A large group linguistic Z-DEMATEL approach for identifying key performance indicators in hospital performance management

In hospital management, performance measurement is of vital importance for improving healthcare service quality. The performance of a healthcare organization is often influenced by numerous indicators, and it is unrealistic to manage them all due to the restriction of resources. In addition, the performance measurement for improvement relates to the benefits of many departments, and it is necessary for large number of experts with different backgrounds to participate in the evaluation process of healthcare indicators. In response, this study develops a large group evaluation approach using linguistic Z-numbers and decision-making trial and evaluation laboratory (DEMATEL) to determine key performance indicators (KPIs) for hospital management. For this approach, the complex and uncertain interrelation evaluations among indicators are given by experts using linguistic Z-numbers. An extended DEMATEL method is proposed to determine KPIs based on the cause and effect relationships of performance indicators. Finally, a case study in a rehabilitation hospital is presented to illustrate the effectiveness and usefulness of the proposed large group linguistic Z-DEMATEL approach. The results indicate that incidents/errors, accidents/adverse events, nosocomial infection, nursing technology pass rate, and length of stay are KPIs for the given application.     

A taxonomy for key performance indicators management

In recent years, research on Key Performance Indicators (KPIs) management has grown exponentially, giving rise to a multitude of heterogeneous approaches addressing any aspect concerning it. In this paper, we plot the landscape of published works related with KPIs management, organizing and synthesizing them by means of a unified taxonomy that encompasses the aspects considered by other proposals, and it captures the overall characteristics of KPIs. Since most of the literature centers on the definition of KPIs, we mainly focus on such an aspect of KPIs management. Our work is intended to provide remarkable benefits such as enhancing the understanding of KPIs management, or helping users decide about the most suitable solution for their requirements.     

A decision support model for identification and prioritization of key performance indicators in the logistics industry

Performance measurement of logistics companies is based upon various performance indicators. Yet, in the logistics industry, there are several vaguenesses, such as deciding on key indicators and determining interrelationships between performance indicators. In order to resolve these vaguenesses, this paper first presents the stakeholder-informed Balanced Scorecard (BSC) model, by incorporating financial (e.g. cost) and non-financial (e.g. social media) performance indicators, with a comprehensive approach as a response to the major shortcomings of the generic BSC regarding the negligence of different stakeholders. Subsequently, since the indicators are not independent of each other, a robust multi-criteria decision making technique, the Analytic Network Process (ANP) method is implemented to analyze the interrelationships. The integration of these two techniques provides a novel way to evaluate logistics performance indicators from logisticians' perspective. This is a matter that has not been addressed in the logistics industry to date, and as such remains a gap that needs to be investigated. Therefore, the proposed model identifies key performance indicators as well as various stakeholders in the logistics industry, and analyzes the interrelationships among the indicators by using the ANP. Consequently, the results show that educated employee (15.61%) is the most important indicator for the competitiveness of logistics companies.     

Recent developments in visual quality monitoring by key performance indicators

In addition to traditional Quality of Service (QoS), Quality of Experience (QoE) poses a real challenge for Internet service providers, audio-visual services, broadcasters and new Over-The-Top (OTT) services. Therefore, objective audio-visual metrics are frequently being dedicated in order to monitor, troubleshoot, investigate and set benchmarks of content applications working in real-time or off-line. The concept proposed here, Monitoring of Audio Visual Quality by Key Performance Indicators (MOAVI), is able to isolate and focus investigation, set-up algorithms, increase the monitoring period and guarantee better prediction of perceptual quality. MOAVI artefacts Key Performance Indicators (KPI) are classified into four categories, based on their origin: capturing, processing, transmission, and display. In the paper, we present experiments carried out over several steps with four experimental set-ups for concept verification. The methodology takes into the account annoyance visibility threshold. The experimental methodology is adapted from International Telecommunication Union – Telecommunication Standardization Sector (ITU-T) Recommendations: P.800, P.910 and P.930. We also present the results of KPI verification tests. Finally, we also describe the first implementation of MOAVI KPI in a commercial product: the NET-MOZAIC probe. Net Research, LLC, currently offers the probe as a part of NET-xTVMS Internet Protocol Television (IPTV) and Cable Television (CATV) monitoring system.     

