A comparative Kano analysis on customer satisfaction based on customer and employment perspectives

These days, there is a tight competition among business units and all production units or service businesses attempt to use different techniques to increase their market shares. In such environment, customer normally says the last word, in fact, customer plays an important role and in many organizations, it is considered as part of their assets. The purpose of this paper is to propose a hybrid method to detect important criteria using Kano three-dimensional method and prioritize them using analytical hierarchy process. We implement the proposed model of this paper for one of Iranian banks called Bank Melli Iran. The study determines 25 different attributes, categorizes them in three different groups based on Kano model, and ranks them in terms of customers and employees' perspective. The results of the survey indicate that customer and employees mostly have similar views since there are 21 common attributes between them. However, the priorities of these 21 items are often different in terms of two groups of employees and customers

Developing Strategic Decision Making Process for Product and Service Planning

It is imperative to forecast advanced or emerging technologies to aid in decision making on firm\u27s R&D investments and business plan for commercialization efforts. Even though a company must align R&D planning with overall business planning such as manufacturing, sales and marketing, personnel, and finance, systematic management approaches are limited in it based upon the prediction of technological change and speed. This paper aims to provide a decision support tool to aid in strategic service planning and technology development in a firm. The study is to enhance strategic development of service and product with the consideration of emerging technologies. This model helps decision makers to easily identify emerging technologies and new research fields with systematic decision making process

Virtuaalinen operointikeskus älykkääseen kunnossapitoon

The concept of Virtual Operation Center (VOC) for smart maintenance is studied in this research. The goal is to find out applications and structure for VOC in the maintenance of paper, board and pulp manufacturing and the potential benefits it can bring. Research structure is exploratory multiple case study with five interviews from other industries using VOC applications and six interviews conducted with the main case Efora Ltd's key personnel. Literature review is done on service innovations, operational excellence, Industrial Internet and service operations. The research shows that several VOC applications have potential to improve maintenance operations making them smarter, leaner and more effective. Applications can also improve network collaboration between maintenance company, its clients and key partners in cooperation profiting all parties. Conclusions are valid in restricted framework of this case study.Diplomityössä tutkitaan Virtuaalisen operointikeskuksen konseptia paperi-, kartonki- ja selluteollisuuden huoltotoiminnassa. Tavoitteena on kartoittaa operointikeskuksen rakennetta ja mahdollisia sovelluksia sekä niiden tuomia potentiaalisia hyötyjä. Tutkimusmenetelmä on kartoittava monitapaustutkimus, johon kerättiin tutkimusdataa haastatteluilla. Tutkimuksen päätapaus on Efora Oy, josta haastaltiin kuusi avainhenkilöä. Lisäksi haastateltiin muilta toimialoilta viisi yritystä, jotka käyttävät Virtuaalista operointikeskusta vastaavia sovelluksia. Kirjallisuuskatsaus käsittelee palveluinnovaatioita, operational excellenceä, teollista internetiä sekä palveluoperaatioita. Tutkimus osoittaa että usealla Virtuaalisen operointikeskuksen sovelluksella on potentiaalia parantaa huoltotoimintoja tehden niistä älykkäämpiä, leanimpia sekä tehokkaampia. Sovellukset voivat parantaa myös verkostoyhteistyötä huoltoyhtiön, asiakkaan ja laitetoimittajien kesken hyödyttäen kaikki osapuolia. Tulokset pätevät rajatussa tapaustutkimuksen viitekehyksessä

Exploring Indoor Health: An In-depth Field Study on the Indoor Air Quality Dynamics

Indoor air pollution, a significant driver of respiratory and cardiovascular diseases, claims 3.2 million lives yearly, according to the World Health Organization, highlighting the pressing need to address this global crisis. In contrast to unconstrained outdoor environments, room structures, floor plans, ventilation systems, and occupant activities all impact the accumulation and spread of pollutants. Yet, comprehensive in-the-wild empirical studies exploring these unique indoor air pollution patterns and scope are lacking. To address this, we conducted a three-month-long field study involving over 28 indoor spaces to delve into the complexities of indoor air pollution. Our study was conducted using our custom-built DALTON air quality sensor and monitoring system, an innovative IoT air quality monitoring solution that considers cost, sensor type, accuracy, network connectivity, power, and usability. Our study also revealed that conventional measures, such as the Indoor Air Quality Index (IAQI), don't fully capture complex indoor air quality dynamics. Hence, we proposed the Healthy Home Index (HHI), a new metric considering the context and household activities, offering a more comprehensive understanding of indoor air quality. Our findings suggest that HHI provides a more accurate air quality assessment, underscoring the potential for wide-scale deployment of our indoor air quality monitoring platform.Comment: 15 pages, 19 figure

