Kano-sarnase mudeli kasutamine avatud innovatsiooni saavutamiseks nõuete analüüsi protsessis

Kui viiakse läbi nõuete analüüsi (inglise k Requirements Engineering, lühend RE), siis sageli järjestatakse nõuded nende olulisuse alusel (inglise k requirements prioritization), et saada selgust, milliste välja pakutud nõuetega funktsioon peaks tarkvaral olemas olema, seega sõltub tarkvara analüüsist tarkvara majandusliku väärtuse suurendamisega seotud otsuste tegemine. Tänapäeval arenevad tooted väga kiiresti ning ka nõuete olulisuse alusel järjestamine (inglise k requirements prioritization) on muutunud kiiremaks. Ettevõtted sooviksid saada kasutajatelt kiiret tagasisidet selle kohta, mis peaks olema järgmises mudelis olemas. Üks häid lahendusi sellele on Kano mudel (inglise k Kano model). Kano mudel selgitab välja kasutajate rahulolu ja toodete tunnuste vahelise suhte. See meetod liigitab kasutajate eelistused nende tähtsuse järjekorras, seega toetab see ka nõuete olulisuse järjekorra moodustamist. Aga Kano mudeli rakendamine on kallis ja aeganõudev ning seda ei saa kiiresti korrata. Veelgi enam – see mudel on keeruline väikeste ettevõtete jaoks, sest neil ei tarvitse olla piisavalt rahalisi jm vahendeid, et kasutajatega ühendust võtta ja neid intervjueerida. See omakorda paneb väikesed ettevõtted, eriti just idufirmad, ebavõrdsesse olukorda suurte ettevõtetega. Et sellele probleemile lahendust leida ja Kano mudeli kasutuselevõttu lihtsamaks ning odavamaks teha, arvame, et Kano mudelit tuleks arendada kahel viisil. Esiteks tuleks kasutada tasuta võrgus leiduvaid kirjalikke andmeid, mida saaks asendada intervjueeritavatelt kogutud vastustega. Teiseks – selleks, et hakkama saada võrgust kogutud kirjalike andmete suure mahuga, ning et kaasa aidata korrapärastele analüüsidele, peaks andmete analüüsimine olema automaatne. Selle uurimuse eesmärk on välja pakkuda meetodeid, et kasutajate avamusi, mis on võrgus saadavatest vabadest allikatest kogutud, (semi-)automaatselt liigitada, ja seda selleks, et aidata otsustajatel otsustada, millised tarkvara nõuded järgmises mudelis kindlasti olemas peaksid olema. Et seda uurimuse eesmärki saavutada, pakume me välja avatud innovatsiooni nõuete analüüsi (OIRE) meetodi, mille abil saavad tarkvarafirmad parema ülevaate kasutajate vajadustest ja sellest, kuivõrd rahul on nad olemasolevate toodetega.When Requirements Engineering (RE) is applied, requirements analysis is often used to determine which candidate requirements of a feature should be included in a software release. This plays a crucial role in the decisions made to increase the economic value of software. Nowadays, products evolve fast, and the process of requirements prioritization is becoming shorter as well. Companies benefit from receiving quick feedback from end users about what should be included in subsequent releases. One effective approach supporting requirements prioritization is the Kano model. The Kano model defines the relationship between user satisfaction and product features. It is a method used to classify user preferences according to their importance, and in doing so, supports requirements prioritization. However, implementing the Kano model is costly and time-consuming, and the application of the Kano model cannot be repeated quickly. Moreover, this is even more difficult for small companies because they might not have sufficient funds and resources to contact end users and conduct interviews. This puts small businesses, especially start-ups, at an unfair disadvantage in competing with big companies. To address this problem and make the application of the Kano model simpler, faster, and cheaper, we propose evolving the Kano model in two aspects. First, free online text data should be used to replace responses collected from interviewees. Second, in order to handle the higher amount of data that can be collected from free online text data and in order to facilitate frequent analyses, the data analysis process should be automated. The goal of this research is to propose methods for (semi-)automatically classifying user opinions collected from online open sources (e.g., from online reviews) to help decision-makers decide which software requirements to include in subsequent product versions. To achieve this research goal, we propose the Open Innovation in Requirements Engineering (OIRE) method to help software organizations gain a better understanding of user needs and satisfaction with existing products. A key element of the OIRE method is its Kano-like model. This Kano-like model mimics the traditional Kano model, except that it uses data from online reviews instead of interviews conducted with select focus groups.https://www.ester.ee/record=b527385

