Product family modeling technology for customized cosmetic packaging design based on basic-element theory

Background:As the market demands change,SMEs(small and medium-sized enterprises)have long faced many design issues,including high costs,lengthy cycles,and insufficient innovation.These issues are especially noticeable in the domain of cosmetic packaging design.Objective:To explore innovative product family modeling methods and configuration design processes to improve the efficiency of enterprise cosmetic packaging design and develop the design for mass customization.Methods:To accomplish this objective,the basic-element theory has been introduced and applied to the design and development system of the product family.Results:By examining the mapping relationships between the demand domain,functional domain,technology domain,and structure domain,four interrelated models have been developed,including the demand model,functional model,technology model,and structure model.Together,these models form the mechanism and methodology of product family modeling,specifically for cosmetic packaging design.Through an analysis of a case study on men’s cosmetic packaging design,the feasibility of the proposed product family modeling technology has been demonstrated in terms of customized cosmetic packaging design,and the design efficiency has been enhanced.Conclusion:The product family modeling technology employs a formalized element as a module configuration design language,permeating throughout the entire development cycle of cosmetic packaging design,thus facilitating a structured and modularized configuration design process for the product family system.The application of the basic-element principle in product family modeling technology contributes to the enrichment of the research field surrounding cosmetic packaging product family configuration design,while also providing valuable methods and references for enterprises aiming to elevate the efficiency of cosmetic packaging design for the mass customization product model.

Novel Early-Warning Model for Customer Churn of Credit Card Based on GSAIBAS-Cat Boost

As the banking industry gradually steps into the digital era of Bank 4.0,business competition is becoming increasingly fierce,and banks are also facing the problem of massive customer churn.To better maintain their customer resources,it is crucial for banks to accurately predict customers with a tendency to churn.Aiming at the typical binary classification problem like customer churn,this paper establishes an early-warning model for credit card customer churn.That is a dual search algorithm named GSAIBAS by incorporating Golden Sine Algorithm(GSA)and an Improved Beetle Antennae Search(IBAS)is proposed to optimize the parameters of the CatBoost algorithm,which forms the GSAIBAS-CatBoost model.Especially,considering that the BAS algorithm has simple parameters and is easy to fall into local optimum,the Sigmoid nonlinear convergence factor and the lane flight equation are introduced to adjust the fixed step size of beetle.Then this improved BAS algorithm with variable step size is fused with the GSA to form a GSAIBAS algorithm which can achieve dual optimization.Moreover,an empirical analysis is made according to the data set of credit card customers from Analyttica official platform.The empirical results show that the values of Area Under Curve(AUC)and recall of the proposedmodel in this paper reach 96.15%and 95.56%,respectively,which are significantly better than the other 9 common machine learning models.Compared with several existing optimization algorithms,GSAIBAS algorithm has higher precision in the parameter optimization for CatBoost.Combined with two other customer churn data sets on Kaggle data platform,it is further verified that the model proposed in this paper is also valid and feasible.

Customer Segment Prediction on Retail Transactional Data Using K-Means and Markov Model

Retailing is a dynamic business domain where commodities and goods are sold in small quantities directly to the customers.It deals with the end user customers of a supply-chain network and therefore has to accommodate the needs and desires of a large group of customers over varied utilities.The volume and volatility of the business makes it one of the prospectivefields for analytical study and data modeling.This is also why customer segmentation drives a key role in multiple retail business decisions such as marketing budgeting,customer targeting,customized offers,value proposition etc.The segmentation could be on various aspects such as demographics,historic behavior or preferences based on the use cases.In this paper,historic retail transactional data is used to segment the custo-mers using K-Means clustering and the results are utilized to arrive at a transition matrix which is used to predict the cluster movements over the time period using Markov Model algorithm.This helps in calculating the futuristic value a segment or a customer brings to the business.Strategic marketing designs and budgeting can be implemented using these results.The study is specifically useful for large scale marketing in domains such as e-commerce,insurance or retailers to segment,profile and measure the customer lifecycle value over a short period of time.

