Search

The outcome economy is a relatively new economic and business paradigm that promotes focusing on the effects that the use of provided products and services create for customers in their markets, rather than focusing on these products or services themselves from the providers’ perspective. This paradigm has been embraced in various fields of business but has not yet been fully integrated with the concept of smart industry. To fill this gap, in this vision paper we provide a framework that does make this integration, showing the full structure of customer outcome management in smart manufacturing, from both business and digital technology perspectives. In applying this structure, a feedback loop is created that spans the markets of provider and customer and supports data-driven product evolution, manufacturing, and delivery. We propose a business reference framework that can be used as a blueprint for designing practical scenarios. We show how integrated digital support for such a scenario can be realized using a well-structured combination of technologies from the fields of the internet of things, business intelligence and federated learning, blockchain, and business process management. We illustrate all of this with a visionary case study inspired by industrial practice in the automotive domain. In doing so, we provide both an academic basis for the integration of several currently dispersed research fields that need to be integrated to further smart manufacturing towards outcome management and a practical basis for the well-structured design and implementation of customer outcome management business cases in smart manufacturing.

We investigate the benefits of on-site printing at remote geographic locations, where access to spare parts is intermittent and supplies are replenished at fixed intervals. Organizations typically have no solution to spare parts shortages other than expensive expediting orders, or waiting for a part to arrive with the next replenishment. We investigate whether on-site three-dimensional (3D) printing of spare parts can bring relief. Our work extends dual-sourcing literature with fixed order cycles by considering two emergency supply options: expediting regular parts and 3D printing lower quality parts. We model the replenishment and emergency supply decisions as a Markov decision process and find that the optimal inventory control policy consists of two thresholds that control when to expedite, when to print and when to wait for regular parts via the next replenishment. We apply our model to a case study of the Royal Netherlands Army (RNLA) for her United Nations peacekeeping mission in Mali. Our results show that on-site 3D printing, much more so than expediting, leads to large operational cost savings through on-site inventory reductions and increased asset availability, thus increasing the ability of the RNLA to operate in remote locations. These results extend to many other organizations that operate in remote locations, for example, those in the mining and offshore industry.

We study how web publishers should set their floor prices in order to maximize expected revenues when they have access to two selling mechanisms, namely an ad exchange and header bidding, in order to sell impressions on the real-time bidding market. We consider the publisher’s problem under incomplete information, propose bandit-type algorithms, and show that their regret – the performance loss compared to the optimal algorithm – is sub-linear in the time horizon. Experiments illustrate the effectiveness of our algorithms.

International audience; Digital service platforms are the facilitators of value co-creation within service ecosystems. They are instrumental in the design of compelling value propositions.Despite the substantial amount of contributions for the conceptualization of digital service platforms, the methodological contributions concerning the engineering of service platforms are scarce. In this paper, we adopt a service systems perspective and present a methodfor the identification of digital service platform requirements. The methodis driven by the value propositions that are based on the capabilities of the actors in service exchange networks. In the paper, we demonstrate the methodby applying it to an international Mobility-as-a-Service platform development project.

We study repeated posted-price auctions where a single seller repeatedly interacts with a single buyer for a number of rounds. In previous works, it is common to consider that the buyer knows his own valuation with certainty. However, in many practical situations, the buyer may have a stochastic valuation. In this paper, we study repeated posted-price auctions from the perspective of a utility maximizing buyer who does not know the probability distribution of his valuation and only observes a sample from the valuation distribution after he purchases the item. We first consider non-strategic buyers and derive algorithms with sub-linear regret bounds that hold irrespective of the observed prices offered by the seller. These algorithms are then adapted into algorithms with similar guarantees for strategic buyers. We provide a theoretical analysis of our proposed algorithms and support our findings with numerical experiments. Our experiments show that, if the seller uses a low-regret algorithm for selecting the price, then strategic buyers can obtain much higher utilities compared to non-strategic buyers. Only when the prices of the seller are not related to the choices of the buyer, it is not beneficial to be strategic, but strategic buyers can still attain utilities of about 75% of the utility of non-strategic buyers.

Conformance checking techniques are widely adopted to pinpoint possible discrepancies between process models and the execution of the process in reality. However, state of the art approaches adopt a crisp evaluation of deviations, with the result that small violations are considered at the same level of significant ones. This affects the quality of the provided diagnostics, especially when there exists some tolerance with respect to reasonably small violations, and hampers the flexibility of the process. In this work, we propose a novel approach which allows to represent actors' tolerance with respect to violations and to account for severity of deviations when assessing executions compliance. We argue that besides improving the quality of the provided diagnostics, allowing some tolerance in deviations assessment also enhances the flexibility of conformance checking techniques and, indirectly, paves the way for improving the resilience of the overall process management system., Comment: 15 pages, 5 figures

We are interested in the influence of spare part service measures on the performance of the front-end wafer fabrication process. This process is characterized by re-entrant flows due to the multiple layers present on chips. This exacerbates the effects of flow variability. We focus on the bottleneck resource. We consider three different service measures. The traditional aggregate fill rate and downtime waiting for parts do not take into account the number of outliers in the case of stock out. The third service measure is the number of extreme long downs , specifically designed at limiting outliers. We develop an improved method, combining optimization and simulation, to compare the performance of a front-end wafer fabrication process under these three service measures. In the studied case, the extreme long down service measure delivers superior performance and transparency. Our approach can help practitioners to choose the appropriate spare parts service measure, increasing fab performance.

