Udayan Chanda

Optimal allocation of testing effort during testing and debugging phases: a control theoretic approach

Allocation of efforts to a software development project during the testing phase is a multifaceted task for software managers. The challenges become stiffer when the nature of the development process is considered in the dynamic environment. Many software reliability growth models have been proposed in last decade to minimise the total testing-effort expenditures, but mostly under static assumption. The main purpose of this article is to investigate an optimal resource allocation plan to minimise the cost of software during the testing and operational phase under dynamic condition. An elaborate optimisation policy based on the optimal control theory is proposed and numerical examples are illustrated. This article also studies the optimal resource allocation problems for various conditions by examining the behaviour of the model parameters and also suggests policy for the optimal release time of the software. The experimental results greatly help us to identify the contribution of each selected parameter and its weight.

Economic order quantity model with demand influenced by dynamic innovation effect

Inventory control policies for new-product items are highly perceptive to different marketing policies especially for innovation effects at the earlier stage of the product life cycle but unfortunately classical economic order quantity (EOQ) model do not recognises the innovation driven demand model. In this paper, a time dependent innovation driven demand model has been introduced in the basic EOQ model to calculate the different optimal policies. The proposed model acknowledged relationship between the innovation coefficient and the optimal policies. Four hypotheses were framed in this paper based on the numerical exercise that could explain the impact of dynamic pattern of the innovation coefficient on different optimal policies.

Economic order quantity model on inflationary conditions with demand influenced by innovation diffusion criterion

In this paper, an inventory model has been proposed based on the explicit assumptions of interaction of marketing parameters to the optimal inventory replenishment policy. This study applies the discounted cash flow (DCF) approach for the analysis of the replenishment problem over a finite planning horizon. The demand rate is a function of time and is assumed to be driven by innovation diffusion process. In addition, a numerical example is performed justifying the need of incorporating the effect of innovation along with the effect of inflation on the optimal inventory replenishment. Sensitivity analysis is also performed to discuss the effectiveness of the proposed framework.

A Model for First and Substitution Adoption of Successive Generations of a Product

Abstract Modelling the new product sales growth and forecasting the future sales has been an important area of research in Marketing Science. Models have been proposed to map the consumer buying behaviour against explanatory variables like price, promotional effort, quality, time, etc. In comparison high technology products have received less attention. The proposed model uses the relationship between the repeat purchasers and the new purchasers in the overall diffusion of a new technology over multiple generations, by separately identifying the two types of adopters. It also includes the adopters skipping an intermediate generation while buying two different generation products. The proposed model imbibes the features of some well-known model and has been validated on historical data.

Economic order quantity model under fuzzy sense with demand follows Bass’s innovation diffusion process

The economic order quantity (EOQ) model is usually not paid attention to make the model more realistic. The realistic EOQ model can bring a significant change while evaluating the profit and loss of any organisation. In this paper a mathematical model has been developed for obtaining the EOQ in which the demand of the product is assumed to follow an innovative imitative behaviour as proposed by Bass (1969). The theory of innovation-diffusion has been incorporated in this model. To make the model more realistic an attempt has been made to solve the model in light of fuzzy set theory under the trapezoidal membership function. The coefficient of innovation, the coefficient of imitation and the inventory carrying cost is assumed to be fuzzy numbers with trapezoidal membership function. By the median rule of defuzzification, total cost formula has been derived in the fuzzy sense in order to obtain the optimal order quantity. The effectiveness of this model is illustrated with a numerical example and sensitivity analysis of the optimal solution with respect to different parameters of the system is performed.

Innovation diffusion of successive generations of high technology products

As most of the high-technology companies are trying to be more demand driven, technological innovation and diffusion have become important force in markets today. It critically affects the fortunes of consumers, firms, and nations. Despite research across many disciplines, many important areas still remain to be explored fully. Consumer adoption decision for multi-generation innovation is one such important research area in this field. High technology product comes in generations where a new innovation offers a significant improvement in performance or benefits over the previous generation. And, often two successive generations under the same product category compete in the market. Yet technology substitution has received little attention in the diffusion of innovation literature. Research for consumer durables has been dominated by studies of first purchase adoption which do not explicitly consider the presence of an existing technology. Only a handful of papers explicitly deal with the diffusion of technology substitution. In this work, we propose multigenerational diffusion model to study the marketing dynamics of Indian Television Market (both Black & White and Color Television). The model consider repeat-adoption-substitution diffusion framework. Comparisons with different existing multigenerational models are made. The results of this study could be advanced for forecasting new technologies of television product.

Bayesian network on labour dissonance: a social sector development challenge to India

Indias unorganized labour force contributes about one third of the total labour sector. The scenario is even worse in the Indian automotive industry which employs a little over 7% on a permanent basis. Problems get exaggerated due to the outdated labour laws, ironically established to support and protect workers. The disappointing areas in the labour contract act and labour laws have led to unfair wage practices and a hostile work environment, giving way to labour discord. This research paper discusses the key issues of labour dissonance in the Indian automobile industry using a Bayesian network analysis. Real-life case-study examples from the Indian automobile industry were considered to identify the rationale behind labour unrest. Bayesian analysis of a set of 250 responses helped us to understand the associations among key attributes of labour dissatisfaction.

A mathematical model for diffusion of products with multiple generations

Modelling the new product sales growth and forecasting the future over the product life-cycle has been an important area of research in Marketing. Many models on this topic have also been extended for describing the diffusion of products having multiple technological generations. But factors influencing diffusion of such products are very distinctive and therefore the modelling approach needs to be different. This paper proposes a model that improves upon some well-know models to study the sales growth of successive generation of products. The model has been validated on a sales-data and has also been compared with an established model.

Developing a Bayesian belief network model for prediction of R&D project success

The project success is critical to the business performance in the era of fierce competition and globalization. The basis for project success lies in the capabilities of managing risks effectively. Innovation has always been considerably risky; however, managing Research and Development (R&D) project risks has become even more important given today’s tight schedules and limited resources. Risk management has to be an integral part of the development process. The purpose of this research is to develop a model to assess and estimate the risk exposure of an R&D project. A risk quantification model based on the Bayesian belief network is proposed, which is effective in capturing the interaction between various risk factors. The aim of this model is to empower the project managers to predict the failure risk probability of R&D projects.

Optimal duration of advertising campaigns for successive technology generations using innovation diffusion theory

Global market and tough competition compels a firm to continuously conceive new ideas and introduce new technologies in the market. As a result, often more than one generation products compete in the same market; creating an incredible pressure on managers for balanced advertising campaigns for the existing product generations. Advertising of multi-generation product involves selection of appropriate advertising medium, analysing the target market and appropriate utilisation of the available advertising budget. Effective advertising campaign is critical for success of a product in the market. Hence, finding the optimal advertising campaign duration is important as huge chunk of a firms budget is allocated for this purpose. For, successive technology generations, advertising at right time become even more important. This study developed a mathematical model to determine the optimal duration of advertising campaigns for successive generations product based on diffusion of information in a social group. The optimal timing depends on diffusion coefficient, population size, advertising cost per time unit, unit price, etc.