are of practical size, the more simple approach of the SPC diagnostic approach is recommended.

This study aims to examine the impact that diagnostic factors such as duration of diagnostic period, number of professionals consulted and perceived social support have on parental stress during the diagnostic process of autism spectrum disorder (ASD).

Forty parents of 2–18 year-old children/adolescents with a formal ASD diagnosis recruited from five specialized private clinics in Lisbon completed a survey which included diagnostic questionnaire, parental stress scale and social support scale from April to December 2022.

Data analysis indicated that the mean age of the diagnosis was 5.6 years with a delay of 3.95 years from first concern until final diagnosis. On average, parents consulted a mean of 3.62 professionals. Other findings indicated that as age of parents increased, levels of parental stress decreased. Parents with higher levels of social support had lower levels of parental stress. Furthermore, higher age of child at first concern predicted higher affective social support.

This study reflects on the experience of obtaining the ASD diagnosis in Portugal, raising awareness on the importance of providing early detection and social support for distressed parents.

Purpose – The purpose of this chapter is to theorize the relationship between diagnosis and medicalization through an examination of the medicalization of childhood gender variance and the Gender Identity Disorder of Childhood diagnosis.

Methodology/approach – The chapter examines textual data (published clinical and research literatures, and critiques of the diagnosis appearing in a range of venues) to track how childhood gender variance is medicalized over time and the role of diagnosis in that medicalization.

Findings – While diagnosis certainly plays a role in shoring up medicalization, this case study reveals the many ways in which diagnoses may also become key tools in attempts to curtail medicalization.

Research limitations/implications – As a case study, the findings are not generalizable to all diagnoses. As a study of an instance of the medicalization of deviance, these findings may be particularly applicable to analogous cases.

Social implications – These findings show the sometimes tenuous nature of medicalization processes, and the social uses of diagnoses in those processes.

Originality/value of paper – This chapter sheds light on a relationship that is often assumed to be unidirectional (e.g., that the formation of diagnosis results in increased medicalization), and answers calls for a more nuanced sociology of diagnosis, including greater attention to the relationship between diagnosis and medicalization.

This paper aims to present a prototype of medical transportation robot whose positioning accuracy can reach millimeter-level in terms of patient transportation. By using this kind of mobile robot, a fully automatic image diagnosis process among independent CT/PET devices and the image fusion can be achieved.

Following a short introduction, a large-load 4WD-4WS (four-wheel driving and four-wheel steering) mobile robot for carrying patient among multiple medical imaging equipments is developed. At the same time, a specially designed bedplate with self-locking function is also introduced. For further improving the positioning accuracy, the authors proposed a calibration method based on Gaussian process regression (GPR) to process the measuring data of the sensors. The performance of this robot is verified by the calibration experiment and Image fusion experiment. Finally, concluding comments are drawn.

By calibrating the robot’s positioning system through the proposed GPR method, one can obtain the accuracy of the robot’s offset distance and deflection angle, which are 0.50 mm and +0.21°, respectively. Independent repeated trials were then set up to verify this result. Subsequent phantom experiment shows the accuracy of image fusion can be accurate within 0.57 mm in the front-rear direction and 0.83 in the left-right direction, respectively, while the clinical experiment shows that the proposed robot can practically realize the transportation of patient and image fusion between multiple imaging diagnosis devices.

The proposed robot offers an economical image fusion solution for medical institutions whose imaging diagnosis system basically comprises independent MRI, CT and PET devices. Also, a fully automatic diagnosis process can be achieved so that the patient’s suffering of getting in and out of the bed and the doctor’s radiation dose can be obviated.

The general bedplate presented in Section 2 that can be mounted on the CT and PET devices and the self-locking mechanism has realized the catching and releasing motion of the patient on different medical devices. They also provide a detailed method regarding patient handling and orientation maintenance, which was hardly mentioned in previous research. By establishing the positioning system between the robot and different medical equipment, a fully automatic diagnosis process can be achieved so that the patient’s suffering of getting in and out of the bed and the doctor’s radiation dose can be obviated.

The GPR-based method proposed in this paper offers a novel method for enhancing the positioning accuracy of the industrial AGV while the transportation robot proposed in this paper also offers a solution for modern imaging fusion diagnosis, which are basically predicated on the conjoint analysis between different kinds of medical devices.