The papers in this special section focus on recent developments and emerging topics in the area of deep fuzzy models that address some of the problems and limitations above. These models have been known under different names, such as hierarchical fuzzy systems and fuzzy networks. They are usually well suited for performing multiple functional compositions at either crisp or linguistic level. Deep learning has gained significant attention within the computational intelligence community in recent years. Its success has been mainly due to the increased power of modern computational platforms in terms of their ability to collect, store, and process large volumes of data. This has led to a substantial increase in the effectiveness and efficiency of data management. As a result, it has become possible to achieve high accuracy for some benchmark learning tasks, such as object classification and image recognition within a short time frame. The most common implementation of deep learning has been through neural networks due to the ability of their layers of neurons to perform multiple functional compositions as part of a multistage learning process.

Nightshift workers go against the natural sleep–wake rhythm. Light can shift the circadian clock but can also induce acute alertness. This placebo-controlled exploratory field study examined the effectiveness of light glasses to improve alertness while reducing the sleep complaints of hospital nurses working nightshifts. In a crossover within-subjects design, 23 nurses participated, using treatment glasses and placebo glasses. Sleepiness and sleep parameters were measured. A linear mixed model analysis on sleepiness revealed no significant main effect of the light intervention. An interaction effect was found indicating that under the placebo condition, sleepiness was significantly higher on the first nightshift than on the last night, while under the treatment condition, sleepiness remained stable across nightshift sessions. Sleepiness during the commute home also showed a significant interaction effect, demonstrating that after the first nightshift, driver sleepiness was higher for placebo than for treatment. Subjective sleep quality showed a negative main effect of treatment vs. placebo, particularly after the first nightshift. In retrospect, both types of light glasses were self-rated as effective. The use of light glasses during the nightshift may help to reduce driver sleepiness during the commute home, which is relevant, as all participants drove home by car or (motor) bike.

Effective and efficient mobility of people and freight is a major topic of attention in modern society around the globe, especially in urban environments. To operate a transportation system under increasing complex conditions, a proper synchronization of all involved parties is of utmost importance. These parties include governmental institutions, non-commercial and commercial transport operators, infrastructure managing organizations, added-value service providers, and last but not least the travelers and shippers themselves. To synchronize the parties, they need to exchange data using digital mobility platforms, thereby forming cooperative, intelligent transportation systems, or C-ITS in short. Consequently, C-ITS form the digital backbone for many integrated mobility solutions. The development of C-ITS is, however, typically seen as a major technological endeavor, in which a spectrum of low-level technological solutions has to be integrated to into a complex, interoperable system that facilitates the data exchange and processing enabling optimal transport. In other words, the emphasis in such development is usually on the technical how of system development. After the development of C-ITS solutions, it often becomes clear that there was little attention for the reasons why the various stakeholders would participate in the collaborative use of the solution. Put shortly: often, there is no business case for a developed solution. Consequently, solutions are not or poorly used in practice. In this book, we address exactly this issue: we put the emphasis on the development of these business cases that are the starting point for the development of collaborative mobility solutions. We do this in the form of collaborative business models that take into account the main characteristics of all stakeholders involved and that enable a structured analysis of values, activities, costs and benefits of these stakeholders to arrive at a balanced and viable interplay. The approach we have used is based on the service-dominant business model design method, which takes a collaborative ecosystem as a starting point. We present a catalog of business models blueprints that are based on research and innovation work across Europe in the past five years.

In the current economy, we see a shift of focus from delivering products or services to delivering value or outcomes to customers, reflected in the concept of the outcome economy. The concept has been embraced by research andpractice but lacks proper operationalization to make it fit for the digital transformation of supply chains. In this paper, we translate the concept into a cybernetic model and accompanying technique and tool that can be used for outcome-based business engineering, leading to structures for Internet-of-Thingsenabled, data-driven value delivery in supply chains. Based on a design science research approach, we discuss the development of the Alpha version of this model and tool, the application in business practice, and the use of this experience to develop a Beta version. We provide a bridge from a leading contemporary business concept to structured application in practice, positioning Internet-of-Things and Business Intelligence technologies for data-driven business execution.

In this paper, we propose an approach that combines optimization techniques with machine learning to improve capacitated barge planning with uncertain container arrivals. The main idea is to use the predictions of a decision tree in the scenario generation of a 2-stage stochastic program to plan the barge calls (i.e. visits) of a barge operator. The predictions of container arrivals help to generate more accurate scenarios, which in turn leads to more informed decisions and less costs. The approach is tested with an iterative method of periodic planning and simulation for a one year duration so that the long term performance is evaluated. A computational experiment is conducted using the historical data of an inland terminal and the Port of Rotterdam. The results show that the proposed approach improves total costs up to 2.07% over the traditional stochastic approach, and up to 4.57% over the current method used in industry.

