Browsing by Author "Sharma, Satyendra Kumar"

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Analysis of book sales prediction at Amazon marketplace in India: a machine learning approach
(Springer, 2019-09) Sharma, Satyendra Kumar
Prediction of customer demand is an important part of Supply Chain Management, as it helps to avoid over or under production and reduces delivery time. In the context of e-commerce, accurate prediction of customer demand, typically captured by sales volume, requires careful analysis of multiple factors, namely, type of product, country of purchase, price, discount rate, free delivery option, online review sentiment etc., and their interactions. For e-tailers such as, Amazon, this kind of prediction capability is also extremely important in order to manage the supply chain efficiently as well as ensure customer satisfaction. This study investigates the efficacy of various modeling techniques, namely, regression analysis, decision-tree analysis and artificial neural network, for predicting the sales of books at amazon.in, using various relevant factors and their interactions as predictor variables. Sentiment analysis is carried out to measure the polarity of online reviews, which are included as predictors in these models. The importance of each independent predictor variable, such as discount rate, review sentiment etc., is analyzed based on the outcome of each model to determine top significant predictors which can be controlled by the marketer to influence sales. In terms of accuracy of prediction, the artificial neural network model is found to perform better than the decision-tree based model. In addition, the regression analysis, with and without sentiment and interaction factors, generates comparable results. The comparative analysis of these models reveals several significant findings. Firstly, all three models confirm that review volume is the most important and significant predictor of sales of books at amazon.in. Secondly, discount rate, discount amount and average ratings have minimal or insignificant effect on sales prediction. Thirdly, both negative sentiment and positive sentiment of the reviews are individually significant predictors as per regression and decision-tree model, but they are not significant at all as per neural network model. This observation from the neural network model is contrary to the extant research which claims that both negative and positive sentiment are significant with the former having more influence in predicting sales. Finally, the interaction effects of review volume with negative and positive sentiment are also found to be significant predictors as per all three models. Hence, overall, out of various factors used for sales prediction of books, review volume, negative sentiment, positive sentiment and their interactions are found to be the most significant ones across all models. The results of this study can be utilized by online sellers to accurately predict the sales volume by adjusting these significant factors, thereby managing the supply chain effectively.
Analysis of manufacturing supply chain agility performance using Taguchi loss functions and design of experiment
(Emerald, 2018-11) Rout, Bijay Kumar; Sharma, Satyendra Kumar; Routroy, Srikanta
The purpose of this paper is to evaluate the agility performance level of manufacturing supply chains using Taguchi loss functions (TLFs) and design of experiment (DoE).
Analysis of supply chain vulnerability factors in manufacturing enterprises: a fuzzy DEMATEL approach
(Taylor & Francis, 2022-06) Sharma, Satyendra Kumar; Routroy, Srikanta
With an increase in supply chain disruptions, managing supply chain vulnerability has become a key factor to build resilient supply chains. Although supply chain vulnerability and associated factors are mostly studied individually or with selected groups, the supply chain literature lacks comprehensive and systematic studies. The current study aims to identify and segregate the supply chain vulnerability factors for manufacturing enterprises based on causal–effect relationships that exist between them. The supply chain vulnerability factors are identified from the extant literature review. Its applicability in Indian manufacturing enterprises is discussed with experts drawn from industry and academia. The cause–effect relationships of selected supply chain vulnerability factors are analysed using the fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL) approach. The obtained results indicate that supply design and supply chain efficiency-related factors are effect factors, whereas supply chain collaboration and information technology-related factors are causal factors. Complexity, centralisation, supplier concentration and low-cost sourcing are found to be the most prominent factors. This research contributes to the literature on supply chain vulnerability by describing the causal relationships among key factors impacting it. It would help managers to develop appropriate disruptions mitigation strategy(s) to make a resilient and robust supply chain.
Assessing working capital management efficiency of Indian manufacturing exporters
(Emerald, 2020-09) Chadha, Saurabh; Sharma, Satyendra Kumar
The purpose of this paper is to empirically investigate the relationship between working capital management (WCM) efficiency and exogenous variables of the Indian manufacturing sector along with its sub-industries that are involved in export activities.