Asymmetric Release Planning-Compromising Satisfaction against Dissatisfaction

Maximizing satisfaction from offering features as part of the upcoming release(s) is different from minimizing dissatisfaction gained from not offering features. This asymmetric behavior has never been utilized for product release planning. We study Asymmetric Release Planning (ARP) by accommodating asymmetric feature evaluation. We formulated and solved ARP as a bi-criteria optimization problem. In its essence, it is the search for optimized trade-offs between maximum stakeholder satisfaction and minimum dissatisfaction. Different techniques including a continuous variant of Kano analysis are available to predict the impact on satisfaction and dissatisfaction with a product release from offering or not offering a feature. As a proof of concept, we validated the proposed solution approach called Satisfaction-Dissatisfaction Optimizer (SDO) via a real-world case study project. From running three replications with varying effort capacities, we demonstrate that SDO generates optimized trade-off solutions being (i) of a different value profile and different structure, (ii) superior to the application of random search and heuristics in terms of quality and completeness, and (iii) superior to the usage of manually generated solutions generated from managers of the case study company. A survey with 20 stakeholders evaluated the applicability and usefulness of the generated results

Natural Language Processing in-and-for Design Research

We review the scholarly contributions that utilise Natural Language Processing (NLP) methods to support the design process. Using a heuristic approach, we collected 223 articles published in 32 journals and within the period 1991-present. We present state-of-the-art NLP in-and-for design research by reviewing these articles according to the type of natural language text sources: internal reports, design concepts, discourse transcripts, technical publications, consumer opinions, and others. Upon summarizing and identifying the gaps in these contributions, we utilise an existing design innovation framework to identify the applications that are currently being supported by NLP. We then propose a few methodological and theoretical directions for future NLP in-and-for design research

Sustainable lighting product development underpinned by online data mining and life cycle assessment

The accurate acquisition of customer requirement information is an important part in product planning and positioning, it plays a decisive role in the success of products in the market. the rapid development of e-commerce makes increasing more consumers shopping online and a big volume of customer reviews are posted on different Websites. The online reviews contain valuable opinions of customers, enabling designers to understand their concerns. In this research, an integrated approach has been developed to mine customer requirements according to the online reviews collected from e-commerce sites to form product design specifications. The main research contents include the following aspects: (1) development of useful online review prediction and classification approach; (2) online review implicit product features and sentiment analysis based on the constructed feature and sentiment lexicon; (3) built a knowledge base containing customer requirements mined from online reviews; (4) conduct a dedicated environmental and social LCA on the proposed domestic lighting product by using a professional LCA software. In this study, multiple models and technologies/methods have been successfully implemented: review helpfulness classification model has been constructed based on the training set and test set by tuning and optimizing; proposes a new approach to implicit feature and sentiment analysis, based on explicit formal feature-emotion sentences, implicit feature sentences and implicit sentiment sentences, combined with a feature lexicon, a 1V1/1Vn sentiment-feature rule base and the feature-emotion word pairs are extracted; based on the preliminary analysis results of feature extraction and sentiment analysis, combined with KANO model to establish user requirement mining rules, and consider satisfaction, propose the user demand priority to obtain the final list of user requirements; a real industrial context with lighting product manufacturer (ONA) in Spain has involved with the lighting product life cycle analysis and development for new product. The analytical results of these studies present an in-depth modelling and analysis on the sustainable lighting product lifecycle with the aid of real manufacturing data

CPPS-3D: a methodology to support cyber physical production systems design, development and deployment

Master’s dissertation in Production EngineeringCyber-Physical Production Systems are widely recognized as the key to unlock the full potential benefits of the Industry 4.0 paradigm. Cyber-Physical Production Systems Design, Development and Deployment methodology is a systematic approach in assessing necessities, identifying gaps and then designing, developing and deploying solutions to fill such gaps. It aims to support and drive enterprise’s evolution to the new working environment promoted by the availability of Industry 4.0 paradigms and technologies while challenged by the need to increment a continuous improvement culture. The proposed methodology considers the different dimensions within enterprises related with their levels of organization, competencies and technology. It is a two-phased sequentially-stepped process to enable discussion, reflection/reasoning, decision-making and action-taking towards evolution. The first phase assesses an enterprise across its Organizational, Technological and Human dimensions. The second phase establishes sequential tasks to successfully deploy solutions. Is was applied to a production section at a Portuguese enterprise with the development of a new visual management system to enable shop floor management. This development is presented as an example of Industry 4.0 technology and it promotes a faster decision-making, better production management, improved data availability as well as fosters more dynamic workplaces with enhanced reactivity to problems