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The companies incubated in business incubators are expected to grow availing the services provided by the incubators. When the services provided are matching with the requirements of these companies, the incubation process become effective and the companies will be satisfied. It is seen that certain services are more valued and certain others are not, by the companies which are the clients of these incubators. The study conducted brings out the perception of the companies incubated in the Indian university-based, government funded incubators, about the services offered and the effectiveness of the services. It throws light to the fact that providing some services more effectively can give more satisfied clients, and thus giving suggestions on how the resources available, especially those created with the government support, with the incubator could be used optimally.

The study explores the factors that could influence the performance of incubators hosted by Indian universities. Sustainability of these TBIs depends on the determiners of these/performance. The study tries to identify if there exist any differentiator among the TBIs like the sector, public or private TBIs that perform better. The methodology adopted is primary data collection using a questionnaire designed for this purpose. Complete enumeration method is adopted for data collection. The questionnaire was circulated to the incubator heads of 67 incubators and 30 responses were obtained. It was found that number of years of operation and government grant are two significant factors that contribute to the successful outcome of incubators. No significant difference in the incubator performance was found between private and public universities to which the incubator is attached. However, sector specificity is proven to be significant in the success or otherwise of an incubator.

Lean manufacturing (LM) has become a popular manufacturing technology and it has been successfully executed by industries all over the world. It is reported that LM has a high influence on the revenues of many industries. Several studies have been conducted on the implementation of lean manufacturing. However, there is a limited understanding existing in terms of its role to develop employee involvement, management commitment and organisational performance. Hence, the present study addresses this gap in tyre manufacturing industry by conducting a questionnaire-based survey. The data analysis was conducted using exploratory factor analysis, confirmatory factor analysis and structural equation modelling. The analysis of results show that adoption of lean manufacturing helped to develop employee involvement and management commitment and in turn better organisational performance. This unique understanding of the present study will help the manufacturing companies on the proper implementation of LM and achieve better organisational outcomes.

The study is an attempt to develop a unique composite dynamic capability index to assess the capability of the new product development (NPD) unit and benchmark its performance with respect to competition. Combining the concepts of dynamic capability, quality management capability and data envelopment analysis (DEA), a unique composite dynamic capability index is proposed. New product development (NPD) units in the electronics industry are specifically chosen for the study due to its environmental dynamism where dynamic capabilities play a significant role in maintaining competitiveness. The proposed index is validated through an empirical study among 50 NPD units in the electronics manufacturing sector. The index is able to explain 71.1% of the variance in competitive performance, 62.8% variance in customer satisfaction and 46.8% variance in development time - three of the four NPD performance parameters chosen for analysis. The application of DEA suggests specific targets for improving the efficiency of the NPD process.

We consider a production inventory problem of a deteriorating product that begins to deteriorate immediately on production. Two models of three echelon supply chain are developed whose rate of demand, production and deterioration are assumed to be constant. The second model allows backlogging of the demand, while the first model does not allow backlogging. In both models, the producer's cycle starts with zero stock and the production continues till the stock level reaches an optimal value. There will be demand and deterioration and consequently stock level falls to zero during the non-production phase. The demand subsequently received is backordered in the second model and a fraction of it is met by restarting the production process. Optimal lot sizes of distributor and retailer is identified within each of the producer's cycle. A numerical example is illustrated. Comparison between the models shows that backordering reduces the total supply chain cost.

Typically, the primary goal of lean technology (LT) is to improve profits and create value by minimising waste. The consideration of demand variability in a multi-dimensional lean manufacturing environment is an innovation in production system engineering. However, manufacturing systems that fail to recognise demand variability generate high work-in-process (WIP) and low throughput in the lean manufacturing process. Moreover, many companies have found it difficult to successfully implement and sustain lean manufacturing. It is, therefore, very important for companies to identify and understand the critical success factors for successfully implementing lean manufacturing. To overwhelm the challenges in the implementation of lean manufacturing, this paper plan to introduce an advanced model and this model is analysed using structural equation modelling.

This paper provides an analysis of the performance of two simplified approaches for forecasting the aggregate demand for a product family consisting of two interrelated items. The correlated demand characteristic of the product family is modelled by bivariate vector IMA(1, 1) process. The forecasting approaches chosen for evaluation are univariate scheme and aggregate scheme. The performances of these methods are analysed by theoretically determining the forecast mean square error (MSE) in terms of observable data characteristics. Further, subsequent evaluation of percentage changes of these MSEs with respect to the MSE of original bivariate model has been conducted to determine how far the forecast MSE values deviate from the original model. Through numerical experimentation, the necessary and sufficient conditions in terms of observable data characteristics are determined for the two approaches to be equal in terms of forecast MSE of the original bivariate model.

