Small farmers in emerging economies face numerous constraints, including financial distress and limited access to profitable markets. Thus, procurement contracts having provisions for resource inputs are essential in linking them to the mainstream agri-supply chains. Consequently, we frame a decision-making model for a risk-neutral firm offering advance payment contracts to such credit-constrained smallholders. The firm allows them to commit their supply quantities and pays a fraction of the per-unit guaranteed price for the crop as advance. Behaviorally, the decision-biased farmers are loss-averse and hyperbolic discounters. They have a subjective perception of crop yield based on the (timing of) advance. Our model establishes a criterion for the firm to shortlist the farmers for contracting. Subsequently, it predicts their commitment quantities under limited information on their behavioral parameters using the Prospect Theory framework. Under the model assumptions, we calculate the upper and lower bounds on their commitment quantities, thereby establishing the limits on the firm’s production quantity and profitability. Lastly, our model determines the optimal timing for paying the advances to the eligible farmers using two different strategies. The first favors a firm’s profit maximization objective, while the second maximizes its marginal cost savings from contracting over spot buying. Through analytical and numerical calculations, we establish that the latter approach significantly raises the farmers’ utilities without severely affecting the firm’s profit. Thus, making it fit for a social entrepreneur on a mission to create a socially responsible and economically viable procurement strategy. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

World University root node

Department of Management Studies

Annals of Operations Research

Enabling socially responsible operations: A decision-making model for a firm contracting with decision-biased smallholders

Direct metal laser sintering (DMLS) process technique is one of the widely used additive manufacturing (AM) method. Some of the challenges in this process are minimizing residual stress and part distortions due to the high thermal gradient during printing. Using an adequate amount of support structures will reduce part distortion but over usage of supports results in increased cost, time and support removal effort. In this study, an attempt is made to optimize support structure required to build part successfully, using finite element analysis (FEA)-based numerical optimization scheme. The problem is formulated to minimize the volume fraction (VF) of the support structure while maintaining support yield strength, and printed part distortion as constraints. Homogenization of perforated block support structure into simple solid continuum technique is adopted in this study to reduce model complexity and reduction of computational cost associated with distortion prediction in FEA. A cantilever beam test sample is printed in Inconel 718 material using an EOS M280™ machine to demonstrate the efficacy of this approach. The optimization result of volume fraction is used as a support generation parameter to create actual support geometry and is verified using FEA. Distortion simulation shows good agreement with optimization results. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Department of Engineering Design

Lecture Notes in Mechanical Engineering

DMLS Support Structure Optimization Using Homogenized Model Distortion Prediction

The present investigation focuses on the effect of heat treatment parameters on the hardening and fracture behavior of 17-4 PH stainless steel, thereby assessing the susceptibility to failure at intense loading conditions. The tensile tests were conducted at an effective strain rate of 47 s−1 on a GleebleTM 3800 thermo-mechanical simulator at room temperature and 75 °C. X-ray diffraction (XRD) analyses revealed the presence of a predominant martensitic phase with significant reverted austenite content at higher aging times in the 480 °C condition, thereby resulting in the softening phenomenon and lower strength values. Peak hardness and superior strength values were obtained for higher aging times in the 450 °C condition and lower aging times in the 480 °C condition due to precipitate-particle segregation at packet junctions and finer lath microstructure. Consequently, the fractographic observations from scanning electron microscopy (SEM) exhibited a semi-brittle mode of failure. In the over-aged conditions, a transition toward ductile fracture mode with dimple morphology was depicted. The higher testing temperature produced a uniform set of failure strain values compared to the scattered strains at ambient conditions. © 2022, ASM International.

Department of Mechanical Engineering

Journal of Materials Engineering and Performance

Microstructure-Induced High-Strain-Rate Deformation Behavior of Heat-Treated 17-4 PH Stainless Steel

Department of Mathematics

GENERAL

Department Of Physics

IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.