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1 – 9 of 9Anurag Mishra, Pankaj Dutta and Naveen Gottipalli
The supply chain (SC) of the fast-moving consumer goods (FMCG) sector in India witnessed a significant change soon after introducing the Goods and Services Tax (GST). With the…
Abstract
Purpose
The supply chain (SC) of the fast-moving consumer goods (FMCG) sector in India witnessed a significant change soon after introducing the Goods and Services Tax (GST). With the initiation of this tax, companies started moving from individual state-wise warehouses to consolidation warehouses model to save costs. This paper proposes a model that frames a mathematical formulation to optimize the distribution network in the downstream SC by considering the complexities of multi-product lines, multi-transport modes and consolidated warehouses.
Design/methodology/approach
The model is designed as mixed-integer linear programming (MILP), and an algorithm is developed that works on the feedback loop mechanism. It optimizes the transportation and warehouses rental costs simultaneously with impact analysis.
Findings
Total cost is primarily influenced by the critical factor transportation price rather than the warehouse rent. The choice of warehouses at prime locations was a trade-off between a lower distribution cost and higher rent tariffs.
Research limitations/implications
The study enables FMCG firms to plan their downstream SC efficiently and to be in line with the recent trend of consolidation of warehouses. The study will help SC managers solve complexities such as multi-product categories, truck selection and consolidation warehouse selection problems and find the optimum value for each.
Originality/value
The issues addressed in the proposed work are transporting products with different sizes and weights, selecting consolidated warehouses, selecting suitable vehicles for transportation and optimizing distance in the distribution network by considering consolidated warehouses.
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Ravikantha Prabhu, Sharun Mendonca, Pavana Kumara Bellairu, Rudolf Charles DSouza and Thirumaleshwara Bhat
The purpose of this study is to investigate the impact of titanium oxide (TiO2) filler on the abrasive wear properties of bamboo fiber reinforced epoxy composites (BFRCs) using a…
Abstract
Purpose
The purpose of this study is to investigate the impact of titanium oxide (TiO2) filler on the abrasive wear properties of bamboo fiber reinforced epoxy composites (BFRCs) using a Taguchi approach. The study aims to enhance the abrasive wear resistance of these composites by introducing TiO2 filler as a potential reinforcement, thus contributing to the development of sustainable and environmentally friendly materials.
Design/methodology/approach
This study focuses on the fabrication of epoxy/bamboo composites infused with TiO2 particles within the Wt.% range of 0–8 Wt.% using hand layup techniques. The resulting composites were subjected to wear testing according to ASTM G99-05 standards. Statistical analysis of the wear results was carried out using the Taguchi design of experiments (DOE). Additionally, an analysis of variance (ANOVA) was used to determine the influential control factors impacting the specific wear rate (SWR) and coefficient of friction (COF).
Findings
The study illuminates how integrating TiO2 filler enhances abrasive wear in epoxy/bamboo composites. Statistical analysis of SWR highlights abrasive grit size (grit) as the most influential factor, followed by normal load, Wt.% of TiO2 and sliding distance. Analysis of the COF identifies normal load as the primary influential factor, followed by grit, Wt.% of TiO2 and sliding distance. The Taguchi predictive model closely aligns with experimental results, validating its reliability. The morphological study revealed significant differences between the unfilled and TiO2-filled composites. The inclusion of TiO2 improved wear resistance, as evidenced by reduced surface damage and wear debris.
Originality/value
This research paper aims to integrate TiO2 filler and bamboo fibers to create an innovative hybrid composite material. TiO2 micro and nanoparticles show promise as filler materials, contributing to improved tribological properties of epoxy composites. The utilization of Taguchi’s DOE and ANOVA for statistical analysis provides valuable guidance for academic researchers and practitioners in optimizing control variables, especially in the context of natural fiber reinforced composites.
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Pallavi Chaturvedi, Durgesh Agnihotri and Vikas Tripathi
The current study investigates the role of consumer ethnocentrism (CE) in the context of locally produced organic food. This research work further extends the extended theory of…
Abstract
Purpose
The current study investigates the role of consumer ethnocentrism (CE) in the context of locally produced organic food. This research work further extends the extended theory of planned behavior (TPB) model by examining the mediating effect of extended TPB variables (ATT, SN, PBC, PV) between CE and PI for locally produced organic food.
Design/methodology/approach
Data were obtained from the visitors of two shopping malls situated in a large, heavily populated city of India using survey method. Further, two-step approach was applied to analyze the hypothesized model.
