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Improvement of reverse supply chains requires accurate and timely information about the patterns of consumption. In the consumer context, the ways to generate and access such use-visibility data are in their infancy. The purpose of this study is to demonstrate how the Internet of Things (IoT) may be operationalised in the domestic setting to capture data on a consumer’s use of products and the implications for reverse supply chains.

This study uses an explorative case approach drawing on data from studies of six UK households. “Horizontal” data, which reveals patterns in consumers’ use processes, is generated by combining “vertical” data from multiple sources. Use processes in the homes are mapped using IDEF0 and illustrated with the data. The quantitative data are generated using wireless sensors in the home, and qualitative data are drawn from online calendars, social media, interviews and ethnography.

The study proposes four generic measurement categories for operationalising the concept of use-visibility: experience, consumption, interaction and depletion, which together address the use of different household resources. The explorative case demonstrates how these measures can be operationalised to achieve visibility of the context of use in the home. The potential of such use-visibility for reverse supply chains is discussed.

This explorative case study is based on an in-depth study of the bathroom which illustrates the application of use-visibility measures (UVMs) but provides a limited use context. Further research is needed from a wider set of homes and a wider set of use processes and contexts.

The case demonstrates the operationalisation of the combination of data from different sources and helps answer questions of “why?”, “how?”, “when?” and “how much?”, which can inform reverse supply chains. The four UVMs can be operationalised in a way that can contribute to supply chain visibility, providing accurate and timely information of consumption, optimising resource use and eliminating waste.

IDEF0 framework and case analysis is used to identify and validate four UVMs available through IoT data – that of experience, consumption, interaction and depletion. The UVMs characterise IoT data generated from a given process and inform the primary reverse flow in the future supply chain. They provide the basis for future data collection and development of theory around their effect on reverse supply chain efficiency.

The purpose of this paper is to contribute to the understanding of the servitization phenomenon of product-centric companies, by identifying the resources, capabilities and organizational aspects needed to successfully deploy a servitized business model (BM).

By adopting a literature-based approach, this paper develops a servitization maturity model (SeMM) aimed at assessing and positioning companies in the servitization journey. The paper also illustrates the model application to two small and medium-sized enterprises (SMEs), a machinery and a forklift truck company.

The SeMM identifies a set of 85 critical requirements that are used to evaluate the servitization level of product-centric companies, through a specific five-stage measurement scale. The requirements are categorized into: five maturity dimensions (organizational approach, process management, performance management, tools, capabilities) and nine BM Canvas components. The empirical application exemplifies how the SeMM can support managers in identifying and bridging the gaps in their servitization journey.

The SeMM adopts an original bi-dimensional approach and provides an operationalization of the servitization process through the identification of specific critical requirements framed on established BM and maturity dimensions taken from the literature. Moreover, the model responds to a call for research to develop practitioner-oriented tools and guidelines to support the servitization process, in particular for SMEs, and to the need to go beyond to measures of servitization based on indicators about number of services offered or their turnover.

The safety and reliability of high-speed trains rely on the structural integrity of their components and the dynamic performance of the entire vehicle system. This paper aims to define and substantiate the assessment of the structural integrity and dynamical integrity of high-speed trains in both theory and practice. The key principles and approaches will be proposed, and their applications to high-speed trains in China will be presented.

First, the structural integrity and dynamical integrity of high-speed trains are defined, and their relationship is introduced. Then, the principles for assessing the structural integrity of structural and dynamical components are presented and practical examples of gearboxes and dampers are provided. Finally, the principles and approaches for assessing the dynamical integrity of high-speed trains are presented and a novel operational assessment method is further presented.

Vehicle system dynamics is the core of the proposed framework that provides the loads and vibrations on train components and the dynamic performance of the entire vehicle system. For assessing the structural integrity of structural components, an open-loop analysis considering both normal and abnormal vehicle conditions is needed. For assessing the structural integrity of dynamical components, a closed-loop analysis involving the influence of wear and degradation on vehicle system dynamics is needed. The analysis of vehicle system dynamics should follow the principles of complete objects, conditions and indices. Numerical, experimental and operational approaches should be combined to achieve effective assessments.

The practical applications demonstrate that assessing the structural integrity and dynamical integrity of high-speed trains can support better control of critical defects, better lifespan management of train components and better maintenance decision-making for high-speed trains.

