Nitin Sachdeva

An innovation diffusion model for consumer durables with three parameters

Strategic innovation diffusion converts newly created knowledge into increasing a firm’s value primarily through innovative product offerings. In this paper, we present a time-based adoption pattern with pricing and promotional expenditure as a three-dimensional innovation diffusion model (3D-IDM). In our proposed 3D-IDM, we assume that value of the product plays a crucial role of being the major driver of diffusion, and is classified into the following three main factors: (1) continuation time of the product in the market – representing goodwill of the product; (2) price of the product – indicating consumers’ buying behaviour; and (3) marketing efforts of the firm. A special form of the Cobb–Douglas production function is used to design the three-dimensional framework. An empirical study is performed on number of consumer-durable sales data to validate and compare the proposed model. Various performance measures are treated uniquely using the Mahalanobis distance-based approach (DBA) to determine the rel...

When to stop testing multi upgradations of software based on cost criteria

Software testing is an important phase of the software development life cycle to achieve highly reliable software. Due to the time and resource limitation during the testing phase, firms do not attempt to deliver a complete and perfect product in one development cycle. They plan multi upgradations of software by adding new functionalities. Many models have been developed in the past which discuss about when to stop testing and when to release the software to the users. But they have been limited to the study of single version only. In the present framework, we describe a unified approach to address an important issue of when to stop testing the multi-upgradation of software, which is a complex process. The total debugging cost for each upgradation includes the cost of debugging in the warranty period along with the testing cost. It is assumed that the software is supported till the warranty period is over. In the proposed cost model for each upgradation, we consider that some of the remaining faults of pr...

Innovation diffusion models incorporating time lag between innovators and imitators adoption

Since time immemorial, managers have been interested in the diffusion of innovations. The philosophy being, if one can better understand how innovations diffuse, one can better predict and manage that diffusion. Innovation diffusion has become highly multifaceted ever since 1969, when the famous Bass model was proposed. Although innovation diffusion modeling has been an area of widespread research in the last 50 years or so, which has resulted in a body of literature, however we consider unfolding and incorporating market trends and letting certain assumptions of existing models to relax in order to better fit into the current global scenario is the need of the hour. In this paper, an attempt is made to relax one of the assumptions of the Bass model which states that both the innovators and imitators contribute to the initial purchases of the product with the only difference of their buying influence. In a more realistic scenario, we consider that there exists a distinction in the two categories based on their timings of adoption. Though, imitators would adopt throughout the diffusion process but initial purchases of the product would be majorly through innovators. Then only the word-of-mouth effect would spread in masses which results in product adoption by imitators. Based on this assumption of time delay between the adoption of product by an innovator and then by an imitator, we propose time-delay effect based innovation diffusion models. Sometimes mutually dependent imitators would adopt a product if and only if the innovators have already adopted the product. Therefore, into the innovation diffusion modeling framework, we incorporate a concept of innovators dependent imitation process. New models are proposed and they are tested against real life data sets. Investigational results demonstrate precise forecasting ability for the proposed framework.

Multi-criteria intuitionistic fuzzy group decision analysis with TOPSIS method for selecting appropriate cloud solution to manage big data projects

Today technology that learns from data to forecast future behavior of individuals, organizations, government and country as a whole, is playing a crucial role in the advancement of human race. In fact, the strategic advantage most of the companies today strive for are use of new available technologies like cloud computing and big data. However, today’s dynamic business environment poses severe challenges in front of companies as to how to make use of the power of big data with the technical flexibility that cloud computing provides? Therefore, evaluating, ranking and selecting the most appropriate cloud solution to manage big data project is a complex concern which required multi criteria decision environment. In this paper we propose a hybrid TOPSIS method combined with intuitionistic fuzzy set to select appropriate cloud solution to manage big data projects in group decision making environment. In order to collate individual opinions of decision makers for rating the importance of various criteria and alternatives, we employed intuitionistic fuzzy weighted averaging operator. Lastly sensitivity analysis is performed so as to evaluate the impact of criteria weights on final ranking of alternatives.

Measuring software testing efficiency using two-way assessment technique

Software has become a driving force. Its impact on our society continues to be profound. Customers have come to rely on the functionality and accuracy of software. A bug free software is an illusion and infinite testing is simply not a viable option.. But with scientific software development technology, quality control and systematic testing, number of bugs could be minimized. Software testing is an important activity to improve software quality. However, it is well known that it is costly (Yang et al, 2008; Bertolino, 2008). Thus, there has always been a need to increase the efficiency of testing while, in parallel, making it more effective in terms of finding & removing defects. Measuring critical attributes of software testing process enables software developers with further insight in to software testing process. Thereafter, optimal utilization of their resources for testing purpose. In this paper, we propose a two way assessment framework for software developers to find out overall utility from the testing process that can serve as an important indicator of the efficiency and effectiveness of software testing process. It illustrates a two way assessment technique, which can be adopted to inculcate a culture of continuous improvement in the overall software testing lifecycle. The framework takes into consideration 1). Managements/Developers perspective to highlight the importance of various attributes of testing; & 2). Testers/Team Members perspective, to assess functioning and working of each of the attribute.