Findings
Findings indicate that CE is a substantial determinant of PI for locally produced organic food. Moreover, extended TPB mediates the relation between CE and PI for locally produced organic food.
Practical implications
Post Covid-19, market size of organic food is rapidly growing in India. In this regard, this study presents useful implications for the marketers of organic food for gaining better consumer insights to further develop appropriate marketing strategies.
Originality/value
CE has been found to be a useful predictor of their food attitudes. However, studies, investigating the role of CE in the context of sustainable food consumption, are scant. Moreover, studies exploring the mediating effect of extended TPB variables are also very limited.
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The study examines the IPO resilience grounded on the firm’s intrinsic factors.
Abstract
Purpose
The study examines the IPO resilience grounded on the firm’s intrinsic factors.
Design/methodology/approach
We examine the association of IPO performance and post-listing firm’s performance with issuers' pre-listing financial and qualitative traits using panel data regression.
Findings
IPOs floated in the Indian market from July 2009 to March 31, 2022, evince the notable influence of issuers' pre-IPO fundamentals and legitimacy traits on IPO returns and post-listing earning power. Where the pandemic’s favorable impact is discerned on the post-listing year earning power of the issuer firms, the loss-making issuers appear to be adversely affected by the Covid disruption. Perhaps, the successful listing equipped the issuers with the financial flexibility to combat market challenges vis-à-vis failed issuers deprived of desired IPO proceeds.
Research limitations/implications
High initial returns followed by a declining pattern substantiate the retail investors to be less informed vis-à-vis initial investors, valuers and underwriters, who exit post-listing after profit booking. Investing in the shares of the newly listed ventures post-listing in the secondary market can shield retail investors from the uncertainty losses of being uninformed. The IPO market needs stringent regulations ensuring the verification of the listing valuation, the firm’s credentials and the intent of utilizing IPO proceeds. Healthy development of the IPO market merits reconsidering the listing of ventures with weak fundamentals suspected to withstand the market challenges.
Originality/value
Given the tremendous rise in the new firm venturing into the primary market and the spike in IPOs countering the losses immediately post-opening, the study examines the loss-making and young firms IPOs separately, adding novelty to the study.
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Md Doulotuzzaman Xames, Fariha Kabir Torsha and Ferdous Sarwar
The purpose of this paper is to predict the machining performance of electrical discharge machining of Ti-13Nb-13Zr (TNZ) alloy, a promising biomedical alloy, using artificial…
Abstract
Purpose
The purpose of this paper is to predict the machining performance of electrical discharge machining of Ti-13Nb-13Zr (TNZ) alloy, a promising biomedical alloy, using artificial neural networks (ANN) models.
Design/methodology/approach
In the research, three major performance characteristics, i.e. the material removal rate (MRR), tool wear rate (TWR) and surface roughness (SR), were chosen for the study. The input parameters for machining were the voltage, current, pulse-on time and pulse-off time. For the ANN model, a two-layer feedforward network with sigmoid hidden neurons and linear output neurons were chosen. Levenberg–Marquardt backpropagation algorithm was used to train the neural networks.
Findings
The optimal ANN structure comprises four neurons in input layer, ten neurons in hidden layer and one neuron in the output layer (4–10-1). In predicting MRR, the 60–20-20 data split provides the lowest MSE (0.0021179) and highest R-value for training (0.99976). On the contrary, the 70–15-15 data split results in the best performance in predicting both TWR and SR. The model achieves the lowest MSE and highest R-value for training in predicting TWR as 1.17E-06 and 0.84488, respectively. Increasing the number of hidden neurons of the network further deteriorates the performance. In predicting SR, the authors find the best MSE and R-value as 0.86748 and 0.94024, respectively.
Originality/value
This is a novel approach in performance prediction of electrical discharge machining in terms of new workpiece material (TNZ alloys).
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I. Wayan Edi Arsawan, Viktor Koval, Dwi Suhartanto, Ni Kadek Dessy Hariyanti, Natalіia Polishchuk and Viktoriia Bondar
The present study aims to examine the relationship between green economic incentives (GEIs) and environmental commitment (EC) as drivers of the circular economy (CE) practices in…
Abstract
Purpose
The present study aims to examine the relationship between green economic incentives (GEIs) and environmental commitment (EC) as drivers of the circular economy (CE) practices in small and medium enterprises (SMEs).
Design/methodology/approach
A cross-sectional study of 594 assistant managers and SME managers was used while data were analyzed through Smart PLS.
