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The purpose of this study is to investigate the integrated application of business process modeling and notation (BPMN) and value stream mapping (VSM) to improve knowledge work performance and productivity in police services. In order to explore the application of the hybrid BPMN-VSM approach in police services, this study uses the department of digital crime investigation (DCI) in one Norwegian police district as a case study.

Service process identification was the next step after selecting an appropriate organizational unit for the case study. BPMN-VSM-based current state mapping, including time and waste analyses, was used to determine cycle and lead time and identify value-adding and nonvalue-adding activities. Subsequently, improvement opportunities were identified, and the current state process was re-designed and constructed through future state mapping.

The study results indicate a 44.4% and 83.0% reduction in process cycle and lead time, respectively. This promising result suggests that the hybrid BPMN-VSM approach can support the visualization of bottlenecks and possible causes of increased lead times, followed by the systematic identification and proposals of avenues for future improvement and innovation to remedy the discovered inefficiencies in a complex knowledge-work environment.

This study focused on one department in a Norwegian police district. However, the experience gained can support researchers and practitioners in understanding lean implementation through an integrated BPMN and VSM model, offering a unique insight into the ability to investigate complex systems.

Complex knowledge work processes generally characterize police services due to a high number of activities, resources and stakeholder involvement. Implementing lean thinking in this context is significantly challenging, and the literature on this topic is limited. This study addresses the applicability of the hybrid BPMN-VSM approach in police services with an original public sector case study in Norway.

This paper aims to delve into the complexities of terminology mapping and annotation, particularly within the context of the COVID-19 pandemic. It underscores the criticality of harmonizing clinical knowledge organization systems (KOS) through a cohesive clinical knowledge representation approach. Central to the study is the pursuit of a novel method for integrating emerging COVID-19-specific vocabularies with existing systems, focusing on simplicity, adaptability and minimal human intervention.

A design science research (DSR) methodology is used to guide the development of a terminology mapping and annotation workflow. The KNIME data analytics platform is used to implement and test the mapping and annotation techniques, leveraging its powerful data processing and analytics capabilities. The study incorporates specific ontologies relevant to COVID-19, evaluates mapping accuracy and tests performance against a gold standard.

The study demonstrates the potential of the developed solution to map and annotate specific KOS efficiently. This method effectively addresses the limitations of previous approaches by providing a user-friendly interface and streamlined process that minimizes the need for human intervention. Additionally, the paper proposes a reusable workflow tool that can streamline the mapping process. It offers insights into semantic interoperability issues in health care as well as recommendations for work in this space.

The originality of this study lies in its use of the KNIME data analytics platform to address the unique challenges posed by the COVID-19 pandemic in terminology mapping and annotation. The novel workflow developed in this study addresses known challenges by combining mapping and annotation processes specifically for COVID-19-related vocabularies. The use of DSR methodology and relevant ontologies with the KNIME tool further contribute to the study’s originality, setting it apart from previous research in the terminology mapping and annotation field.

Autonomous mobile robots (AMRs) play a crucial role in industrial and service fields. The paper aims to build a LiDAR-based simultaneous localization and mapping (SLAM) system used by AMRs to overcome challenges in dynamic and changing environments.

This research introduces SLAM-RAMU, a lifelong SLAM system that addresses these challenges by providing precise and consistent relocalization and autonomous map updating (RAMU). During the mapping process, local odometry is obtained using iterative error state Kalman filtering, while back-end loop detection and global pose graph optimization are used for accurate trajectory correction. In addition, a fast point cloud segmentation module is incorporated to robustly distinguish between floor, walls and roof in the environment. The segmented point clouds are then used to generate a 2.5D grid map, with particular emphasis on floor detection to filter the prior map and eliminate dynamic artifacts. In the positioning process, an initial pose alignment method is designed, which combines 2D branch-and-bound search with 3D iterative closest point registration. This method ensures high accuracy even in scenes with similar characteristics. Subsequently, scan-to-map registration is performed using the segmented point cloud on the prior map. The system also includes a map updating module that takes into account historical point cloud segmentation results. It selectively incorporates or excludes new point cloud data to ensure consistent reflection of the real environment in the map.

The performance of the SLAM-RAMU system was evaluated in real-world environments and compared against state-of-the-art (SOTA) methods. The results demonstrate that SLAM-RAMU achieves higher mapping quality and relocalization accuracy and exhibits robustness against dynamic obstacles and environmental changes.

