An integrated performance measurement framework for restaurant chains: A case study in Istanbul

Companies that continue to operate in a competitive market strive the most efficient use of their resources in order to remain competitive. Nowadays, with increasing customer feedback, properly analyzing customer needs and requests and producing services in accordance with expectations have become increasingly important due to the large number of companies competing in the same market, and this is especially important to be at the forefront of competitors in the food services industry. There are risks and uncertainties owing to the continuously changing demand for food service enterprises, the difficulty to regulate interest and comparable charges, the competitive environment, and currency rate hikes. In light of all of these circumstances, restaurants require a versatile tool to effectively measure and analyze their performance. Therefore, this study combines Principal Component Analysis (PCA) and Categorical Data Envelopment Analysis (CAT-DEA) to analyze the performance of 15 dealers in Istanbul, divided into three categories: steakhouse, kebab, and meatball-doner. The results demonstrate that each category has just one efficient restaurant, for a total of three efficient restaurants out of fifteen. In addition to the suggested CAT-DEA-based framework, three research hypotheses are constructed and analyzed to investigate the link between restaurant performance and various environmental factors (or relevant indicators) in the food service industry.Rekabetçi bir piyasada faaliyet göstermeye devam eden şirketler, rekabetçi kalabilmek için kaynaklarını en verimli şekilde kullanmaya çalışırlar. Artan müşteri geri bildirimleri ile birlikte, aynı pazarda rekabet eden çok sayıda firma nedeniyle, müşteri ihtiyaç ve isteklerini doğru analiz etmek ve beklentilere uygun hizmet üretmek giderek daha önemli hale geldi ve bu durum özellikle gıda hizmetleri endüstrisinde rekabette ön planda olmak için önemlidir. Yiyecek hizmeti işletmelerine yönelik sürekli değişen talep, faiz ve karşılaştırılabilir ücretlerin düzenlenmesindeki zorluk, rekabet ortamı ve kur artışları nedeniyle bu sektörde riskler ve belirsizlikler bulunmaktadır. Tüm bu koşullar ışığında restoranlar, performanslarını etkin bir şekilde ölçmek ve analiz etmek için çok yönlü bir araca ihtiyaç duyarlar. Bu nedenle, bu çalışma, İstanbul'da et lokantası, kebap ve köfte-döner olmak üzere üç kategoriye ayrılmış 15 bayinin performansını analiz etmek için Temel Bileşenler Analizi (PCA) ve Kategorik Veri Zarflama Analizini (CAT-DEA) birleştirmektedir. Sonuçlar, her bir kategorinin yalnızca bir verimli restorana sahip olduğunu ve on beş bayiden toplamda üç bayinin verimli olduğunu göstermektedir. Önerilen CAT-DEA tabanlı yaklaşıma ek olarak, yemek hizmeti endüstrisinde restoran performansı ile çeşitli çevresel faktörler (veya ilgili göstergeler) arasındaki bağlantıyı araştırmak için üç araştırma hipotezi oluşturulmuş ve analiz edilmiştir

Toward a Model of Information System Development Success: Perceptions of Information Systems Development Team Members

Many information systems development (ISD) projects are deemed a failure in the field. However, several practitioners and researchers argue these projects could actually be considered successful if we used a broader definition of software development project success. Answering the call for further research on what makes ISD projects successful, this paper describes the process used to build the model of ISD Success, which includes a thorough literature review to create an initial model followed by semi-structured interviews conducted to validate the model and to allow for the discovery of emergent constructs, sub-constructs, and hypotheses. The model is tested with data collected from practitioners using Partial Least Squares (PLS) analysis. The paper concludes with a discussion of the findings and conclusions

Large-scale internet user behavior analysis of a nationwide K-12 education network based on DNS queries

ANII Fondo Sectorial de Investigación a partir de datos (FSDA_1_2018_1_154853)To the best of our knowledge, this paper presents the first Internet Domain Name System (DNS) queries data study from a national K-12 Education Service Provider. This provider, called Plan Ceibal, supports a one-to-one computing program in Uruguay. Additionally, it has deployed an Information and Communications Technology (ICT) infrastructure in all of Uruguay’s public schools and high-schools, in addition to many public spaces. The main development is wireless connectivity, which allows all the students (whose ages range between 6 and 18 years old) to connect to different resources, including Internet access. In this article, we use 9,125,888,714 DNS-query records, collected from March to May 2019, to study Plan Ceibal user’s Internet behavior applying unsupervised machine learning techniques. Firstly, we conducted a statistical analysis aiming at depicting the distribution of the data. Then, to understand users’ Internet behavior, we performed principal component analysis (PCA) and clustering methods. The results show that Internet use behavior is influenced by age-group and time of the day. However, it is independent of the geographical location of the users. Internet use behavior analysis is of paramount importance for evidence-based decision making by any education network provider, not only from the network-operator perspective but also for providing crucial information for learning analytics purposes