Industry 4.0 is expected to significantly transform industrial value creation. However, research on business models affected through Industry 4.0, and on small- and medium-sized enterprises (SMEs), remains scarce. In response, the purpose of this paper is to address both aspects, further elaborating on the role that SMEs can take toward Industry 4.0 as provider or user.

The paper used an exploratory research design based on 43 in-depth expert interviews within the three most important German industry sectors, mechanical and plant engineering, electrical engineering and automotive suppliers. Interviews were conducted with leading personnel of the respective enterprises, including 22 CEOs. They assign business model implications through Industry 4.0, referring to the Business Model Canvas, while the paper delineates between Industry 4.0 providers and users.

The paper finds that key resources and value proposition are among the most affected elements of the business model, whereas channels are the least affected. Furthermore, distinct characteristics between Industry 4.0 providers and users can be delineated. In general, Industry 4.0 providers’ business models are significantly more affected than users, except for key partners and customer relationships.

Industry 4.0 remains at its early stages of implementation. As a result, many interviewees’ answers remain at a rather general level.

Strategies for the further alignment of the business models are provided for Industry 4.0 providers and users.

The paper is among the few that investigate Industry 4.0 in the context of SMEs and business models.

The purpose of this study is to develop a cylindrical capacitive sensor that has the advantages of high resolution, small size and designability and can be easily installed on lubricant pipeline to monitor lubricant oil debris.

A theoretical model of the cylindrical capacitive sensor is presented to analyze several parameters’ effectiveness on the performance of sensor. Numerical simulations are then conducted to determine the optimal parameters for preliminary experiments. Experiments are finally carried out to demonstrate the detectability of developed capacitive sensors.

It is clear from experimental results that the developed capacitive sensor can monitor the debris in lubricant oil well, and the capacitance values increase almost linearly when the number and size of debris increase.

There is lot of further work to do to apply the presented method into the application. Especially, it is necessary to consider several factors’ influence on monitoring results. These factors include the flow rate of the lubricant oil, the temperature, the debris distribution and the vibration. Moreover, future work should consider the influence of the oil degradation to the capacitance change and other contaminations (e.g. water and dust).

This work conducts a feasibility study on application of capacitive sensing principle for detecting debris in aero engine lubricant oil.

The novelty of the presented capacitance sensor can be summarized into two aspects. One is that the sensor structure is simple and characterized by two coaxial cylinders as electrodes, while conventional capacitive sensors are composed of two parallel plates as electrodes. The other is that sensing mechanism and physical model of the presented sensor is verified and validated by the simulation and experiment.

For many decades, it has been recognized that maintenance activities should be adapted to the specific usage of a system. For that reason, many advanced policies have been developed, such as condition-based and load-based maintenance policies. However, these policies require advanced monitoring techniques and rather detailed understanding of the failure behavior, which requires the support of an OEM or expert, prohibiting application by an operator in many cases. The present work proposes a maintenance policy that relieves the high (technical) demands set by these existing policies and provides a more accurate specification of the required (dynamic) maintenance interval than traditional usage-based maintenance.

The methodology followed starts with a review and critical assessment of existing maintenance policies, which are classified according to six different aspects. Based on the need for a technically less demanding policy that appears from this comparison, a new policy is developed. The consecutive steps required for this functional usage profiles based maintenance policy are then critically discussed: usage profile definition, monitoring, profile severity quantification and the possible extension to the fleet level. After the description of the proposed policy, it is demonstrated in three case studies on real systems.

A maintenance policy based on a simple usage registration procedure appears to be feasible, which enables a significantly more efficient maintenance process than the traditional usage-based policies. This is demonstrated by the policy proposed here.

The proposed maintenance policy based on functional usage profiles offers the operators of fleets of systems the opportunity to increase the efficiency and effectiveness of their maintenance process, without the need for a high investment in advanced monitoring systems and in experts interpreting the results.

The original contribution of this work is the explicit definition of a new maintenance policy, which combines the benefits of considering the effects of usage or environment severity with a limited investment in monitoring technology.

Heavy fog results in low visibility, which increases the probability and severity of traffic crashes, and fog warning system is conducive to the reduction of crashes by conveying warning messages to drivers. This paper aims at exploring the effects of dynamic message sign (DMS) of fog warning system on driver performance.