Optimal profit for manufacturers in product remanufacturing diffusion dynamics

Abstract In remanufacturing, companies take the product back from customers when the product reaches its end-of-life, so as to use as a feedstock for manufacturing of new products. Thereby, it reduces the risk of environmental degradation to be caused by these end-of-life products. The real efficiency in remanufacturing lies in the cost-effective use of reused components. In this paper, we formulate an optimization model based on famous Bass diffusion model to depict not only the quantity returned in the remanufacturing process, but also the amount of cost-saving a manufacturer does with efficient remanufacturing. The proposed approach can enable product designers and remanufacturers to quantify the total component reuse volume by considering the quantity returned, diffusion dynamics with coefficient of innovation and imitation and time to begin remanufacturing. Analytical reasoning & numerical illustration to validate the proposed optimization model and explanation of remanufacturing diffusion dynamics are provided.

Utility based Tool to Assess Overall Effectiveness of HRD Instruments

Humans practically measure everything in life. For any business, they measure revenue, sales, profits, earning per share, stake holders value and so forth. In HR management they measure staff attrition, cost of training per candidate, rating awarded in performance management system etc. Measurement of Effectiveness of HRD instruments namely training and development, compensation, feedback and coaching, job design, career planning etc. are required for any organization, regardless of size, industry, location or success. In this paper an attempt has been made to analyze prominent HR instruments to determine their individual measure of effectiveness and the Total Expected Utility Measure based on Multiple-Attribute Utility analysis, from both the employees’ and employers’ view point. To assign relative importance to each HRD instrument, Analytical Hierarchical Process has been used. The methodology adopted was questionnaire, to a random sample of teaching faculty in a large private university in north India. The findings indicate that the total expected utility measure is an important measure and facilitates comparing firms in the same industry, region, size, etc. KEywORDS Analytical Hierarchical Process, HRD Instruments, Measure of Effectiveness, Multi-Attribute Utility Analysis, Total Expected Utility Measure

Selecting appropriate cloud solution for managing big data projects using hybrid AHP-entropy based assessment

Today technology that learns from data to forecast future behavior of individuals, organizations, government and country as a whole, is playing a crucial role in the advancement of human race. In fact, the strategic advantage most of the companies today strive for are use of new available technologies like cloud computing and big data. However, todays dynamic business environment poses severe challenges in front of companies as to how to make use of the power of big data with the technical flexibility that cloud computing provides? Therefore, evaluating, ranking and selecting the most appropriate cloud solution to manage big data project is a complex concern which requires multi criteria decision environment. In this paper we propose a hybrid entropy method combined with Analytical Hierarchical Process (AHP) to select appropriate cloud solution to manage big data projects in group decision making environment. In order to collate individual opinions of decision makers for rating the importance of various criteria and alternatives, we employed usability analysis using the proposed hybrid AHP-Entropy method.

Two-Dimensional Framework to Optimize Release Time and Warranty

Today’s customer expectations to have well-developed complex enterprise level software, delivered within no time, have left developers with a dilemma as for how to attain the desired level of software reliability with shortened testing time? In this paper, we examine a two-dimensional testing time and effort-based model using Cobb-Douglas production function, for separate release and testing stop time strategy for software in order to optimize overall testing and market opportunity cost. Here we propose a generalized framework for software developers to achieve multiple objectives of minimizing overall testing and market opportunity cost, optimizing warranty length, and optimizing release and testing stop time. We make use of software reliability growth models (SRGMs) to model an average number of bugs to be detected by testers (users) during the pre-/(post)release of the software. Numerical illustration based on real-life Tandem Computers data set by considering exponential distribution along with the sensitivity of important parameters is provided to validate the proposed two-dimensional cost modeling framework.

When to Start Remanufacturing Using Adopter Categorization

Remanufacturing of goods involves taking the product back from the customer so as to use it as a feedstock for manufacturing new products. It provides double benefits of cost and environmental saving to the manufacturer and society, respectively. One of the major challenges faced by original equipment manufacturer (OEM) here is forecasting quantity returned by the customers and when to start remanufacturing. In this paper, we formulate a strategy for OEMs to optimize time to start remanufacturing using the adopter categorization approach as proposed by Roger. We propose an optimization model based on famous Bass diffusion model to depict not only the quantity returned in the remanufacturing process but also the amount of cost saving a manufacturer does with efficient remanufacturing. With data on 11 consumer durable products, we compare product returned under various adopter categorization time point. An application examining the diffusion of these products under remanufacturing scenario is documented to illustrate the usefulness of the proposed strategy.