Engineering changes (ECs) are new product development activities addressing external or internal challenges, such as market demand, governmental regulations, and competitive reasons. The corresponding EC processes, although perceived as standard, can be very complex and inefficient. There seem to be significant differences between what is the “officially” documented and the executed process. To better understand this complexity, we propose a data-driven approach, based on advanced text analytics and process and data mining techniques. Our approach sets the first steps toward an automatic analysis, extracting detailed events from an unstructured event log, which is necessary for an in-depth understanding of the EC process. The results show that the predictive accuracy associated with certain EC types is high, which assures the method applicability. The contribution of this article is threefold: 1) a detailed model representation of the actual EC process is developed, revealing problematic process steps (such as bottleneck departments); 2) homogeneous, complexity-based EC types are determined (ranging from “standard” to “complex” processes); and 3) process characteristics serving as predictors for EC types are identified (e.g., the sequence of initial process steps determines a “complex” process). The proposed approach facilitates process and product innovation, and efficient design process management in future projects.

We propose an algorithm to find a starting point for iterative methods. Numerical experiments show empirically that the algorithm provides starting points for different iterative methods (like Newton method and its variants) with low computational cost.

In this paper, we propose an algorithm to estimate the radius of convergence for the Picard iteration in the setting of a real Hilbert space. Numerical experiments show that the proposed algorithm provides convergence balls close to or even identical to the best ones. As the algorithm does not require to evaluate the norm of derivatives, the computing effort is relatively low.

In this chapter, the authors continue the systematisation by adding an assessment aid, although a word of caution is needed. They present different aspects that they believe are relevant to any notion of social sustainability. The authors operationalise social sustainability in terms of stakeholder, knowledge, learning and organisational issues, where the last can be seen in terms of co-ordination mechanisms and organisational structures. The chapter describes the instrument. In general, an instrument in the form of a questionnaire can have at least three forms. First, it can be a categorisation structure. Second, it is possible to attach ordinal scales to answers. Third, a questionnaire can have the form of a construct that is measured and of which the outcome is an indication of a certain correct or incorrect state of affairs. With respect to both the individual level of description and the various kinds of organisational levels of description, the authors try to operationalise units, teams, groups, firms and societies.

Although sustainability is often discussed solely in ecological terms, it cannot be disconnected from the way humans behave in their social environment. This article presents a theoretical approach toward sustainability that takes a human behavior and knowledge view on sustainability as a starting point. This approach requires that human behavior should change, individually and collectively, in order to achieve sustainability. Knowledge is identified as the driving force behind human behavior and its effect on the ecological and social environment. In connecting knowledge with sustainability, two concepts are introduced: knowledge of sustainability (KoS), which refers to the sustainability content of knowledge, and sustainability of knowledge (SoK), which denotes the dynamics of the continuing process of knowledge creation and application. To apply SoK and KoS, we argue that a cognitive interpretation of human behavior should be formulated within a knowledge management approach that incorporates the stages of knowledge creation, integration, and application and that ensures the critical evaluation of created knowledge. In order to show that our new approach is practical, we use existing research from the Dutch starch potato industry to reformulate possibilities for the enhancement of sustainability in terms of KoS and SoK. In addition, the research led to the development of a mechanism for evaluating knowledge. Group interaction, information technology, and decision support systems are used to realize knowledge integration. The combination of conceptual design and domain of application is common in the engineering sciences, where a design methodology is used to make the steps from conceptual design to functional design and technical implementation. A conceptual design may show ways to improve existing practices, which in turn might result in superior practices. Of course, it is necessary to empirically test the interventions in reality. For AGROBIOKON that has not been done, yet.

From a knowledge (management) perspective, behaviour is identified as the consequence of an individual's knowledge (Jorna, 2006). In other words, a change of behaviour requires that an individual changes his knowledge. Innovation brings about the necessity for an individual to change his current knowledge, thus abandoning old insights, and learning new. Knowledge management is the discipline that focuses on such processes of changing knowledge.

Clustering semantically related terms is crucial for many applications such as document categorization, and word sense disambiguation. However, automatically identifying semantically similar terms is challenging. We present a novel approach for automatically determining the degree of relatedness between terms to facilitate their subsequent clustering. Using the analogy of ensemble classifiers in Machine Learning, we combine multiple techniques like contextual similarity and semantic relatedness to boost the accuracy of our computations. A new method, based on Yarowsky’s word sense disambiguation approach, to generate high-quality topic signatures for contextual similarity computations, is presented. A technique to measure semantic relatedness between multi-word terms, based on the work of Hirst and St. Onge is also proposed. Experimental evaluation reveals that our method outperforms similar related works. We also investigate the effects of assigning different importance levels to the different similarity measures based on the corpus characteristics.