Run-time prediction of business process indicators using evolutionary decision rules

Predictive monitoring of business processes is a challenging topic of process mining which is concerned with the prediction of process indicators of running process instances. The main value of predictive monitoring is to provide information in order to take proactive and corrective actions to improve process performance and mitigate risks in real time. In this paper, we present an approach for predictive monitoring based on the use of evolutionary algorithms. Our method provides a novel event window-based encoding and generates a set of decision rules for the run-time prediction of process indicators according to event log properties. These rules can be interpreted by users to extract further insight of the business processes while keeping a high level of accuracy. Furthermore, a full software stack consisting of a tool to support the training phase and a framework that enables the integration of run-time predictions with business process management systems, has been developed. Obtained results show the validity of our proposal for two large real-life datasets: BPI Challenge 2013 and IT Department of Andalusian Health Service (SAS).     

A multi-granular linguistic model for management decision-making in performance appraisal

The performance appraisal is a relevant process to keep and improve the competitiveness of companies in nowadays. In spite of this relevance, the current performance appraisal models are not sufficiently well-defined either designed for the evaluation framework in which they are defined. This paper proposes a performance appraisal model where the assessments are modelled by means of linguistic information provided by different sets of reviewers in order to manage the uncertainty and subjectivity of such assessments. Therefore, the reviewers could express their assessments in different linguistic scales according to their knowledge about the evaluated employees, defining a multi-granular linguistic evaluation framework. Additionally, the proposed model will manage the multi-granular linguistic labels provided by appraisers in order to compute collective assessments about the employees that will be used by the management team to make the final decision about them.     

Predictive monitoring of temporally-aggregated performance indicators of business processes against low-level streaming events

Monitoring the performances of a business process is a key issue in many organizations, especially when the process must comply with predefined performance constraints. In such a case, empowering the monitoring system with prediction capabilities would allow us to know in advance a constraint violation, and possibly trigger corrective measures to eventually prevent the violation. Despite the problem of making run-time predictions for a process, based on pre-mortem log data, is an active research topic in Process Mining, current predictive monitoring approaches in this field only support predictions at the level of a single process instance, whereas process performance constraints are often defined in an aggregated form, according to predefined time windows. Moreover, most of these approaches cannot work well on the traces of a lowly-structured business process when these traces do not refer to well-defined process tasks/activities. For such a challenging setting, we define an approach to the problem of predicting whether the process instances of a given (unfinished) time window will violate an aggregate performance requirement. The approach mainly rely on inducing and integrating two complementary predictive models: (1) a clustering-based predictor for estimating the outcome of each ongoing process instance, (2) a time-series predictor for estimating the performance outcome of “future” process instances that will fall in the window after the moment when the prediction is being made (i.e. instances, not started yet, that will start by the end of the window). Both models are expected to benefit from the availability of aggregate context data regarding the environment that surrounds the process. This discovery approach is conceived as the core of an advanced performance monitoring system, for which an event-based conceptual architecture is here proposed. Tests on real-life event data confirmed the validity of our approach, in terms of accuracy, robustness, scalability, and usability.     

Strategy archetypes for digital transformation: Defining meta objectives using business process management

Digital transformation dominates the practical and scientific discourse. Still, many companies do not have a clear plan on how to approach it. Particularly, small- and medium-sized enterprises struggle to initiate their digital journey as they lack resources and expertise. In response, we examine how five companies use business process management (BPM) to implement digital transformation. We perform a qualitative interview study, and analyze the capabilities of BPM based on six requirements of digital transformation. Thereby, we carve out 17 recommendations, which must be adapted according to companies’ meta objectives. We derive three strategy archetypes to serve as implementation blueprints.     

Achieving agility using business model stress testing

Business Model (BM) Innovations aim at making systemic changes in the business logic of companies when they are bringing innovative products and services to the market. Companies should be sensitive to changes in their business environment and able to modify their BMs in an agile way. To assess the agility of BMI during specific market entry situations, this paper uses a method that stress tests the value proposition and the components of a BM against contingent uncertainties. We present three qualitative case studies of companies that differ in their market entry approach. Starting from their strategic orientation, these case companies’ stress test their Business models and BM components, using a scenario-based identification of uncertainties. The BM Stress Test method contributes to a quick understanding of the components their BM needs to monitor, reconsider, or improve. Such stress testing helps enhance business agility. The research contributes to market strategy and business modelling research by introducing BM Stress Testing as a new method that can achieve and maintain agility regarding BM uncertainties.     