Examining Quality Factors Influencing the Success of Data Warehouse

Increased organizational dependence on data warehouse (DW) systems has drived the management attention towards improving data warehouse systems to a success. However, the successful implementation rate of the data warehouse systems is low and many firms do not achieve intended goals. A recent study shows that improves and evaluates data warehouse success is one of the top concerns facing IT/DW executives. Nevertheless, there is a lack of research that addresses the issue of the data warehouse systems success. In addition, it is important for organizations to learn about quality needs to be emphasized before the actual data warehouse is built. It is also important to determine what aspects of data warehouse systems success are critical to organizations to help IT/DW executives to devise effective data warehouse success improvement strategies. Therefore, the purpose of this study is to further the understanding of the factors which are critical to evaluate the success of data warehouse systems. The study attempted to develop a comprehensive model for the success of data warehouse systems by adapting the updated DeLone and McLean IS Success Model. Researcher models the relationship between the quality factors on the one side and the net benefits of data warehouse on the other side. This study used quantitative method to test the research hypotheses by survey data. The data were collected by using a web-based survey. The sample consisted of 244 members of The Data Warehouse Institution (TDWI) working in variety industries around the world. The questionnaire measured six independent variables and one dependent variable. The independent variables were meant to measure system quality, information quality, service quality, relationship quality, user quality, and business quality. The dependent variable was meant to measure the net benefits of data warehouse systems. Analysis using descriptive analysis, factor analysis, correlation analysis and regression analysis resulted in the support of all hypotheses. The research results indicated that there are statistically positive causal relationship between each quality factors and the net benefits of the data warehouse systems. These results imply that the net benefits of the data warehouse systems increases when the overall qualities were increased. Yet, little thought seems to have been given to what the data warehouse success is, what is necessary to achieve the success of data warehouse, and what benefits can be realistically expected. Therefore, it appears nearly certain and plausible that the way data warehouse systems success is implemented in the future could be changed

Developing and testing green performance measures for the supply chain

Performance measurements evolve as new challenges are met and the natural environment is one of the biggest challenges facing society and the evolution of performance measurement today. Consequently, a cross-disciplinary interest in the field of green supply chain management (GSCM) has grown amongst researchers and practitioners in recent years because of climate change issues, diminishing raw materials, excess waste production, increasing levels of pollution and because it is a source of competitive advantage. Yet, there has been little work done in developing and incorporating green measures into the existing bank of supply chain performance measures. Only 18 articles have been published in the last 18 years on green supply chain performance measurement (GSCPM). The aim of this thesis is to address this challenge by empirically developing and testing green performance measures for the supply chain.Based on an extensive literature review, five research questions were proposed for this thesis to address gaps in the body of knowledge. This is a new area of theory development and demanded theoretical and methodological triangulation to maximize the amount of data collected to explore the research phenomena from different perspectives. The study used a rigorous three-phased methodological framework originally developed by Churchill (1979) for items and scales development. The first phase comprised generating variables and constructs from the extant literature and focus groups. The second phase involved testing these items and constructs in a survey. Finally, a focus group was conducted in Phase Three to verify and validate the overall results.The thesis proposes a battery of 29 GSCPM variables and 12 GSCPM constructs that can be used by organisations to measure their impact on the environment. The study found that GSCPM variables used by organisations, such as usual performance measures, remain primarily driven by cost. Furthermore, there are significant differences in the capabilities and the way in which organisations view the importance, enablers, barriers and benefits of GSCPM. This thesis contributes to knowledge by proposing a universal set of GSCPM variables and reporting tools that organisations can use to manage their GSCPM. Finally, the use of methodological pluralism in this research has helped to provide a more complete picture of this phenomenon and represents one of only a few studies which have explored GSCPM in this way

