Purpose: The purpose of this paper is to develop an approach to measuring the performance of motor vehicle manufacturers (MVMs) from economic and environmental (E&E) perspectives. Design/methodology/approach: Eight measures are identified for benchmarking the performance from E&E perspectives. A new company performance index I_MVM is constructed to quantitatively generate the historical data of MVMs’ company performance. Autoregressive integrated moving average (ARIMA) models are built to generate the forecast data of the I_MVM. The minimum Akaike information criteria value is used to identify the model of the best fit. Forecast accuracy of the ARIMA models is tested by the mean absolute percentage error. Findings: The construction of the index I_MVM is benchmarked against three frameworks by six benchmark metrics. The I_MVM satisfies all of its applicable metrics while the three frameworks are incapable to satisfy their applicable metrics. Out of 15, 4 MVMs are excluded for benchmarking future performance due to their non-stationary time series data. Based on the forecast I_MVM data, GM is the best performer among the 15 samples in the FY2018. Originality/value: This research highlights the environmental perspective during vehicles’ production. The development of this approach is based on publicly available data and transparent about the methods it used. The data out of the approach can benefit stakeholders with insights by benchmarking the historical performance of MVMs as well as their future performance.

Composite indicators (CIs) are needed for decision makers to effectively benchmark holistic company performance. Composite indicators at macro levels are inappropriate to be implemented at the company level. By a literature survey, this article identified 29 individual methods for constructing CIs, 17 specific business sectors where CIs have been utilized in practice, and the motor vehicle manufacturing sector as the most studied sector. This article identified nine problems and provided four recommendations for future research.

The Automated People Mover (APM) is an important asset for many airports to transport passengers inside or between terminal and satellite buildings An APM system normally runs on fixed schedules throughout the day, which means that the capacity of the APM is pre-determined and not depending on the actual demand. This at times can cause either an overcapacity, which leads to a waste of resources, or an under capacity, which results in passengers waiting at the station. Especially the latter factor is problematic, as it reduced passenger experience and can negatively affect the transfer process between airport facilities. In order to better match the offered APM capacity with the demand, it is proposed in this paper to use sensor-based predictive control system, which adapts the APM system capacity to real-time demand. By means of sensor data, passenger numbers are determined before they walk onto the stations platforms, and subsequently the APM system capacity is adjusted to the measured demand. In principle there are two methods to change the APM system capacity, i.e.: 1) by changing the APM capacity (i.e. more cars per train) or 2) by changing the frequency. A simulation test case was designed to provide numerical insight in the potential of adaptively changing the capacity of an APM, based on sensor derived real-time demand. The test case was derived from a variety of typical systems used worldwide and represents a complex APM system. From the simulation results it is concluded that an intelligent design of the control system results in significant improvements in terms of passenger experience, operational cost, capital cost and emission footprint. The favourable method of adjusting capacity to demand is by increase train capacity, before reducing the headway between trains.

Purpose: The development of bulk material handling equipment can be accelerated and made less expensive when testing of virtual prototypes is adopted. However, the modelling of a grab unloader requires a large volume (77 m3) of iron ore pellets, making the computational costs prohibitive. This paper investigates the extent to which the original particles can be substituted by larger, coarser grains. It is crucial that this particle upscaling does not alter the realistic behaviour of the simulated bulk material, nor its interaction with the bulk handling equipment. Approach: First, our coarse graining technique is explained and set out for the particle system at hand. The material behaviour is then characterized using three laboratory experiments (two angle of repose tests and a penetration test). Next, the results of simulations using two contact models with and without coarse graining with different scale factors are compared with the measured material behaviour and material-equipment interaction. This includes a comparison of the macrobehaviour of the bulk material and the tool interaction of coarser grains in a cutting and sliding process. After reaching a satisfactory verified solution on the laboratory scale, the material behaviour and interaction behaviour of a large-scale experiment are modelled. A simulation model of a grab unloader was used for validation of the chosen coarse graining approach. Findings: Using the scaling method presented, the macroscopic tests indicated consistent material behaviour, regardless of the chosen particle scale for two contactmodels. Scaling of the tool interaction process produced mixed results: the sliding process scaled consistently but the penetration process did not, most likely because it is significantly harder for coarser grains to move since they have to move further to the sides before the tool can pass, leading to higher normal forces and frictional forces on the tip. This inconsistency was compensated for by adjusting the wall friction coefficient in the tip of the penetration tool. Once this adapted coarse graining scheme was applied to the industrial-scale simulation of a grab unloader, it produced consistent particle-scale invariant results. Originality/value: This research is the first to show how coarse graining schemes for DEM simulations can be applied to large-scale bulk handling equipment involving dominance of material equipment interaction through penetration of the bulk material.

