Mrinalini Shah

Bio: Mrinalini Shah is an academic researcher from Institute of Management Technology, Ghaziabad. The author has contributed to research in topics: Supply chain & Fuzzy logic. The author has an hindex of 4, co-authored 12 publications receiving 90 citations. Previous affiliations of Mrinalini Shah include Athens University of Economics and Business & University of Warsaw.

E-governance in India: Dream or reality

Abstract: India is moving towards achieving e-governance. E-governance can be attained in four steps: Information or Cataloguing, Transaction, Vertical Integration & horizontal integration. India has already achieved the first and the second stage of e-governance. And presently the country is on the verge of attaining the third stage, and moving towards the fourth or the final stage, that is, horizontal integration, which is most challenging. Still there are number of issues untouched. Geographical, social, & economical disparities are the biggest barriers for full-fledged egovernance. Illiteracy, lack of infrastructure, security and privacy of personal and financial data are other constraints. This article discusses the position of India in e-governance environment and issues and challenges ahead.

Fuzzy based trend mapping and forecasting for time series data

Abstract: The study demonstrates the superiority of fuzzy based methods for non-stationary, non-linear time series. Study is based on unequal length fuzzy sets and uses IF-THEN based fuzzy rules to capture the trend prevailing in the series. The proposed model not only predicts the value but can also identify the transition points where the series may change its shape and is ready to include subject expert's opinion to forecast. The series is tested on three different types of data: enrolment for Alabama university, sales volume of a chemical company and Gross domestic capital of India: the growth curve. The model is tested on both kind of series: with and without outliers. The proposed model provides an improved prediction with lesser MAPE (mean average percentage error) for all the series tested.

An insight into agri-food supply chains: a review

Abstract: Organised retailing being the present and future, the paper is an attempt to provide an over view of varied existing agri-food supply chain models across the world and factors responsible for the evolution. Considering the diversity in economies, culture, geography and climatic zones, eight countries have been considered for the study. The study provides an insight into evolution, procurement and marketing strategies and business models for supply of fresh fruits and vegetables across the world. The study compares the present agri-food supply chain structures with business propositions of Tesco Plc. for correlating as well as understanding the implications of varied supply chain structures upon a multinational company’s performance. The present study is an effort to compile and compare the various types of supply chain present in different part of the world and will pave the base for the future research for issues related with supply chain integration and modelling.

Fuzzy logic: a realistic tool for management of customer relation

Abstract: Fuzzy based software allows the customer to be in several classes at a time but up to a different degree. A fuzzy customer class follows the human reasoning and thus treats the customer according to their real value. The concept of membership function allows that degree up to which the customer falls with the concept of that class. To create a modal for the analysis of customer relationships, fuzzy logic based software is proposed. Fuzzy logic, unlike statistical data mining techniques, enables the use of non-numerical values and introduces the notion of linguistic variables. Using linguistic terms and variables will result in a more human oriented querying process. The modal reduces the complexity of customer data and extracts valuable hidden information through a fuzzy logic based classification of customers.

Innovation in procurement from rural India using enterprise mobility strategy: a case study

Abstract: Purpose – The fundamental rule for sustenance in the business world for organizations is to explore new ways to discover themselves and to realign the business strategies with the changing environment, apply new management concepts and adopt new technologies so as to have a faster response to the changing business situation. With more than 600 million user base of mobile phones in India, it may be useful for the Indian companies to set up an enterprise mobility strategy akin to their information technology strategy and take maximum advantage of this mobile wave. The paper aims to discuss these issues. Design/methodology/approach – The paper discusses methodology adopted to bring in and manage change in its process of procurement in a big organization “Marico,” one of the largest players in the Indian FMCG sector. A detailed process which “Marico” adopted to bring change in procurement process and its supply chain was studied with the help of long interviews and available secondary data. Findings – Heindl ...