An Integrated Framework to Assess ‘Leanness’ Performance in Distribution Centres

The theory behind lean philosophy is to create more value with less. Effective lean management enables organisations to exceed customer expectations while reducing costs. Despite the fact that numerous practices and approaches are used in the process of implementing lean philosophy and reducing waste within supply chain systems, little effort has been directed into assessing the leanness level of distribution and its impact on overall performance. Given the vital role of distribution units within supply chains, this research aims to develop a comprehensive lean assessment framework that integrates a selected set of statistical, analytical, and mathematical techniques in order to assess the ‘leanness’ level in the distribution business. Due to the limited number of published articles in the area of lean distribution, there are no clear definitions of the underlying factors and practices. Therefore, the primary phase of the proposed framework addresses the identification of lean distribution dimensional structure and practices. The other two phases of the framework discuss the development of a structured model for lean distribution and address the process to find a quantitative lean index for benchmarking lean implementation in distribution centres. Integrating the three phases provides the decision makers with an indicator of performance, subject to applying various lean practices. Incorporating the findings of a survey that sent to 700 distribution businesses in Ireland along with value stream mapping, modelling, simulation, and data envelopment analysis, has given the framework strength in the assessment of leanness. Research outcomes show that lean distribution consists of five key dimensions; workforce management, item replenishment, customers, transportation, and process quality. Lean practices associated with these dimensions are mainly focused on enhancing the communication channels with customers, simplifying the distribution networks structure, people participating in problem solving and a continuous improvement process, and increasing the reliability and efficiency of the distribution operations. The final output of the framework is two key leanness indices; one is set to measure the tactical leanness level, while the second index represents the leanness at the operational level. Both indices can effectively be used in evaluating the lean implementation process and conducting a benchmarking process based on the leanness level

Synthesizing Quality Open Data Assets from Private Health Research Studies

International audienceGenerating synthetic data represents an attractive solution for creating open data, enabling health research and education while preserving patient privacy. We reproduce the research outcomes obtained on two previously published studies, which used private health data, using synthetic data generated with a method that we developed, called HealthGAN. We demonstrate the value of our methodology for generating and evaluating the quality and privacy of synthetic health data. The dataset are from OptumLabs R Data Warehouse (OLDW). The OLDW is accessed within a secure environment and doesn't allow exporting of patient level data of any type of data, real or synthetic, therefore the HealthGAN exports a privacy-preserving generator model instead. The studies examine questions related to comorbidites of Autism Spectrum Disorder (ASD) using medical records of children with ASD and matched patients without ASD. HealthGAN generates high quality synthetic data that produce similar results while preserving patient privacy. By creating synthetic versions of these datasets that maintain privacy and achieve a high level of resemblance and utility, we create valuable open health data assets for future research and education efforts

Design, implementation and realization of an integrated platform dedicated to e-public health, for analysing health data and supporting the management control in healthcare companies.

In healthcare, the information is a fundamental aspect and the human body is the major source of every kind of data: the challenge is to benefit from this huge amount of unstructured data by applying technologic solutions, called Big Data Analysis, that allows the management of data and the extraction of information through informatic systems. This thesis aims to introduce a technologic solution made up of two open source platforms: Power BI and Knime Analytics Platform. First, the importance, the role and the processes of business intelligence and machine learning in healthcare will be discussed; secondly, the platforms will be described, particularly enhancing their feasibility and capacities. Then, the clinical specialties, where they have been applied, will be shown by highlighting the international literature that have been produced: neurology, cardiology, oncology, fetal-monitoring and others. An application in the current pandemic situation due to SARS-CoV-2 will be described by using more than 50000 records: a cascade of 3 platforms helping health facilities to deal with the current worldwide pandemic. Finally, the advantages, the disadvantages, the limitations and the future developments in this framework will be discussed while the architectural technologic solution containing a data warehouse, a platform to collect data, two platforms to analyse health and management data and the possible applications will be shown

Searching For Phenotypes Of Sepsis: An Application Of Machine Learning To Electronic Health Records

SEARCHING FOR PHENOTYPES OF SEPSIS: AN APPLICATION OF MACHINE LEARNING TO ELECTRONIC HEALTH RECORDS. Michael J. Boyle (Sponsored by R. Andrew Taylor). Department of Emergency Medicine, Yale University School of Medicine, New Haven, CT. Sepsis has historically been categorized into discrete subsets based on expert consensus-driven definitions, but there is evidence to suggest it would be better described as a continuum. The goal of this study was to perform an exhaustive search for distinct phenotypes of sepsis using various unsupervised machine learning techniques applied to the electronic health record (EHR) data of 41,843 Yale New Haven Health System emergency department patients with infection between 2013 and 2016. Specifically, the aims were to develop an autoencoder to reduce the high-dimensional EHR data to a latent representation amenable to clustering, and then to search for and assess the quality of clusters within that representation using various clustering methods (partitional, hierarchical, and density-based) and standard evaluation metrics. Autoencoder training was performed by minimizing the mean squared error of the reconstruction. With this exhaustive search, no convincing consistent clusters were found. Various clustering patterns were produced by the different methods but all had poor quality metrics, while evaluation metrics meant to find the ideal number of clusters did not agree on a consistent number but seemed to suggest fewer than two clusters. Inspection of one promising arrangement with eight clusters did not reveal a statistically significant difference in admission rate. While it is impossible to prove a negative, these results suggest there are not distinct phenotypic clusters of sepsis