Traditionally, the optimal resource allocation in a business process is determined by manually exploring a number of options, but whether this leads to the optimal solution is questionable; it is possible that an unexplored option produces even better results. Therefore, an automated approach is needed that explores all possible options in a structured manner to find the best one. To address this need this paper proposes three iterations of a search strategy for resource optimization that searches through the space of possible resource allocations to find the best one. The performance of the search strategy is evaluated theoretically with guided experiments. The experiments show that the proposed search strategies scale very well and obtain the optimal value in only a fraction of the number of steps needed by an exhaustive exploration of the solution space. In this manner, this paper also forms the basis for future research into resource optimization with extensions into multi-process optimization, advanced, and operational resource optimization.

In the Industry 4.0 era, manufacturers strive to remain competitive by using advanced technologies such as collaborative robots, automated guided vehicles, augmented reality support and smart devices. However, only if these technological advancements are integrated into their system context in a seamless way, they can deliver their full potential to a manufacturing organization. This integration requires a system architecture as a blueprint for positioning and interconnection of the technologies. For this purpose, the HORSE framework, resulting from the HORSE EU H2020 project, has been developed to act as a reference architecture of a cyber-physical system to integrate various Industry 4.0 technologies and support hybrid manufacturing processes, i.e., processes in which human and robotic workers collaborate. The architecture has been created using design science research, based on well-known software engineering frameworks, established manufacturing domain standards and practical industry requirements. The value of a reference architecture is mainly established by application in practice. For this purpose, this paper presents the application and evaluation of the HORSE framework in 10 manufacturing plants across Europe, each with its own characteristics. Through the physical deployment and demonstration, the framework proved its goal to be basis for the well-structured design of an operational smart manufacturing cyber-physical system that provides horizontal, cross-functional management of manufacturing processes and vertical control of heterogeneous technologies in work cells. We report on valuable insights on the difficulties to realize such systems in specific situations. The experiences form the basis for improved adoption, further improvement and extension of the framework. In sum, this paper shows how a reference architecture framework supports the structured application of Industry 4.0 technologies in manufacturing environments that so far have relied on more traditional digital technology.

Because of high downtime costs of capital goods, it is becoming increasingly more important to have spare parts available as fast as possible. In order to ensure this, companies are resorting to the use of so-called lateral and emergency shipments in case the stock is not available at the closest local warehouse. As a result, managing the inventory of spare parts in a large network becomes increasingly more complex. In this paper we present a spare parts inventory model for a two-echelon spare parts network with lateral and emergency shipments and an emergency warehouse which is intended to allow for reserving stock nearby the central warehouse dedicated to emergency requests. Although any sequence is possible, the order is commonly fixed as it is based on the time and thus costs to get a part from each other location to the customer. We first present an accurate approximate procedure to evaluate such a complex network. Using simulation we find that this approximate evaluation procedure is accurate, especially for higher availability levels. We then look at optimized basestock levels, obtained via a smart enumeration procedure, to show the benefit of making use of this emergency warehouse. We find that savings up to 30% can be obtained via an emergency warehouse.

The application of smart industry frameworks and technologies propels manufacturing into a new era with increased production flexibility, closed-loop quality management, more efficient use of resources, and more agile production processes. These characteristics lay the basis for the support of highly customized production, short lead times and increased product quality at lower prices – all characteristics that are demanded by the global market in which modern manufacturing finds itself. This book presents the approach and results of the five-year European HORSE project in the field of smart manufacturing. HORSE focuses on bringing flexible, safe human-robot collaboration and vertical and horizontal process support in an integrated fashion to the manufacturing work floor. The HORSE system is realized on the basis of a highly modular architecture, such that the system can be configured to the manufacturing context at hand. This architecture is aligned with the ISA95 and RAMI 4.0 frameworks. The book presents the development of the architecture, the realization of the prototype system, and the application of the system in ten industrial cases across Europe, discussing the details of seven of these. The book is aimed at manufacturing professionals, system integrators and consultants, researchers in the smart manufacturing domain, as well as advanced students in manufacturing curricula.

Task illuminance is one of the most used parameters in office lighting design and is often used as a ‘single number criterion’ to verify that a lighting design meets the requirements. Although other parameters, such as wall luminance, are often highly correlated with task illuminance, not taking these explicitly into account means critical user criteria such as comfort and satisfaction are left to chance. In this study, we investigated the effect of varying desk illuminance(150 lux to 1500 lux) while keeping wall luminance constant, isolating the effects of illuminance on the desk and eye on office users’ overall perception of the space, their mental state and their performance (visual and cognitive). While both visual acuity (paper-based) and perceived brightness increased significantly with higher desk illuminance, the room’s attractiveness did not. Even though illuminance at the eye increased considerably with desk illuminance (from 118 lux to 796 lux), only minor effects were found on subjective alertness and cognitive performance. This suggests that focusing on horizontal task illuminance as a design parameter is appropriate in view of visual acuity but has little to no effect on the space’s attractiveness, nor on cognitive performance or mental state of the office worker.

