This article illustrates a method to measure, visualize, and statistically test tamping effectiveness on track geometry using and comparing two different measurement systems: an onboard system mounted on a regular passenger train and a specialized measurement train system. The proposed analysis method can be generalized to assess the impact of other track maintenance activities. Track quality variables, such as the longitudinal level, were measured to assess tamping effects on a 1250 m section of the Swedish Iron Ore Line in autumn 2022. The onboard system provided frequent measurements before and after tamping, capturing immediate to 3-month effects, while the regular system enabled a 21-month follow-up. The analysis segmented the tamped and comparative track sections into 100 m segments, employing t-tests for statistical analysis. For our studied section, tamping induced a direct improvement of track quality by significantly reducing the standard deviations of longitudinal level and alignment, decreasing maximum twist values, and increasing the composite track quality index (QS). The paper also compares the two measurement systems, highlighting their combined value in assessing tamping efficacy. Both measurement systems yielded comparable track geometry measures, highlighting the potential of combining frequent data from onboard-mounted systems with regular measurements for maintenance analysis and decision-making.

Quality management (QM) has shown an impressive ability to update and evolve. The purpose of this paper is to highlight themes that have been identified as vital and important for research projects within QM during the coming decade. The paper is also an attempt to initiate research for the emerging 2030 agenda for QM, here referred to as ‘Quality 2030’. This article is based on extensive data gathered during a workshop process conducted in two main steps: (1) a collaborative brainstorming workshop with 22 researchers and practitioners (spring 2019) and (2) an appreciative inquiry summit with 20 researchers and practitioners (autumn 2019). The process produced five collectively elaborated and designed future research themes for QM: (a) systems perspectives applied, (b) stability in change, (c) models for smart self-organising, (d) integrating sustainable development, and (e) higher purpose as QM booster. The process also identified a positive core of QM, defined as core values and aspects in the field and practice that need to be preserved and nurtured in the future.

The accuracy of the measurement system is vital for reliable process monitoring using statistical process control charts. The applied chart’s effectiveness depends on the measurement system's performance. Measurement uncertainty can lead to incorrect decisions like unnecessary stops or failure to intervene. In this paper, we investigated the effect of measurement errors on the performance of four well-established combined charts for monitoring the mean of normally distributed processes: Shewhart-CUSUM, Shewhart-Crosier’s CUSUM, Shewhart-EWMA and Shewhart-GWMA charts. To deal with measurement errors we considered the additive measurement error model. Detailed run length profiles of these charts are studied in terms of average run length (ARL), extra quadratic loss, relative ARL, and performance comparison index through Monte Carlo simulations under different sizes of measurement errors. It was found that measurement errors significantly reduce the power of the combined charts. Thus, multiple measurements scheme is incorporated as a remedy to this effect. The Shewhart-Crosier’s CUSUM performed best of four charts, while the Shewhart-EWMA chart did worst. To demonstrate the effect of measurement uncertainty and highlight implications further, a simulated dataset with a shift in the process mean is considered.

Timely planning and scheduling of railway infrastructure maintenance interventions are crucial for increased safety, improved availability, and reduced cost. We propose a data-driven decision-support framework integrating track condition predictions with tactical maintenance planning and operational scheduling. The framework acknowledges prediction uncertainties by using a Wiener process-based prediction model at the tactical level. We also develop planning and scheduling algorithms at the operational level. One algorithm focuses on cost-optimisation, and one algorithm considers the multi-component characteristics of the railway track by grouping track segments near each other for one maintenance activity. The proposed framework's performance is evaluated using track geometry measurement data from a 34 km railway section in northern Sweden, focusing on the tamping maintenance action. We analyse maintenance costs and demonstrate potential efficiency increases by applying the decision-support framework.

Railway track maintenance and renewal are vital for railway safety, train punctuality, and travel comfort. Therefore, having cost-effective maintenance is critical in managing railway infrastructure assets. There has been a considerable amount of research performed on mathematical and decision support models for improving the application of railway track maintenance planning and scheduling. This article reviews the literature in decision support models for railway track maintenance planning and scheduling and transforms the results into a problem taxonomy. Furthermore, the article discusses current approaches in optimising maintenance planning and scheduling, research trends, and possible gaps in the related decision-making models.

The active radio frequency identification (RFID) technique is used for in-situ measurement of acceleration and temperature in the distribution chain of iron ore pellets. The results of this paper are based on two experiments, in which active RFID transponders were released into train wagons or product bins. RFID exciters and readers were installed downstream in a harbour storage silo to retrieve data from the active transponders. Acceleration peaks and temperatures were recorded. The results imply that in-situ data can aid the understanding of induced stresses along the distribution chain to, for example, reduce pellet breakage and dusting. In-situ data can also increase understanding of product mixing behaviour and product residence times in silos. Better knowledge of stresses, product mixing and residence times are beneficial to process and product quality improvement, to better understand the transportation process, and to reduce environmental impacts due to dusting.

The concurrent use of statistical process control and engineering process con-trol involves monitoring manipulated and controlled variables. One multivari-ate control chart may handle the statistical monitoring of all variables, butobserving the manipulated and controlled variables in separate control chartsmay improve understanding of how disturbances and the controller perfor-mance affect the process. In this article, we illustrate how step and ramp dis-turbances manifest themselves in a single-input–single-output system bystudying their resulting signatures in the controlled and manipulated variables.The system is controlled by variations of the widely used proportional-integral-derivative(PID) control scheme. Implications for applying control charts forthese scenarios are discussed.

Researchers have promoted statistical improvement methods as essential for product and process improvement for decades. However, studies show that their use has been moderate at best. This study aims to assess the use of statistical process control (SPC), process capability analysis, and design of experiments (DoE) over time. The study also highlights important barriers for the wider use of these methods in Sweden as a follow-up study of a similar Swedish study performed in 2005 and of two Basque-based studies performed in 2009 and 2010. While the survey includes open-ended questions, the results are mainly descriptive and confirm results of previous studies. This study shows that the use of the methods has become more frequent compared to the 2005 study. Larger organisations (>250 employees) use the methods more frequently than smaller organisations, and the methods are more widely utilised in the industry than in the service sector. SPC is the most commonly used of the three methods while DoE is least used. Finally, the greatest barriers to increasing the use of statistical methods were: insufficient resources regarding time and money, low commitment of middle and senior managers, inadequate statistical knowledge, and lack of methods to guide the user through experimentations.