Compared to other SOTA methods in simulation and real environments, SLAM-RAMU showed higher mapping quality, faster initial aligning speed and higher repeated localization accuracy.

To address the absence of Lean in transportation logistics in the digital era, this study aims to investigate the application of Lean transportation (LT) tools to reduce waste and facilitate the digital transformation of dedicated road transportation in the offshore industry.

The study adopts action research with a multimethod approach, including a scoping review, focus groups (FG) and participant observation. The research is conducted within the offshore supply chain of a major oil and gas company.

Implementing LT’s continuous improvement tools, particularly value stream mapping (VSM), reduces offshore transportation waste and provides empirical evidence about the intersection of Lean and digital technologies. Applying techniques drawn from organisational learning theory (OLT), stakeholders involved in VSM mapping and FGs engage in problem-solving and develop action plans, driving digital transformation. Waste reduction in loading and unloading stages leads to control actions, automation and process improvements, significantly reducing downtime. This results in an annual monetary gain of US$1.3m. The study also identifies waste related to human effort and underutilised digital resources.

This study contributes to theory and practice by using action research and LT techniques in a real intervention case. From the lens of OLT, it highlights the potential of LT tools for digital transformation and demonstrates the convergence of waste reduction through Lean and Industry 4.0 technologies in the offshore supply chain. Practical outputs, including a benchmarking questionnaire and a plan-do-check-act cycle, are provided for other companies in the same industry segment.

Understanding and tailoring the solidification characteristics and microstructure evolution in as-built parts fabricated by laser powder bed fusion (LPBF) is crucial as they influence the final properties. Experimental approaches to address this issue are time and capital-intensive. This study aims to develop an efficient numerical modeling approach to develop the process–structure (P-S) linkage for LPBF-processed Inconel 718.

In this study, a numerical approach based on the finite element method and cellular automata was used to model the multilayer, multitrack LPBF build for predicting the solidification characteristics (thermal gradient G and solidification rate R) and the average grain size. Validations from published experimental studies were also carried out to ensure the reliability of the proposed numerical approach. Furthermore, microstructure simulations were used to develop P-S linkage by evaluating the effects of key LPBF process parameters on G × R, G/R and average grain size. A solidification or G-R map was also developed to comprehend the P-S linkage.

It was concluded from the developed G-R map that low laser power and high scan speed will result in a finer microstructure due to an increase in G × R, but due to a decrease in G/R, columnar characteristics are also reduced. Moreover, increasing the layer thickness and decreasing the hatch spacing lowers the G × R, raises the G/R and generates a coarse columnar microstructure.

The proposed numerical modeling approach was used to parametrically investigate the effect of LPBF parameters on the resulting microstructure. A G-R map was also developed that enables the tailoring of the as-built LPBF microstructure through solidification characteristics by tuning the process parameters.

Construction activities conducted in urban areas are often a source of significant noise disturbances, which cause psychological and health issues for residents as well as long-term auditory impairments for construction workers. The limited effectiveness of passive noise control measures due to the close proximity of the construction site to surrounding neighborhoods often results in complaints and eventually lawsuits. These can then lead to delays and cost overruns for the construction projects.

The paper proposes a novel approach to integrating construction noise as an additional dimension into scheduling construction works. To achieve this, a building information model, including the three-dimensional construction site layout object geometry, resource allocation and schedule information, is utilized. The developed method explores further project data that are typically available, such as the assigned equipment to a task, its precise location, and the estimated duration of noisy tasks. This results in a noise prediction model by using noise mapping techniques and suggesting less noisy alternative ways of construction. Finally, noise data obtained from sensors in a case study contribute real values for validating the proposed approach, which can be used later to suggest solutions for noise mitigation.

The results of this study indicate that the proposed approach can accurately predict construction noise given a few available parameters from digital project planning and sensors installed on a construction site. Proactively integrating construction noise control measures into the planning process has benefits for both residents and construction managers, as it reduces construction noise-related disturbances, prevents unexpected legal issues and ensures the health and well-being of the workforce.

While previous research has concentrated on real-time data collection using sensors, a more effective solution would also involve addressing and mitigating construction noise during the pre-construction work planning phase.

The study sets out to demonstrate how a lifecycle perspective on complex, public-sector procurement projects can be used for making qualitative assessments of procurement policy and practice and reveal those procurement capabilities that are most impactful for operating effectively.

Agency theory, institutional theory and the lifecycle analysis technique are combined to abductively develop a framework to identify, analyse and compare complex procurement policies and practices in public sector organisations. Defence is the focal case and is compared with cases in the Nuclear, Local Government and Health sectors.

