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Sir Clive Sinclair – an electronics genius – has developed a number of completely innovative products and made remarkable breakthroughs in technology. He has had great success, but also some disasters, the most notable being the C5 electric vehicle. The reasons for this failure are examined mainly from a marketing perspective. Sinclair ignored the basic principles of marketing and new product development, and thereby made a number of fundamental errors. The market was badly defined, there was a lack of both quantitative and qualitative market research, the safety and performance aspects of the vehicle failed to meet the manufacturer′s claims, the advertising messages were confusing, the product and price unsatisfactory and the distribution methods chosen were inappropriate to such a new product concept – in general, marketing principles were totally ignored. Suggestions as to how the C5 failure could have been avoided are presented.

The purpose of the food traceability is used to retain the good quality of raw material supply, diminish the loss and reduced system complexity.

The proposed hybrid algorithm is for food traceability to make accurate predictions and enhanced period data. The operation of the internet of things is addressed to track and trace the food quality to check the data acquired from manufacturers and consumers.

In order to survive with the existing financial circumstances and the development of global food supply chain, the authors propose efficient food traceability techniques using the internet of things and obtain a solution for data prediction.

The operation of the internet of things is addressed to track and trace the food quality to check the data acquired from manufacturers and consumers. The experimental analysis depicts that proposed algorithm has high accuracy rate, less execution time and error rate.

This paper elicits information, an excess of information and the stakeholders’ role in the use of information. The purpose of this paper is to create a “C5 model” (clients, connectivity, content, copyright and cost) for the consortium management to support and improve its academic and administrative decision-making skills and optimize returns.

The methodology used for this study was “strength, weakness, opportunity and threat” (SWOT) analysis. As a tool, it was used as one of the strategic analytical techniques to evaluate various aspects of consortium management. The purpose of choosing this method was to derive a set of strategies for comparing the internal (strengths/weaknesses) and external (opportunities/threats) forces of the “C5 model” of consortium management.

From the SWOT analysis of the “C5 model,” it was observed that four combinations, maxi-maxi (strengths/opportunities (SO), mini-maxi (weaknesses/opportunities (WO), maxi-mini (strengths/threats (ST)) and mini-mini (weaknesses/threats (WT)), were drawn to derive a set of strategies to benefit the consortium management. The SO strategies such as building a client-centric resource management system, centralized gateway, collaborative acquisition model, common copyright communication and clearance center and cost-benefit analysis were built on the basis of the strengths and opportunities identified. Likewise, the WO strategies such as organizational planning, equality in ICT infrastructure facilities, quality content, public disclosure of copyright obligations and effective economic models were drawn to minimize the weaknesses of each component by taking advantage of the identified opportunities. The ST strategies were worked out using strengths of the model to possibly mitigate its threats. Similarly, the WT strategies were created to minimize the weaknesses and avoid the threats as much as possible. However, these strategies would not only help consortium management in the decision-making process, but also optimize returns and improve the intellectual outcome of organizations.

Not many research studies have been developed regarding the SWOT analysis of the “C5 model” for consortium management. This study attempts to benefit a large community of library and information science professionals, consortia and their clients.

Determining the relevant information using financial measures is of great interest for various stakeholders to analyze the performance of the firm. This paper aims at identifying these financial measures (ratios) which critically affect the firm performance. The authors specifically focus on discovering the most prominent ratios using a two-step process. First, the authors use an exploratory factor analysis to identify the underlying dimensions of these ratios, followed by predictive modeling techniques to identify the potential relationship between measures and performance.

The study uses data of 25 financial variables for a sample of 1923 Indian manufacturing firms which exist continuously between 2011 and 2018. For prediction models, four popular decision tree algorithms [Chi-squared automatic interaction detector (CHAID), classification and regression trees (C&RT), C5.0 and quick, unbiased, efficient statistical tree (QUEST)] were investigated, and the information fusion-based sensitivity analyses were performed to identify the relative importance of these input measures.

Results show that C5.0 and CHAID algorithms produced the best predictive results. The fusion sensitivity results find that net profit margin and total assets turnover rate are the most critical factors determining the firm performance in an Indian manufacturing context. These findings may enable managers in their decision-making process and also have vital implications for investors in assessing the performance of the firm.

