Dynamic Analysis of Customer Demand Based on Intuitionistic Fuzzy Number in Product Planning

To address the fuzziness and variability in determining customer demand importance,a dynamic analysis method based on intuitionistic fuzzy numbers is proposed.First,selected customers use intuitionistic fuzzy numbers to represent the importance of each demand.Then,the preference information is aggregated using customer weights and time period weights through the intuitionistic fuzzy ordered weighted average operator,yielding a dynamic vector of the subjective importance of the demand index.Finally,the feasibility of the proposed method is demonstrated through an application example of a vibrating sorting screen.

Methodology for Obtaining Electricity-Price Patterns in Customer Response Programs

The main objective of electricity regulators when establishing electricity markets is to decrease the cost of electricity through competition. However, this scenario cannot be achieved without a full participation of the electricity demand by reacting against electricity prices. The aim of this research is to develop tools for helping customers and aggregators to join price and demand response programs, while helping them to hedge against the risk of short-term price volatility. In this way, the capacity of and hybrid methodology （Self-Organizing Maps and Statistical Ward＇s Linkage） to classify high electricity market prices is analysed. Besides, with the help of Non-Parametric Estimation, some price-patterns were found in the abovementioned clusters. The contained knowledge within these patterns supplies customer market-based information on which to base its energy use decisions. The interest for this participation of customers in markets is growing in developed countries to obtain a higher elasticity in demand. Results show the capability of this approach to improve data management and select coherent policies to accomplish cleared demand offers amongst different price scenarios in a more flexible way.

Research on Multi-stage Optimum Design Technology for Virtual Supply Chain Based on Cybermediary in Manufacturing Industry

Virtual supply chain based on cybermediary (VSC-CM) is an innovative VSC pattern meeting informationization development requirements in the manufacturing industry. Methods and features of customer-demand-oriented optimum VSC design adopted by CM are discussed. A customer demand goal system applying to VSC-CM design and quantifying methods of these goals are accordingly given. Then a three-stage optimum VSC design scheme based on dynamic goals is designed, which considers both the holistic optimization of VSC and individuation demands of member enterprises. To implement the scheme, an optimum algorithm synthesizing fuzzy c-means clustering algorithm and topsis comprehensive evaluation algorithm is presented. Feasibility and rapidity of this scheme is proved through a case analysis finally.

Analysis of the Evolution and Influence on the Industrial Value Chain in Telecommunications

This paper discusses the evolution of telecommunication industrial value chain （TIVC）, analyzes the influence of technical innovation and customer demand on TIVC, and establishes the model. The appearance of the circuit switching technology and packet switching technology together with the diversity of the demand of customers change the structure of TIVC, causing vibration in value creation and distribution systems. With changes of the TIVC, enterprises in the chain will accordingly alter their business models, products （services） and the internal organizational structures. All these changes will lead to the reconstruction and optimization of the TIVC, and consequently, promote the development of the telecommunication industry.

Product Customer Demand Mining and Its Functional Attribute Configuration Driven by Big Data

The maturity of big data analysis theory and its tools improve the efficiency and reduce the cost of massive data mining.This paper discusses the method of product customer demand mining based on big data,and further studies the configuration of product function attributes.Firstly,the Hadoop platform was used to perform product attribute data participle and feature word extraction based on Apriori algorithm was used to mine product customer demand information.And then the MapReduce model on the big data platform was applied into efficient parallel data processing,obtaining product attributes with research value,and their weights and attribute levels.After that,the cloud model and the MNL model were employed to construct the product function attribute configuration model,and the improved artificial bee colony algorithm was used to solve the model.The optimal solution of the product function attribute configuration model was got.Finally,an example was given to illustrate the feasibility of the proposed method in this paper.