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This study aims to explore and empirically test variables influencing material delivery schedule inaccuracies?

A mixed-method case approach is applied. Explanatory variables are identified from the literature and explored in a qualitative analysis at an automotive original equipment manufacturer. Using logistic regression and random forest classification models, quantitative data (historical schedule transactions and internal data) enables the testing of the predictive difference of variables under various planning horizons and inaccuracy levels.

The effects on delivery schedule inaccuracies are contingent on a decoupling point, and a variable may have a combined amplifying (complexity generating) and stabilizing (complexity absorbing) moderating effect. Product complexity variables are significant regardless of the time horizon, and the item’s order life cycle is a significant variable with predictive differences that vary. Decoupling management is identified as a mechanism for generating complexity absorption capabilities contributing to delivery schedule accuracy.

The findings provide guidelines for exploring and finding patterns in specific variables to improve material delivery schedule inaccuracies and input into predictive forecasting models.

The findings contribute to explaining material delivery schedule variations, identifying potential root causes and moderators, empirically testing and validating effects and conceptualizing features that cause and moderate inaccuracies in relation to decoupling management and complexity theory literature?

This paper aims to overcome the inability of both comparing loss costs and accounting for production resource losses of Overall Equipment Effectiveness (OEE)-related approaches.

The authors conducted a literature review about the studies focusing on approaches combining OEE with monetary units and/or resource issues. The authors developed an approach based on Overall Equipment Cost Loss (OECL), introducing a component for the production resource consumption of a machine. A real case study about a smart multicenter three-spindle machine is used to test the applicability of the approach.

The paper proposes Resource Overall Equipment Cost Loss (ROECL), i.e. a new KPI expressed in monetary units that represents the total cost of losses (including production resource ones) caused by inefficiencies and deviations of the machine or equipment from its optimal operating status occurring over a specific time period. ROECL enables to quantify the variation of the product cost occurring when a machine or equipment changes its health status and to determine the actual product cost for a given production order. In the analysed case study, the most critical production orders showed an actual production cost about 60% higher than the minimal cost possible under the most efficient operating conditions.

The proposed approach may support both production and cost accounting managers during the identification of areas requiring attention and representing opportunities for improvement in terms of availability, performance, quality, and resource losses.

The paper identifies and explores the perspectives of middle school educators regarding the benefits and challenges of an ongoing, emerging school–university partnership. Over five years, professors at one comprehensive, Midwestern university, formed a partnership with a local middle school. While progress has been made to develop the partnership, the authors recognized a lack of shared governance (Essential 7). In particular, they were unsure the partnership was mutually beneficial. The authors interviewed teachers, the principal, assistant principals, guidance counselors, the instructional coach and the youth service center director to gain their perspectives on the partnership.

The authors used an intrinsic, evaluative case study to examine educators perceptions of the benefits and challenges of the partnership (Guba & Lincoln, 1981; Patton, 2002). This approach was used because within this bounded system the authors have an interest in obtaining information to help improve the program and partnership.

Results suggest the partnership was beneficial in a number of ways including hiring of and offering fresh ideas to teachers. Educators also felt there were many benefits for university candidates. Challenges included scheduling, technology access and candidate disposition. Implications are discussed.

Inherent within the research methodology, researchers’ inclusion in the data collection process may affect participants responses.

Researchers discuss the implications of this work, including the role of hiring candidates and clear articulation of a mission for the partnership.

This work adds to research on school site stakeholders’ perspectives on the value of school–university partnerships and includes teachers and the schools’ entire leadership team.

Large classes pose challenges in managing different types of skills (e.g. maths, subject-specific knowledge, writing, confidence and communication), facilitating interactions, enabling active learning and providing timely feedback. This paper shares a design of a set of assessments for a large undergraduate economics course consisting of students from diverse cultural backgrounds. The benefits, challenges and learning experiences of students are analysed.

Students worked in groups to complete an assessment with several questions which would be useful as a revision for the individual assessment, the following week. Survey questionnaires with Likert-type questions and open-ended questions were used to analyse the learning and skill development that occurred because of the group work. Responses to the open-ended survey questions were coded and analysed by identifying the themes and categorising the various issues that emerged.

