Mass Customization: A Review of the Paradigm Across Marketing, Engineering and Distribution Domains

2010 ◽  
Author(s):  
Scott Ferguson ◽  
Andrew Olewnik ◽  
Priyesh Malegaonkar ◽  
Phil Cormier ◽  
Saket Kansara
Keyword(s):  
Mass Customization ◽  
State Of The Art ◽  
Specific Information ◽  
Design Practice ◽  
Process Innovations ◽  
Lack Of Information ◽  
Two Factors ◽  
Development Approach ◽  
Dominant Paradigm ◽  
The Ideal

Introduced nearly 25 years ago, the paradigm of mass customization (MC) has largely not lived up to its promise. Despite great strides in information technology, engineering design practice, and manufacturing production, the necessary process innovations that can produce products and systems with sufficient customization and economic efficiency have yet to be found in wide application. In this paper, the state-of-the-art in MC is explored in order to answer the question of “why not?” and to highlight areas for specific research in the MC paradigm. To establish perspective for this work, we consider MC to be a product development approach which allows for the production of goods — after a customer places an order — which minimize the tradeoff between the ideal product and the available product by fulfilling the needs and preferences of individuals functionally, emotionally and anthropologically. Results of this research were generated by reviewing 88 papers from various journals that span three domains of interest (marketing, engineering, and distribution) and explore proposed methodologies, specific information inputs and outputs, proposed metrics, and barriers toward the implementation of MC. Qualitatively, we show that the lack of MC in application is due to two factors: 1) a lack of marketing tools capable of capturing individual needs that can be mapped to the technical space; and 2) a lack of information relation mechanisms that connect the domains of marketing, engineering, and distribution. In the end it is our belief that MC is realizable and that eventually it will emerge as a dominant paradigm in the design and delivery of products and systems. However, pursuing the opportunities for research presented in this work will hopefully speed this emergence.

scholarly journals Understanding the needs of carers in Vietnam and the development of online supportive resources

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
C Jenkins ◽  
H t Ho ◽  
O Santin
Keyword(s):  
Cancer Diagnosis ◽  
Healthcare Providers ◽  
Treatment Decision ◽  
Nutrition Support ◽  
Specific Information ◽  
Supportive Services ◽  
Middle Income ◽  
Treatment Decision Making ◽  
Lack Of Information ◽  
Online Resource

Abstract Issue Informal carers provide an important and often overlooked role in the care of people with a cancer diagnosis. Our study sought to better understand carers needs and develop an online resource to help address the needs identified. There are not currently any widespread or embedded support services for cancer carers in Vietnam. Description of the Problem We conducted in-depth interviews and focus groups with both carers (n = 20) and healthcare providers (n = 22) to understand the needs and challenges of caring for someone with a cancer diagnosis. We discussed what resources would alleviate challenges and used these discussions to inform a process of co-designing an online resource. This process was modelled off a peer-led online resource intervention developed in the United Kingdom. This process of co-design is transferable to other contexts, and when adapted could help meet the needs of cancer carers in other lower and middle income countries. Results Carers in Vietnam reported (i) economic challenges of care; (ii) not being able to access facilities and secure accommodation when caring for inpatients; (iii) lack of information about cancer and nutrition; (iv) lacking emotional support; and (v) requiring training to support both the treatment and recovery of people under their care. Suggestions for content of an online resource included the need for contextually appropriate Vietnamese content, specific information on diet and nutrition, support in making decisions around treatment, and signposting for other services. Lessons Successful co-design of resources requires input from multiple key stakeholders. This is necessary to successful adapt and modify interventions for new contexts. Our process revealed new information about the roles and needs of carers, and enabled us to incorporate solutions to these needs within our online resource. Given the lack of other supportive services for carers, the development of such resources should be considered a priority. Key messages Cancer carers in Vietnam experience specific challenges including provision of nutrition, supporting navigation of hospital administration, and taking a central role in treatment decision-making. Co-designed online resources have the potential to support carers in providing relevant and appropriate information and signposting to other important services.