Modeling customer satisfaction for bus rapid transit in Changzhou, China

This paper aims to develop a customer satisfaction model for bus rapid transit （BRT）. Both the socio-economic and travel characteristics of passengers were considered to be independent variables. Changzhou BRT was taken as an example and on which on-board surveys were conducted to collect data. Ordinal logistic regression （OLR） was used as the modeling approach. The general OLR-based procedure for modeling customer satisfaction is proposed and based on which the customer satisfaction model of Changzhou BRT is developed. Some important findings are concluded： Waiting sub-journey affects customer satisfaction the most, riding sub- journey comes second and arriving station sub-journey has relatively fewer effects. The availability of shelter and benches at stations imposes heavy influence on customer satisfaction. Passengers＇ socio-economic characteristics have heavy impact on customer satisfaction.

A Custom Manipulator for Dental Implantation Through Model-Based Design

This paper presents a Model-Based Design(MBD)approach for the design and control of a customized manipulator intended for drilling and position-ing of dental implants accurately with minimal human intervention.While performing an intra-oral surgery for a prolonged duration within a limited oral cavity,the tremor of dentist's hand is inevitable.As a result,wielding the drilling tool and inserting the dental implants safely in accurate position and orientation is highly challenging even for experienced dentists.Therefore,we introduce a customized manipulator that is designed ergonomically by taking in to account the dental chair specifications and anthropomorphic data such that it can be readily mounted onto the existing dental chair.The manipulator can be used to drill holes for dental inserts and position them with improved accuracy and safety.Further-more,a thorough multi-body motion analysis of the manipulator was carried out by creating a virtual prototype of the manipulator and simulating its controlled movements in various scenarios.The overall design was prepared and validated in simulation using Solid works,MATLAB and Simulink through Model Based Design(MBD)approach.The motion simulation results indicate that the manipulator could be built as a prototype readily.

CSI-Model-Based Research on Factors Affecting Rate Satisfaction of Government and Corporate CustomersTaking Special-Line Products as an Example

The rate satisfaction of government and corporate customers,to which telecom companies pay special attention,is an important part of the overall satisfaction of the market segment.Focusing on specialline products and based on interview with experts and literature review,this article builds an effect model comprising four factors affecting the major government and corporate customers' satisfaction with the rate,including cost performance,discount degree,rate communication and bill composition.Besides,employing AMOS and other analytical tools,it reveals significant positive effects of the aforementioned four factors on the customer's rate satisfaction,and arranges them in sequence according to the path coefficient of each influence.Based on quantified results,this article proposes management recommendations to improve the rate satisfaction,providing basis for research on the overall rate satisfaction optimization of government and corporate customers.

Analysis of Influence of Price to Customer Satisfaction Based on the Prediction Models

For the purpose of improvement of the sales, confirming the influence of price to the sales and customer satisfaction of the product is important. The most suitable price should be determined from the view point of customers, and it is extremely important to implement a high quality product corresponding to the real need of customers. It may have close relationship between cost and an expense to implement the individual inherent attribute of system product. Also, it may have close relationship between production cost and price of product. For the purpose of improvement of the customer satisfaction for quality of system product, the method of quantitative quality requirement and evaluation based on the ISO/IEC9126 quality model that includes six quality characteristics is widely recognized. In the precedent study, I have introduced the requirements definition method for the quality of system product based on the system quality model defined in ISO/IEC9126 and proposed the effectiveness of it statistically. In the previous study, I have also confirmed the relationship between inherent attributes of the product and quantitative result of the measured value of total customer satisfaction from the view point of six quality characteristics statistically. I performed the development of the prediction model to estimate the total customer satisfaction for the system product from the view point of inherent attribute of the product. And, I have proposed the effectiveness of application of the estimated prediction model and possibility of improvement of the total customer satisfaction of a system product. Based on the result of previous study, in this paper, I propose the result of investigation of influence of price to customer satisfaction, and the possibility of application of estimated prediction model for improvement of the total customer satisfaction of system product based on the price of product. Also, based on the result of investigation of relationship among price and inherent attributes of product, I propose the possibility of application of estimated prediction model and improvement of the price of system product from the view point of inherent attributes of product.

