Umakanta Mishra
@vit.ac.in
Assistant Professor of Mathematics at
VIT University Vellore, Tamil Nadu, India.
RESEARCH INTERESTS
Supply Chain
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Abin Thomas and Umakanta Mishra
Elsevier BV
Najaf Ali Wani and Umakanta Mishra
Elsevier BV
Wakhid Ahmad Jauhari, Shabrina Chairunnisa Novia Ramadhany, Cucuk Nur Rosyidi, Umakanta Mishra, and Hawa Hishamuddin
Elsevier BV
Alpesh Kumar Dauda, Ambarish Panda, and Umakanta Mishra
Elsevier BV
Ezhilarasan Peter John and Umakanta Mishra
Springer Science and Business Media LLC
Ambarish Panda and Umakanta Mishra
Elsevier BV
Ezhilarasan Peter John and Umakanta Mishra
Elsevier BV
G. Durga Bhavani, Umakanta Mishra, and G. S. Mahapatra
Springer Science and Business Media LLC
Najaf Ali Wani and Umakanta Mishra
Springer Science and Business Media LLC
Vishnupriya Kalathil Sahadevan and Umakanta Mishra
Springer Science and Business Media LLC
Umakanta Mishra, Abu Hashan Md Mashud, Sankar Kumar Roy, and Md Sharif Uddin
American Institute of Mathematical Sciences (AIMS)
<p style='text-indent:20px;'>Price rebate is only permitted when purchases made by the customer exceed a predefined limit and they later buy other items from the purchaser. There are various forms of rebate used by production companies. This study provides a deteriorating inventory model of four-level production rates and derives the rebate-value-based demand with the product selling price under shortages. This model gives preference to optimal replenishment time, ordering quantity, rebate value, and selling price while maximizing total profit. This model first explores and discusses the demand function, which discretely hinges on the selling price of rebate value, followed by discussions on demand based on the selling price. This study proposes a solution through unique propositions and the construction of two algorithms that are suitable for four-level production; this has not yet been explored in-depth in the literature. Illustrative examples and a sensitivity analysis demonstrate the applicability of the proposed algorithms; the customer decides to buy a product that is larger than the minimum suitable for a price rebate and the buyer can then deal with a higher price rebate. The benefit of rebate marketing helps production companies increases conversion rates and encourages customers to purchase goods. This model demonstrates that proposing rebates can consume substantial pricing and inventory inferences and can result in a substantial increase in profit.</p>
Najaf Ali Wani and Umakanta Mishra
Elsevier BV
Abin Thomas and Umakanta Mishra
Elsevier BV
Abu Hashan Md Mashud, Dipa Roy, Yosef Daryanto, Umakanta Mishra, and Ming-Lang Tseng
Elsevier BV
Rofin T.M., Umakanta Mishra, and Jei-Zheng Wu
MDPI AG
The skyrocketing growth of e-commerce and traditional retailing contributes to a large proportion of carbon emissions in any supply chain. Nevertheless, the literature related to carbon emission has focused on manufacturers and their potential for emission reduction. Therefore, it is imperative to understand the role of the retailing sector in reducing carbon emissions. Therefore, this study considers emission-sensitive demands which are faced by an r-store (brick and mortar retailer) and an e-store (online retailer) under different channel power structures. The competition between the channel members is modeled using game theory for the following channel structures, i.e., (i) r-store and e-store have commensurate channel power, (ii) r-store holds higher channel power, and (iii) e-store holds higher channel power. Equilibrium analysis was carried out to obtain the optimal pricing strategies and the r-store’s optimal profit and e-store. Further, the pricing strategies and resulting sales volumes were compared analytically and followed by a numerical validation. Three subcases were considered under numerical examples based on the parameter values with special reference to the base demand. It was found that competition between the r-store and the e-store having commensurate channel power will make them worse off. Therefore, the channel leadership is neither helping the r-store nor the e-store obtain more profit when the customer demand is emission sensitive.
