Search results

The purpose of this paper is to offer improvement in routing and collection load decisions for a green logistics system that delivers lunch boxes.

A mathematical model is introduced into the literature for the 130 years old logistics systems whose delivery accuracy is better than the Six Sigma standard without using sophisticated tools. A simulated annealing (SA) approach is then used to find the routing and collection load decisions for the lunch box career.

The findings establish that we can improve the world-class lunch box delivery (LBD) system. The suggested improvement in terms of reduction in distance travel is nearly 6%. This could be a huge relief for thousands of lunch box careers. The uniformity in collection load decisions suggested by the proposed approach can be more effective for the elderly lunch box carriers.

The research provides a mathematical framework to study an important logistics system that is running with a supreme level of service accuracy. Collecting primary data was challenging as there is no scope for recording and maintaining data in the present logistics system. The replicability of the system for some other city in the world is a challenging question to answer.

Better routing and collection load decisions can help many lunch box careers save time and bring homogeneity in workload into the system.

An efficient routing decision can help provide smoother traffic movements, and uniformity in collection load can help avoid unwanted injuries to about 5,000 lunch box careers.

The originality of this paper lies in the proposed mathematical model and finding the routing and collection load decisions using a nature-inspired probabilistic search technique. The LBD system of Mumbai was never studied mathematically. The study is the first of its kind.

Owing to highly volatile and chaotic external events, predicting future movements of cryptocurrencies is a challenging task. This paper advances a granular hybrid predictive modeling framework for predicting the future figures of Bitcoin (BTC), Litecoin (LTC), Ethereum (ETH), Stellar (XLM) and Tether (USDT) during normal and pandemic regimes.

Initially, the major temporal characteristics of the price series are examined. In the second stage, ensemble empirical mode decomposition (EEMD) and maximal overlap discrete wavelet transformation (MODWT) are used to decompose the original time series into two distinct sets of granular subseries. In the third stage, long- and short-term memory network (LSTM) and extreme gradient boosting (XGB) are applied to the decomposed subseries to estimate the initial forecasts. Lastly, sequential quadratic programming (SQP) is used to fetch the forecast by combining the initial forecasts.

Rigorous performance assessment and the outcome of the Diebold-Mariano’s pairwise statistical test demonstrate the efficacy of the suggested predictive framework. The framework yields commendable predictive performance during the COVID-19 pandemic timeline explicitly as well. Future trends of BTC and ETH are found to be relatively easier to predict, while USDT is relatively difficult to predict.

The robustness of the proposed framework can be leveraged for practical trading and managing investment in crypto market. Empirical properties of the temporal dynamics of chosen cryptocurrencies provide deeper insights.

This paper aims to prioritize preventive maintenance actions on process equipment by evaluating the risk associated with failure modes using predictive maintenance data instead of maintenance history alone.

In process plants, maintenance task identification is based on the failure mode and effect analysis (FMEA). To eliminate or mitigate risk caused by failure modes, maintenance tasks need to be prioritized. Risk priority number (RPN) can be used to rank the risk. RPN is estimated invariably using maintenance history. However, maintenance history has deficiencies, like limited data, inconsistency etc. To overcome these deficiencies, the proposed approach uses the predictive maintenance data clubbed with expert domain knowledge. Unlike the traditional single step approach, RPN is estimated in two steps, i.e. Step 1 estimates the “Possibility of failure mode detection” and Step 2 estimates RPN using output of step 1. Fuzzy sets and approximate reasoning are used to handle the uncertainty/imprecision in data and subjectivity/vagueness of expert domain knowledge. Fuzzy inference system is developed using MATLAB® 6.5.

The proposed approach is applied to a large gearbox in an integrated steel plant. The gearbox is covered under a predictive maintenance program. RPN for each of the failure modes is estimated with the proposed approach and compared with the maintenance task schedule. The illustrative case study results show that the proposed approach helps in detection of failure modes more scientifically and prevents “Over maintenance” to ensure reliability.

This approach gives an opportunity to integrate the predictive maintenance data and subjective/qualitative domain expertise to evaluate the possibility of failure mode detection (POD) quantitatively, which is otherwise purely estimated using subjective judgments. The approach is generic and can be applied to a variety of process equipment to ensure reliability through prioritized maintenance scheduling.

The purpose of this article is to report the application of failure mode and effect analysis (FMEA) to an automotive leaf spring manufacturing organization.

FMEA has been used as a decision‐making tool to prioritize the corrective actions so as to enhance product/system performance by reducing the failure rate. Both design and process FMEA documents have been developed by the systematic formation of team.