Recognizing CEO personality and its impact on business performance: Mining linguistic cues from social media

Upper echelons theory suggests that CEO personality will influence organizational performance. However, difficulty in measuring CEO personality restrains related research. We capture linguistic cues CEOs leaving on social media and recognize their personality by text mining. To our knowledge, it is the first study introducing social media text mining approaches into the research stream that empirically inquires and extends upper echelons theory. Then, we investigate the CEO personality’s impact on both operational and financial performance. Results show that CEO Extraversion, Emotional Stability, and Agreeableness improve Cost Efficiency and Profitability, while CEO Conscientiousness reduces them. CEO Openness to Experience negatively influences Profitability, and all facets of CEO personality improve Employee Productivity except for CEO Conscientiousness. The contribution of our research is multi-sided: (1). methodologically, we introduce a text mining approach to measure CEO personality; (2). theoretically, we provide empirical evidence for upper echelons theory; (3). practically, our results help companies evaluate CEO candidates from a personality perspective.     

Lean rules extraction methodology for lean PSS design via key performance indicators monitoring

Lean PSS (Product-Service System) design comprises a promising strategy for delivering sustainable PSS offerings, considering several well-established lean practices. However, automated ways to apply lean practices and more specifically lean rules in industry are limited. This work proposes a methodology for improving the leanness of PSS design, by combining real-time KPI monitoring with lean principles and practices. Through a correlation of typical wastes with the metrics used in the calculation of KPIs, the Total Leanness Index (TLI) of the procedures is defined. Based on automatically identified trade-off values for TLI, lean rules are extracted to improve the performance of PSS lifecycle phases. The proposed lean rules extraction methodology (LeanREM) is validated through a case study of power waste reduction and the concurrently maintenance time decrease in a mould-making company.     

Sentiment analysis of financial news articles using performance indicators

Mining financial text documents and understanding the sentiments of individual investors, institutions and markets is an important and challenging problem in the literature. Current approaches to mine sentiments from financial texts largely rely on domain-specific dictionaries. However, dictionary-based methods often fail to accurately predict the polarity of financial texts. This paper aims to improve the state-of-the-art and introduces a novel sentiment analysis approach that employs the concept of financial and non-financial performance indicators. It presents an association rule mining-based hierarchical sentiment classifier model to predict the polarity of financial texts as positive, neutral or negative. The performance of the proposed model is evaluated on a benchmark financial dataset. The model is also compared against other state-of-the-art dictionary and machine learning-based approaches and the results are found to be quite promising. The novel use of performance indicators for financial sentiment analysis offers interesting and useful insights.     

The business model portfolio as a strategic tool for value creation and business performance

The number of organisations that operate multiple business models continues to increase. However, operating multiple business models can be complex, as they often need to be harmonised within a broader portfolio due to their interdependencies. This complexity is exacerbated by the increasing role of digital technology and data – enabling new opportunities but also coming with related paradoxes. This paper examines the growing body of literature on business model portfolios revealing that they are evolving into a strategic tool for value creation and business performance. While there are concomitant value opportunities arising from complementarities and synergies, there are also paradoxes emerging from tensions that need to be considered. Employing a developmental literature review, we present a synthesis of recent empirical case studies to gain insight into the management of business model portfolios. Firstly, we identify different strategic intents: diversifying, sensing, and complementing. Secondly, we distil different themes for value opportunities and paradoxes and categorise them according to a business model framework. Thirdly, we identify and discuss the role of digital technologies and data for business model portfolios. Overall, we contribute to an emerging stream of studies on multiple business models in relation to strategy, innovation and technology. By adopting a holistic perspective on the management of business model portfolios, we explore strategic intent, value opportunities and paradoxes, and discuss how portfolios can play a role in strategic management and planning.     