Customer Retention: Behaviour Perspective Model of Ghanaian Telecommunication Industry Using Multinomial Regression Analysis

To stay competitive, the mobile telecommunication companies spend millions of Ghana cedi each year on building long-term relationships with their customers. Marketing managers are constantly challenged with the problem of where to channel the limited resources in order to retain existing customers. This study approaches the customer retention problem in the mobile phone sector from a behavioural perspective, applying the Behavioural Perspective Model as the main analytical framework and further exploits some other factors that influence customer retention. The model includes a set of pre-behaviour and post-behaviour factors to study consumer choice, and explains its relevant drivers in a viable and comprehensive way, grounded in radical behaviourism. Data for the analysis were collected from tertiary students from Accra and Takoradi. Data collected were analysed using the multinomial regression technique. Analysis of the data revealed that the Behaviour setting factor is the only significant element in Behaviour Perspective Model. Further exploitation of behaviour situation revealed that the number of networks a customer uses, previous experience of a customer and customer’s intention are significant factors in determining customer retention in Ghana’s mobile telecommunication industry.

A Support Vector Machine-based Evaluation Model of Customer Satisfaction Degree in Logistics

This paper presents a novel evaluation model of the customer satisfaction degree （CSD） in logistics based on support vector machine （SVM）. Firstly, the relation between the suppliers and the customers is analyzed. Seondly, the evaluation index system and fuzzy quantitative methods are provided. Thirdly, the CSD evaluation system including eight indexes and three ranks based on one-against-one mode of SVM is built, last simulation experint is presented to illustrate the theoretical results.

Prediction Models for Total Customer Satisfaction Based on the ISO/IEC9126 System Quality Model

The profitability of the system product is decided on the sales of the product. Furthermore, a customer satisfaction for products quality and a price have a big influence on the sales of the product. It spends limited financial resources effectively to raise the profitability of the system product, and it is necessary to realize the high quality product correspond to the customer needs as much as possible. There may be close relationship between cost of a product and an expense to implement the individual inherent attribute of system product. For the purpose of improvement of the customer satisfaction for quality of system product, the method of quantitative quality requirement and evaluation based on the ISO/IEC9126 quality model that includes six quality characteristics is widely recognized. However, independency among each quality characteristic has not been sure and the suitability of method for quality requirement of system product by using these six quality characteristics could not certified statistically. In the precedent study, introduced the requirements definition method for the quality of system product based on the system quality model defined in ISO/IEC9126 and proposed the effectiveness of it statistically. This study have measured the customer satisfaction for the system quality from the viewpoint of six quality characteristics quantitatively and confirmed the effectiveness of the technique to evaluate. In this study, we have confirmed the relationship between inherent attributes of the product and quantitative result of a measured value of total customer satisfaction from the view point of six quality characteristics statistically. This study performed the trial to clarify the relations with the inherent attributes that quantitative result of a measurement of the customer satisfaction based on six quality characteristics by the quality model of ISO/IEC9126. In addition, this study performed the development of the prediction model to estimate the total customer satisfaction for the system product from the view point of inherent attribute of the product. In this paper, we propose the effectiveness of application of the estimated prediction model and possibility of improvement of the total customer satisfaction of a system product.

Customer Satisfaction and Hotel Brand Equity： A Structural Equation Modelling Study

In this paper, a structural equation modelling technique was used to examine the effect of brand equity on customer satisfaction in the Egyptian hotel industry. Brand equity was separated into four constructs （brand loyalty, perceived quality, brand awareness and brand associations）, and each one was examined individually. A sample of hotel guests, who accommodated at four- and five-star branded hotels, in four tourism destinations in Egypt, was selected. A structured questionnaire that included established scales was then used to measure brand loyalty, perceived quality, brand awareness, brand associations and customer satisfaction. Data were collected from 280 usable questionnaires answered by hotel guests, and the relationships among the four variables of brand equity and customer satisfaction were examined using correlation analysis and structural equation modelling. The study found that brand loyalty, brand awareness and brand associations all had a significant positive effect on customer satisfaction. Perceived quality, however, did not have any effect. These findings suggest that customer satisfaction is a reasonable measure for the success of branding activities, and that branding managers should focus on brand loyalty, awareness and associations to raise their customers＇ satisfaction.