Assessment for risk of logistics infrastructure projects using analytic network process
(Inder Science, 2021-08) Sharma, Satyendra Kumar; Singh, Ajit Pratap
Mega construction projects are subject to multiple risks which influence project objectives, such as, cost, quality and time. Risk intensity varies across different stages of the life cycle of the project. While most of the studies in risk management have been on identifying risks in various projects, very few of them have explored risks in air logistics infrastructure development projects. In this study, we have designed risks prioritising model for such projects using analytic network process (ANP) technique with paired risk comparison. The data was collected through extensive literature review and detailed interview/questionnaire inputs from subject matter experts involved in development of mega air logistics development projects. The analysis through ANP technique has revealed comparison of various risks, their interdependencies, their relative ranking based on risk priority indices. This has revealed that the financial and the construction risks play a very important and critical role for the success of infrastructure projects. Finally, the study presents insights into risk implications with their relative importance that will rationalise the management's effort.
Assessment for risk of logistics infrastructure projects using analytic network process
(Inderscience, 2021-08-24) Singh, Ajit Pratap; Sharma, Satyendra Kumar; Sharma, Vijay Kumar
Mega construction projects are subject to multiple risks which influence project objectives, such as, cost, quality and time. Risk intensity varies across different stages of the life cycle of the project. While most of the studies in risk management have been on identifying risks in various projects, very few of them have explored risks in air logistics infrastructure development projects. In this study, we have designed risks prioritising model for such projects using analytic network process (ANP) technique with paired risk comparison. The data was collected through extensive literature review and detailed interview/questionnaire inputs from subject matter experts involved in development of mega air logistics development projects. The analysis through ANP technique has revealed comparison of various risks, their interdependencies, their relative ranking based on risk priority indices. This has revealed that the financial and the construction risks play a very important and critical role for the success of infrastructure projects. Finally, the study presents insights into risk implications with their relative importance that will rationalise the management's effort.
Bayesian Belief Network Approach for Supply Risk Modelling
(IGI Global, 2022) Sharma, Satyendra Kumar; Routroy, Srikanta
Today’s global and complex world increased the vulnerability to risks exponentially and organizations are compelled to develop effective risk management strategies for its mitigation. The prime focus of research is to design a supply risk model using Bayesian Belief Network bear in mind the tie-in of risk factors (i.e. objective and subjective) those are critical to a supply chain network. The proposed model can be re-engineered as per new information available at disclosure, so risk analysis will be current and relevant along the timeline as so situation is strained. The top three factors which influenced profitability were transportation risk and price risks. Netica is the platform used for designing and running simultaneous simulations on the Bayesian Network. The proposed methodology is demonstrated through a case study conducted in an Indian manufacturing supply chain taking inputs from supply chain/risk management experts. .
Benchmarking the efficiency model for working capital management: data envelopment analysis approach
(Emerald, 2021-10) Sharma, Satyendra Kumar; Chadha, Saurabh
This paper evaluates the working capital management (WCM) efficiency of the Indian manufacturing industries through data envelopment analysis (DEA) and empirically investigates the influence of several exogenous variables on the WCM efficiency.
Capital budgeting practices: a survey in the selected Indian manufacturing firms
(IDEAS is a RePEc, 2019) Chadha, Saurabh; Sharma, Satyendra Kumar
The purpose of this study is to investigate the capital budgeting practices prevalent in the selected Indian manufacturing firms. The study also determines the factors which sample firms consider for the ascertainment of their cost of capital. Further, the study throws a light on some of the issues which led to the ignorance of scientific evaluation techniques of project appraisal. The study was conducted with the help of survey method. The findings of the survey show that payback period method and NPV method are the two most popular techniques of capital budgeting in the Indian manufacturing sector. Although, the overall weightage of the non-discounting technique is more when compared to the discounting methods. It was also found that WACC is the most used method to determine the cost of capital in the sample firms. Thus, this study provides a useful insight of capital budgeting practices of Indian manufacturing firms. The study will be helpful for investors who are looking to invest in Indian manufacturing sector under the Make in India program and it is the first study of its kind which covers the ignorance aspect of the capital budgeting techniques.
