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Agility and sustainability are different manufacturing paradigms. Ideally, companies should merge the two concepts so as to enhance competitive advantage. Agility, which has evolved from lean manufacturing, is the ability of an enterprise to respond quickly to a wide range of customer demands. On the other hand sustainability is a condition in which a company optimizes economic, social and environmental factors by taking into account indirect and long-term consequences. Merging the two concepts could result in holistically improving the system, by fulfilling performance without depleting available resources. Thus, identifying and simultaneously improving two areas with common goals could lead to developing a successful and an emerging enterprise. The paper aims to discuss these issues.

The authors identify agility-sustainability criteria which complement each other from amongst a set of differentiating criteria. The conceptual model is built by integrating five enablers with 25 criteria and 75 attributes. The combined criteria are modeled using graph theory which considers interdependencies and contributing factors on the organization. Within each enabler, the authors investigate the interdependencies between criteria and focus further on those that have potential to improve overall score. The methodology was applied in a manufacturing organization that operates on ad hoc basis depending on projects’ availability.

The model determines a range of values for extreme scenarios under theoretical and practical situations, along with the current level of business. The scenarios are tested for assessing performance by identifying the gaps in the weaker areas.

Approach can serve as a managerial tool for monitoring performance of the organization along agility and sustainability dimensions. Further, government entities can use the approach in devising policy for improving economies by considering holistic approach rather than only environmental aspect. Biased and conflicting opinions of experts might deter chances for comprehensive and consistent decision.

The study integrates a framework for modeling and performance evaluation for a system that incorporates agility and sustainability characteristics. The study will enable practicing managers and decision makers to identify suitable characteristics that would simultaneously improve an organization’s agile performance by considering aspects of sustainability.

The outset of the COVID-19 pandemic caused disruptions of all forms in the supply chain globally for almost two and a half years. This study identifies various challenges in the effective functioning of the existing supply chain during COVID-19. The focus is to see the disruptions impacting the energy storage supply chains.

The procedure entails a thorough analysis of scholarly literature pertaining to various supply chain interruptions, confirmed and verified by experts working in an energy storage company in India. These experts also confirmed the occurrence of more disruptive factors during their interviews and questionnaire survey. Moreover, this process attempts to filter out the relevant causal disruption factors in an energy storage company by using the integrated approach of qualitative and quantitative methodologies.

The results provide practical insights for the company management in planning and devising new strategies to manage supply chain disruptions. Supply chains for companies in other industry sectors can also benefit from the proposed framework and results in making them more robust to counter future disastrous events.

The study provides an easily adaptable decision framework to different industries by closely examining supply chain disruptions and identifying associated causes for building a robust supply chain focused on the energy storage sector. It examines four disruption dimensions and investigates possible outcomes and impacts of disruptions.

The “quality” of any product or service defines the agility of the product and its life cycle in dynamic environment. The demand of high “quality” becomes an imperative concern, when “software” is acting as a product or a service. Since the nature of the software is intangible and more complex, therefore the assurance of providing accurate results is anxiety for companies. The overall quality of the software is based upon many individual factors (or attributes) that makes software reliable, inclined and a long-lasting product in the marketplace. But how these factors can influence each other is significant to identify. Therefore, the purpose of this paper is to study the quality aspect of the software and analyse the interrelationship of impactful attributes.

The analysis has been done through responses sought from software development teams/clients in India. The questionnaire related to the software quality was administered to the sample population. Interconnection among impactful characteristics has been analysed by using a qualitative technique called interpretive structural modelling (ISM). The driving and dependency of the attributes under consideration has been classified using cross-impact matrix multiplication applied to classification (MICMAC) analysis. The procedure of applying ISM method has been automated and provided it as package “ISM” using R software.

In general, it is very complex job to determine the most impactful attribute of software quality. By applying ISM and MICMAC analysis on the set of attributes under consideration, it has been found that “reliability” along with “usability” and “performance” is the most influential attribute of software quality and preferred most.

Though ISM provides an organized modelling framework yet its results are considered less statistically significant. Therefore, it would be interesting to concatenate the present findings with the findings of any analytical methodology; which gives statistically significant results.