Business process management (BPM) is often employed as a driver of integration, by articulating business processes that cross the boundaries of individual business functions. Business process modelling as part of BPM has shown its potential in administrative environments, such as banking and insurance organizations. However, business process modelling remains unproven for all the processes encountered in manufacturing enterprises, including the physical operations processes that transform input materials into the required product. These processes have physical characteristics that make them essentially different from administrative processes with a highly digital nature, like limited physical buffers for intermediate products and transportation times between activities. In this paper, we present an approach to use business process models for the specification of these physical operations processes. Our approach is based on a catalogue of common process fragments that are adapted to the physical nature of manufacturing. These process fragments serve as reusable building blocks for the specification of manufacturing processes. We use the industry standard Business Process Model and Notation (BPMN) to represent both fragments and processes. We demonstrate the use of our catalogue of fragments by modelling and enacting the real-world processes of ten manufacturing organizations. The results show strong support for the use of business process models for both representation and enactment of manufacturing processes. This work closes part of the gap between the support for administrative processes and physical industrial processes, thus contributing to the advent of the smart manufacturing concept in the context of Industry 4.0.

Barge transport suffers from a high degree of uncertainty resulting from unreliable container arrivals (e.g. due to deep sea vessel delays), and dynamism that leads to limited information availability during planning. This paper studies the impact of uncertainty and dynamism using real data of an inland terminal. Our method consists of iterating weekly operations for one year to evaluate the long term performance. Each iteration involves solving a stochastic program for planning barge calls, and then simulating actual events. We show that uncertainty has an impact of up to 53% and dynamism up to 20% on total costs.

The use of dynamic light scenarios for indoor environments may be motivated by the aim to support our circadian rhythms or provide a fascinating and/or informative environment. Additionally, dynamic lighting may be beneficial as it could acutely provide support at certain times of day, or for specific tasks or activities. A systematic literature search was performed to explore the dynamic scenarios that have been designed and tested to date. We explored what the motivations behind them were and what the effectiveness of these scenarios was. On all aspects, the fourteen included articles varied substantially. The most promising effect seemed to be improved sleep due to an increased light dose during the day. However, for future work, it is recommended to explicitly explain and carefully align the theoretical rationale, the dynamic light scenario and the study protocol, including outcome measures. This review suggests potential beneficial effects of dynamic light scenarios on human functioning, but also clearly indicates that consolidated strategies for their description and assessment are currently lacking.

The widespread deployment of digital technologies has resulted in a hyperconnected context for business organizations. Currently many ventures, both start-ups and incumbents, consider engaging in a Digital Platform Business Strategy to create and capture value in collaborative ecosystems. This type of strategy strongly differs from a conventional Business Strategy in speed, scale, scope and interaction with the ecosystem. Business Models are used to realize a strategy, but little guidance and support exists for the specification of business models realizing a Digital Business Platform Business Strategy. This affects an organization's responsiveness to digital opportunities. To address this gap, this paper proposes a novel method to support the derivation of Business Model Design Requirements from a Digital Platform Business Strategic Objective. The method uses a Catalogue of Strategy Elements as a starting point to generate Subobjectives and Business Model Design Requirement Specification Cards as a structuring tool to facilitate the transformation of these Subobjectives into Business Model Design Requirements. The method is demonstrated using a historical platform case study. We show that our approach generates Business Model Design Requirements in a structured way and in line with Business Strategy. As a positive by-product the method creates options for business model evolution.

Providing more diurnal light to people working indoors serves vitality and healthy entrainment of our internal clock via the non-image forming pathway. But we do not exactly know how much light people are exposed to in their office, nor do we know how much extra light they receive if we raise the overall illuminance. In this study, we tracked office workers’ personal exposure during two three-week field intervention studies, one in winter, one in late spring. Electric lighting provided, on average, either approximately 125 or 900 lux horizontally on the desk (40 lux vs. 300 lux vertically, at the eye), on top of daylight conditions. The aim of the study was to demonstrate the amount of variance in exposure of office employees in a modern office space. Detailed graphical visualizations are presented to illustrate personal exposure patterns. Data collected with a spectrometer at a reference desk clearly revealed the light intervention, despite variations in weather conditions and sun position. Yet the person-based data revealed large differences between - and within - participants in terms of light received at the eye, despite them working in the same office space. Moreover, whereas for some individuals the light intervention resulted in marked increases in exposure, light exposure patterns of others showed virtually no signs of the additional lighting. We discuss implications for research and practice.

De technologische ontwikkelingen in de supply chain volgen elkaar razendsnel op. Volgens professor Paul Grefen van de Technische Universiteit Eindhoven blijft het potentieel ervan echter vaak onderbenut. “Veel organisaties wenden artificiële intelligentie, IoT of cloud logistics enkel aan voor om bestaande samenwerkingsvormen te verbeteren, terwijl disruptieve tools net uitnodigen om op een andere manier naar de supply chain te kijken”, luidt het. De professor adviseert bedrijven om eerst over nieuwe businessmodellen na te denken, alvorens de technologiekeuze op de agenda te plaatsen.