A survey on forecasting of time series data

Abstract: Time series analysis and forecasting future values has been a major research focus since years ago Time series analysis and forecasting in time series data finds it significance in many applications such as business, stock market and exchange, weather, electricity demand, cost and usage of products such as fuels, electricity, etc and in any kind of place that has specific seasonal or trendy changes with time The forecasting of time series data provides the organization with useful information that is necessary for making important decisions In this paper, a detailed survey of the various techniques applied for forecasting different types of time series dataset is provided This survey covers the overall forecasting models, the algorithms used within the model and other optimization techniques used for better performance and accuracy The various performance evaluation parameters used for evaluating the forecasting models are also discussed in this paper This study gives the reader an idea about the various researches that take place within forecasting using the time series data

High-order fuzzy-neuro expert system for time series forecasting

Abstract: In this article, we present a new model based on hybridization of fuzzy time series theory with artificial neural network (ANN). In fuzzy time series models, lengths of intervals always affect the results of forecasting. So, for creating the effective lengths of intervals of the historical time series data set, a new ''Re-Partitioning Discretization (RPD)'' approach is introduced in the proposed model. Many researchers suggest that high-order fuzzy relationships improve the forecasting accuracy of the models. Therefore, in this study, we use the high-order fuzzy relationships in order to obtain more accurate forecasting results. Most of the fuzzy time series models use the current state's fuzzified values to obtain the forecasting results. The utilization of current state's fuzzified values (right hand side fuzzy relations) for prediction degrades the predictive skill of the fuzzy time series models, because predicted values lie within the sample. Therefore, for advance forecasting of time series, previous state's fuzzified values (left hand side of fuzzy relations) are employed in the proposed model. To defuzzify these fuzzified time series values, an ANN based architecture is developed, and incorporated in the proposed model. The daily temperature data set of Taipei, China is used to evaluate the performance of the model. The proposed model is also validated by forecasting the stock exchange price in advance. The performance of the model is evaluated with various statistical parameters, which signify the efficiency of the model.

A brief review of modeling approaches based on fuzzy time series

Abstract: Recently, there seems to be increased interest in time series forecasting using soft computing (SC) techniques, such as fuzzy sets, artificial neural networks (ANNs), rough set (RS) and evolutionary computing (EC). Among them, fuzzy set is widely used technique in this domain, which is referred to as “Fuzzy Time Series (FTS)”. In this survey, extensive information and knowledge are provided for the FTS concepts and their applications in time series forecasting. This article reviews and summarizes previous research works in the FTS modeling approach from the period 1993–2013 (June). Here, we also provide a brief introduction to SC techniques, because in many cases problems can be solved most effectively by integrating these techniques into different phases of the FTS modeling approach. Hence, several techniques that are hybridized with the FTS modeling approach are discussed briefly. We also identified various domains specific problems and research trends, and try to categorize them. The article ends with the implication for future works. This review may serve as a stepping stone for the amateurs and advanced researchers in this domain.

Probabilistic Forecasting of Sensory Data With Generative Adversarial Networks – ForGAN

Abstract: Time series forecasting is one of the challenging problems for humankind. The traditional forecasting methods using mean regression models have severe shortcomings in reflecting real-world fluctuations. While new probabilistic methods rush to rescue, they fight with technical difficulties like quantile crossing or selecting a prior distribution. To meld the different strengths of these fields while avoiding their weaknesses, as well as, to push the boundary of the state-of-the-art, we introduce ForGAN - one step ahead probabilistic forecasting with generative adversarial networks. ForGAN utilizes the power of the conditional generative adversarial network to learn the data generating distribution and compute probabilistic forecasts from it. We argue how to evaluate ForGAN in opposition to regression methods. To investigate probabilistic forecasting of ForGAN, we create a new dataset and demonstrate our method abilities on it. This dataset will be made publicly available for comparison. Furthermore, we test ForGAN on two publicly available datasets, namely Mackey-Glass dataset and Internet traffic dataset (A5M), where the impressive performance of ForGAN demonstrate its high capability in forecasting future values.