The study provides a framework for undertaking a lifecycle analysis to understand the challenges and capabilities of complex, public-sector buyers. Eighteen hierarchically-arranged themes are identified and used in conjunction with agency theory and institutional theory to explain complex procurement policy and practice variation in some of the UK’s highest-profile public buyers. The study findings provide a classification of complex buyers and offer valuable guidance for practitioners and researchers navigating complex procurement contexts.

The lifecycle approach proposed is a new research tool providing a bespoke application of theory by considering each lifecycle phase as an individual but related element that is governed by unique institutional pressures and principal-agent relationships.

The purpose of this paper is twofold: first, a case study on applying lean principles in manufacturing operations to redesign and optimize an electronic device assembly process and its impact on performance and second, introducing cardboard prototyping as a Kaizen tool offering a novel approach to testing and simulating improvement scenarios.

The study employs value stream mapping, root cause analysis, and brainstorming tools to identify root causes of poor performance, followed by deploying a Kaizen event to redesign and optimize an electronic device assembly process. Using physical models, bottlenecks and opportunities for improvement were identified by the Kaizen approach at the workstations and assembly lines, enabling the testing of various scenarios and ideas. Changes in lead times, throughput, work in process inventory and assembly performance were analyzed and documented.

Pre- and post-improvement measures are provided to demonstrate the impact of the Kaizen event on the performance of the assembly cell. The study reveals that implementing lean tools and techniques reduced costs and increased throughput by reducing assembly cycle times, manufacturing lead time, space utilization, labor overtime and work-in-process inventory requirements.

This paper adds a new dimension to applying the Kaizen methodology in manufacturing processes by introducing cardboard prototyping, which offers a novel way of testing and simulating different scenarios for improvement. The paper describes the process implementation in detail, including the techniques and data utilized to improve the process.

Properly allocating an organization's activities within a building is vital to reducing the relational complexity arising from process–environment interactions. Multiple relationships are mapped, and certain interferences are only identified after these have been processed. The method/software employed for this task is Mapping Activity Environment Allocation (MAEA). However, data input and interpretation of results depend on the usability conditions of the organization's agents. This paper presents MAEA's usability test results.

Test sessions and interviews were carried out with seven agents registered at a University Hospital. Participants were instructed to think aloud during its use, and immediately afterward, responded to semi-structured interviews. Test sessions were audio recorded and screen captured.

Participants found the software easy to use and pointed out valuable implications for professional and academic use. In addition to relationship, priority and parallelism data, customized visualizations were created, including organizational charts, flowcharts and activity flow routes on the floor plan.

MAEA's simplicity allows non-designers to conduct evidence-based assessments and decisions. It allows designers to test their proposals during the programming and outline proposal stages.

A more detailed definition of design requirements from the beginning increases the conditions to successfully achieve project goals.

The ability to map the allocation of activity-spaces in the pre-design phase of building architecture allows for early identification of interactions, aiding in the development of more robust project requirements during programming.

Although accounting and reporting visualisations (i.e. graphs, maps and grids) are often used to veil organisations’ untransparent actions, these practices perform irrespectively of their ability to represent facts. In this research, the authors explore accounting and reporting visualisations beyond their persuasive and representational purpose.

By building on previous research on the rhetoric of visualisations, the authors illustrate how the design of accounting visualisations within integrated reports engages managers in a recursive process of knowledge construction, interrogation, reflection and speculation on what sustainable value creation means. The authors articulate the theoretical framework by developing a longitudinal field study in International Fashion Company, a medium-sized company operating in the fashion industry.

This research shows that accounting and reporting visualisations do not only contribute to creating unclear and often contradicting representations of organisations’ sustainable performance but, at the same time, “open up” and support managers’ unfolding search for “sustainable value” by reducing its unknown meaning into known and understandable categories. The inconsistencies and imperfections that accounting and reporting visualisations leave constitute the conditions of possibility for the interrogation of the unknown to happen in practice, thus augmenting managers’ questioning, reflections and speculation on what sustainable value means.

This study shows that accounting and reporting visualisations can represent good practices (the authors are not saying a “solution”) through which managers can re-appreciate the complexities of measuring and defining something that is intrinsically unknown and unknowable, especially in contexts where best practices have not yet consolidated into a norm. Topics such as climate change and sustainable development are out there and cannot be ignored, cannot be reduced through persuasive accounts and, therefore, need to be embraced.