The present proposal deals with the interpretation of the software quality attributes and their contextual relationship but with more effective and efficient manner. It can help management to understand the complexity of relationship amongst attributes (which are quality attributes here) more accurately and precisely. Since today is an era of automation, the manual part is being substituted so as to reduce the labour cost, improve safety, security and product quality to increase production. This study is, therefore, an effort and a helping hand in making the hassle free calculations for obtaining intermediate matrices and doing eventual calculations.

n numbers of parameters can be selected to analyse the interrelationship of any project/study. Eradication human errors in applying transitivity law or applying any other operation in solving problem. The package created here can save precious time of users. Provides well-formatted and readable excel output files that make interpretation easier.

Software is one such product/service which plays a significant role in this high-technological world, where each and every firm try their best to be on the top of the list of consumers’ preference. For this purpose, companies reduce manual efforts by converting it into qualitative software that provides deliverables in a systematic manner. Therefore, it becomes imperative to study various interrelated quality attributes of the software. On the similar platform, ISM is a widely used technique and just to provide a helping hand in quantification of the qualitative attributes this paper facilitates the readers with algorithm developed using R software.

The objective of this research work is to study the artificial intelligence (AI)-based product benefits and problems of the agritech industry. The research variables were developed from the existing review of literature connecting to AI-based benefits and problems, and 90 samples of primary data from agritech industry managers were gathered using a survey of a well-structured research questionnaire. The statistical package of IBM-SPSS 21 was utilized to analyze the data using the statistical techniques of descriptive and inferential statistical analysis. Results show that better information for faster decision-making has been ranked as the topmost AI benefit. This implies that the executives of agritech units have a concern about the quality of decisions they make and resistance to change from employees and internal culture has been ranked as the topmost AI problem.

The ideology of this article is to study the performance concerns of SDN Controllers, with the help of developed SRGM and thereby obtain its optimal testing duration. The effect of undetected uncertainty in the parameter values have also been catered in the proposal.

These uncertainties in the parameter values are studied as the risk of not meeting desired set of requirements, whose removal causes additional cost. Considering these two constructs as attributes of MAUT, the controller's optimal testing duration is obtained.

The article focuses towards obtaining the optimal duration for which the SDN Controllers must be tested. It was observed that the inculcation of risk-attribute has provided the higher utility value as compared to any other existing scenarios.

Plenty of SRGM have been proposed in the literature which talks about the testing stop time determination problems. But, none of them have considered the impact of risk of not meeting the requirements (reliability) along with cost to obtain its testing stop time. Further, validation of the proposed model in presented with the help of two releases versions of SDN controller platform, ONOS, entitled as “Kingfisher” and “Loon” and has acquired promising results.

The purpose of the present work is to mathematically model the reliability growth of a multi-version software system that is affected by infected patches.

The work presents a mathematical model that studies the reliability change due to the insertion of an infected patch in multi-version software. Various distribution functions have been considered to highlight the varied aspects of the model. Furthermore, weighted criteria approach has been discussed to facilitate the choice of the model.

The model presented here is able to quantify the effect of an infected patch on multi-version software. The model captures the hike in bug content due to an infected patch.

Multi-version systems have been studied widely, but the role of an infected patch has not been yet explored. The effect of an infected patch has been quantified by modeling the extra bugs generated in the system. This bug count would prove helpful in further studies for optimal resource allocation and testing effort allocation.

There is a strong inducement to develop new inorganic materials to substitute the current industrial pigments, which are known for their poor ultraviolet absorbent and low photoluminescence (PL) properties. The purpose of this paper is to invent a better rare-earth-based pigment material as a spectral modifier with good luminescence properties to enhance the spectral response for photovoltaic panel application.

Different phosphor samples made of nano-calcium carbonate (CaCO₃) with varied wt.% of the dopant Dysprosium doped calcium borophosphate (CBP/Dy) as (W0 – 0%, W1 – 3,85%, W2 – 7.41%, W3 –10.71% and W4 –13.79%) were prepared via the solid-state diffusion method at 600 °C for 6 h using a muffle furnace. The structural, morphological and luminescence properties of the CaCO₃:CBP/Dy powder samples were examined using scanning electron microscopy (SEM), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR) and PL test.