Real-time indoor air quality (IAQ) monitoring system for smart buildings

Mestrado de dupla diplomação com a UTFPR - Universidade Tecnológica Federal do ParanáIndoor air quality (IAQ) is a term describing the air quality of a room, it refers to the health and comfort of the occupants. Normally, people spend around 90% of their time in indoor environments where the concentration of air pollutants, such CO, CO2, VOCs, SO2, O3 and NOx, may be two to five times — and occasionally, more than 100 times — higher than outdoor levels. According to the World Health Organization (WHO), the indoor air pollution is responsible for the deaths of 3.8 million people annually. It has been indicated that IAQ in residential areas or buildings is significantly affected by three primary factors: (i) Outdoor air quality, (ii) human activity in buildings, and (iii) building and construction materials, equipment, and furniture. In this contest, this work consist in a real time IAQ system to monitoring and control thermal comfort and gas concentration. The system has a data acquisition stage, where the data is measured by a set of sensors and then stored on InfluxDB database and displayed in Grafana. To track the behavior of the measured parameters, two machine learning algorithms are developed, a mathematical model linear regression, and an artificial intelligence model neural network. In a test made to see how precise were the prediction of the two models, linear regression model performed better then neural network, presenting cases of up to 99.7% and 98.1% of score prediction, respectively. After that, a test with smoke was done to validate the models where the results shows that both learning models can detect adverse cases. Finally, prediction data are storage on InfluxDB and displayed on Grafana to monitoring in real-time measured data and prediction data

Spatial-temporal business partnership selection in uncertain environments

Small and Medium (SME) companies are facing growing challenges while trying to implement globalized business strategies. Contemporary business models need to account for spatial-temporal changeable environments, where lack of confidence and uncertainty in data are a reality. Further, SMEs are finding it increasingly difficult to include all required competences in their internal structures; therefore, they need to rely on reliable business and supplier partnerships to be successful. In this paper we discuss a spatial-temporal decision approach capable of handling lack of confidence and imprecision on current and/or forecast data. An illustrative case study of business' partner selection demonstrates the approach suitability, which is complemented by a statistical analysis with different levels of uncertainty to assess its robustness in uncertain environments.The authors wish to acknowledge the support of the Fundacao para a Ciencia e Tecnologia (FCT), Portugal, through the grant: "Projeto Estrategico - PEst2015-2020, reference: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio

