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All supply chain around the world are established with an aim of reducing customer order cycle time, drive customer value and facilitate financial success. This also results in implementing effective service recovery strategies to achieve these goals. This paper identifies and analyses various factors which influences and contributes to service recovery process by exploring the existing literature on service recovery. Based on these factors, decision-making trial and evaluation laboratory (DEMATEL) method is illustrated to precisely measure causal relations between all factors and define the processes influencing these factors. The results of this study state that proactive recovery capability, communication from suppliers in form of early warnings, focus on service outcome failures and moment of truth are the key enabling factors for service recovery, while customer commitment level, original cost and placing inventory close to customers are the most direct influencing factors.

The purpose of this paper is to examine the existing service quality measurement scales in the literature on automobile after-sales service. It seeks to develop and validate a service quality measurement scale for Indian passenger car industry's after-sales segment, thereby enhancing service quality (SQ) and customer satisfaction. The study's research design consists of a combination of literature review, exploratory interview and a questionnaire survey conducted through interviews. This paper develops a pilot SQ measurement scale to assess SQ in the Indian passenger car industry's after-sales segment. Apart from the five SERVQUAL dimensions in the extant literature, two new dimensions of service failure and service recovery have been identified and included in the scale developed to address the specific features of the Indian automobile industry. The scale developed in this research will provide a framework towards the development of a measurement scale for after sales services in auto industry.

This research work puts forward the inventory optimisation model for the high technology multi-generation products under the situation of limited warehouse storage space. It is assumed that the manufacturer has its own space which has a lesser opportunity cost of usage as compared to another rented space. Therefore, the manufacturer utilises the own storage space for the period of time where his own space is sufficient to keep the inventories. While the research work has been done earlier on various demand patterns under such a scenario, there is no research present under storage space constraints for the generations of innovative products whose demand follows the Norton Bass Model of Innovation Diffusion. This paper lays down such a model for inventory optimisation, but also puts forward a few theorems on the dynamics of inventory decisions, and also performs numerical illustrations of the proposed model.

ABSTRACTOrganisations’ vulnerability to risks exponentially increased in the past decade, thereby highlighting the need to develop additional effective risk management strategies. This research uses a systematic literature review as a foundation for designing a supply risk model that uses a Bayesian belief network. The proposed model aims to identify the most critical objective and subjective risk factors influencing supply chain networks. Moreover, the proposed methodology has been demonstrated through a case study conducted in an Indian manufacturing, in which inputs were taken from supply chain and risk management experts. Hugin Expert software was used to design and run simultaneous simulations on the Bayesian network. The top three factors found to influence business profitability were delays, product technology, and fuel price. The proposed model can be reengineered as conditions change and new information becomes available, thereby ensuring that risk analysis remains current and relevant along the timeline of the any disruption.

In this paper, a new Economic Order Quantity (EOQ) model for a successive generation of technology products has been discussed. The classical EOQ model is based on the assumption that the demand rate is constant. Hence it cannot be used for technology products where competition-substitution among products is a usual phenomenon. To address this problem, the EOQ model proposed in this article is considered a demand model for a technology product that follows the innovation-diffusion process. A numerical example has been illustrated and a comprehensive sensitivity analysis is conducted to understand the path of the optimal planning horizon and optimal costs under varied innovation and imitation effect. The sensitivity analysis of the introduction timing of the second generation has been performed to know the applicability of the model in actual circumstances. The behavior of the model has been discussed in detail in the numerical illustration section.

In this paper, the multi-period EOQ model is developed for the technology products that have multiple generations co-existing in the market, with each of them having a very short product life cycle. The paper first develops the framework for computation of inventory-related costs and then minimizes the total replenishment costs using random search technique and approximating the non-linear expressions while using Simpson’s Rule for integration. The paper also provides numerical illustrations and establishes a few important theorems that relate the EOQ to the innovation of diffusions. It is found that the total replenishment cost curve, drawn on the EOQ axis in the case of technology generations is convex to the origin. Since the objective function is highly non-linear, the genetic algorithm has been used to find the solution to the problem. The study also suggests that the faster diffusion of the next generations has a conflicting effect on the EOQ of the first generation in the case of pooled and non-pooled logistics.