Customer-oriented Configuration Model for Modular MechatronicProducts: Application in Industrial Robot Design

The contradiction between manufacturing costs and customer demand of mechatronic products can be balanced by configuration design. The article proposes a customer-oriented configuration model for modular mechatronic products which makes up the shortfall in meeting customer needs for the traditional configuration de- sign mode. The elements of the modular mechatronic products configuration model including module, module connection and configuration knowledge are analyzed. And a formal representation method for configuration model elements combining with their characteristics is given. Based on the above research, an abstract configuration model of modular mechatronic products is designed. Finally, an industrial robot is used as an application exam- ple to build a customer-oriented industrial robot configuration model.

Application of a Bayesian Network Complex System Model Examining the Importance of Customer-Industry Engagement to Peak Electricity Demand Reduction

This paper explores the importance of customer-industry engagement (CIE) to peak energy demand by means of a newly developed Bayesian Network (BN) complex systems model entitled the Residential Electricity Peak Demand Model (REPDM). The REPDM is based on a multi-disciplinary perspective designed to solve the complex problem of residential peak energy demand. The model provides a way to conceptualise and understand the factors that shift and reduce consumer demand in peak times. To gain insight into the importance of customer-industry engagement in affecting residential peak demand, this research investigates intervention impacts and major influences through testing five scenarios using different levels of customer-industry engagement activities. Scenario testing of the model outlines the dependencies between the customer-industry engagement interventions and the probabilities that are estimated to govern the dependencies that influence peak demand. The output from the model shows that there can be a strong interaction between the level of CIE activities and interventions. The influence of CIE activity can increase public and householder support for peak reduction and the model shows how the economic, technical and social interventions can achieve greater peak demand reductions when well-designed with appropriate levels of CIE activities.

Configuration knowledge modeling of customizable products based on semantic web technologies

In order to solve the problem of modeling product configuration knowledge at the semantic level to successfully implement the mass customization strategy, an approach of ontology-based configuration knowledge modeling, combining semantic web technologies, was proposed. A general configuration ontology was developed to provide a common concept structure for modeling configuration knowledge and rules of specific product domains. The OWL web ontology language and semantic web rule language （SWRL） were used to formally represent the configuration ontology, domain configuration knowledge and rules to enhance the consistency, maintainability and reusability of all the configuration knowledge. The configuration knowledge modeling of a customizable personal computer family shows that the approach can provide explicit, computerunderstandable knowledge semantics for specific product configuration domains and can efficiently support automatic configuration tasks of complex products.

Evaluation of the Customer Satisfaction Index for Wellhead Blowout Preventers of China’s Petroleum Industry——A new method based on PLS

This paper establishes an evaluation model of the customer satisfaction index for the wellhead blowout preventers of China＇s petroleum industry based on evaluation models of the customer satisfaction index at home and aboard, and by considering the consuming situation in China and the features of the China＇s petroleum industry. For the existence of： （1） multiple correlations among the factors in the model; （2） the variables need to be explained, but that are hard to observe; （3） the customer satisfaction degree of observation variables appears the shape of skewness or two or three peaks, the correlations between the satisfaction index and its factors cannot be described by common multiple regression. This paper uses a partial least squares （PLS） method based on principal components and typical correlative analysis to solve the problem. When PLS is used in the model of the customer satisfaction index of the wellhead blowout preventers, the latent variables and the explanation degree coefficient of the manifest variable to the corresponding latent variables are estimated by PLS path analysis, and the influencing coefficient among the latent variables in the model is estimated by PLS regression analysis. PLS is also be used to calculate and analyze the model and disclose the correlations among the structural variables as well as the correlation between structural variables and its corresponding observation variables, evaluating results of which provide useful information for petroleum industry to improve the product quality and to the enhancement of the customer satisfaction to the product.

