scholarly journals Reinforcing the Supply Chain of COVID-19 Therapeutics with Expert-Coded Retrosynthetic Software

2020 ◽  
Author(s):  
Yingfu Lin ◽  
Zirong Zhang ◽  
babak mahjour ◽  
di wang ◽  
rui zhang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Supply Chain ◽  
General Solution ◽  
Supply Chains ◽  
Raw Materials ◽  
Drug Repurposing ◽  
Hand Sanitizer ◽  
Toilet Paper ◽  
Global Demand

Supply chains become stressed when demand for essential products increases rapidly in times of crisis. This year, the scourge of coronavirus highlighted the fragility of diverse supply chains, affecting the world’s pipeline of hand sanitizer, 1 toilet paper,2 and pharmaceutical starting materials. 3 Many drug repurposing studies are now underway. 4 If a winning therapeutic emerges, it is unlikely that the existing inventory of the medicine, or even the chemical raw materials needed to synthesize it,5 will be available in the quantities required to satisfy global demand. We show the use of a retrosynthetic artificial intelligence (AI) 6-10 to navigate multiple parallel synthetic sequences, and arrive at plausible alternate reagent supply chains for twelve investigational COVID-19 therapeutics. In many instances, the AI utilizes C–H functionalization logic, 11-13 and we have experimentally validated several syntheses, including a route to the antiviral umifenovir that requires functionalization of six C–H bonds. This general solution to chemical supply chain reinforcement will be useful during global disruptions, such as during a pandemic.

scholarly journals Reinforcing the Supply Chain of COVID-19 Therapeutics with Expert-Coded Retrosynthetic Software

2020 ◽  
Author(s):  
Yingfu Lin ◽  
Zirong Zhang ◽  
babak mahjour ◽  
di wang ◽  
rui zhang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Supply Chain ◽  
General Solution ◽  
Supply Chains ◽  
Raw Materials ◽  
Drug Repurposing ◽  
Hand Sanitizer ◽  
Toilet Paper ◽  
Global Demand

Supply chains become stressed when demand for essential products increases rapidly in times of crisis. This year, the scourge of coronavirus highlighted the fragility of diverse supply chains, affecting the world’s pipeline of hand sanitizer, 1 toilet paper,2 and pharmaceutical starting materials. 3 Many drug repurposing studies are now underway. 4 If a winning therapeutic emerges, it is unlikely that the existing inventory of the medicine, or even the chemical raw materials needed to synthesize it,5 will be available in the quantities required to satisfy global demand. We show the use of a retrosynthetic artificial intelligence (AI) 6-10 to navigate multiple parallel synthetic sequences, and arrive at plausible alternate reagent supply chains for twelve investigational COVID-19 therapeutics. In many instances, the AI utilizes C–H functionalization logic, 11-13 and we have experimentally validated several syntheses, including a route to the antiviral umifenovir that requires functionalization of six C–H bonds. This general solution to chemical supply chain reinforcement will be useful during global disruptions, such as during a pandemic.

scholarly journals Reinforcing the Supply Chain of Umifenovir and Other Antiviral Drugs with Retrosynthetic Software

2021 ◽  
Author(s):  
Yingfu Lin ◽  
Zirong Zhang ◽  
Babak Mahjour ◽  
Di Wang ◽  
Rui Zhang ◽  
...  
Keyword(s):  
Supply Chain ◽  
Supply Chains ◽  
Raw Materials ◽  
Antiviral Drug ◽  
Drug Repurposing ◽  
Antiviral Drugs ◽  
Multiple Drug ◽  
General Strategy ◽  
Inflammatory Drugs ◽  
Anti Inflammatory

Abstract The global disruption caused by the 2020 coronavirus pandemic stressed the supply chain of many products, including pharmaceuticals. Multiple drug repurposing studies for COVID-19 are now underway. If a winning therapeutic emerges, it is unlikely that the existing inventory of the medicine, or even the chemical raw materials needed to synthesize it, will be available in the quantities required. We used retrosynthetic software to arrive at alternate chemical supply chains for the antiviral drug umifenovir, as well as eleven other antiviral and anti-inflammatory drugs. We have experimentally validated four routes to umifenovir and one route to bromhexine. In several instances, the software utilizes C–H functionalization logic, and one route to umifenovir employs functionalization of six C–H bonds. The general strategy we apply can be used to identify distinct starting materials, and relieve stress on existing supply chains.

scholarly journals Reinforcing the supply chain of umifenovir and other antiviral drugs with retrosynthetic software

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Yingfu Lin ◽  
Zirong Zhang ◽  
Babak Mahjour ◽  
Di Wang ◽  
Rui Zhang ◽  
...  
Keyword(s):  
Supply Chain ◽  
Supply Chains ◽  
Functional Groups ◽  
Raw Materials ◽  
Antiviral Drug ◽  
Drug Repurposing ◽  
Antiviral Drugs ◽  
Multiple Drug ◽  
Search Results ◽  
Inflammatory Drugs