Equivariant Adversarial Network for Image-to-image Translation

2021 ◽  
Vol 17 (2s) ◽  
pp. 1-14
Author(s):  
Masoumeh Zareapoor ◽  
Jie Yang
Keyword(s):  
State Of The Art ◽  
Generative Models ◽  
Generative Model ◽  
Target Domain ◽  
Adversarial Network ◽  
Proposed Model ◽  
Image Translation ◽  
Great Performance ◽  
Representative Model ◽  
The Ideal

Image-to-Image translation aims to learn an image from a source domain to a target domain. However, there are three main challenges, such as lack of paired datasets, multimodality, and diversity, that are associated with these problems and need to be dealt with. Convolutional neural networks (CNNs), despite of having great performance in many computer vision tasks, they fail to detect the hierarchy of spatial relationships between different parts of an object and thus do not form the ideal representative model we look for. This article presents a new variation of generative models that aims to remedy this problem. We use a trainable transformer, which explicitly allows the spatial manipulation of data within training. This differentiable module can be augmented into the convolutional layers in the generative model, and it allows to freely alter the generated distributions for image-to-image translation. To reap the benefits of proposed module into generative model, our architecture incorporates a new loss function to facilitate an effective end-to-end generative learning for image-to-image translation. The proposed model is evaluated through comprehensive experiments on image synthesizing and image-to-image translation, along with comparisons with several state-of-the-art algorithms.

Some Possible Lines of Development in Aircraft Engines

1921 ◽  
Vol 25 (123) ◽  
pp. 130-165
Keyword(s):  
Present State ◽  
State Of The Art ◽  
Point Of View ◽  
Aircraft Engines ◽  
Aero Engine ◽  
The Ideal

In the following paper the writer's aim is to indicate certain possible lines of development and research which his own investigations and preliminary experiments have shown to be at least worthy of serious consideration.If we review the present state of the art we find the position to be substantially as follows :—From a thermodynamic point of view the performance of the modern aero engine has approached so nearly to the ideal obtainable from the cycle on which it operates that there is little scope for improvement.

scholarly journals Fixel-based Analysis of Diffusion MRI: Methods, Applications, Challenges and Opportunities

2020 ◽  
Author(s):  
Thijs Dhollander ◽  
Adam Clemente ◽  
Mervyn Singh ◽  
Frederique Boonstra ◽  
Oren Civier ◽  
...  
Keyword(s):  
Diffusion Mri ◽  
State Of The Art ◽  
Specific Information ◽  
Analysis Framework ◽  
Wide Range ◽  
Challenges And Opportunities ◽  
P Diffusion ◽  
Modelling Techniques ◽  
The Rich

Diffusion MRI has provided the neuroimaging community with a powerful tool to acquire in-vivo data sensitive to microstructural features of white matter, up to 3 orders of magnitude smaller than typical voxel sizes. The key to extracting such valuable information lies in complex modelling techniques, which form the link between the rich diffusion MRI data and various metrics related to the microstructural organisation. Over time, increasingly advanced techniques have been developed, up to the point where some diffusion MRI models can now provide access to properties specific to individual fibre populations in each voxel in the presence of multiple "crossing" fibre pathways. While highly valuable, such fibre-specific information poses unique challenges for typical image processing pipelines and statistical analysis. In this work, we review the "fixel-based analysis" (FBA) framework that implements bespoke solutions to this end, and has recently seen a stark increase in adoption for studies of both typical (healthy) populations as well as a wide range of clinical populations. We describe the main concepts related to fixel-based analyses, as well as the methods and specific steps involved in a state-of-the-art FBA pipeline, with a focus on providing researchers with practical advice on how to interpret results. We also include an overview of the scope of current fixel-based analysis studies (until August 2020), categorised across a broad range of neuroscientific domains, listing key design choices and summarising their main results and conclusions. Finally, we critically discuss several aspects and challenges involved with the fixel-based analysis framework, and outline some directions and future opportunities.