The study results indicated the actions that lead to improvement in design. There has been improvement in key decision factors apart from conventional factors. In addition, the quality of leaf springs produced also has been improved.

Conventional design and process FMEA approaches have been developed. In future, fuzzified FMEA can be used.

FMEA has been systematically deployed in a typical industrial scenario. The real improvements have been gained as a result of implementation.

The article presents the results of the case study conducted in an industrial scenario. The contributions of the study are original and valuable.

The purpose of this paper is to find out the status/levels of using crowdsourcing in galleries, libraries, archives and museums (GLAM) around the globe and to give suggestions on how Indian GLAM can take the benefit of this global trend.

The study is based on the analytical study of the literature available on the embracing crowdsourcing for diverse tasks with special emphasis on the efforts of GLAM domain regarding the development of digital repositories.

Meticulous analysis of literature and case studies give an overview of the diverse practices of public participation/crowd collaboration in the development of digital repositories around the globe. However, Indian GLAM are far behind in adopting such practices.

With the rapid growth in digital information and Web-based technology, GLAM around the world encourage and engage public participation in various digitization projects to enrich and enhance their digital collections and place them on the Web. However, Indian GLAM still refrain to accept and adopt such practices. Thus, this paper will encourage and motivate the Indian GLAM to enrich and enhance their collection with crowd contribution and uploading them on Web.

This is an original paper and has great implementation value. During the study, enormous literature was available on crowd participation in various areas around the globe, as well as in India. International examples of crowd participation in GLAM creation are found in the literature; however, not sufficient evidences are found regarding crowd contribution in Indian GLAM. Hence, the paper, by presenting the evidences of crowd participation in GLAM domain, proposes the Indian GLAM to exploit the benefits of this practice for Indian digital repositories to expedite the creation and development of various national digital repositories.

The case describes the first four years of Dhruva, a tax advisory firm set up by Dinesh Kanabar, ex-Deputy CEO of KPMG. Dinesh and other founding partners had worked with the Big-4 firms and were familiar with some of the tensions in the overall ecosystem of Professional Services Firms. Dinesh wanted to build a distinctive professional service firm driven by values of cooperation, high quality work, transparency and stewardship. Very early in its journey, Dhruva's founding team decided that they would use organizational culture as the North Star for guiding decisions related to growth, internal organization design and even admission of new members including Partners. The first four years turned out to be highly successful for the firm. Since inception, it was ranked as Tier-1 firm in the tax advisory space. It was apparent that the firm had succeeded in building a model of alternate organizational paradigm for professional service firms. The next challenge was to test the scalability of this model as the firm embarked on an ambitious growth journey.

This study aims to investigate the influence of Big Five Personality traits (i.e. openness to experience, conscientiousness, extraversion, agreeableness and neuroticism) on young consumers’ intentions to share branded viral video advertisements. Further, this study also demonstrates that the advertising appeal (informational versus emotional) used in the viral advertisement moderates the effects of specific personality traits on the sharing of viral ads.

A conceptual framework is proposed based on the Five-Factor Model of Personality (McCrae and John, 1992) and advertising effectiveness literature. Using experiments, responses from young consumers were collected and hypotheses were tested using hierarchical regression and ANOVA.

Results reveal that the two personality traits, extraversion and openness to experiences, are positively associated with consumers’ viral ad sharing intentions, whereas conscientiousness, agreeableness and neuroticism are not. Moreover, individuals scoring high on openness and extraversion prefer sharing branded viral ads containing informational appeal vis-ã-vis those containing emotional appeals.

Studies decoding the factors behind the success of viral advertisements have more often focussed on the ad content rather than on personality dimensions of the ad sharers. This study bridges this gap by investigating the influence of Big Five Personality traits on young consumers’ intention to forward viral ads, in interaction with ad appeal. Young consumers represent key audience segments consuming and sharing viral content online, and hence, it is important to have a deeper understanding of this market segment.

The purpose of the present work is to improve the industry performance by identifying and quantifying the risks faced by the Indian pharmaceutical industry (IPI). The risk values for the prominent risks and overall industry are determined based on the four risk parameters, which would help determine the most contributive risks for mitigation.

An extensive literature survey was done to identify the risks, which were also validated by industry experts. The finalized risks were then evaluated using the fuzzy synthetic evaluation (FSE) method, which is the most suitable approach for the risk assessment with parameters having a set of different risk levels.

The three most contributive sub-risks are counterfeit drugs, demand fluctuations and loss of customers due to partners' poor service performance, while the main risks obtained are demand, financial and logistics. Also, the overall risk value indicates that the industry faces medium to high risk.