Wheel impact load detectors are widespread railway systems used for measuring the wheel–rail contact force. They usually measure the rail strain and convert it to force in order to detect high impact forces and corresponding detrimental wheels. The measured strain signal can also be used to identify the defect type and its severity. The strain sensors have a limited effective zone that leads to partial observation from the wheels. Therefore, wheel impact load detectors exploit multiple sensors to collect samples from different portions of the wheels. The discrete measurement by multiple sensors provides the magnitude of the force; however, it does not provide the much richer variation pattern of the contact force signal. Therefore, this paper proposes a fusion method to associate the collected samples to their positions over the wheel circumferential coordinate. This process reconstructs an informative signal from the discrete samples collected by multiple sensors. To validate the proposed method, the multiple sensors have been simulated by an ad hoc multibody dynamic software (VI-Rail), and the outputs have been fed to the fusion model. The reconstructed signal represents the contact force and consequently the wheel defect. The obtained results demonstrate considerable similarity between the contact force and the reconstructed defect signal that can be used for further defect identification.

Purpose: Today, most of the car manufacturers world-wide have embraced the principles of lean manufacturing on strategic and operational level. On strategic level car companies like Toyota (Womack et al., 1990) shifted 63 per cent of the value of the car towards the first, second and third tier suppliers for the co-production and co-development of cars as an effect of lean implementation. However, lean implementation was also followed by for instance Ford and GM in the USA, the latter company faced a sudden disruption in 2009 due to the break-out of the financial crisis in 2008, while Ford survived. Could this be foreseen? The exclusive use of (classic) financial performance indicators may give a false image of a company’s current and future performance. There is a need for a model to identify “the stars and the laggards’ regarding car companies by taking into account non-financial and intangible dimensions as advocated by Neely et al. (2003) regarding the third generation of business performance measurement systems. The purpose of this paper is therefor to propose a method to measure and benchmark car company performance which includes the non-financial R&D dimension as well as supply chain, value creating and employee dimensions. These dimensions are present in the value leverage model (van Blokland et al., 2012a, 2012b) which can serve as a basis for this method. The aim is to contribute to the third generation business performance measurement systems by further development of the value leverage model towards a maturity model for benchmarking car company performance. The proposed method can provide a big picture and give insight regarding company performance and direction of the performance. Design/methodology/approach: Value leverage can be measured by a correlation analysis regarding three dimensions, namely, supply chain, R&D and value creation, all relative to the employee or capita which results in the average value leverage (AVL) factor. This AVL factor can be used to compose a combined relative and absolute ranking. The score regarding the AVL results in a relative ranking expressing the level of stability regarding the car companies value chain and system. For the absolute ranking the car companies receive per variable parameter a score according to their absolute performance relative to the other car companies. The relative and absolute ranking are presented on the vertical and horizontal axes forming a matrix. The matrix is the basis for the stability-value leverage maturity model for measuring and benchmarking company performance. With the proposed method, the following main research question can be answered: “How can company performance be measured and benchmarked from a stability-value leverage perspective?”. Findings: With the proposed method, stability-value leverage performance can be measured. The relative ranking on the vertical axis and the absolute ranking form together a matrix which is presented by a scatterplot. A matrix with four maturity levels emerged from the analysis by introducing the average score of all the car companies together in the data set crossing the matrix vertical and horizontal. The four levels are as follows: Level I, low stability – low value leverage; Level II, low stability – high value leverage; Level III, high stability – low value leverage; and Level IV, high stability – high value leverage. Stability-value leverage performance of car companies can be measured over time which makes it possible to observe to which direction the car company migrates for instance from Level I to Level III, before and after the financial crises in 2008. The car companies BMW, Daimler, Audi, Ford and Honda are the best performing companies in stability-value leverage over the period 2000-2014, as they are situated at Level IV. With the findings, the main research question can be answered. The value leverage indicators can be used for measuring and benchmarking company performance regarding four maturity levels of stability and value leverage. The direction of performance can be observed as well. Research/limitations/implications: This research is limited to the car industry. Further research is devised to test the indicators for instance on the truck manufacturing industry. Further research towards new variables is part of the ongoing research. Practical/implications: With the value leverage maturity model, it is possible to inform stakeholders about stability, value leverage and value creation capability of car companies. Weak performing companies can be identified in an early stage with this method to anticipate for instance on possible discontinuation of a car company effecting in merger an acquisition processes. Social/implications: With the method stakeholders such as employees, users of cars and investors can be informed about how and why car companies perform in an unstable or stable manner. Originality/value: This research towards ranking and classification of car companies aligns with theories regarding lean manufacturing and maturity models, as these models are used to compare companies on their level of perfection or excellence.