AbstractThe global disruption caused by the 2020 coronavirus pandemic stressed the supply chain of many products, including pharmaceuticals. Multiple drug repurposing studies for COVID-19 are now underway. If a winning therapeutic emerges, it is unlikely that the existing inventory of the medicine, or even the chemical raw materials needed to synthesize it, will be available in the quantities required. Here, we utilize retrosynthetic software to arrive at alternate chemical supply chains for the antiviral drug umifenovir, as well as eleven other antiviral and anti-inflammatory drugs. We have experimentally validated four routes to umifenovir and one route to bromhexine. In one route to umifenovir the software invokes conversion of six C–H bonds into C–C bonds or functional groups. The strategy we apply of excluding known starting materials from search results can be used to identify distinct starting materials, for instance to relieve stress on existing supply chains.

AI technologies and their impact on supply chain resilience during COVID-19

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sachin Modgil ◽  
Shivam Gupta ◽  
Rébecca Stekelorum ◽  
Issam Laguir
Keyword(s):  
Artificial Intelligence ◽  
Supply Chain ◽  
Information Processing ◽  
Supply Chains ◽  
Failure Modes ◽  
Stress Test ◽  
Weak Links ◽  
Content Type ◽  
Supply Chain Resilience

PurposeCOVID-19 has pushed many supply chains to re-think and strengthen their resilience and how it can help organisations survive in difficult times. Considering the availability of data and the huge number of supply chains that had their weak links exposed during COVID-19, the objective of the study is to employ artificial intelligence to develop supply chain resilience to withstand extreme disruptions such as COVID-19.Design/methodology/approachWe adopted a qualitative approach for interviewing respondents using a semi-structured interview schedule through the lens of organisational information processing theory. A total of 31 respondents from the supply chain and information systems field shared their views on employing artificial intelligence (AI) for supply chain resilience during COVID-19. We used a process of open, axial and selective coding to extract interrelated themes and proposals that resulted in the establishment of our framework.FindingsAn AI-facilitated supply chain helps systematically develop resilience in its structure and network. Resilient supply chains in dynamic settings and during extreme disruption scenarios are capable of recognising (sensing risks, degree of localisation, failure modes and data trends), analysing (what-if scenarios, realistic customer demand, stress test simulation and constraints), reconfiguring (automation, re-alignment of a network, tracking effort, physical security threats and control) and activating (establishing operating rules, contingency management, managing demand volatility and mitigating supply chain shock) operations quickly.Research limitations/implicationsAs the present research was conducted through semi-structured qualitative interviews to understand the role of AI in supply chain resilience during COVID-19, the respondents may have an inclination towards a specific role of AI due to their limited exposure.Practical implicationsSupply chain managers can utilise data to embed the required degree of resilience in their supply chains by considering the proposed framework elements and phases.Originality/valueThe present research contributes a framework that presents a four-phased, structured and systematic platform considering the required information processing capabilities to recognise, analyse, reconfigure and activate phases to ensure supply chain resilience.

Genetic Algorithm to Solve Multi-Period, Multi-Product, Bi-Echelon Supply Chain Network Design Problem

2009 ◽  
Vol 2 (4) ◽  
pp. 24-42 ◽  
Author(s):  
R. Dhanalakshmi ◽  
P. Parthiban ◽  
K. Ganesh ◽  
T. Arunkumar
Keyword(s):  
Supply Chain ◽  
Supply Chains ◽  
Raw Materials ◽  
Optimization Techniques ◽  
Network Design Problem ◽  
Production Plant ◽  
Two Stage ◽  
Cost Structures ◽  
Multi Stage ◽  
Product Costs

In many multi-stage manufacturing supply chains, transportation related costs are a significant portion of final product costs. It is often crucial for successful decision making approaches in multi-stage manufacturing supply chains to explicitly account for non-linear transportation costs. In this article, we have explored this problem by considering a Two-Stage Production-Transportation (TSPT). A two-stage supply chain that faces a deterministic stream of external demands for a single product is considered. A finite supply of raw materials, and finite production at stage one has been assumed. Items are manufactured at stage one and transported to stage two, where the storage capacity of the warehouses is limited. Packaging is completed at stage two (that is, value is added to each item, but no new items are created), and the finished goods inventories are stored which is used to meet the final demand of customers. During each period, the optimized production levels in stage one, as well as transportation levels between stage one and stage two and routing structure from the production plant to warehouses and then to customers, must be determined. The authors consider “different cost structures,” for both manufacturing and transportation. This TSPT model with capacity constraint at both stages is optimized using Genetic Algorithms (GA) and the results obtained are compared with the results of other optimization techniques of complete enumeration, LINDO, and CPLEX.