This assessment design developed group working skills, created opportunities to interact and enhanced learning. The analysis of the responses found that working with peers enabled the students to generate their own feedback, clear doubts and learn to solve problems. Effective communication, planning meetings and working around the diverse group members’ strengths and weaknesses are some graduate skills that are developed in this group assessment. The challenges were arranging meetings, finalising assessments, engagement of group members and unreliable technology. However, the students found ways to overcome these challenges.

This assessment design can be useful in higher education practice by introducing a mechanism for authentic collaborative practice. This paper adds to the literature on peer interactions and group work and enables effective learning at scale.

The interruption event will seriously affect the normal operation of urban rail transit lines,causing a large number of passengers to be stranded in the station and even making the train stranded in the interval between stations. This study aims to reduce the impact of interrupt events and improve service levels.

To address this issue, this paper considers the constraints of train operation safety, capacity and dynamic passenger flow demand. It proposes a method for adjusting small loops during interruption events and constructs a train operation adjustment model with the objective of minimizing the total passenger waiting time. This model enables the rapid development of train operation plans in interruption scenarios, coordinating train scheduling and line resources to minimize passenger travel time and mitigate the impact of interruptions. Regarding the proposed train operation adjustment model, an improved genetic algorithm (GA) is designed to solve it.

The model and algorithm are applied to a case study of interruption events on Beijing Subway Line 5. The results indicate that after solving the constructed model, the train departure intervals can be maintained between 1.5 min and 3 min. This ensures both the safety of train operations on the line and a good match with passengers’ travel demands, effectively reducing the total passenger waiting time and improving the service level of the urban rail transit system during interruptions. Compared to the GA algorithm, the algorithm proposed in this paper demonstrates faster convergence speed and better computational results.

This study explicitly outlines the adjustment method of using short-turn operation during operational interruptions, with train departure times and station stop times as decision variables. It takes into full consideration safety constraints on train operations, train capacity constraints and dynamic passenger demand. It has constructed a train schedule optimization model with the goal of minimizing the total waiting time for all passengers in the system.

Healthcare organisations are often described as less innovative than other organisations, since organisational culture works against innovations. In this paper, the authors ask whether it has to be that way or whether is possible to nurture an innovative culture in a healthcare organisation. The aim of this paper is to describe and analyse nurturing an innovative culture within a healthcare organisation and how culture can support innovations in such a healthcare organisation.

Based on a qualitative case study of a healthcare unit that changed, within a few years, from having no innovations to repeatedly generating innovations, the authors describe important aspects of how innovative culture can be nurtured in healthcare. Data were analysed using inductive and deductive analysis steps.

The study shows that it is possible to nurture an innovative culture in a healthcare organisation. Relationships and competences beyond healthcare, empowering structures and signalling the importance of innovation work with resources all proved to be important. All are aspects that a manager can influence. In this case, the manager's role in nurturing innovative culture was very important.

This study highlights that an innovative culture can be nurtured in healthcare organisations and that managers can play a key role in such a process.

The paper describes and analyses an innovative culture in a healthcare unit and identifies important conditions and strategies for nurturing innovative culture in healthcare organisations.

The primary objective of this paper is to show a systematic and methodological approach for the digitalization of critical clinical pathways (CPs) within the healthcare domain.

The methodology entails the integration of service design (SD) and action research (AR) methodologies, characterized by iterative phases that systematically alternate between action and reflective processes, fostering cycles of change and learning. Within this framework, stakeholders are engaged through semi-structured interviews, while the existing and envisioned processes are delineated and represented using BPMN 2.0. These methodological steps emphasize the development of an autonomous, patient-centric web application alongside the implementation of an adaptable and patient-oriented scheduling system. Also, business processes simulation is employed to measure key performance indicators of processes and test for potential improvements. This method is implemented in the context of the CP addressing transient loss of consciousness (TLOC), within a publicly funded hospital setting.

The methodology integrating SD and AR enables the detection of pivotal bottlenecks within diagnostic CPs and proposes optimal corrective measures to ensure uninterrupted patient care, all the while advancing the digitalization of diagnostic CP management. This study contributes to theoretical discussions by emphasizing the criticality of process optimization, the transformative potential of digitalization in healthcare and the paramount importance of user-centric design principles, and offers valuable insights into healthcare management implications.