Belt conveyor systems are widely utilized for continuous transport of bulk materials. Maintenance activities are essential to ensure the reliability of belt conveyor systems. Conventional diagnosis decision is achieved based on empirical constant thresholds. The Challenge of this study is to propose a framework of integrated maintenance decision making for belt conveyor idlers. Information from operational conditions, reliability estimation of idlers and condition monitoring data are integrated for accurate decision making. Innovatively, in the proposed framework threshold of the monitoring parameter can vary according to real time operational conditions and reliability estimation results. A simulation study is presented to demonstrate the effectiveness of framework. Simulation results show that the framework can result in more accurate maintenance decision making compared to conventional approaches.

The ultimate strength of metallic pipelines will be inevitably affected when they have suffered from structural damage after mechanical interference. The present experiments aim to investigate the residual ultimate bending strength of metallic pipes with structural damage based on large-scale pipe tests. Artificial damage, such as a dent, metal loss, a crack, and combinations thereof, is introduced to the pipe surface in advance. Four-point bending tests are performed to investigate the structural behavior of metallic pipes in terms of bending moment-curvature diagrams, failure modes, bending capacity, and critical bending curvatures. Test results show that the occurrence of structural damage on the pipe compression side reduces the bending capacity significantly. Only a slight effect has been observed for pipes with damage on the tensile side as long as no fracture failure appears. The possible causes that have introduced experimental errors are presented and discussed. The test data obtained in this paper can be used to further quantify damage effects on bending capacity of seamless pipes with similar D/t ratios. The comparison results in this paper can facilitate the structural integrity design as well as the maintenance of damaged pipes when mechanical interference happens during the service life of pipelines.

Purpose: Bulk material-handling equipment development can be accelerated and is less expensive when testing of virtual prototypes can be adopted. However, often the complexity of the interaction between particulate material and handling equipment cannot be handled by a single computational solver. This paper aims to establish a framework for the development, verification and application of a co-simulation of discrete element method (DEM) and multibody dynamics (MBD). Design/methodology/approach: The two methods have been coupled in two directions, which consists of coupling the load data on the geometry from DEM to MBD and the position data from MBD to DEM. The coupling has been validated thoroughly in several scenarios, and the stability and robustness have been investigated. Findings: All tests clearly demonstrated that the co-simulation is successful in predicting particle–equipment interaction. Examples are provided describing the effects of a coupling that is too tight, as well as a coupling that is too loose. A guideline has been developed for achieving stable and efficient co-simulations. Originality/value: This framework shows how to achieve realistic co-simulations of particulate material and equipment interaction of a dynamic nature.

Wood pellet imports are expected to increase in the European Union and Southeast Asia by 2030, considering pellets are the main feedstock used for co-firing in power plants throughout these regions. Due to the material's physical and biological properties, the equipment at an import terminal need to be different than what is used for other bulk material. Thus, most of the common problems associated with handling can be avoided. Dust emission and explosions, degradation in storage, self-heating and ignition are important criteria when designing a wood pellet port terminal, and can greatly affect associated logistics. Despite some availability of data concerning the handling of pellets, there is a lack of insight into the equipment and operations of actual handling facilities. A detailed literature research was performed in order to ascertain the level of the scientific background on the subject. Subsequently, visits in pellet facilities in the Netherlands and in-depth interviews with representatives were conducted and serve as a means of gaining an overview of current industry practices and equipment used for the handling of wood pellets. The main objective of this work is to evaluate the state-of-the-art in wood pellet handling in import terminals. This way, future bottlenecks can be identified and actions needed to overcome them can be determined. The analysis performed shows that while wood pellet terminals might be able to cope with the low amounts being traded currently, a reexamination and redesign of terminal facilities to accommodate the increased volumes will probably be required by 2030.