Nowadays, organizations have to adjust their business processes along with the changing environment in order to maintain a competitive advantage. Changing a part of the system to support the business process implies changing the entire system, which leads to complex redesign activities. In this paper, a bottom-up process mining and simulation-based methodology is proposed to be employed in redesign activities. The methodology starts with identifying relevant performance issues, which are used as basis for redesign. A process model is “mined” and simulated as a representation of the existing situation, followed by the simulation of the redesigned process model as prediction of the future scenario. Finally, the performance criteria of the current business process model and the redesigned business process model are compared such that the potential performance gains of the redesign can be predicted. We illustrate the methodology with three case studies from three different domains: gas industry, government institution and agriculture.

Designing and personalising systems for specific user groups encompasses a lot of effort with respect to analysing and understanding user behaviour. The goal of our paper is to provide a new methodology for determining navigational patterns of behaviour of specific user groups. We consider agricultural users as a specific user group, during the usage of a decision support system supporting cultivar selection - OPTIRas(TM). Combining process mining techniques with insights from decision making theories, we provide a method of analysing logs resulted from usage of decision support systems. For instance, farmers show difficulties in fulfilling the goal of OPTIRas, while other agricultural users seems to manage better. The results of our analysis can be used to support the redesign and personalization of decision support systems.

Process mining is to extract business process models from event logs, the mining process is an important learning task. However, the discovery of these processes poses many challenges, including noise, non-local, non-free choice constructs and so on. In the study, we give out the definition of the behavior redundancy degree which is benefit to analyze the behavior conformance. Then, in order to build the optimal the process model, a process mining method based on Discrete Particle Swarm Optimization (DPSO) is presented. The method can take into account the basic Petri net structure and the metrics of behavior conformance and avoid the blindness of building process model. Finally, a DPSO process mining plug-in is developed and a number of event log is tested in the DPSO mining plug-in based on PROM platform. Theoretical analysis and experimental results show that DPSO-based mining method has …

When modeling or redesigning a process, the knowledge-management perspective is seldomly used. Using the knowledge categorization developed by van Heusden and Jorna, we propose a knowledge-management perspective to provide a strategy for modeling and redesigning a business process. As an illustration of our approach, we use hospital data of multidisciplinary patients. This specific group of patients requires the involvement of different specialisms for their medical treatment that leads to more efforts regarding the coordination of care for these patients. In order to increase the care efficiency, knowledge that supports the reorganization of care for multidisciplinary patients should be provided. We use the above-mentioned knowledge-management perspective for creating new multidisciplinary units, in which different specialisms coordinate the treatment of specific groups of patients.

Processes such as tendering, ordering, delivery, and paying are executed by several parties in almost all supply chains. However, none of these parties has a proper overview over the whole set of activities executed. Therefore, none of the parties can take the lead in business process redesign. Business processes are often not described in an explicit manner, and therefore they are not available for analysis. However, in the information system of each supply chain party, partial information about the business process are recorded. We claim that the overall distributed process can be induced, by using this partial information of all involved parties.In this paper we present an overview of methods available to discover processes across supply chains, based on the assumption that there is a common point of reference at all involved parties, e.g. an order number. Such an induced or discovered process enables analysis across the supply chain, and can become an important tool to facilitate business process redesign in networked organizations.

Many of today’s information systems are driven by explicit process models. Workflow management systems, but also ERP, CRM, SCM, and B2B, are configured on the basis of a workflow model specifying the order in which tasks need to be executed. Creating a workflow design is a complicated time-consuming process and typically there are discrepancies between the actual workflow processes and the processes as perceived by the management. To support the design of workflows, we propose the use of workflow mining. Starting point for workflow mining is a so-called “workflow log” containing information about the workflow process as it is actually being executed. In this paper, we introduce the concept of workflow mining and present a common format for workflow logs. Then we discuss the most challenging problems and present some of the workflow mining approaches available today.

We develop a diagnostic method for detecting problematic dialogue strategies in spoken dialogue systems (SDS). Our tool classifies relations between semantically-pragmatically labelled system prompts and user inputs in a Dutch SDS corpus, and constructs a dependency/frequency graph that provides rich information about the underlying interaction process. The graph can be directly analysed to specify those system events from which more paths lead to problematic user events than to the unproblematic semantic equivalents of those user inputs. As a result, we can pinpoint bad sequences of prompting strategies in the system and suggest ways to replace such prompts with more effective ones. Our approach is a general method that provides straightforward output for evaluating a SDS design, given some labelled data.

Effective information systems require the existence of explicit process models; a completely specified process design needs to be developed in order to enact a given business process. This development is time consuming and often subjective and incomplete. We propose a method that discovers the process model from process logs where process events are recorded as they have been executed over time. We induce a rule-set that predict causal, exclusive, and parallel relations between process events. The rule-set is induced from simulated process log data that are generated by varying process characteristics (e.g. noise, log size). Tests reveal that the induced rule-set has a high performance on new data. Knowing the causal, exclusive and parallel relations we can build the process model expressed in the Petri net formalism. We also evaluate the results using a real-world case study.