The Business Impact of Social Media - Sentiment Analysis Approach -

이 연구의 목적은 소셜 미디어에서 추출된 7개의 감성 도메인이 자동차 시장 점유율 예측에 대한 감성 분석 실험을 위한 데이터로서 적합한 지에 대한 신뢰성을 확인하고 고객들의 의견이 기업의 성과에 어떻게 영향을 미치는 지에 대하여 확인하기 위한 것이다. 본 연구는 총3단계에 걸쳐서 진행되었습니다. 첫 번째 단계는 감성사전 구축의 단계로서 2013년 1월 1일부터 2015년 12월 31일까지 미국 내 26개의 자동차 제조 회사의 고객의 소리 (VOC: Voice of the Customer) 총 45,447개를 자동차 커뮤니티로부터 크롤링 (crawling)하여 POS (Part-of-Speech) 즉 품사정보를 추출하는 태깅 (tagging)과정을 거쳐 부정적, 긍정적 감성의 빈도수를 측정하여 감성사전을 구축하였고, 이에 대한 극성을 측정하여 7개의 감성도메인을 만들었습니다. 두 번째 단계는 데이터에 대한 신뢰성 분석의 단계로서 자기상관관계분석 (Auto-correlation Analysis)과 주성분분석 (PCA: Principal Component Analysis)을 통해 데이터가 실험에 적합한지를 검증하였다. 세 번째 단계에서는 2개의 선형회귀분석 모델로 7개의 감성영역이 미국내 자동차 제조 회사 중 GM, 포드, FCA, 폭스바겐 등 총 4개의 자동차 생산 기업을 선정하여 이들 기업의 성과 즉, 자동차 시장점유율에 어떤 영향을 미치고 있는 지 실험하였다. 그 결과, 우리는 4,815개의 부정적인 어휘들과 2,021개의 긍정적인 감성어휘들을 추출하여 감성사전을 구축하였으며, 구축된 감성사전을 바탕으로, 추출되고 분류된 부정적이고 긍정적인 어휘들을 자동차 산업에 관련된 어휘들과 조합하였고, 자기상관분석과 PCA (주성분 분석)를 통해 감성의 특성을 조사하였다. 실험 결과에 따르면, 자기상관분석에 의해서 감성 데이터에 어떤 일정한 패턴이 존재한다는 것이 발견되었고, 각각의 감성 영역의 감성이 자기상관성이 있으며, 감성의 시계열성 또한 관찰되었다. PCA에 의한 결과로서, 7개 감성영역이 부정성, 긍정성, 중립성을 주성분으로 연결되어 있음을 확인할 수 있었다. 자기상관분석과 PCA를 통한 VOC 감성 데이터에 대한 신뢰성을 바탕으로 2개의 선형회귀분석 모델을 구축하여 실험을 진행하였다. 첫 번째 모델은 주성분 분석에서 부정적 감성의 Sadness, Anger, Fear와 긍정적 감성도메인인 Delight, Satisfaction을 독립변수로 선정하고, 시장점유율을 종속변수로 선정하여 실행하였고 두 번째 모델은 첫 번째 모델에 주성분이 중립성으로 결과가 나온 Shame, Frustration을 독립변수에 추가하여 중립성을 띠고 있는 감성이 시장 점유율에 유의미한 영향을 미치고 있는 지를 확인하였다. 분석 결과, 각 기업 마다 시장점유율에 유의미한 영향을 미치는 감성들이 존재하고 모델 1과, 모델 2에서의 감성 영향력이 차이가 있음을 발견하였다. 본 연구를 통해, 데이터 상에 나타난 정보를 가진 감성이 과거 값에 기초하여 자동차 시장에서 변화를 수반할 수 있다는 것을 나타내고 있음을 확인하였다. 또한, 우리가 시장 데이터의 가용성을 적용하려고 할 때, 자동차 시장 관련 정보나 감성의 자기상관성을 잘 활용할 수 있다면, 감정 분석에 대한 연구에 큰 기여를 할 수 있을 뿐만 아니라, 실제 시장에서의 비지니스 성과에도 다양한 방법으로 기여할 수 있을 것으로 기대된다.List of Tables iv List of Figures v Abstract 1 1. Introduction 1.1 Back Ground 3 1.2 Necessity of Study 6 1.3 Purpose & Questions 8 1.4 Structure 9 2. Literature Reviews of VOC Analysis 2.1 Importance of VOC 11 2.2 Data Mining 15 2.2.1 Concept & Functionalities 15 2.2.2 Methodologies of Data mining 20 2.3 Text Mining 24 2.4 Sentiment Analysis 26 2.5 Research Trend in Korea 30 3. Methodology 3.1 Research Flow 32 3.2 Proposed Methodologies 34 3.2.1 Sentiment Analysis 34 3.2.2 Auto-correlation Analysis 37 3.2.3 Principal Component Analysis (PCA) 38 3.2.4 Linear Regression 40 4. Experiment & Analysis 4.1 Phase I: Constructing Sentiment Lexicon & 7 Sentiment Domains 43 4.1.1 The Subject of Analysis & Crawling Data 43 4.1.2 Extracting POS Information 44 4.1.3 Review Extracting POS Information 46 4.2 Phase II : Reliability Analysis 49 4.2.1 Auto-correlation Analysis of Sentiment 51 4.2.2 Principal Component Analysis of Sentiment 55 4.3 Phase III : Influence on Automotive Market Share 58 4.3.1 Linear Regression Model 58 4.3.2 Definition of Variables 60 4.3.3 The Result of Linear Regression Analysis 62 5. Conclusion 5.1 Summary of Study 73 5.2 Managerial Implication and Limitation 75 5.3 Future Study 77 References 79Docto

Transportation Capital Programming in Massachusetts

Recommends implementing an explicit, policy-driven framework and criteria for prioritizing transportation capital spending at the Massachusetts Highway Department and the Massachusetts Bay Transportation Authority. Outlines benchmarks and key elements