Abin Thomas and Umakanta Mishra
Elsevier BV
Ming-Lang Tseng, Tat-Dat Bui, Ming K. Lim, Minoru Fujii, and Umakanta Mishra
Elsevier BV
Arash Sepehri, Umakanta Mishra, and Biswajit Sarkar
Elsevier BV
Ambarish Panda, Kathleen B. Aviso, Umakanta Mishra, and Ipseeta Nanda
Wiley
Abu Hashan Md Mashud, Magfura Pervin, Umakanta Mishra, Yosef Daryanto, Ming-Lang Tseng, and Ming K. Lim
Elsevier BV
Umakanta Mishra, Abu Hashan Md Mashud, Ming-Lang Tseng, and Jei-Zheng Wu
MDPI AG
This study investigated how greenhouse managers should invest in preservation and green technologies and introduce trade credit to increase their profits. We propose a supply chain inventory model with controllable deterioration and emission rates under payment schemes for shortage and surplus, where demand depends on price and trade credit. Carbon emissions and deterioration are factors affecting global warming, and many greenhouse managers have focused on reducing carbon emissions. Carbon caps and tax-based incentives have been used in many greenhouses to achieve such reduction. Because of the importance of reducing carbon emissions for developing a green supply chain, various studies have investigated how firms deal with carbon emission constraints. In this continuation, we have used green technology to curb the excessive emissions from the environment or make it clean from CO2. In a seller–buyer relationship, the seller can offer a trade credit period to the buyer to manage stock and stimulate demand. Deterioration may become a challenge for most firms as they are under time constraints control, and preservation technology could help. This study proposes three novel inventory strategies for a sustainable supply chain (full backorder, partial backorder, and no backorder), linking all these important issues. The solution optimizes total annual profit for inventory shortage or surplus. We conducted a numerical study with three examples to evaluate the model’s authenticity and effectiveness and demonstrate the solution technique. The deterioration and emission rates can be included in a trade credit policy to increase greenhouse profits. The results suggest that greenhouse managers could apply the proposed model to manage real-world situations.
Arash Sepehri, Umakanta Mishra, Ming-Lang Tseng, and Biswajit Sarkar
MDPI AG
Reducing carbon emissions plays a significant role in developing sustainable inventory systems. In a seller-buyer relationship, an allowable delay in payment is considered for the buyer to manage the stock and simulate the demand. Deteriorating items that usually have specific maximum lifetimes have become a challenge for most firms. Contrary to the importance of these issues, very little research has studied the impact of carbon emissions on deteriorating inventory systems. This paper provides a price-dependent demand for perishable items when carbon cap-and-trade regulation fills the mentioned gap. This model provides a carbon reduction investment scheme and illustrates this investment’s effect on the inventory system. This paper determines the optimal replenishment cycle and selling price, in which: (a) perishable items have specific maximum lifetimes, (b) a specific period of delay in payment is allowed for the buyer to accumulate revenue, (c) carbon is emitted due to ordering and storage operations and carbon cap and trade is regulated along with allowable carbon reduction investment. After developing the model, optimal values are obtained from necessary and sufficient conditions of optimality. Numerical experiments are proposed to validate the model. By developing an algorithm, the optimal values of replenishment cycle, selling price, and carbon reduction technology investment are obtained, and the impact of carbon emissions and efforts to control emissions are outlined. Finally, some managerial applications are mentioned, and future research directions are exposed.
Umakanta Mishra, Jei-Zheng Wu, and Biswajit Sarkar
Elsevier BV
Abstract Reducing carbon emissions is crucial in the development of a green supply chain. Greenhouse firms regulate carbon emission constraints to achieve sustainable supply chains. Deterioration can be controlled by utilizing a preservation technology investment in most of greenhouse firms as they have a specific (summer) time. Despite the importance of these concerns, few researches have been done on the effect of carbon emissions on the lot sizing sustainable inventory management. To fill this gap, this study develops a carbon cap and tax-regulated sustainable inventory management for a buyer utilizing a linear and non-linear price-dependent demand. This is the first study to explore sustainable inventory management under controllable carbon emissions from a greenhouse farm. An investment in preservation and green technology efforts under different backorder situations is needed. This model furnishes both theoretical and numerical solutions to determine optimal strategies relating to preservation technology (PRT) and green technology (GRT) investment, selling price, cycle time, and the fraction period length for sustainable inventory management. Several numerical examples are presented to validate all sustainable economic order quantity models. This problem is solved by theoretical and analytical methods. The results of this paper, in a controllable sustainable inventory management partial backordering case, exhibit a justifiable amount of profit compared with other backordering cases. The results of this paper are sustainable inventory management in the carbon tax and cap partial backlogging case has a better justifiable profit with the highest cycle time and the lowest value of the fraction length period as well as the lower green technology investment cost compared with other models. Finally, sensitivity analysis is discussed in relation to the managerial implications of the optimal feasible solution with respect to key parameters.