The integrated procurement, production and distribution planning (IPPDP) problem consists of multiple suppliers who supply raw materials to multiple plants to produce a single product to meet the demand in multiple time horizons. The present research focuses on the analysis of the effect of backorders on the IPPDP problem in a three-echelon supply chain that consists of suppliers, manufacturers and retailers. The IPPDP problem is formulated as a mixed integer linear programming (MILP) model and the optimal solution is obtained with the solver LINGO. The total net profit obtained for the scenario involving lost sales is compared with that for the scenario which involves lost sales and backordering. For a set of problem instances, an increase of 10.29% in the average total net profit and 4.80% in the average fill rate is obtained for the lost sales and backordering scenario compared to the lost-sales-only scenario.

Cost minimisation is one of the key challenges faced by logistic industries across the world. As the number of customers increases, it becomes absolutely essential to make maximum utilisation of the existing resources to ensure quality customer service. The huge amount of data available with the firm under study is analysed using data envelopment analysis (DEA) and turned into valuable insights that could be used by management in taking right informed decision. The results show that about half of the transporters have efficiency value less than the average, indicating a huge scope for improvement. Related suggestions for efficiency improvement using DEA slack results are also presented. The decision making units (DMU) that are considered are the individual trucks. The findings of the study provide useful insights to the management in decision making at all the three levels, viz. operational, tactical and strategic.

This paper presents the salient aspects of the performance analysis of two mechanisms for shop floor control namely, order review/release (ORR) and dispatching rules in an assembly job shop system in a multi-objective environment. A simulation model of an assembly job shop is developed for experimentation with eight dispatching rules and four ORR mechanisms from the literature. An ORR policy based on the concept of backward scheduling and interval release is developed and incorporated in the simulation model. Grey relational analysis is used for ranking the ORR policy-dispatching rule combinations. Multivariate and univariate hypotheses are developed. The results are analysed using ANOVA and MANOVA. The analysis of results reveals that integrating dispatching rules with ORR policies provides better system performance than using dispatching rules alone. The proposed ORR policy namely, backward scheduling with interval release policy provides better results than other ORR polices.

To retain a customer in a logistics industry is difficult as it highly depends on the customer relationship management (CRM) and the quality of the service delivered. The existing customers are to be retained for longer period of time for maximum profit contribution. Hence, it is important to perform a data analysis on the CRM data to identify the right segment of customers who are to be retained to maximise profit. This study segments the customers depending on the parameters recency, frequency, and the monetary value, known as RFM analysis. Customer lifetime value estimation is done on segments obtained using the RFM Analysis to identify the profit contribution of each of the segments. The analysis carried out aids the management to adopt decisions on marketing strategies based on the RFM scores and CLV of each segment. Company can also take decisions on budget spending in retaining the right customers.

An integrated production distribution problem in an environment of only order refusal and backorder and order refusal is studied in this work. Plant side capacity and storage restrictions are also considered in this analysis. The effect of this consideration on the supply chain profit is investigated. The model considers fixed cost, setup cost, and core demand. The complexity of the integrated production and distribution planning problems are empirically studied using this model. It is identified that the increase in the number of products increases the complexity than the increase in the number of retailers. Further a sensitivity analysis is carried out to assess the capability of the model.

This paper addresses the scheduling problem in assembly job shop systems with machine breakdowns. The present study intends to rank the dispatching rules that are applied in an assembly job shop production system. A simulation model of an assembly job shop is developed for the purpose of this ranking and eight dispatching rules from the literature are incorporated in the simulation model. The product structures considered in this study are single-level assembly structure, two-level assembly structures and three level assembly structures. The machines are subjected to non-availability due to of breakdowns. Five performance measures are considered for analysis. The performance of each dispatching rule for each performance measure is calculated individually. To identify which of the dispatching rule provides the optimum result when all the performance measures are equally important, grey relational analysis is adopted to rank the dispatching rules. The results indicate that earliest completion time rule performs better in comparison with the other rules investigated in this study for single-level structure with multiple objectives.