Transport demand for containers has been increasing for decades, which places pressure on road transport. As a result, rail transport is stimulated to provide better intermodal freight transport services. This paper investigates mathematical models for the planning of container movements in a port area, integrating the inter-terminal transport of containers (ITT, within the port area) with the rail freight formation and transport process (towards the hinterland). An integer linear programming model is used to formulate the container transport across operations at container terminals, the network interconnecting them, railway yards and the railway networks towards the hinterland. A tabu search algorithm is proposed to solve the problem. The practical applicability of the algorithm is tested in a realistic infrastructure case and different demand scenarios. Our results show the degree by which internal (ITT) and external (hinterland) transport processes interact, and the potential for improvement of overall operations when the integrated optimization proposed is used. Instead, if the planning of containers in the ITT system is optimized as a stand-alone problem, the railway terminals may suffer from longer delay times or additional train cancellations. When planning the transport of 4060 TEU containers within one day, the benefits of the ITT planning without considering railway operations account for 17% ITT cost reduction but 93% railway operational cost growth, while the benefits of integrating ITT and railway account for a reduction of 20% in ITT cost and 44% in railway operational costs.

In railway operations, when a disruption occurs, train dispatchers aim to adjust the affected schedule and to minimize negative consequences during and after the disruption. As one of the most important components of the railway system, railway signals are used to guarantee the safety of train services. We study the train dispatching problem with consideration of railway signaling commands under the fixed-block signaling system. In such a system, signaling commands dynamically depend on the movement of the preceding trains in the network. We clarify the impact of the signaling commands on train schedules, which has so far been neglected in the literature on railway train dispatching, and we propose an innovative set of signaling constraints to describe the impact. The determination of the signal indicators is presented using “if-then” constraints, which are further transformed into linear inequalities by applying two transformation properties. Activation of the train speed limits that result from the signaling commands is the core purpose of the signaling constraints, and this is implemented by using the signal indicators. Moreover, we formulate the Greenwave (GW) policy, which requires that trains always proceed under green signals, and we further investigate the impact of the GW policy on delays. In numerical experiments, the proposed signaling constraints are employed within a time-instant optimization problem, which is a mixed-integer linear programming (MILP) problem. The experimental results demonstrate the effectiveness of the proposed signaling constraints and show the impact of the signaling commands and GW policy on the train dispatching solution.

A periodic out-of-round wheel generates a range of excitation frequencies that may approach to the fundamental resonance of the track and vehicle. Detecting these defects by current condition monitoring systems is challenging. This paper focuses on the polygonal defect detection. VI-Rail is used to model the wheel-rail interaction to generate required data. A fusion algorithm reconstructs the defect signals in the space domain. The comparisons between the reconstructed defect signals and the wheel defects present a great potential for defect type identification. The results show that different polygonal defects can be correctly recognized and the dominant harmonics can be successfully decomposed.

Fresh cut flower supply chains are aware of the need for reducing spoilage and increasing customer satisfaction. This paper focuses on a part of the cut rose supply chain, from auction house to several end customers. A new business mode is considered that would allow end customers to subscribe to florists and have a continuous supply of bouquets of roses. To make this business mode feasible, we propose to benefit from real-time information on roses’ remaining vase life. First, a quality-aware modeling technique is applied to describe supply chain events and quality change of cut roses among several supply chain players. Then, a distributed model predictive control strategy is used to make up-to-date decisions for supply chain players according to the latest logistics and quality information. This approach provides a tool for multiple stakeholders to collaboratively plan the logistics activities in a typical cut rose supply chain based on roses’ estimated vase life in real time. The proposed approach is compared with a currently used business mode in simulation experiments. Results illustrate that the new business mode and the planning approach could reduce unmet demand and spoilage in a cut rose supply chain.

Wheel defects are detrimental for railway train and track components and should be detected and identified as early as possible. Wheel Impact Load Detector (WILD) is a commercial condition monitoring system used for detecting the defective wheels. This system usually measures the rail strain at different points by multiple sensors. WILD converts the measured strains to the force and uses the peak force, dynamic force, and ratio of the peak force to the static force to estimate the condition of the in-service wheels. These methods are useful for detecting the severe defects contributing to the contact force to the extent that exceed a predetermined threshold. Therefore, in the prior research a fusion method has been developed to reconstruct a new informative pattern from the data collected by the multiple sensors. The reconstructed pattern provides a comprehensive description of the wheel condition. This paper validates the fusion method using a set of lab tests to investigate the applicability of the proposed method. For this purpose, a test rig has been built consisting of a circular rail, a rotating arm, and a wheel. Six strain sensors have been installed under the rail in the symmetric locations over the rail circle with 60 degree intervals. The fusion method used to reconstruct a signal from the bending strain signals measured by the multiple sensors. Different wheel defects including the flat and out-of-round wheels have been tested and the results validated the fusion method by providing informative patterns.