Data mining-based algorithm for assortment planning
(Taylor & Francis, 2020-02) Sharma, Satyendra Kumar
With increasing varieties and products, management of limited shelf space becomes quite difficult for retailers. Hence, an efficient product assortment, which in turn helps to plan the organization of various products across limited shelf space, is extremely important for retailers. Products can be distinguished based on quality, price, brand, and other attributes, and decision needs to be made about an assortment of the products based on these attributes. An efficient assortment planning improves the financial performance of the retailer by increasing profits and reducing operational costs. Clustering techniques can be very effective in grouping products, stores, etc. and help managers solve the problem of assortment planning. This paper proposes data mining approaches for assortment planning for profit maximization with space, and cost constraints by mapping it into well-known knapsack problem
Developing a Bayesian belief network model for prediction of R&D project success
(Taylor & Francis, 2017-03) Sharma, Satyendra Kumar; Chanda, Udayan
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.
Developing a Bayesian Network Model for Supply Chain Risk Assessment
(Taylor & Francis, 2016-05) Sharma, Satyendra Kumar
The Bayesian network based probability model is very new to the field of business management. Its use for risk assessment to predict the supply chain disruption and their consequences on the supply chain goals is very limited. The purpose of this research, is to develop a risk assessment tool to assess and to determine the risk exposure faced by a supply chain. In a global economy with ever-growing competition the firms are facing uncertain disruptions in their supply chains that further dent their brand value. The proposed probabilistic model that updates itself in the light of new evidences and calculates marginal probabilities for all risk variables and supply chain goals through conditional probability tables. The proposed model empowers the supply chain managers to predict the chances of any disruptive risk factors in the supply chain.
Developing a Framework for Analyzing Global Supply Chain Security
(SSRN, 2015-12) Sharma, Satyendra Kumar
Global supply chain security became a critical issue for governments and firms alike after the attacks of 9/11 in New York city. Past researchers have gone ahead of the classical notion of security in the context of supply chain as protection of facilities and inventory only. The paper aims at analyzing and explaining global supply chain security in an overall comprehensive way regarding all factors — the need for supply chain security, the measures that can be taken, their barriers to implementation and outcomes. A framework was formed to explain global supply chain security from all angles. The framework consists of elements like drivers (the need for supply chain security), strategies (measures to secure supply chains), barriers (limitations to implementation of security measures) and outcomes (results of security measures taken). The framework given in the paper can give a brief idea to practitioners and researchers about supply chain security on a comprehensive level. Each element of the framework can be picked up by researchers for future research.
Development of an automobile industry risk index
(Inder Science, 2019-11) Sharma, Satyendra Kumar
The aim of this research is to construct a Bayesian belief network (BBN) model, which encompasses all the risk factors relevant to the Indian automotive sector that can give a fair, empirical idea as to how much the risk factors drive down the gross turnover of the industry. The BBN model is used to gauge business, economic and external risks and evaluate its impact on gross turnover of the industry. Empirical model draws a lot of implications to streamline the risk effects in the industry, but it clearly shows that the three factors - business risks, economic risks and external risks are not entirely independent and are positively correlated with each other. Bayesian networks provide a very useful risk assessment tool that takes into account the advantages of both quantitative and qualitative risk assessment methods. This is a novel, empirical effort to provide a generalised model to integrate all risks - domestic, global, economic, legal - relevant to the automotive industry.
An empirical exploration of supply chain design factors and their influence on supply chain performance
(IDEAS is a RePEc, 2013) Bhat, Anil Kumar; Sharma, Satyendra Kumar
Supply chain management has become a competitive tool for companies to increase their profitability and supply chain performance is getting the attention of business executives. They have started to focus on various factors, (that are) responsible for supply chain performance. We present an extant literature review on various factors that enable supply chain performance. Further, we conducted a survey in the Indian automobile industry and have attempted to test empirically the relationship between various supply chain design factors and supply chain performance. Factors such as aligning incentives, integration, adaptability and contingency planning were found to be most important factors. However, similar studies need be conducted in other industries to generalise the findings of our survey.