Background The interpretability of results predicted by the machine learning models is vital, especially in the critical fields like healthcare. With the increasingly adoption of electronic healthcare records (EHR) by the medical organizations in the last decade, which accumulated abundant electronic patient data, neural networks or deep learning techniques are gradually being applied to clinical tasks by utilizing the huge potential of EHR data. However, typical deep learning models are black-boxes, which are not transparent and the prediction outcomes of which are difficult to interpret. Methods To remedy this limitation, we propose an attention neural network model for interpretable clinical prediction. In detail, the proposed model employs an attention mechanism to capture critical/essential features with their attention signals on the prediction results, such that the predictions generated by the neural network model can be interpretable. Results We evaluate our proposed model on a real-world clinical dataset consisting of 736 samples to predict readmissions for heart failure patients. The performance of the proposed model achieved 66.7 and 69.1% in terms of accuracy and AUC, respectively, and outperformed the baseline models. Besides, we displayed patient-specific attention weights, which can not only help clinicians understand the prediction outcomes, but also assist them to select individualized treatment strategies or intervention plans. Conclusions The experimental results demonstrate that the proposed model can improve both the prediction performance and interpretability by equipping the model with an attention mechanism.

ACHTERGROND Er zijn aanwijzingen dat bij nieuwbouw van psychiatrische ziekenhuizen aspecten van licht en de inrichting van de groene ruimte in aanmerking moeten worden genomen, om zo het herstel van patiënten positief te beïnvloeden. DOEL Positieve en duurzame effecten van licht- en groenvoorziening in de ggz op de mentale gezondheid onder de aandacht brengen. METHODE Literatuurstudie. RESULTAAT Dag- en kunstlicht, en natuur kunnen een positieve invloed op herstel en mentaal welbevinden hebben van patiënten en medewerkers in zorginstellingen. CONCLUSIE Het is de moeite waard al in de ontwerpfase van nieuwbouw voor psychiatrische ziekenhuizen rekening te houden met lichtinval, verlichting en groenvoorziening. Dit draagt bij aan het psychisch functioneren van de gebruikers.

We propose the use of linguistic summarization concept to support business model design evaluation. In contrast to traditional linguistic summarization, we do not infer these linguistic summaries from data, but use their structure to express intentions or conditions for stakeholders to participate in the designed business model. Early phase business model design is highly uncertain, for which it is often difficult to quantify under what conditions the business model design would be acceptable for the stakeholders involved. Through generation of intentional linguistic summaries (ILS), stakeholders can make explicit under what conditions a certain business model design would be acceptable. Therefore ILS can allow a discussion on benefits and costs without focusing too much on details, which are rarely available in the early phases of business model design. These intentional linguistic summaries can consequently be used as soft KPIs to be validated once the business model is operational.

Several high-tech manufacturing technologies are emerging to meet the demand for mass customized products. These technologies include configurable robots, augmented reality and the Internet-of-Things. Manufacturing enterprises can leverage these new technologies to pursue increased flexibility, i.e., the ability to perform a larger variety of activities within a shorter time. However, the flexibility offered by these new technologies is not fully exploited, because current operations management techniques are not dynamic enough to support high variability and frequent change. The HORSE Project investigated several of the new technologies to find novel ways to improve flexibility, as part of the Horizon 2020 research and innovation program. The purpose of the project was to develop a system, integrating these new technologies, to support efficient and flexible manufacturing. This article presents the core result of the project: a reference architecture for a manufacturing operations management system. It is based on the application and extension of business process management (BPM) to manage dynamic manufacturing processes. It is argued that BPM can complement current operations management techniques by acting as an orchestrator in manufacturing processes augmented by smart technologies. Building on well-known information systems&rsquo, architecting frameworks, design science research is performed to determine how BPM can be applied and adapted in smart manufacturing operations. The resulting reference architecture is realized in a concrete HORSE system and deployed and evaluated in ten practical cases, of which one is discussed in detail. It is shown that the developed system can flexibly orchestrate the manufacturing process through vertical control of all agents, and dynamic allocation of agents in the manufacturing process. Based on that, we conclude that BPM can be applied to overcome some of the obstacles toward increased flexibility and smart manufacturing.

In this paper we study a repeated posted-price auction between a single seller and a single buyer that interact for a finite number of periods or rounds. In each round, the seller offers the same item for sale to the buyer. The seller announces a price and the buyer can decide to buy the item at the announced price or the buyer can decide not to buy the item. In this paper we study the problem from the perspective of the buyer who only gets to observe a stochastic measurement of the valuation of the item after he buys the item. Furthermore, in our model the buyer uses fuzzy sets to describe his satisfaction with the observed valuations and he uses fuzzy sets to describe his dissatisfaction with the observed price. In our problem, the buyer makes decisions based on the probability of a fuzzy event. His decision to buy or not depends on whether the satisfaction from having a high enough valuation for the item out weights the dissatisfaction of the quoted price. We propose an algorithm based on Thompson Sampling and demonstrate that it performs well using numerical experiments.