We study the integration of real-time traffic management and train control by using mixed-integer nonlinear programming (MINLP) and mixed-integer linear programming (MILP) approaches. Three innovative integrated optimization approaches for real-time traffic management that inherently include train control are developed to deliver both a train dispatching solution (including train routes, orders, departure and arrival times at passing stations) and a train control solution (i.e., train speed trajectories). Train speed is considered variable, and the blocking time of a train on a block section dynamically depends on its real speed. To formulate the integrated problem, we first propose an MINLP problem (P_NLP), which is solved by a two-level approach. This MINLP problem is then reformulated by approximating the nonlinear terms with piecewise affine functions, resulting in an MILP problem (P_PWA). Moreover, we consider a preprocessing method to generate the possible speed profile options for each train on each block section, one of which is further selected by a proposed MILP problem (P_TSPO) with respect to safety, capacity, and speed consistency constraints. This problem is solved by means of a custom-designed two-step approach, in order to speed up the solving procedure. Numerical experiments are conducted using data from the Dutch railway network to comparatively evaluate the effectiveness and efficiency of the three proposed approaches with heterogeneous traffic. According to the experimental results, the MILP approach (P_TSPO) yields the best overall performance within the required computation time. The experimental results demonstrate the benefits of the integration, i.e., train delays can be reduced by managing train speed.

Application of an M-out-of-N redundancy architecture is a well-known measure for improving the reliability of safety systems. Most scientific papers addressing the reliability assessment of such systems consider a conventional homogeneous M-out-of-N redundancy architecture that is performed for identical channels. However, often in practice, an M-out-of-N redundancy architecture does not have identical channels. Reliability assessment of such heterogeneous systems (electrical/electronic and mechanical) with nonidentical channels and a combination of constant and nonconstant failure rates is considered in this paper. Such type of M-out-of-N redundancy architecture is introduced in this research as "asymmetrical redundancy". It can be used for enhancing the reliability of old mechanical systems or for reducing mutual influence of channels and increase of diagnostic coverage. This paper also presents a new "window-based Markov method" for PFDavg (average probability of failure on demand) and PFH (average frequency of dangerous failures) calculation for systems with an asymmetrical redundancy architecture and compares the results with those obtained by using the steady-state semi-Markov method and Monte-Carlo simulation. The applicability of the developed method is demonstrated in a simple case study.

On the basis of an experimental investigation [1], numerical investigation is conducted in this paper on damaged seamless metallic pipelines with metal loss (diameter-to-thickness ratio D/t around 21) through nonlinear finite element method (FEM). Numerical models are developed and validated through test results by using the measured material properties and specimen geometry, capable of predicting the residual ultimate strength of pipes in terms of bending capacity (M_cr) and critical curvature (κ_cr). By changing the metal loss parameters, i.e. length (l_m), width (w_m) and depth (d_m), a series of numerical simulations are carried out. Results show that the larger the d_m or l_m is, the less the bending capacity will be. The increase of notch width slightly reduces the pipe strength, presenting a linear tendency. Based on the FEM results, empirical formulas are proposed to predict the residual ultimate strength of metallic pipes with metal loss under pure bending moment. The prediction results match well with the results from the tests, the numerical simulations as well as the theoretical derivation. Such formulas can be therefore used for practice purposes and facilitate the decision-making of pipe maintenance after mechanical interference.

Belt conveyor systems are widely used in bulk material handling and transport applications. Within a belt conveyor, depending on its distance, there can be tens of thousands of idler rolls which face random failure. However, condition monitoring solutions for belt conveyor idlers is underdeveloped. This is because the choice of monitoring parameters is still arbitrary. This paper aims to investigate which parameters can represent technical condition of idler rolls for the purpose of condition monitoring. A belt conveyor test rig is developed in laboratory. Temperature and vibration sensors are applied to monitor idler rolls induced with different types of failures. Patten of temperature evolution and RMS level of vibration are extracted from the signal and analysed. It is concluded that temperature measurement at roll shafts is a straightforward and effective manner for condition monitoring of belt conveyor idlers.

Current research in the field of performance measurement hasn't presented a rigorous composite indicator for quantifying company performance, with environmental indicators for automobile companies. This paper aims to construct this missing composite indicator. A new approach is developed, including techniques of fuzzy logic, analytic network process, the entropy theory and a geometric mean with unequal weights. The method is transparent, and the composite indicator derived can serve as a statistical tool for benchmarking. A case study is conducted in six leading automobile companies with data from the fiscal year 2016.