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The purpose of this paper is to propose a grey target decision model based on cobweb area in order to overcome the effect and influence from the extreme value of the index on the decision result. However, it does not take into account the impact of the correlation between indicators on the angle of the index, and produce a certain degree decision information distortion as a result of the equal angle between the indicators. In order to solve the above problems, a novel grey decision-making model based on cone volume is proposed.

In this paper, the model uses the whitening weight function to whiten the interval grey number, and the Delphi method and the maximal entropy method are exploited to integrate the weight of the index. On the basis of this, the center of the bull’s eye, the weight and the index value are constructed as the center circle, the radius, and the high cone, respectively. The scheme is selected by the volume of the cone, the decision is made according to the order relation, and the example is utilized to prove and analyze the validity of the proposed model.

The results show that the proposed model can well improve the traditional grey target decision-making model from the modeling object and modeling method.

The method exposed in the paper can be used to deal with the grey target decision-making problems which characteristics are multi-indexes, and the attribute values are interval grey numbers.

The paper succeeds in overcoming the disadvantages of grey target decision making based on the target center distance and the cobweb area.

The purpose of this study is to master the development process and the construction effectiveness of backbone circulation network in an all-round way, formulate regional logistics development planning as well as promote the development of logistics industry by scientifically evaluating the logistics development of node cities with a view to analyzing their spatial differentiation features.

In this paper, an integrated evaluation model is constructed by adopting factor analysis, gray target decision-making model based on cone volume and other methods so as to evaluate the logistics development of node cities. The dimensionality of three-dimensional panel data is reduced by factor analysis at first. Then, the gray target decision-making method based on cone volume is adopted to evaluate the development of node cities, whose evaluation results are carried out through the clustering analysis. The clustering analysis is used to determine the development level of node cities and to extract the spatial differentiation features of node cities.

The results show that the proposed model can comprehensively evaluate the logistics level of node cities and clarify the overall logistics development and spatial differentiation of node cities, which could provide objective evidence for formulating national policies as well as promoting the balanced and coordinated development of regional logistics in China.

The paper succeeds in overcoming the disadvantages of existing methods assessing the logistics development level, such as principal component analysis and factor analysis, which are not applicable to panel data.

Introduction Consider a hi‐fi loudspeaker manufacturing company acquired on the brink of insolvency by an American multinational. The new owners discover with growing concern that the product range is obsolete, that manufacturing facilities are totally inadequate and that there is a complete absence of any real management substance or structure. They decide on the need to relocate urgently so as to provide continuity of supply at the very high — a market about to shrink at a rate unprecedented in its history.

This special “Anbar Abstracts” issue of the Marketing Intelligence & Planning is split into nine sections covering abstracts under the following headings: Business Strategy; Marketing Strategy; Customer Service; Sales Management; Promotion; Marketing Research/Customer Behaviour; Product Management; Logistics and Distribution; Sundry.

Internet of Things (IoT) and artificial intelligence are two leading technologies that bought revolution to each and every field of humans using in daily life by making everything smarter than ever. IoT leads to a network of things which creates a self-configuring network. Improving farm productivity is essential to meet the rapidly growing demand for food. In this chapter, the authors have introduced a smart greenhouse by integration of two leading technologies in the market (i.e., Machine Learning and IoT). In proposed model, several sensors are used for data collection and managing the environment of greenhouse. The idea is to propose an IoT and Machine Learning based smart nursery that helps in healthy growing and monitoring of the seed. The structure will be a dome-like structure for observation and isolation of an egg with various sensors like pressure, humidity, temperature, light, moisture, conductivity, air quality, etc. to monitor the nursery internal environment and maintain the control and flow of water and other minerals inside the nursery. The nursery will have a solar panel from which it stores the electricity generated from the sun, a small fan to control the flow of air and pressure. A camera will also be equipped inside the nursery that will use computer vision technology to monitor the health of the plant and will be trained on the past data to notify the user if the plant is diseased or need attention.

IN the middle of a January afternoon an audience which packed the National Film Theatre was held in thrall by a film. These people drawn from Government departments, trade unions, employers, technical colleges and local productivity committees were not wasting precious time watching the miming of famous film stars.

The following is an introductory profile of the fastest growing firms over the three-year period of the study listed by corporate reputation ranking order. The business activities in which the firms are engaged are outlined to provide background information for the reader.

With the growth of unstructured data, opportunities to generate insights into supply chain risks in low cost countries (LCCs) are emerging. Sourcing risk has primarily focused on short-term mitigation. This paper aims to offer an approach that uses newsfeed data to assess regional supply base risk in LCC’s for the apparel sector, which managers can use to plan for future risk on a long-term planning horizon.

This paper demonstrates that the bulk of supplier risk assessments focus on short-term responses to disruptions in developed countries, revealing a gap in assessments of long-term risks for supply base expansion in LCCs. This paper develops an approach for predicting and planning for long-term supply base risk in LCC’s to address this shortfall. A machine-based learning algorithm is developed that uses the analysis of competing hypotheses heuristic to convert data from multiple news feeds into numerical risk scores and visual maps of supply chain risk. This paper demonstrates the approach by converting large amounts of unstructured data into two measures, risk impact and risk probability, leading to visualization of country-level supply base risks for a global apparel company.

This paper produced probability and impact scores for 23 distinct supply base risks across 10 countries in the apparel sector. The results suggest that the most significant long-term risks of supply disruption for apparel in LCC’s are human resource regulatory risks, workplace issues, inflation costs, safety violations and social welfare violations. The results suggest that apparel brands seeking suppliers in the regions of Cambodia, India, Bangladesh, Brazil and Vietnam should be aware of the significant risks in these regions that may require mitigative action.

This approach establishes a novel approach for objectively projecting future global sourcing risk, and yields visually mapped outcomes that can be applied in forecasting and planning for future risks when considering sourcing locations in LCC’s.