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Outpatient care delivery is one of the key revenue sources of a hospital which plays a salient role in timely care delivery. The key purpose of the study is to propose a multi-objective simulation-based decision support model that considers the cost of care delivery and patient dissatisfaction as its two key conflicting objectives. Patient dissatisfaction considers service fairness. Patient idiosyncrasies such as no-show, unpunctuality and balking have been considered in the model involving multiple classes of patients.

A model has been designed using data collected from field investigations. In the first stage, queuing theory based discrete event simulation model has been developed. Genetic algorithm has been used to solve the scalarized problem and obtain actionable insights. In the second stage, non-dominated sorting genetic algorithm II (NSGA-II) has been involved to achieve the Pareto optimal fronts considering equal priority of the two objectives.

The computational results considering various parameter settings can help in efficient resource planning while ensuring better care delivery. The model proposed in the study provides structural insights on the business strategy of healthcare service providers on optimizing the dual goals of care delivery cost and service fairness.

The study is one of the early works that helps to improve the care delivery process by taking into consideration the environmental factors as well as service fairness. The study demonstrates the usage of simulation-based multi-objective optimization to provide a more sustainable patient centric care delivery.

The purpose of this paper is to determine the inventory replenishment policies when demand rate is a function of the inventory space allocated to the products on retail shelves. Existing results on inventory policies with inventory-level-dependent demand (ILDD) assume deterministic functional forms of the demand rate. In this paper, the authors model the inventory decisions when demand is a function of shelf-space allocation and random uncertainty. The authors provide managerial insights of this paper's results.

The demand rate is assumed to be a function of shelf-space allocation based on two settings in the literature. First, the authors model the demand rate as a function of initial shelf-space allocation. In the next setting, the authors assume that the demand rate is a function of instantaneous inventory level on shelves. In both the settings, the authors also model random demand uncertainty in addition to the shelf-space dependency of demand rate. The objective is to maximize the expected profit and determine the inventory parameters.

In addition to the demand uncertainty, the authors consider linear, power and exponential functional forms of demand rate. Inventory policy that maximizes expected profit is determined when demand rate is a function of initial allocation and displayed inventory level. The results are implementable for practitioners for optimizing the shelf-space allocation and related inventory policy.

Most of the extant results on inventory policy with shelf-space-dependent demand do not model the demand uncertainty. The authors model a variety of functional forms of demand rate with ILDD in addition to the demand uncertainty. The results are a building block for more applications in inventory management for real-life applications.

The purpose of this paper is to understand the practices and policies unique to high-tech manufacturing start-ups in emerging economies, such as India. The study analyzes the main features and challenges of the high-tech manufacturing sector, and the way in which enabling environment including policy making, supply chain capability and related technologies through Industry 4.0 could be leveraged to foster growth.

The paper undertakes an exploratory approach through in-depth semi-structured interviews conducted with high-tech manufacturing firms in various stages of their growth. The paper provides evidence of the challenges that high-tech manufacturing firms face in India, the strategies they adopt and highlights the role of institutional structures and policies.

Findings show that high-tech manufacturing start-ups in India face various challenges in the upstream, production and downstream supply chain processes. Further, issues related to availability of quality material, quality suppliers, contracts, funding and access to markets remain. Results also show that enabling operational and financial levers could be created by policy makers and other stakeholders to help the high-tech manufacturing start-ups scale faster and create value.

This paper contributes to the R&D intensive industry and high-tech manufacturing literature in the context of emerging economies, such as India, and provides a rigorous overview of the start-up ecosystem in high-tech manufacturing.

Many pharmaceutical companies outsource their research and development and manufacturing operations to value chain partners. Effective evaluation of outsourcees' capabilities and relationship management are often central for outsourcers to secure sustainable competitive advantage. This study aims to investigate how to evaluate outsourcees and manage outsourcing relationships in the pharmaceutical industry based on the theory of dynamic capability (DC).

The investigation used an exploratory multiple case study approach. The data collection, spanning a period of 12 months, entailed a multinational pharmaceutical company (PharmCo) with its headquarters in Europe, and four contract research and manufacturing organizations from China and India.

The results show that PharmCo evaluates its outsourcing partners based on their dynamic capabilities, which include processes (project deliverables, communication, and accuracy of costs), positions (financial assets, number of scientists, spectrum of services, and geographical presence), and paths (past experiences). The findings indicate that a pharmaceutical company outsources to partners with high operational capabilities, whereas it builds fully integrated outsourcing relationships only with those that have high dynamic capabilities.

Findings from this study provide guidelines for practitioners in manufacturing industries to efficiently and effectively evaluate and manage outsourcees to deal with the challenges and risks associated with strategic outsourcing.

The paper contributes to the literature by providing empirical evidence on the role of DC in outsourcee evaluation and outsourcing relationship management in the pharmaceutical industry. Moreover, the paper illustrates how to conceptualize and measure the DC as a multi-dimensional construct. The analysis also indicates that partners' dynamic and operational capabilities play different roles in outsourcing relationship management.

The purpose of this paper is to develop a framework for mapping the Indian Public Distribution System (PDS) using multi-agent system (MAS). The entire PDS supply chain from purchase to the distribution is mapped in detail by integrating stages of PDS supply chain.

Literature related to PDS, food grain supply chain (FGSC) and MAS is reviewed and critically assessed. Based on this a framework is proposed which will help in improving functioning of PDS.

The PDS has many shortcomings arising from its complex structure and practices which are used to implement it. The authors propose an MAS to model it in which each entity will be modelled as an agent. The authors propose two stages of supply chain. First stage models the processes from procurement to storage of food grain and second stage model the distribution process.

This paper will be of interest to the policy makers and decision makers involved in the PDS by providing the shortfalls in the system and also suggesting a method to model the PDS based on practices of food supply chains.

This paper provides the decision makers in the PDS, a framework to model and assess the entire supply chain. This will help them in effective implementation of the PDS and also improve in the areas of concerns which are pointed the study.

The case primarily focused on a real-life problem and shows that existing operations management tools can be used to solve a complex problem. Through this case, the participants will learn the application of the factor loading method and aggregate planning.

This case revolves around the Modi Agro Pvt. Ltd, a mango procurement and distribution business established in 1994 by Mr Dhanush Modi in Mumbai, India. Mr Mahendra Modi, son of the company owner, observed that the different seasons of cultivation and varied customer demands lead to changing workforce requirements during the procurement process. In addition, the production quality, variety, available resources, procurement location and cost play a significant role in establishing a long-term relationship with the customers. This case highlights the problem faced by Mahendra in determining an appropriate location among all available options for mango procurement and the optimal workforce for each month to meet the varying customers’ demands.

The case can be used as teaching material for participants of the course Service Operations Management, Operations Management, Decision Analysis and Quantitative Techniques

Teaching notes are available for educators only.

CSS 9: Operations and logistics.