An empirical investigation of the contribution of supply chain design characteristics on supply chain risks in Indian automobile industry
(Inder Science, 2012) Sharma, Satyendra Kumar; Bhat, Anil Kumar
The importance of supply chain risk management is attracting the attention of academicians and business managers globally. As companies experience globalisation and cut throat competition, companies are forced to adopt measures to contain risk due to their supply chain design characteristics. The purpose of this paper is to identify supply chain design characteristics and investigate their relationship with overall supply chain risk. This research presents the empirical investigation of supply chain risk drivers. Analysis of responses from 102 executives from Indian automobile industry, give an insight into the relationship between key supply chain risk drivers viz. (supply chain complexity, focus on efficiency, dependence on few members and node criticality), and supply chain risks.
An Empirical Study on Supply Chain Risk Management Strategies in Indian Automobile Industry
(SSRN, 2015-08) Sharma, Satyendra Kumar; Bhat, Anil Kumar; Routroy, Srikanta
Supply chain disruptions, major or minor, have affected supply chains negatively. Literature shows that supply chain disruptions also lead to reduction in shareholder value. Supply Chain Risk Management (SCRM) has become a concern for boardrooms, and companies have started to think how they can better manage their supply chains to survive and continue operations. This paper provides insights into SCRM in Indian automobile sector. Strategies used to deal with supply chain risks are explored in this paper. First a taxonomy is suggested based on the review of the extant literature, i.e., proactive strategies, coping strategies, aligning strategies, early warning strategies and survival strategies. These strategies are then empirically tested in the context of Indian automobile industry. A total SCRM strategy index is calculated and the status of Indian automobile industry in SCRM is discussed.
Evaluating value requirement for Industrial Product-Service System in circular economy for wind power-based renewable energy firms
(Elsevier, 2022-03) Sharma, Satyendra Kumar
In India, Wind farms have been operating with obsolete wind energy generator and are facing troubles due to the end-of-service life of the wind turbines and other components. Only two options are available to the wind farms under such a scenario-repowering or life extension. At present, life extension is the only plausible solution to aging wind turbine as repowering initiative has been very slow due to many reasons. Life extension of wind turbine generates circular value and includes reconditioning of parts, reuse of few components, and finally remanufacturing. To achieve circular value, wind turbine original equipment manufacturers (WT OEMs) need to redesign their products and services to meet customer expectations. Literature hasn't discussed value requirements in the design and development of the Industrial Product-Service System (IPS2) for the life cycle extension of product such as wind turbines. To address this gap, we proposed a research framework, which is composed of two stages – first deals with the elicitation of value requirement, and second describes the evaluation methodology to prioritize and rank the value requirement with Fuzzy AHP. Stage-I reveals that there are five customer value elements (Pre-sales Services, Product Reverse Flow, Refurbished Product Services, Installation and Site Services, Repair, Upgrade, and Debugging Services) catering to the circular services and two elements (Product differentiation and Benchmarking, Optimized Performance) catering to circular product. The analysis step in stage-II reveals that Repair, Upgrade, and Debugging (RUD) has got the highest priority and value requirement named as “Improved perceived performance” and “Smart monitoring” has got the highest weight under this category. This study contributes to the literature on product-service system, circular economy, and requirement engineering and puts forward theoretical and practical implications in the above area.
Exploring predictors of working capital management efficiency and their influence on firm performance: an integrated DEA-SEM approach
(Emerald, 2021-04) Chadha, Saurabh; Sharma, Satyendra Kumar
This study develops an integrated approach combining data envelopment analysis (DEA) and structural equation modeling (SEM) for estimating the working capital management (WCM) efficiency and evaluating the effects of diverse exogenous variables on the WCM efficiency and firms' performance.
Force field analysis of Indian automotive strategic sourcing risk management enablers and barriers
(Emerald, 2018-09) Sharma, Satyendra Kumar; Matai, Rajesh
Strategic sourcing and supply risk management have become interesting topics of research in the recent years. Automotive industry experts are increasingly focussing on improving the supply efficiency and performance towards gaining sustainable competitive advantage. This study aims to classify, through an exhaustive review of past literature, the various enablers and barriers of strategic sourcing risk management (SSRM) and use them to identify the problems in the automobile secto