In this age of digitalization, when every industry is undergoing technological disruption, there is a big role of digital gadgets and technology products. A key feature of these digital gadgets is the short length of the product life cycle, since the newer and more advanced generations of technologies are developed regularly to replace the earlier conventional technologies. The traditional EOQ models that assume a constant demand cannot be used here. This research paper formulates an inventory optimization model for the multi-generational products under the trade credits and the credit-linked and innovation diffusion dependent demand. The study also performs a numerical illustration of the proposed model, and establishes important dynamics among the key variables. It also performs the sensitivity analysis with the cost of credit and the trade credit period. The paper concludes with the managerial implications for the inventory practitioners and the possible areas of extension for this research in the future.

In this article, we discussed optimal replenishment policies for two succeeding generations' technology products under partial trade credit financing. It is often seen that in technology market, advanced generation product plays an important role in cannibalising the market of existing generation product. Thus, precise estimation of demand of technology generations' product is critical for taking any policy decisions. Demand estimation of technology products is a complex process, as the consumer buying behaviour of technology generational products is not only depends on marketing mix variables but also associated with the time-to-market phenomenon of new technologies. In technology market, interaction among users of different generational products controls the rate of substitution of older technology products with the new one. Therefore, due to the substitution nature of demand of multi-generation product, it is important to incorporate the interaction-substitution effect in replenishment policies for this kind of products. We used life cycle dynamics to project demand rates of technology generations. In this paper, we formulate the total cost function for five different situations depending upon the new generation introduction timing and length of the trade credit period. A detail sensitivity analysis is been performed to explore the efficacy of the model in a given situation.

In recent years several studies have been made to understand the impact of Socially Responsible HRM practices on Organizational Performance. Employee progress, community and environment play an important role in the sustainable growth of an organization. Thus, organizations are always looking for the ways to improve the employee satisfaction vis-a-vis commitment to improve the performance. Recent studies have shown that as employees are important stakeholder, hence formulating proper Socially Responsible HRM practices may help organization to better the returns on assets. The main objective of the study is to identify the relationship among various dimensions of Socially Responsible HRM practices with dimensions of employee satisfaction, employee commitment and organizational performance for Indian manufacturing sector by using Bayesian Network approach. Results of the study establish the relationship between dimensions of Socially Responsible HRM and Organizational Performance.

The objective of this review article is to study the published research on the inventory modeling and optimization for substitutable technology products with a short product life cycle. This review explains the demand dynamics of technology generation products and how to administer supply chain management of this kind of products than any other functional products. The article does a review of the demand models proposed on the diffusion of innovation products, and then, moves on to the literature review of the multi-item inventory models, followed by the literature review of the inventory modeling of substitutable items. In order to ensure the quality, this study covers the review of the research papers mostly published in reputed SCI or ABDC rated journals. The study discovers that the research that has been done on the inventory optimization of multi generation technology products is not only rare but also very restrictive in its scope and assumptions. The study then proposes the directions for the future research.

Traditional inventory models are mostly ignorant of the life cycle dynamics of a technology product; hence, they often fail to identify different dimensions of inventory research. This paper attempts to investigate the relationship between adoption behavior of customers using life cycle dynamics and associated trade credit policies in order to optimize the total inventory cost. The demand model used in this paper treats sales as a function of awareness diffusion and adoption. Awareness is considered as a function of feedback effects from users/customers. Retailer’s optimal strategies for short life cycle product under credit financing were determined analytically. Finally, numerical examples have been used to support the theoretical results. Theoretical results have further been used to gain some managerial insights.

AbstractAccurate prediction and knowledge of adoption pattern is critical to formulate inventory strategies for any technology product. Prior researches in this area recommended that aggregate level demand models often forecast imprecise sales figure. We argue that it is essential to recognize the technology specific adoption dynamics prior to formulate the inventory strategies. This paper aims to develop an Economic Order Quantity (EOQ) model to find strategy for a firm that sells technology products’ over a defined planning horizon. Demand is considered to follow trial-repeat purchase phenomenon. The fuzzy criterion is incorporated to address the problems of uncertain nature of marketing parameters. The model is illustrated with a numerical example and a comprehensive sensitivity analysis on the optimal solution with respect to different parameters has also been performed.

One of the major concerns for the technology market is the demand volatility and its impact on inventory policies. Demand volatility in the technology sector may arise due to many factors namely customer choices, competition, growing market size, and so on. Often companies use rented warehouses to absorb any fluctuations in demand. Unfortunately, warehouse and inventory researches ignore the phenomenon of growing market size to formulate policy decisions. In this paper, we proposed a two-warehouse inventory model with deterioration for technology products with linearly increasing market size where demand follows innovation diffusion criterion. The model is based on the assumption that the holding costs in the rented warehouse are more than the own warehouse. A simple solution procedure also discussed to solve nonlinear cost function. Numerical example and sensitivity analysis are also used to describe the utility of the model.