[EN] The Internet of Things (IoT) enables to connect the physical world to digital business processes (BP). By using the IoT, a BP can, e.g.: 1) take into account real-world data to take more informed business decisions, and 2) automate and/or improve BP tasks. To achieve these benefits, the integration of IoT and BPs needs to be successful. The first step to this end is to support the modeling of IoT-enhanced BPs. Although numerous researchers have studied this subject, it is unclear what is the current state of the art in terms of current modeling solutions and gaps. In this work, we carry out a Systematic Mapping Study (SMS) to find out how current solutions are modelling IoT into business processes. After studying 600 papers, we identified and analyzed in depth a total of 36 different solutions. In addition, we report on some important issues that should be addressed in the near future, such as, for instance the lack of standardization., This research has been funded by Internal Funds KU Leuven (Interne Fondsen KU Leuven) and the financial support of the Spanish State Research Agency under the project TIN2017-84094-R and co-financed with ERDF.

In recent work [J. Opt. Soc. Am. A 36, 1022 (2019)JOAOD60740-323210.1364/JOSAA.36.001022], we found that Δ E ab ∗ / s in CIELAB is not suitable for describing the perceived speed of temporal color changes in full-room illumination. Two hue transitions with the same physical speed of change, in terms of Δ E ab ∗ / s , were not perceived to change at the same speed. This is not really surprising, since CIELAB was not designed to characterize the perception of temporal color transitions in illumination. In this study, we further investigate the temporal uniformity of CIELAB. The stimuli were presented in a square of 4.3° visual angle surrounded by a 4000 K adapting field, similar to the viewing condition for which CIELAB was designed (i.e., where color stimuli are presented on-axis surrounded by a static adaptation field). The human observers viewed pairs of temporal color transitions which were presented sequentially, and were asked to select the one that appeared to change faster. The results confirmed that under these conditions CIELAB was also not temporally uniform. We present preliminary attempts to improve the temporal uniformity for both CIELAB and cone-excitation spaces (i.e., LMS and DKL (Derrington–Krauskopf–Lennie [J. Physiol. 357, 241 (1984)JPHYA70022-375110.1113/jphysiol.1984.sp015499]).

Quantitative business process analysis is a powerful approach for analyzing timing properties of a business process, such as the expected waiting time of customers or the utilization rate of resources. Multiple techniques are available for quantitative business process analysis, which all have their own advantages and disadvantages. This paper presents a novel technique, based on queueing models, that combines the advantages of existing techniques, in that it leads to accurate analyses, is computationally inexpensive, and feature complete with respect to its support for basic process modeling constructs. An extensive quantitative evaluation has been performed that compares the presented queueing model to existing queueing models from literature. This evaluation shows that the presented model outperforms existing models with one order of magnitude on accuracy. The resulting queueing model can be used for fast and accurate timing predictions of business process models. These properties are useful in optimization scenarios.

Multimodal transport is widely acknowledged to be more economic and environmental friendly compared to road transport, and has attracted a significant research effort from the field of operations research. Hinterland transport, particularly is of interest, as managing the freight flows between the port and the inland via road, waterway or railway transport offers a wide range of operational problems. Quite often though, there is a significant gap between how these problems are perceived and modeled by academia, and how they really exist in real life. This gap causes the academic solutions to be ineffective or inapplicable in solving the problems in practice. This paper is intended to describe how hinterland transport is executed in practice, and to increase the understanding of researchers. For this purposes, 12 in-depth interviews are conducted with practitioners of hinterland transport with different roles and perspectives to provide a wider picture of the business practices. Meanwhile, the view of academic research is studied via a literature review, and the findings are compiled and provided in this paper.

Fuzzy inference systems (FIS) gained popularity and found application in several fields of science over the last years, because they are more transparent and interpretable than other common (black-box) machine learning approaches. However, transparency is not automatically achieved when FIS are estimated from data, thus researchers are actively investigating methods to design interpretable FIS. Following this line of research, we propose a new approach for FIS simplification which leverages graph theory to identify and remove similar fuzzy sets from rule bases. We test our methodology on two data sets to show how this approach can be used to simplify the rule base without sacrificing accuracy.

Tremor severity assessment is an important element for the diagnosis and treatment decision-making process of patients suffering from Essential Tremor (ET). Classically, questionnaires like the ETRS and QUEST surveys have been used to assess tremor severity. Recently, attention around computerized tremor analysis has grown. In this study, we use regression trees to map the relationship between tremor data that is collected using the TREMOR12 smartphone application with ETRS and QUEST scores. We aim to develop a model that is able to automatically assess tremor severity of patients suffering from Essential Tremor without the use of more subjective questionnaires. This study shows that tremor data gathered using the TREMOR12 application is useful for constructing machine learning models that can be used to support the diagnosis and monitoring of patients who suffer from Essential Tremor.