The study’s relevance lies in its ability to enhance healthcare practices without necessitating disruptive and resource-intensive process overhauls. This pragmatic approach aligns with the imperative for healthcare organizations to improve their operations efficiently and cost-effectively, making the study’s findings relevant.

Output from the Irish Dairy Industry has grown rapidly since the abolition of quotas in 2015, with processors investing heavily in capacity expansion to deal with the extra milk volumes. Further capacity gains may be achieved by extending the processing season into the winter, a key enabler for which being the reduction of duration of the winter maintenance overhaul period. This paper aims to investigate if Lean Six Sigma tools and techniques can be used to enhance operational maintenance performance, thereby releasing additional processing capacity.

Combining the Six-Sigma Define, Measure, Analyse, Improve, Control (DMAIC) methodology and the structured approach of Turnaround Maintenance (TAM) widely used in process industries creates a novel hybrid model that promises substantial improvement in maintenance overhaul execution. This paper presents a case study applying the DMAIC/TAM model to Ireland’s largest dairy processing site to optimise the annual maintenance shutdown. The objective was to deliver a 30% reduction in the duration of the overhaul, enabling an extension of the processing season.

Application of the DMAIC/TAM hybrid resulted in process enhancements, employee engagement and a clear roadmap for the operations team. Project goals were delivered, and original objectives exceeded, resulting in €8.9m additional value to the business and a reduction of 36% in the duration of the overhaul.

The results demonstrate that the model provides a structure that promotes systematic working and a continuous improvement focus that can have substantial benefits for wider industry. Opportunities for further model refinement were identified and will enhance performance in subsequent overhauls.

To the best of the authors’ knowledge, this is the first time that the structure and tools of DMAIC and TAM have been combined into a hybrid methodology and applied in an Irish industrial setting.

Contingency studies within logistics and supply chain management have shown a need for longitudinal studies on fit. The purpose of this paper is to investigate the logistics strategy from a process of establishing fit perspective.

A large Swedish building contractor's logistics strategy process was analysed using a longitudinal single-case study for a period of 11 years (2008–2019).

The case study reveals three main constraints to logistics strategy implementation: a dominant purchasing organisation, a lack of incentives and diverging top-management priorities. This suggests that logistics strategy fit is not a conscious choice determined by contextual factors.

Establishing fit is a continuous cycle of regaining fit between the logistics context and logistics strategy components. Fit can be achieved by a change to the logistics context or to logistics strategy components.

Logistics managers may need to opt for satisfactory fit in view of the costs incurred by changing strategy versus the benefits to be gained from a higher degree of fit.

This paper adopts a longitudinal case design to study the fit between the logistics context and strategy, adding to the body of knowledge on organisational design and strategy in logistics and supply chain management.

A real-time production scheduling method for semiconductor back-end manufacturing process becomes increasingly important in industry 4.0. Semiconductor back-end manufacturing process is always accompanied by order splitting and merging; besides, in each stage of the process, there are always multiple machine groups that have different production capabilities and capacities. This paper studies a multi-agent based scheduling architecture for the radio frequency identification (RFID)-enabled semiconductor back-end shopfloor, which integrates not only manufacturing resources but also human factors.

The architecture includes a task management (TM) agent, a staff instruction (SI) agent, a task scheduling (TS) agent, an information management center (IMC), machine group (MG) agent and a production monitoring (PM) agent. Then, based on the architecture, the authors developed a scheduling method consisting of capability & capacity planning and machine configuration modules in the TS agent.

The authors used greedy policy to assign each order to the appropriate machine groups based on the real-time utilization ration of each MG in the capability & capacity (C&C) planning module, and used a partial swarm optimization (PSO) algorithm to schedule each splitting job to the identified machine based on the C&C planning results. At last, we conducted a case study to demonstrate the proposed multi-agent based real-time production scheduling models and methods.

This paper proposes a multi-agent based real-time scheduling framework for semiconductor back-end industry. A C&C planning and a machine configuration algorithm are developed, respectively. The paper provides a feasible solution for semiconductor back-end manufacturing process to realize real-time scheduling.