scholarly journals Understanding Cognitive Dissonance of Indian Customers for Financial Products: A Multi-Dimensional Scale Development Approach

2020 ◽  
Vol 4 (1) ◽  
pp. 20-32 ◽  
Author(s):  
Brajesh Bolia ◽  
Sumi Jha ◽  
Manoj K Jha
Keyword(s):  
Cognitive Dissonance ◽  
Measurement Scale ◽  
Research Approach ◽  
Focus Group Discussions ◽  
Group Discussions ◽  
Financial Products ◽  
Two Factors ◽  
Development Approach ◽  
Financial Concern ◽  
Post Purchase

The aim of the study was to understand the dynamics of cognitive dissonance in the context of financial product purchase. A mixed methodology research approach was undertaken to explore the attitudinal and behavioural dimensions (qualitative) and subsequent empirical validation (quantitative) with a sample of customers of financial products. Qualitative research was conducted through focus group discussions to arrive at a pool of 99 items which were then pruned and validated with the help of academic and industry experts. The items were empirically tested and validated with the help of appropriate statistical tools to arrive at a “5 factor and 25 items” measurement scale for cognitive dissonance. The study found two factors “Emotional Gain” & “Financial Concern” as distinguishing factors emerging out as key findings. The arousal of cognitive dissonance after the purchase decision taken by consumer can be a major concern for marketers as it might result in order cancellations, loss of trust for the brand as well as loss of loyal customers. Measuring dissonance in financial product context post purchase can help marketers devise appropriate strategies to reduce dissonance, thereby retaining and attracting customers.

scholarly journals Joint Representation Learning of Legislator and Legislation for Roll Call Prediction

2020 ◽  
Author(s):  
Yuqiao Yang ◽  
Xiaoqiang Lin ◽  
Geng Lin ◽  
Zengfeng Huang ◽  
Changjian Jiang ◽  
...  
Keyword(s):  
Neural Networks ◽  
State Of The Art ◽  
Ideal Point ◽  
Representation Learning ◽  
Context Information ◽  
Roll Call ◽  
Triplet Loss ◽  
Joint Representation ◽  
Narrative Description ◽  
The Ideal

In this paper, we explore to learn representations of legislation and legislator for the prediction of roll call results. The most popular approach for this topic is named the ideal point model that relies on historical voting information for representation learning of legislators. It largely ignores the context information of the legislative data. We, therefore, propose to incorporate context information to learn dense representations for both legislators and legislation. For legislators, we incorporate relations among them via graph convolutional neural networks (GCN) for their representation learning. For legislation, we utilize its narrative description via recurrent neural networks (RNN) for representation learning. In order to align two kinds of representations in the same vector space, we introduce a triplet loss for the joint training. Experimental results on a self-constructed dataset show the effectiveness of our model for roll call results prediction compared to some state-of-the-art baselines.

scholarly journals DeepTable: a permutation invariant neural network for table orientation classification

2020 ◽  
Vol 34 (6) ◽  
pp. 1963-1983
Author(s):  
Maryam Habibi ◽  
Johannes Starlinger ◽  
Ulf Leser
Keyword(s):  
Deep Neural Networks ◽  
State Of The Art ◽  
Superior Performance ◽  
Web Pages ◽  
Specific Information ◽  
Silver Standard ◽  
Present Information ◽  
Previous State ◽  
Novel Method