The operational efficiency of the food and beverages industry is determined by identifying the technical and scale efficiencies by applying data envelopment analysis (DEA) models. A set of 46 companies with average data collected for the year 2005-2006 to 2009-2010 is used for the study. The analysis begins with a benchmark study, followed by identification of peers and their followers. Nine firms emerge as peers. The slacks of the inefficient firms are identified. Regression analysis is used to estimate the causal relationship between operational efficiency and the other chosen variables. Analyses of peer firms are conducted to identify the influencing variables of the peer firms. The first stage DEA analysis reveals that the inefficiencies such as scale and technical inefficiencies exist in the Indian food and beverages industry and a majority of inefficient firms are operating in the increasing returns to scale region. The second stage analysis using the OLS regression to identify the determinants of these inefficiencies reveals that current ratio and financial assets to total assets are contributing significantly to the inefficiencies.

Most self-help groups (SHGs) in India are social enterprises owned and operated by poor women. They have helped to create women entrepreneurs in the rural areas of the country. Though these SHGs are producing a large number of products with superior quality, they fail to sustain in the market. To address this problem of sustainability and marketability of SHG in Kerala, India, an attempt has been made to develop a sustainable marketing model of SHG using analytical hierarchical process (AHP). The objective of the study is to enable a SHG find out the most appropriate mode of finance, branding and positioning strategy, promotional strategy and appropriate production aspects of such products. Based on the feedback obtained on the critical areas and the practice of successful self-help groups in Kerala, this paper develops a marketing model for SHGs in Kerala.

Assembly job shop scheduling problem (AJSSP) is an important NP-hard scheduling problem very difficult to solve because of its complex precedence relationship between jobs and product structures. In the present work, a systematic multi-attribute decision-making methodology namely, preference selection index method is adopted for ranking priority dispatching rules for scheduling an assembly job shop. A simulation model of an assembly job shop with seven work stations and one assembly station is developed for the purpose of experimentation. Eight priority dispatching rules from literature are identified and incorporated in the simulation model. Performance measures considered for analysis include mean flow time, maximum flow time, mean tardiness, maximum tardiness and machine utilisation. Products with single level assembly structure, two level assembly structure and three level assembly structure are examined to demonstrate and to check the applicability of the proposed methodology. The best performing priority dispatching rules are determined in a multi-objective environment.

Corporations worldwide adopted stringent safety and risk management standards after the Bhopal Union Carbide plant disaster that killed more than 2,500 people in India in 1984. Potential product and substance dangers are now clearly identified and public information on hazards is widely published. People in developing countries where plants are located are educated on safety practices and identification and elimination of potential hazards. The Indian government has instituted safety standards for all hazardous industries and has enacted hazardous waste prevention laws., On the morning of December 3, 1984, the world awoke to a disaster in Bhopal, India, a city of just under one million people, where more than 2,500 people died. [...]

This work deals with the significant aspects of a simulation based experimental study of two shop floor control polices: order review/release (ORR) and dispatching rules for scheduling an assembly job shop in which multi-objective criteria is considered. A simulation of model of an assembly job shop which consists of one assembly work station with two machines and seven machining work stations with two machines in each work station is developed. Six dispatching rules and four ORR mechanisms identified from the literature are incorporated in the simulation model. Grey relational analysis is used for ranking the dispatching rule-ORR combinations. The performance measures considered in this study are mean flow time, mean tardiness and machine utilization. Simulation experiments have been conducted in an environment with products consisting of single level assembly structure, two level assembly structure and three level assembly job structures. The results indicate that Job Due Date rule with Interval Release policy performs better in comparison with the other dispatching rule- ORR combinations investigated in this study.

Flow shop scheduling problem (FSSP) is a combinatorial optimization problem. This work, with the objective of optimizing the makespan of an FSSP uses a particle swarm optimization (PSO) approach. The problems are tested on Taillard’s benchmark problems. The results of Nawaz, Encore and Ham (NEH) heuristic are initialized to the PSO to direct the search into a quality space. Variable neighbourhood search (VNS) is employed to overcome the early convergence of the PSO and helps in global search. The results are compared with standalone PSO, traditional heuristics and the Taillard’s upper bounds. Five problem set are taken from Taillard’s benchmark problems and are solved for various problem sizes. Thus a total of 35 problems are solved. The experimental results show that the solution quality of FSSP can be improved if the search is directed in a quality space based on the proposed PSO approach (PSO-NEH-VNS).