To the best of the authors’ knowledge, the current paper is the first to address the application of decision tree algorithms to predict the performance of manufacturing firms in an emerging economy such as India, with the latest data. This practical perspective helps the organizations in managing the critical parameters for the firm’s growth.

The purpose of this paper is to discuss and assess the structural characteristics (conceptual utility) of the most popular classification and predictive techniques employed in customer relationship management and customer scoring and to evaluate their classification and predictive precision.

A sample of customers' credit rating and socio‐demographic profiles are employed to evaluate the analytic and classification properties of discriminant analysis, binary logistic regression, artificial neural networks, C5 algorithm, and regression trees employing Chi‐squared Automatic Interaction Detector (CHAID).

With regards to interpretability and the conceptual utility of the parameters generated by the five techniques, logistic regression provides easily interpretable parameters through its logit. The logits can be interpreted in the same way as regression slopes. In addition, the logits can be converted to odds providing a common sense evaluation of the relative importance of each independent variable. Finally, the technique provides robust statistical tests to evaluate the model parameters. Finally, both CHAID and the C5 algorithm provide visual tools (regression tree) and semantic rules (rule set for classification) to facilitate the interpretation of the model parameters. These can be highly desirable properties when the researcher attempts to explain the conceptual and operational foundations of the model.

Most treatments of complex classification procedures have been undertaken idiosyncratically, that is, evaluating only one technique. This paper evaluates and compares the conceptual utility and predictive precision of five different classification techniques on a moderate sample size and provides clear guidelines in technique selection when undertaking customer scoring and classification.

The development of the Sinclair C5 is outlined from its origins to its launch in January 1985. The development is then compared with the “textbook” stages of new product development to highlight differences. Factors that contributed to the failure are examined, encompassing such criticisms as lack of market research, actual development, safety and performance aspects, and so on. Other possible marketing opportunities for the C5 that may have avoided failure for the product are suggested.

This paper aims to identify the interplay of standard Capesize optimal speeds for time charter equivalent (TCE) maximization in the Australia–China iron ore route and the optimal speeds as an operational tool for compliance with the International Maritime Organization (IMO) carbon intensity indicator (CII).

The TCE at different speeds have been calculated for four standard Capesize specifications: (1) standard Capesize with ecoelectronic engine; (2) standard Capesize with non-eco engine (3) standard Capesize vessel with an eco-electronic engine fitted with scrubber and (4) standard Capesize with non-eco engine and no scrubber fitted.

Calculations imply that in a highly inflationary bunker price context, the dollar per ton freight rates equilibrates at levels that may push optimal speeds below the speeds required for minimum CII compliance (C Rating) in the Australia–China trade. The highest deviation of optimal speeds from those required for minimum CII compliance is observed for non-eco standard Capesize vessels without scrubbers. Increased non-eco Capesize deployment would see optimal speeds structurally lower at levels that could offer CII ratings improvements.

While most of the studies have covered the use of speed as a tool to improve efficiency and emissions in the maritime sector, few have been identified in the literature to have examined the interplay between the commercial and operational performance in the dry bulk sector stemming from the freight market equilibrium. The originality of this paper lies in examining the above relation and the resulting optimal speed selection in the Capesize sector against mandatory environmental targets.

The purpose of this paper is to propose a new simplified numerical model, based on a very compact semi-empirical formulation, able to simulate the fluid dynamics behaviors of an electrohydraulic servovalve taking into account several effects due to valve geometry (e.g. flow leakage between spool and sleeve) and operating conditions (e.g. variable supply pressure or water hammer).

The proposed model simulates the valve performance through a simplified representation, deriving from the linearized approach based on pressure and flow gains, but able to evaluate the mutual interaction between boundary conditions, pressure saturation and leak assessment. Its performance was evaluated comparing with other fluid dynamics numerical models (a detailed physics-based high-fidelity one and other simplified models available in the literature).

Although still showing some limitations attributable to its simplified formulation, the proposed model overcomes several deficiencies typical of the most common fluid dynamic models available in the literature, describing the water hammer and the nonlinear dependence of the delivery differential pressure with the spool displacement.

Although still based on a simplified formulation with reduced computational costs, the proposed model introduces a new nonlinear approach that, approximating with suitable precision the pressure-flow fluid dynamic characteristic of a servovalve, overcomes the shortcomings typical of such models.