Abstract Tables are a common way to present information in an intuitive and concise manner. They are used extensively in media such as scientific articles or web pages. Automatically analyzing the content of tables bears special challenges. One of the most basic tasks is determination of the orientation of a table: In column tables, columns represent one entity with the different attribute values present in the different rows; row tables are vice versa, and matrix tables give information on pairs of entities. In this paper, we address the problem of classifying a given table into one of the three layouts horizontal (for row tables), vertical (for column tables), and matrix. We describe DeepTable, a novel method based on deep neural networks designed for learning from sets. Contrary to previous state-of-the-art methods, this basis makes DeepTable invariant to the permutation of rows or columns, which is a highly desirable property as in most tables the order of rows and columns does not carry specific information. We evaluate our method using a silver standard corpus of 5500 tables extracted from biomedical articles where the layout was determined heuristically. DeepTable outperforms previous methods in both precision and recall on our corpus. In a second evaluation, we manually labeled a corpus of 300 tables and were able to confirm DeepTable to reach superior performance in the table layout classification task. The codes and resources introduced here are available at https://github.com/Marhabibi/DeepTable.

PharmKG: a dedicated knowledge graph benchmark for bomedical data mining

2020 ◽  
Author(s):  
Shuangjia Zheng ◽  
Jiahua Rao ◽  
Ying Song ◽  
Jixian Zhang ◽  
Xianglu Xiao ◽  
...  
Keyword(s):  
Chemical Structure ◽  
State Of The Art ◽  
Critical Role ◽  
Global Network ◽  
Evaluation Metrics ◽  
Complex Nature ◽  
Specific Information ◽  
Biomedical Knowledge ◽  
Domain Specific ◽  
Modeling Methods

Abstract Biomedical knowledge graphs (KGs), which can help with the understanding of complex biological systems and pathologies, have begun to play a critical role in medical practice and research. However, challenges remain in their embedding and use due to their complex nature and the specific demands of their construction. Existing studies often suffer from problems such as sparse and noisy datasets, insufficient modeling methods and non-uniform evaluation metrics. In this work, we established a comprehensive KG system for the biomedical field in an attempt to bridge the gap. Here, we introduced PharmKG, a multi-relational, attributed biomedical KG, composed of more than 500 000 individual interconnections between genes, drugs and diseases, with 29 relation types over a vocabulary of ~8000 disambiguated entities. Each entity in PharmKG is attached with heterogeneous, domain-specific information obtained from multi-omics data, i.e. gene expression, chemical structure and disease word embedding, while preserving the semantic and biomedical features. For baselines, we offered nine state-of-the-art KG embedding (KGE) approaches and a new biological, intuitive, graph neural network-based KGE method that uses a combination of both global network structure and heterogeneous domain features. Based on the proposed benchmark, we conducted extensive experiments to assess these KGE models using multiple evaluation metrics. Finally, we discussed our observations across various downstream biological tasks and provide insights and guidelines for how to use a KG in biomedicine. We hope that the unprecedented quality and diversity of PharmKG will lead to advances in biomedical KG construction, embedding and application.

Influence of braking on dynamic stability of car-trailer combinations

2020 ◽  
pp. 095440702095989
Author(s):  
Ning Zhang ◽  
Jian-hua Wu ◽  
Tian Li ◽  
Zi-qian Zhao ◽  
Guo-dong Yin
Keyword(s):  
Dynamic Stability ◽  
Load Transfer ◽  
Great Influence ◽  
Stability Control ◽  
System Dynamic ◽  
Force Distribution ◽  
Single Track ◽  
Two Factors ◽  
Braking Force ◽  
The Ideal

The influence of braking on dynamic stability of a car-trailer combination (CTC) is studied in this paper. The braking is simply modeled and integrated into a single-track model (STM) with a single-axle trailer. On this basis, some fundamentals and analysis results related to system dynamic stability are given through simulation. Furthermore, it is found that the axle load transfer and braking force distribution have a great influence on system dynamic stability. In order to further analyze the influence of these two factors, both of the braking force distribution and the pitch motion are considered in the modeling. Finally, the ideal braking force distribution domain is proposed. Results can be adopted to explain the experimental phenomenon and serve as a guideline for the differential braking strategy in stability control of the CTC.

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