BACKGROUND The COVID-19 pandemic has necessitated an immediate and large-scale uptake of online treatment for mental health care. However, there is uncertainty about what the “new normal” in mental health care will be like in post–COVID-19 times. To what extent will the experiences gained during the pandemic influence a sustainable adoption and implementation of online mental health care treatment in the future? OBJECTIVE In this paper, we aim to formulate expectations with regard to the sustainability of online mental health care after COVID-19. METHODS In an interview study, 11 mental health care professionals were asked about their experiences and expectations for the future. Participants were recruited from a mental health care organization in the Netherlands. The interviews took place between April 7-30, 2020, at the peak of the COVID-19 crisis in the Netherlands. The data were analyzed using a thematic coding method. RESULTS From the interviews, we learn that the new normal in mental health care will most likely consist of more blended treatments. Due to skill enhancement and (unexpected) positive experiences with online treatment, an increase in adoption is likely to take place. However, not all experiences promise a successful and sustainable upscaling of online treatment in the future. Mental health care professionals are learning that not all clients are able to benefit from this type of treatment. CONCLUSIONS Sustainable upscaling of online mental health care requires customized solutions, investments in technology, and flexibility of mental health care providers. Online treatment could work for those who are open to it, but many factors influence whether it will work in specific situations. There is work to be done before online treatment is inherently part of mental health care.

The complexity of manufacturing processes is increasing due to the production variety implied by mass customization of products. In this context, manufacturers strive to achieve flexibility in their operational processes. Business Process Management (BPM) can help integration, orchestration and automation of these manufacturing operations to reach this flexibility. BPMN is a promising notation for modeling and supporting the enactment of manufacturing processes. However, processes in the manufacturing domain include the flow of physical objects (materials and products) apart from information flow. Buffering, bundling and unbundling of physical objects are three commonly encountered patterns in manufacturing processes, which require fine-grained synchronization in the enactment of multiple process instances. Unfortunately, BPMN lacks strong support for this kind of dynamic synchronization as process instances are modeled and executed from a single, isolated point of view. This paper presents a mechanism based on BPMN 2.0 that enables process modelers to define synchronization points by using the concept of recipes. The recipe system uses a dynamic correlation scheme to control many-to-many interactions among process instances to implement required inter-instance synchronizations. We formally describe the involved BPMN patterns, implement and evaluate them in a manufacturing scenario in the high-tech media printing domain.

Many researchers have used fuzzy set theory and fuzzy logic in a variety of applications related to computer science and engineering, given the capability of fuzzy inference systems to deal with uncertainty, represent vague concepts, and connect human language to numerical data. In this work we propose Simpful, a general-purpose and user-friendly Python library designed to facilitate the definition, analysis, and interpretation of fuzzy inference systems. Simpful provides a lightweight Application Programming Interface that allows to intuitively define fuzzy sets and fuzzy rules, and to perform fuzzy inference. Worthy of note, in Simpful the fuzzy rules are specified by means of strings of text written in natural language. We provide here some practical examples to show that Simpful represents a valuable addition to the open-source software that supports fuzzy reasoning.KeywordsDecision support, Fuzzy logic, Fuzzy networks, Modeling and control, Open- source software, Python library

Processing sequential data and time-dependent data is a problem of constructing computational graph with a certain structure. A computational graph formalizes the structure of a set of computations including mapping temporal inputs and outputs. In this paper we apply graph theory and fuzzy interval representation of uncertain variables to indicate states of the temporal scheduling system. Descriptive model for temporal reasoning on graph, sequence modelling and ordering of fuzzy inputs for scheduling problem is introduced.

Conformance checking techniques are widely adopted to validate process executions against a set of constraints describing the expected behavior. However, most approaches adopt a crisp evaluation of deviations, with the result that small violations are considered at the same level of significant ones. Furthermore, in the presence of multiple data constraints the overall deviation severity is assessed by summing up each single deviation. This approach easily leads to misleading diagnostics; furthermore, it does not take into account user’s needs, that are likely to differ depending on the context of the analysis. We propose a novel methodology based on the use of aggregation functions, to assess the level of deviation severity for a set of constraints, and to customize the tolerance to deviations of multiple constraints.

Living in the era of "data deluge" demands for an increase in the application and development of machine learning methods, both in basic and applied research. Among these methods, in the last decades fuzzy inference systems carved out their own niche as (light) grey box models, which are considered more interpretable and transparent than other commonly employed methods, such as artificial neural networks. Although commercially distributed alternatives are available, software able to assist practitioners and researchers in each step of the estimation of a fuzzy model from data are still limited in scope and applicability. This is especially true when looking at software developed in Python, a programming language that quickly gained popularity among data scientists and it is often considered their language of choice. To fill this gap, we introduce pyFUME, a Python library for automatically estimating fuzzy models from data. pyFUME contains a set of classes and methods to estimate the antecedent sets and the consequent parameters of a Takagi-Sugeno fuzzy model from data, and then create an executable fuzzy model exploiting the Simpful library. pyFUME can be beneficial to practitioners, thanks to its pre-implemented and user-friendly pipelines, but also to researchers that want to fine-tune each step of the estimation process.

