Those who do not move, do not notice their (supply) chains—inconvenient lessons from disruptions related to COVID-19

Settanni, Ettore

doi:10.1007/s00146-020-00988-y

Cited by 5 publications

(3 citation statements)

References 17 publications

(15 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…CM were expected to bring that figure down to 70 days or less (Srai et al, 2015). However, the global disruptions in API supply linked to a recent pandemic demonstrated that the merits of cutting down on inventory are not straightforward to demonstrate in the presence of high dependency on a handful of manufacturing locations (Settanni, 2020).…”

Section: Impact On Cost Inventory and Lead Timesmentioning

confidence: 99%

Continuous manufacturing technologies in upstream pharmaceutical supply chains: Combining engineering and managerial criteria

Aulakh

Settanni

Srai

2021

Multi Criteria Decision Anal

Self Cite

View full text Add to dashboard Cite

The COVID-19 pandemic exposed vulnerabilities in upstream pharmaceutical supply chains (PSC). One is that the global supply of active pharmaceutical ingredients (APIs) is overly dependent on few locations and large-scale batch manufacturing. Regulators hope to enable more dependable location decisions and improved processing quality with the adoption of advanced technologies such as process intensification through continuous manufacturing (CM). Conceptual work suggests that the benefits of shifting from batch to CM accrue end-to-end across the PSC. Yet detailed quantitative information about CM is limited at an early stage of evaluation, and too specialised to inform managerial decisions about PSC reconfiguration. Supply chain and engineering criteria are rarely combined in the early-stage evaluation of alternative CM technologies. Extant CM research typically overlooks implications for supply chain managers. To address the current gap, this article evaluates, at an early stage of adoption, alternative CM reactor technologies for the synthesis of APIs in selected therapeutic areas. With evidence from secondary data, relevant technologies and criteria are identified, and their relative importance is evaluated in a semi-quantitative fashion following analytical hierarchy process (AHP) principles, ensuring that findings are intelligible to both engineers and managers. The proposed empirical work enriches previous conceptual frameworks predicated on volume-variety considerations. Specifically, findings suggest that, all things considered, microreactor technologies outperform alternatives. However, PSC managerial considerations introduce nuances in specific therapeutic areas, for example, antivirals where a tension between complex chemistry and the need for flexibility in unit operations may favour batch manufacturing. For analgesics the need to exploit the existing manufacturing base whilst addressing inventory reduction favours technologies that incorporate elements of batch and CM. The proposed analysis is in line with real-world decisions that global medicines manufacturers are increasingly facing, as governments seek to develop local health countermeasures to the COVID-19 pandemic in the absence of detailed information.Parminder Kaur Aulakh was with the University of Cambridge at the time this research was carried out.

show abstract

Section: Impact On Cost Inventory and Lead Timesmentioning

confidence: 99%

Continuous manufacturing technologies in upstream pharmaceutical supply chains: Combining engineering and managerial criteria

Aulakh

Settanni

Srai

2021

Multi Criteria Decision Anal

Self Cite

View full text Add to dashboard Cite

show abstract

“…The COVID-19 pandemic triggered a wave of panic-buying behavior worldwide at the end of Q1 2020 (e.g., Furutani 2020 ; Lufkin 2020 ; Settanni 2020 ). Consumers rushed to grocery stores and stockpiled large amounts of essential supplies, such as face masks, hand sanitizers, canned food, and toilet paper.…”

Section: Introductionmentioning

confidence: 99%

Using AI to detect panic buying and improve products distribution amid pandemic

Adulyasak

Benomar²,

Chaouachi³

et al. 2023

AI & Soc

View full text Add to dashboard Cite

The COVID-19 pandemic has triggered panic-buying behavior around the globe. As a result, many essential supplies were consistently out-of-stock at common point-of-sale locations. Even though most retailers were aware of this problem, they were caught off guard and are still lacking the technical capabilities to address this issue. The primary objective of this paper is to develop a framework that can systematically alleviate this issue by leveraging AI models and techniques. We exploit both internal and external data sources and show that using external data enhances the predictability and interpretability of our model. Our data-driven framework can help retailers detect demand anomalies as they occur, allowing them to react strategically. We collaborate with a large retailer and apply our models to three categories of products using a dataset with more than 15 million observations. We first show that our proposed anomaly detection model can successfully detect anomalies related to panic buying. We then present a prescriptive analytics simulation tool that can help retailers improve essential product distribution in uncertain times. Using data from the March 2020 panic-buying wave, we show that our prescriptive tool can help retailers increase access to essential products by 56.74%.

show abstract

“…The emergence of global pandemic COVID-19 is attracting the attention of various stakeholders since the start of 2020. A serious issue of the rising number of cases and limited healthcare facilities hit most nations and had a profound impact on the way we see our world and our lives everyday [1]. The alarming rate of infection and pattern of spread threatens the global ecosystem, despite the safety measures put in place to contain the spread of the virus, the containment of the virus is at a nascent stage.…”

Section: Introductionmentioning

confidence: 99%

Leveraging Artificial Intelligence Tools to Combat the COVID-19 Crisis

Gaur

Singh

Agarwal

2021

Communications in Computer and Information Science

View full text Add to dashboard Cite

The severity of the coronavirus disease (COVID-19) has shaken the world forcefully and sent economies into difficult times. As stated by the World Health Organization on 11 March 2020, the impact of the current unprecedented situation will cause the major cost of lives and financial damage across the globe. The various stakeholders, including scientists, doctors, economists, politicians, are seeking the help of data scientist to explore disruptive technologies which can aid in reducing the pandemic's effects. In these unprecedented times, Artificial Intelligence (AI) has emerged as a promising tool which can play a huge role in various domains. There are several fields where disruptive technologies like AI is utilised to the fight against COVID-19 such as generating real-time warnings and alerts, tracing prediction, interactive dashboards, diagnosing risks, suggesting treatments and cures, facilitating "contactless" deliveries, and social control. The focus of the present study is to analyze the contribution of AI to track and fight against COVID-19.

show abstract

Those who do not move, do not notice their (supply) chains—inconvenient lessons from disruptions related to COVID-19

Cited by 5 publications

References 17 publications

Continuous manufacturing technologies in upstream pharmaceutical supply chains: Combining engineering and managerial criteria

Continuous manufacturing technologies in upstream pharmaceutical supply chains: Combining engineering and managerial criteria

Using AI to detect panic buying and improve products distribution amid pandemic

Leveraging Artificial Intelligence Tools to Combat the COVID-19 Crisis

Contact Info

Product

Resources

About