Customer Tiered Purchase Forecast by Mobile Edge Computing Based on Pareto/NBD and SVR

Mobile edge computing is trending nowadays for its computation efficiency and privacy.The rapid development of e-commerce show great interest in mobile edge computing due to numerous rise of small and middle-sized enterprises(SMEs)in the internet.This paper predicts the overall sales volume of the enterprise through the classic ARIMA model,and notes that the behavior and arrival differences between the new and old customer groups will affect the accuracy of our forecasts,so we then use Pareto/NBD to explore the repeated purchases of customers at the individual level of the old customer and the SVR model to predict the arrival of new customers,thus helping the enterprise to make layered and accurate marketing of new and old customers through machine learning.In general,machine learning relies on powerful computation and storage resources,while mobile edge computing typically provides limited computation resources locally.Therefore,it is essential to combine machine learning with mobile edge computing to further promote the proliferation of data analysis among SMEs.

Research on customer loyalty of B2C e-commerce

Customer loyalty of B2C e-commerce is studied by testing the hypotheses on five latent variables using the structural equation modeling method. After correcting model, online store image is obviously fundamental basis on expectations of shopping online, experience of shopping online, customer satisfaction and customer loyalty between which there are seven positive relationships accepted in the AMOS model. The results provide scientific evidence for B2C e-commerce.

Research on Clustering Analysis and Its Application in Customer Data Mining of Enterprise

The paper study improved K-means algorithm and establish indicators to classify customers according to RFM model. Experimental results show that, the new algorithm has good convergence and stability, it has better than single use of FKP algorithms for clustering. Finally the paper study the application of clustering in customer segmentation of mobile communication enterprise. It discusses the basic theory, customer segmentation methods and steps, the customer segmentation model based on consumption behavior psychology, and the segmentation model is successfully applied to the process of marketing decision support.

MODEL OF OPERATING AND BALANCING ASSEMBLY LINE IN A MASS CUSTOMIZATION ENVIRONMENT

The successful implementation of mass customization lies on reengineeringtechnology and management methods to organize the production. Especially in assembly phase, variousproduct configurations, due-time penalties and order-driven strategy challenge the traditionaloperation and management of assembly lines. The business features and the operation pattern ofassembly line based on mass customization are analyzed. And the research emphatically studiesvarious technologic factors to improve customer satisfaction and their corresponding implementmethods in operating assembly line. In addition, the models are proposed for operating assembly lineunder dynamic process environment in mass customization. A genetic approach is developed to providethe optimal solution to the models. The effectiveness of the proposed approach is evaluated with anindustrial application.

Key technologies of artificial intelligence in electric power customer service

As the demand for customer service continues to increase,more companies are attempting to apply artificial intelligence technology in the field of customer service,enabling intelligent customer service,reducing customer service pressure,and reducing operating costs.Currently,the existing intelligent customer service has a limited degree of intelligence and can only answer simple user questions,and complex user expressions are difficult to understand.To solve the problem of low accuracy of multi-round dialogue semantic understanding,this paper proposes a semantic understanding model based on the fusion of a convolutional neural network(CNN)and attention.The model builds an“intention-slot”joint model based on the“encoding–decoding”framework and uses hidden semantic information that combines intent recognition and slot filling,avoiding the problem of information loss in traditional isolated tasks,and achieving end-to-end semantic understanding.Additionally,an improved attention mechanism based on CNNs is introduced in the decoding process to reduce the interference of redundant information in the original text,thereby increasing the accuracy of semantic understanding.Finally,by applying the model to electric power intelligent customer service,we verified through an experimental comparison that the proposed fusion model improves the performance of intent recognition and slot filling and can improve the user experience of electric power intelligent customer services.