The study identifies the critical risks which need to be mitigated for an efficient industry. The industry is most vulnerable to the demand risk category. Therefore, the managers should minimize this risk by mitigating its sub-risks, like demand fluctuations, bullwhip effect, etc. Another critical sub-risk, the counterfeit risk, should be managed by adopting advanced technologies like blockchain, artificial intelligence, etc.

There is insufficient literature focusing on risk quantification. Therefore, this work addresses this gap and obtains the industry's most critical risks. It also discusses suitable mitigation strategies for better industry performance.

The pharmaceutical industry faces multiple risks that adversely affect its performance. Within these risks, some dependencies have been observed, which help in streamlining the mitigation efforts. Therefore, the present work identifies and categorizes various risks/sub-risks in cause–effect groups, considering uncertainty in the decision-making process.

An extensive literature review and experts' opinions were utilized to identify and finalize the risks faced by the pharmaceutical industry. For further analysis, data collection was done using a questionnaire focusing on finalized risks. Based on the data, the causal relation under uncertainty between various risks/sub-risks was identified using a multi-criteria decision making (MCDM) technique, i.e. intuitionistic fuzzy DEMATEL, in a pairwise manner.

The results show that the three most prominent risk categories are operational, demand/customer/market and financial. Also, out of the seven main risks, only supplier and operational are categorized within the effect group and the rest, i.e. financial, demand, logistics, political and technology within the cause group. The sub-risks within each category have also been categorized into cause–effect groups. The mitigation of cause group risks will help in economize the financial resources and improve the performance and resilience of the industry.

There is insufficient research on identifying the causality among the pharmaceutical industry risks. Additionally, an extensive discussion on the identified cause–effect groups is also missing in the literature. Therefore, in this work, efforts have been made to determine the prominent risks for the Indian pharmaceutical industry that will be helpful for channelizing the resources to mitigate risks for a resilient industry.

An integrated intuitionistic fuzzy (IF) modelling-based framework for examining the performance analysis of a packaging unit (PU) in three different stages has been proposed.

For the series and parallel configuration of PU, a mathematical model based on the intuitionistic fuzzy Lambda–Tau (IFLT) approach was developed in order to calculate various reliability parameters at various spreads. For determining membership and non-membership function-based reliability parameters for the top event, AND/OR gate transitions expression was employed.

For 15%–30% spread, unit’s availability for the membership function falls by 0.006442%, and it falls even more by 0.014907% with an increase in spread from 30% to 45%. In contrast, for 15%–30% spread, the availability of non-membership function-based systems reduces by 0.007491% and further diminishes. Risk analysis has presented applying an emerging approach called intuitionistic fuzzy failure mode and effect analysis (IFFMEA). For each of the stated failure causes, the output values of the intuitionistic fuzzy hybrid weighted Euclidean distance (IFHWED)-based IFFMEA have been tabulated. Failure causes like HP1, MT6, FB9, EL16, DR23, GR27, categorized under subsystems, namely hopper, motor, fluidized bed dryer, distributor, grader and bin, respectively, with corresponding IFFMEA output scores 1.0975, 1.0190, 0.8543, 1.0228, 0.9026, 1.0021, were the most critical one to contribute in the system’s failure.

The limitation of the proposed framework lies in the fact that the results obtained for both reliability and risk aspects mainly depend on the correctness of raw data provided by the experts. Also, an approximate model of PU is obtained from plant experts to carry performance analysis, and hence more attention is required in constructing the model. Under IFLT, reliability parameters of PU have been calculated at various spreads to study and analyse the failure behaviour of the unit for both membership and non-membership function in the IFS of [0.6,0.8]. For both membership- and non-membership-based results, availability of the considered system shows decreasing trend. To improve the performance of the considered system, risk assessment was carried using IFFMEA technique, ranking all the critical failure causes against IFHWED score value, on which more attention should be paid so as to avoid sudden failure of unit.

The livelihood of millions of farmers and workers depends on sugar industries. So perpetual running of these industries is very important from this viewpoint. On the basis of findings of reliability parameters, the maintenance manager could frame a correct maintenance policy for long-run availability of the sugar mills. This long-run availability will generate revenue, which, in turn, will ensure the livelihood of the farmers.

Mathematical modelling of the considered unit has been done applying basic expressions of AND/OR gate. IFTOPSIS approach has been implemented for ranking result comparison obtained under IFFMEA approach. Eventually, sensitivity analysis was also presented to demonstrate the stability of ranking of failure causes of PU.