The XRD, SEM and FTIR results verified the crystalline formation, morphological behaviour and vibration bonds of synthesized CBP/Dy-doped CaCO₃ powder samples. XRD pattern revealed that the synthesized powder samples exhibit crystalline structured materials, and SEM results showed irregular shape and porous-like structured morphologies. FTIR spectrum shows prominent bands at 712, 874 and 1,404 cm⁻¹, corresponding to asymmetric stretching vibrations of CO3²⁻ groups and out-of-plane bending. PL characterization of CBP/Dy-doped CaCO₃ (sample W) shows emission at 427 nm (λmax) under the excitation of 358 nm. The intensity of PL emission spectra drops due to the concentration quenching effect, while the maximum PL intensity is observed in the W3 phosphor powder system.

This phosphor powder is expected to find out the potential application such as a spectral modifier which is applied to match the energy of photons with solar cell bandgap to improve spectral absorption and lead to better efficiency.

The introduction of a nano-CaCO₃:CBP/Dy hybrid powder system with good luminescence properties to be used as spectral modifiers for solar cell application has been synthesized in the lab, which is a novel attempt.

The current natural language processing algorithms are still lacking in judgment criteria, and these approaches often require deep knowledge of political or social contexts. Seeing the damage done by the spreading of fake news in various sectors have attracted the attention of several low-level regional communities. However, such methods are widely developed for English language and low-resource languages remain unfocused. This study aims to provide analysis of Hindi fake news and develop a referral system with advanced techniques to identify fake news in Hindi.

The technique deployed in this model uses bidirectional long short-term memory (B-LSTM) as compared with other models like naïve bayes, logistic regression, random forest, support vector machine, decision tree classifier, kth nearest neighbor, gated recurrent unit and long short-term models.

The deep learning model such as B-LSTM yields an accuracy of 95.01%.

This study anticipates that this model will be a beneficial resource for building technologies to prevent the spreading of fake news and contribute to research with low resource languages.

Today, a firm’s major concern is to know the way in which an innovation is adopted in the marketplace. The purpose of this paper is to focus on the two-stage nature of diffusion process in which the time lag between people being informed and their act of making final purchase is considered.

The paper discusses an approach based on the time lag for modeling awareness and adoption process as two separate and yet connected processes. Varying forms of time lag (constant, deterministic or random) have been considered while modeling the required framework. Furthermore, an equivalence approach has been shown between the present framework and the two well-known and established approaches of infinite queuing theory and hazard rate function.

The results are verified on sales data of two different consumer durables and it show good prediction capability of proposed models in capturing the real-life scenario. Further, the equivalence approach helps us to quantify such scenarios which were difficult to be modeled with any one particular approach. Further, the possibility of capturing different market scenarios by studying various distribution functions has been identified.

The proposed methodology is based on a two-stage adoption process. The same can be extended to a multi-stage adoption process as in today’s competitive environment. “Motivation” is one such factor that is highly important which can be considered in some later studies. In future, the authors wish to study the multi-stage adoption process considering the different forms of time lag function.

The equivalence approach discussed in the paper can help to cater the possibility of capturing different market scenarios by studying various distribution functions.

The proposed approach helps to cater the time lag between awareness and adoption process and develop different mean value functions to account for the manner in which sales are happening under different circumstances. The proposed methodical approach can also help decision makers in managing their available resources in a prudent manner.

In the current market scenario, software upgrades and updates have proved to be very handy in improving the reliability of the software in its operational phase. Software upgrades help in reinventing working software through major changes, like functionality addition, feature enhancement, structural changes, etc. In software updates, minor changes are undertaken which help in improving software performance by fixing bugs and security issues in the current version of the software. Through the current proposal, the authors wish to highlight the economic benefits of the combined use of upgrade and update service. A cost analysis model has been proposed for the same.

The article discusses a cost analysis model highlighting the distinction between launch time and time to end the testing process. The number of bugs which have to be catered in each release has been determined which also consists of the count of latent bugs of previous version. Convolution theory has been utilized to incorporate the joint role of tester and user in bug detection into the model. The cost incurred in debugging process was determined. An optimization model was designed which considers the reliability and budget constraints while minimizing the total debugging cost. This optimization was used to determine the release time and testing stop time.

The proposal is backed by real-life software bug dataset consisting of four releases. The model was able to successfully determine the ideal software release time and the testing stop time. An increased profit is generated by releasing the software earlier and continues testing long after its release.

The work contributes positively to the field by providing an effective optimization model, which was able to determine the economic benefit of the combined use of upgrade and update service. The model can be used by management to determine their timelines and cost that will be incurred depending on their product and available resources.