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.

ABSTRACTMarketing strategies such as advertising and pricing can play an important role in the acceptance of technology products by consumers. This phenomenon indicates that diffusion of technology products in the society may have strong linkages with unit selling price and advertisement effort. Therefore, management should sincerely reflect on pricing and advertising strategy during formulation of the inventory policies. This paper aims to develop an economic order quantity model for finding strategy for a firm that sells technology products' over a finite planning horizon. Demand is considered to follow a lifecycle phenomenon with dynamic ceiling on the potential adoptions and sensitive to advertising expenditure and unit selling price. The fuzzy criterion has also been incorporated to address the problems of uncertain nature of marketing parameters. The model is illustrated with a numerical example and a comprehensive sensitivity analysis of the optimal solution with respect to different parameters has also been performed.

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 firm's 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.

Often, sales curve of a technology product exhibit a small peak and then decline, before continuing with the traditional bell shaped curve. Under this situation sales curve of the technology products show bimodal pattern. Traditional EOQ models have ignored the bimodal pattern of demand phenomenon during development of the policy frameworks. The approach in this paper is to study the effect of bimodal demand function, on economic order quantity model. Based on hazard rate demand, an integrated EOQ model is discussed in the paper for permissible delay in payments, under the assumption that supplier may offer credit periods to the retailer. The proposed framework is demonstrated with a numerical example and a comprehensive sensitivity analysis is also performed to validate effectiveness of the model.

Economic ordering policies for a retailer are often strategies subject to the nature of demand, and the kind of benefits received from the suppliers. Globalisation and continuum innovations of new products/technologies put enormous pressure on retailers to attract new customers and look after the current ones concurrently. Thus, a realistic economic order quantity (EOQ) model should link the present business requirements, which can enable the retailer to achieve its business objectives. Based on hazard rate demand, an integrated EOQ model is discussed in the paper for permissible delay in payments, under the assumption that supplier may offer credit periods to the retailer. The demand rate is assumed to be governed by the hazard rate function under dynamic pricing and advertising. The proposed framework is demonstrated with a numerical example and a comprehensive sensitivity analysis is also performed to validate effectiveness of the model.

Diffusion pattern of new products have been well-documented in literature but research on integration of growth model and inventory policies are still scarce. Globalisation and technological breakthroughs' are creating significant risks of obsolescence at the product level. This calls for integration of diffusion dynamics in formulation of economic ordering policies for technology products. Unfortunately, most of the diffusion models available in literature do not recognise uncertainty in diffusion parameters, making it difficult to use in inventory-models. Here, we propose an economic order quantity model using fuzzy logic for new product where demand rate follows innovation diffusion process. Effect of deterioration is incorporated by considering the salvage value in the cost component. The comprehensive sensitivity analysis with respect to different parameters has also been performed to illustrate the effectiveness and behaviour of the model.

India's 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.

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.

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.

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.

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.

Business organisations introduce new products in the market to beat competition and increase profit. For high-technology products, continuous innovations promise better performance, feature enhancements and quality improvement. Often, consecutive technology generations compete in the market, which calls for synergistic decision-making on marketing variables. At around the time of its introduction, the time path of prices for two competing technologies can show interesting patterns vis-a-vis their sales. It is important to understand the influence of marketing variables on consumer psychology to predict the adoption process of new technology. This paper focuses on studying the relative changes in diffusion parameters and marketing decision variables through sales models developed for multiple-generation products.

Modelling the new product sales growth and forecasting 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 such as price, promotional effort, quality, time, etc. In comparison, high technology products have received less attention. These products usually have many generations, each succeeding generation being an improvement over the previous one. This paper discusses some well-known models in this area and also proposes a new one. The new 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. This model has been validated on sales-data and has also been compared to an established model.

In today’s competitive environment, the Indian manufacturing sector is feeling the need to develop new strategies to increase employee performance by improving the job satisfaction and reducing stress. Improving employee performance is one of the important drivers of the organization’s growth. In recent years, organizations are also investing a great deal on human capital to capitalize on employee productivity. The objective of this study is to identify and assess the association among different individual level factors and their impact on the organization performance in the Indian Manufacturing companies. Partial least square (PLS) approach was used to examine the relationships among various factors that lead to employee commitment and organizational performance. Results of this study can provide an important reference for both academicians and practitioners for effectively improving employee satisfaction, employee commitment to increase the economic performance of the organization.