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The purpose of this study is to identify organisational homeostasis factors in the context of healthcare organisations and to develop a conceptual model for green transformation.

The organisational homeostasis factors were determined by review of literature study and the opinions of healthcare experts. Scheduled interviews and closed-ended questionnaires are employed to collect data for this research. This study employed “TISM methodology” and “MICMAC analysis” to better comprehend how the components interact with one another and prioritise them based on their driving and dependence power.

This study identified 10 factors of organisational homeostasis in healthcare organisation. Recognition of interdependence, hormesis, strategic coalignment, consciousness on dependence of healthcare resources and cybernetic principle of regulations are the driving or key factors of this study.

The study's primary focus was on the organisational homeostasis factors in healthcare organisations. The methodological approach and structural model are used in a healthcare organisation; in the future, these approaches can be applied to other industries as well.

The key drivers of organisational homeostasis and the identified factors will be better comprehended and understood by academic and important stakeholders in healthcare organisations. Prioritizing the factors helps the policymakers to comprehend the organisational homeostasis for green transformation in healthcare.

In this study, the TISM and MICMAC analysis for healthcare is proposed as an innovative approach to address the organisational homeostasis concept in the context of green transformation in healthcare organisations.

The paper explores and characterizes antifragility in simple inventory systems exposed to demand variability, providing the initial inroads to operationalizing antifragility in the context of inventory management. Antifragility refers to the feature of a system that can benefit from uncertainty, rather than suffer from it. The paper expands the concept of inventory beyond that of risk mitigation and towards one of enabling antifragility.

The study employs analytical and simulation modelling of an inventory system with two classes of demand. To separate the influence of factors, a simple inventory policy with a range of fixed order quantities is modelled, allowing for the identification of antifragile regions in an experimental space.

Outputs uncover a variety of performance outcomes, ranging from settings where additional inventory yields no benefit, to areas where additional inventory results in increasing normalized profit with increasing uncertainty, demonstrating antifragility. In between these regions, increases in normalized profit are bounded, and confined to specific regions.

This research expands academic understanding of inventory as a vehicle to achieving antifragile outcomes. Although this paper does not solve for an optimal policy as typical inventory research does, it instead characterizes the antifragile outcomes within simple inventory systems. Further research should be carried out to investigate antifragility in settings of greater complexity and design ordering policies leveraging inventory towards a gain from uncertainty.

Typically, inventory is used to buffer against uncertainty, and, with a given amount of inventory, the performance is expected to degrade with increasing variability. In this paper, the authors demonstrate that certain levels of additional inventory can result in antifragility and increase profitability as uncertainty increases, extending beyond traditional conceptualizations of inventory and uncertainty.

Empirical research into designing antifragile outcomes is limited, with very few examples of increasing performance with increases in uncertainty. This article presents an initial empirical exploration of how additional inventory can result in antifragility.