First, a testing platform was established based on driving simulator and driver performance data under DMS were collected. The experiment route was consisted of three different zones (i.e. warning zone, transition zone and heavy fog zone), and mean speed, mean acceleration, mean jerk in the whole zone, ending speed in the warning zone and transition zone, maximum deceleration rate and mean speed reduction proportion in the transition zone and heavy fog zone were selected. Next, the one-way analysis of variance was applied to test the significant difference between the metrics. Besides, drivers’ subjective perception was also considered.

The results indicated that DMS is beneficial to reduce speed before drivers enter the heavy fog zone. Besides, when drivers enter a heavy fog zone, DMS can reduce the tension of drivers and make drivers operate more smoothly.

This paper provides a comprehensive approach for evaluating the effectiveness of the warning system in adverse conditions based on the driving simulation test platform. The method can be extended to the evaluation of vehicle-to-infrastructure technology in other special scenarios.

Poor maintenance management leads to non-negligible economic, environmental and social impacts and obstacles to the sustainable manufacturing paradigm. Studies evaluating maintenance impacts on sustainability underline growing interest in the topic, but reports on the industrial field are lacking. Therefore, this paper investigates the industrial environment and the indicators that manufacturing companies use for measuring their maintenance impacts.

In this pilot survey study, several stakeholders of production enterprises in the south of Italy were interviewed to unveil the spread of the measurement of maintenance impacts on sustainability and the indicators used by those companies.

The interview results showed a low level of awareness among stakeholders about maintenance impacts on sustainability. Maintenance stakeholders are mainly focused on technical and economic factors, whereas environmental, quality and safety stakeholders are becoming more aware of maintenance impacts on environmental and social factors. However, both groups need guidelines to define sustainability indicators to assess such impacts.

This exploratory study allowed us to investigate the current situation in industrial organisations and achieve the first variegated and diversified vision of the awareness of company stakeholders on maintenance impacts on the sustainability of several business functions. This paper provides a valuable contribution to “maintenance and sustainability” research area in production contexts and sheds light on non-negligible maintenance impacts on sustainability, providing preliminary insights on the topic and an effective basis for defining future research opportunities. Moreover, this study enables increased awareness among internal and external manufacturing company stakeholders on the role of maintenance in sustainable production.

The research approach is based on the concept that a failure event is rarely random and is often generated by a chain of previous events connected by a sort of domino effect. Thus, the purpose of this study is the optimal selection of the components to predictively maintain on the basis of their failure probability, under budget and time constraints.

Assets maintenance is a major challenge for any process industry. Thanks to the development of Big Data Analytics techniques and tools, data produced by such systems can be analyzed in order to predict their behavior. Considering the asset as a social system composed of several interacting components, in this work, a framework is developed to identify the relationships between component failures and to avoid them through the predictive replacement of critical ones: such relationships are identified through the Association Rule Mining (ARM), while their interaction is studied through the Social Network Analysis (SNA).

A case example of a process industry is presented to explain and test the proposed model and to discuss its applicability. The proposed framework provides an approach to expand upon previous work in the areas of prediction of fault events and monitoring strategy of critical components.

The novel combined adoption of ARM and SNA is proposed to identify the hidden interaction among events and to define the nature of such interactions and communities of nodes in order to analyze local and global paths and define the most influential entities.

This article aims to describe the thinking behind MASSE, a project in Finland that helps address the fragmentation of care and patient journey disruptions for long-term care. It outlines the conceptualization of an information technology (IT)-assisted solution and presents preliminary findings and research problems in this ongoing project.

The project employs a service engineering and design science approach with the objective of addressing chronic and multimorbid patients in specialized multiprovider environments. It does this by applying information and communication technologies and organizational design. The project has been a cocreative effort with ongoing interviews and workshops with various stakeholders to inform the conceptualization of a solution, an intermediary step before the implementation phase.

Patient journey disruptions occur when caregivers do not know what to do in specific situations. A potential solution is a virtual care operator (VCO) with a personalized patient card that would enable service ecosystem actors to integrate and coordinate their tasks. This article presents the basic design principles of such a solution.

Conceptual ideas and preliminary results only indicative.

Systemic integration efforts like those ongoing in Finland can benefit from the VCO concept encouraging a more collaborative way of thinking about integrative solutions and opening up new avenues of research on business implications and ecosystem strategies.

The VCO concept answers to the continuity of care, the rising costs of health care and the growing numbers of patients with chronic disease and multimorbidity whose care remains fragmented and uncoordinated.

Taking an ecosystem approach to care integration and addressing interoperability issues are on the cutting edge of healthcare system transformation.