In the modern economy, we see a shift towards networked business scenarios. In many contemporary situations, the operation of multiple organizations is tightly coupled in collaborative business networks. To allow this tightly coupled collaboration, business process management (BPM) in these collaborative networks is becoming increasingly important. We discuss automated support for this networked BPM: automated means to manage business processes that span multiple autonomous organizations - thereby combining aspects of process management and e-business. We first provide a conceptual background for networked BPM. We describe a number of research approaches in this area, ranging from early developments to contemporary designs in a service-oriented context. This provides an overview of developments in which we observe several major trends. Firstly, we see a development from support for static business processes to support for highly dynamic processes. Secondly, we see how approaches move from addressing simple business collaboration networks to addressing complex networks. Thirdly, we find a move from the use of dedicated information technology to the use of standard technology. Finally, we observe that the BPM research efforts move through time from pushing new BPM technology into application domains to using BPM to realize business-IT alignment in application contexts.

In this paper we introduce an NP-hard optimization problem and examine preferences of decision-maker towards imprecise alternatives (modes) in a fuzzy temporal graph structure model. Fuzzy Z-number preference matrix is introduced and types of generalized precedence relations in fuzzy multi-mode resource-constrained project scheduling problem (F-MRCPSP) based on expert estimation are discussed. Implementation of Z-numbers allows handling uncertain data and modelling preferences of expert towards uncertain variables. Multi-mode way for activity performance allows considering temporal uncertainty, expert estimations, flexibility in switching from mode to mode and resource levelling profile problem. Geometrical interpretation for fuzzy multi-mode problem (F-MRCPSP) based on box packing is given.

Background Effects of light on well-being and performance are often studied in the controlled confines of a laboratory. In real-life, we need to optimally balance circadian and acute effects on the one hand and visual comfort on the other, as both impact human daytime functioning and wellbeing. Objective Our aim was to investigate concurrent IF and NIF effects of three contrasting light scenarios on alertness, mood and comfort in a real-life office context. Method Two field studies were performed (summer vs. winter) in an office in The Netherlands. The study received approval of the local ethical committee. In summer 12 office workers (1 female, Mage = 45) participated, in winter 11 (1 female, Mage = 43). Each study lasted 3 weeks - one for each scenario. In contrast to a constant neutral electric light scenario (300 lx on desk, 100 lx at the eye, 4000 K), we tested a scenario with morning bright light (900 lx on desk, 300 lux at the eye) and a scenario with afternoon bright light. On top of these electric scenarios, the daylight added substantially to illuminances at desk and eye. We employed experience sampling (alertness (KSS), vitality, mood and light and temperature appraisals, 10 times per day) in combination with continuous person-worn and office-bound sensors (objective light exposure, skin temperature and actigraphy). Findings Preliminary results indicate that bright electric light was perceived as less pleasant in summer. Moreover, alertness and vitality scores were lower throughout days with bright morning light. In contrast, pleasantness was not affected by the conditions in winter, and vitality and mood significantly dropped after morning bright light in the first week. Conclusions and Implications When designing light interventions it is important to take both NIF and IF effects into account. Brighter light is not always better, particularly in real-life environments.

Data-driven business processes involve knowledge workers that process information to take decisions. Such processes have been modelled successfully using artifact-centric process models. Artifacts represent business entities about which the knowledge workers collect and process information. Since information retrieval costs time and money, the key goal is to retrieve only the pieces of information that are needed to make a well-informed decision. To aid knowledge workers in achieving this goal, this paper realizes decision support for declarative artifact-centric process models by showing how declarative artifact-centric process models can be translated into Markov Decision Processes (MDP). The approach is illustrated with an example from the field of financial services.

Currently many webshops rely on a fixed list of product facets to help users find the products of interest. Such a solution suffers from two problems: (1) it is difficult to devise a fixed list of facets that would satisfy all user interests, and (2) the top facets could become obsolete when all product results have these facets. To address these two problems we propose a novel algorithm for dynamic ordering of the product facets based on the query results. This algorithm relies on measures such as specificity and dispersion for qualitative and quantitative facets, respectively, to rank the properties associated with these facets so that users are able to find the products of interest with a minimum number of drill-down steps. Using a large-scale simulation study and a user-based evaluation, we show that our algorithm outperforms the expert-based fixed facets approach, a greedy baseline, and a state-of-The-Art entropy-based solution. This paper is an extended abstract of our previous work [1].

This study examined whether particular recovery activities after work have a positive or negative effect on employee recovery from work (i.e., cognitive, emotional, and physical detachment) and sleep quality. We used a two-wave panel study of 230 health care employees which enabled looking at both short-term and long-term effects (i.e., two-year time interval). Gender, age, marital status, children at home, education level, management position, and working hours were used as control variables. Hierarchical multiple regression analyses showed that work-related off-job activities were negatively associated with cognitive and emotional detachment in both the short and long run, whereas low-effort off-job activities were positively related to cognitive detachment in the short run. Moreover, household/care off-job activities were positively related to sleep quality in the long run, whereas physical off-job activities were negatively associated with sleep quality in the long run. The long-term findings existed beyond the strong effects of baseline detachment and sleep quality. This study highlights the importance of off-job recovery activities for health care employees&rsquo, detachment from work and sleep quality. Practical implications and avenues for further research are discussed.