Findings
The results showed that GEIs positively affect the SMEs' EC and the CE practice model. Furthermore, EC is the mediator between GEIs and the three CE practices: internal environmental management, eco-design and corporate asset management and recovery (CAM&R).
Practical implications
The present study provided a basis for understanding the relevance of SMEs' CE practices and designing a strategic plan for its implementation. Also, it provides insight into that collaboration between triple helix (the government, SMEs and the community) is needed in increasing environmental awareness toward sustainability.
Originality/value
This study enhanced the natural resource-based view (NRBV), describing the mechanisms that view GEIs and EC as pollution prevention in CE practice.
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K.G. Rumesh Samarawickrama, U.G. Samudrika Wijayapala and C.A. Nandana Fernando
The purpose of this study is to extract and characterize a novel natural dye from the leaves of Lannea coromandelica and the extraction with finding ways of dyeing cotton fabric…
Abstract
Purpose
The purpose of this study is to extract and characterize a novel natural dye from the leaves of Lannea coromandelica and the extraction with finding ways of dyeing cotton fabric using three mordants.
Design/methodology/approach
The colouring agents were extracted from the leaves of Lannea coromandelica using an aqueous extraction method. The extract was characterized using analysis methods of pH, gas chromatography-mass spectrometry (GC-MS), Fourier transform infrared (FTIR), ultraviolet-visible (UV-vis) and cyclic voltammetry measurement. The extract was applied to cotton fabric samples using a non-mordant and three mordants under the two mordanting methods. The dyeing performance of the extracted colouring agent was evaluated using colour fastness properties, colour strength (K/S) and colour space (CIE Lab).
Findings
The aqueous dye extract showed reddish-brown colour, and its pH was 5.94. The GC-MS analysis revealed that the dye extract from the leaves of Lannea coromandelica contained active chemical compounds. The UV-vis and FTIR analyses found that groups influenced the reddish-brown colour of the dye extraction. The cyclic voltammetry measurements discovered the electrochemical properties of the dye extraction. The mordanted fabric samples showed better colour fastness properties than the non-mordanted fabric sample. The K/S and CIE Lab results indicate that the cotton fabric samples dyed with mordants showed more significant dye affinities than non-mordanted fabric samples.
Originality/value
Researchers have never discovered that the Lannea coromandelica leaf extract is a natural dye for cotton fabric dyeing. The findings of this study showed that natural dyes extracted from Lannea coromandelica leaf could be an efficient colouring agent for use in cotton fabric.
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Cemalettin Akdoğan, Tolga Özer and Yüksel Oğuz
Nowadays, food problems are likely to arise because of the increasing global population and decreasing arable land. Therefore, it is necessary to increase the yield of…
Abstract
Purpose
Nowadays, food problems are likely to arise because of the increasing global population and decreasing arable land. Therefore, it is necessary to increase the yield of agricultural products. Pesticides can be used to improve agricultural land products. This study aims to make the spraying of cherry trees more effective and efficient with the designed artificial intelligence (AI)-based agricultural unmanned aerial vehicle (UAV).
Design/methodology/approach
Two approaches have been adopted for the AI-based detection of cherry trees: In approach 1, YOLOv5, YOLOv7 and YOLOv8 models are trained with 70, 100 and 150 epochs. In Approach 2, a new method is proposed to improve the performance metrics obtained in Approach 1. Gaussian, wavelet transform (WT) and Histogram Equalization (HE) preprocessing techniques were applied to the generated data set in Approach 2. The best-performing models in Approach 1 and Approach 2 were used in the real-time test application with the developed agricultural UAV.
Findings
In Approach 1, the best F1 score was 98% in 100 epochs with the YOLOv5s model. In Approach 2, the best F1 score and mAP values were obtained as 98.6% and 98.9% in 150 epochs, with the YOLOv5m model with an improvement of 0.6% in the F1 score. In real-time tests, the AI-based spraying drone system detected and sprayed cherry trees with an accuracy of 66% in Approach 1 and 77% in Approach 2. It was revealed that the use of pesticides could be reduced by 53% and the energy consumption of the spraying system by 47%.
Originality/value
An original data set was created by designing an agricultural drone to detect and spray cherry trees using AI. YOLOv5, YOLOv7 and YOLOv8 models were used to detect and classify cherry trees. The results of the performance metrics of the models are compared. In Approach 2, a method including HE, Gaussian and WT is proposed, and the performance metrics are improved. The effect of the proposed method in a real-time experimental application is thoroughly analyzed.
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