Multivariable self-organizing fuzzy logic control using dynamic performance index and linguistic compensators

As far as fuzzy logic based multivariable control systems are concerned, it is not always an easy task to express control strategies in the form of related multi-situations to multi-actions control rules. Decoupled control is one possible and attractive strategy to simplify this problem. However, the control performance of the decoupled controller relies greatly on ‘a prior’ knowledge of the system dynamics to build suitable compensators. This paper aims at introducing a new model-independent decoupled control architecture with the ability of on-line learning, which ensures a fast tracking performance. In this architecture, the dominating controller is developed using a new model-free Self-Organizing Fuzzy Logic Control (SOFLC) architecture whereby the Performance Index table is ‘dynamic’, of a free structure, and starting from no knowledge. Furthermore, a switching mode scheme, with a compensating action triggered by the interaction between the channels, is proposed to improve the tracking performance of the closed-loop system. A series of simulations are carried out on a two-input and two-output biomedical process, with the conclusion that the proposed control mechanism has the ability to deal with varying system dynamics and noise and is tolerant to the choice of the compensator gains effectively.     

Multi-stage multi-objective production planning using linguistic and numeric data-a fuzzy integer programming model

This paper introduces a methodology to solve a multi-stage production planning problem having multiple objectives, which are conflicting, non-commensurable and fuzzy in nature. The production process consists of multiple stages having one or more machines in each stage. Every processing stage produces work-in-process, semi-finished items as an output, which becomes an input to the subsequent stage either fully or partially depending on the cycle times of the machines. Events of machine breakdowns and imbalances in input–output relations in one or more stages may affect both work-in-process (WIP) and final production targets. Our paper provides a methodology based on fuzzy logic to maintain the desired balanced input–output relation at each stage and the targeted production output at the final stage. This is done by procurement of work-in-process inventory (WIP) and installation of new machines at appropriate stages. The objectives or goals expressed in linguistic terms are represented as fuzzy sets. The Induced Ordered Weighted Averaging (IOWA) operator is used to aggregate the objectives as per their priorities and finally to formulate the production process as a Mixed Integer Programming (MIP) problem. The solution to MIP shows the degrees of achievements of the production process objectives. The methodology is illustrated with a real life application of crankshaft productions in an automobile industry.     

经营过程绩效模型库软件的检索与重用机制

探讨了企业战略和外部环境对经营过程绩效体系的影响,在此基础上提出了经营过程绩效模型检索及参考模型生成方法,并以该检索与生成机制为核心构建了经营过程绩效模型库管理软件,以支持绩效模型的重用。最后,结合实际案例介绍了该模型库管理软件的应用。     

Towards an integrated learning analytics framework for quality perceptions in higher education: a 3-tier content,process, engagement model for key performance indicators

ABSTRACT

The integration of quality in Higher Education is multidimensional. Higher Education administration, programs, procedures and evaluation provide the context for the application and diffusion of quality metrics. Our research intends to provide a holistic discussion on Key Performance Indicators (KPIs) related to quality in Higher Education. The analysis of recent literature resulted in the construction of two research tools. The first one is related to a structured agenda for a qualitative interview targeted at Higher Education administrators. The second is related to a quantitative research model that analyses the relations of various quality factors. We provide a mapping of quality perceptions as discussed in previous work and we construct a theoretical model for the affordances of scholars to this perception. The research design includes interviews with academic administrators and teaching staff involved in the creation of academic programmes and courses. The main contribution is an analytic discussion of their perceptions about quality that updates significantly contemporary literature in interesting dimensions. Three-dimensional value space with twenty factors is presented. The outcomes of this research work are used as input for our quantitative study. In fact, a list of 20 quality factors is exploited in three main dimensions of learning analytics namely: content, process and engagement. Key Performance Indicators are highlighted for further investigation.     

Constructing road safety performance indicators using Fuzzy Delphi Method and Grey Delphi Method

The main goal of this paper is to construct three sets of road safety performance indicators, which are regional road safety performance indicators, urban road safety performance indicators and highway safety performance indicators, respectively. Fuzzy Delphi Method and Grey Delphi Method are applied to quantify experts’ attitudes to regional road safety, urban road safety and highway safety. Comparing the results of two methods, the different results of two methods are analyzed, and then the final safety performance indicators are obtained by taking the intersection of results of two methods. Finally, three sets of performance indicators are constructed, which can be described and evaluated the safety level of region, urban road and highway, respectively. The research findings show that the method used in this paper is feasible and practical and can be provided as a reference for the administrative authority of road safety.     

Features of estimation of the main key performance indicators of work of production of technological type. I. Complex of key indicators and feature of estimation of qualitative indexes of production flows

The structure of a complex of key performance indicators of work of production of technological type is considered. Features of determination of the qualitative indexes of work of production entering a complex and an error of their estimates are analyzed.