scholarly journals Only as Strong as the Weakest Link

2011 ◽  
Vol 133 (06) ◽  
pp. 26-31 ◽  
Author(s):  
James B. Rice
Keyword(s):  
Supply Chain ◽  
Supply Chains ◽  
Raw Materials ◽  
Supply Chain Risk Management ◽  
Supply Chain Risk ◽  
Risk Monitoring ◽  
Weakest Link ◽  
Internal Operations ◽  
Supplier Risk ◽  
Simulation Exercises

This article discusses why companies need to shore up their supply chains to guard against disasters. Supply chains, which provide raw materials and distribute finished goods to end customers, now extend through many independent companies, and nearly every chain is global. As a consequence, major events from around the world, both natural and man-made, affect the flow of goods and have an increasingly sharp and visibly evident impact on businesses. It is therefore essential that companies identify their entire upstream supply chain—not just their tier-one suppliers but all suppliers and subsuppliers . They should try to understand their downstream customers and intermediaries as well. Businesses also need to conduct a vulnerability assessment for their extended supply chain, not just internal operations. This includes assessing geographic risk, organizational risk, embedded risk, and supplier risk. Organizations should also develop a plan to create a culture that supports supply chain risk management, including active risk monitoring, education, training, and simulation exercises.

scholarly journals The Semiconductor Supply Chain: Assessing National Competitiveness

2021 ◽  
Author(s):  
Saif Khan
Keyword(s):  
Artificial Intelligence ◽  
United States ◽  
Supply Chain ◽  
Supply Chains ◽  
National Security ◽  
Emerging Technologies ◽  
Scientific Progress ◽  
The United States ◽  
Beneficial Use ◽  
National Competitiveness

Semiconductors are a key component in fueling scientific progress, promoting economic advancement, and ensuring national security. This issue brief summarizes each component of the semiconductor supply chain and where the United States and its allies possess the greatest leverage. A related policy brief, “Securing Semiconductor Supply Chains,” recommends policy actions to ensure the United States maintains this leverage and uses it to promote the beneficial use of emerging technologies, such as artificial intelligence.

scholarly journals Securing Semiconductor Supply Chains

2021 ◽  
Author(s):  
Saif Khan
Keyword(s):  
Artificial Intelligence ◽  
United States ◽  
Supply Chain ◽  
Supply Chains ◽  
Emerging Technologies ◽  
State Of The Art ◽  
The United States ◽  
Beneficial Use ◽  
Semiconductor Chips

The countries with the greatest capacity to develop, produce and acquire state-of-the-art semiconductor chips hold key advantages in the development of emerging technologies. At present, the United States and its allies possess significant leverage over core segments of the supply chain used to produce these chips. This policy brief outlines actions the United States and its allies can take to secure that advantage in the long term and use it to promote the beneficial use of emerging technologies, such as artificial intelligence.

Transforming Logistics Pricing

2017 ◽  
Vol 8 (1) ◽  
pp. 40-54
Author(s):  
Jeffrey Drue Peck Jr ◽  
Michael S Gendron ◽  
Tera Black
Keyword(s):  
Decision Making ◽  
Supply Chain ◽  
Supply Chains ◽  
Business Intelligence ◽  
Multivariate Regression ◽  
Raw Materials ◽  
Market Forces ◽  
Logistics Services ◽  
The Cost ◽  
The Relationship

Raw materials and products are moved and created globally through complex supply chains. Within those supply chains, logistics is what enables the goods to move through distribution and to the end consumer. This is what motivates the researchers to examine the logistics portion of the supply chain and attempt to determine the relationship between various market forces and their impact on the cost of logistics. This will be accomplished with transformative analytics techniques, such as multivariate regression modeling, that should enable logistics managers, researchers, and others to better understand the cost of logistics services, and thus impact pricing of goods dependent on those services. In a world where logistics managers rely heavily on “gut feel”, utilizing business intelligence and analytics can better enable decision making.

Genetic Algorithm to Solve Multi-Period, Multi-Product, Bi-Echelon Supply Chain Network Design Problem

2011 ◽  
pp. 275-289
Author(s):  
R. Dhanalakshmi ◽  
P. Parthiban ◽  
K. Ganesh ◽  
T. Arunkumar
Keyword(s):  
Supply Chain ◽  
Supply Chains ◽  
Raw Materials ◽  
Optimization Techniques ◽  
Network Design Problem ◽  
Production Plant ◽  
Two Stage ◽  
Cost Structures ◽  
Multi Stage ◽  
Product Costs

In many multi-stage manufacturing supply chains, transportation related costs are a significant portion of final product costs. It is often crucial for successful decision making approaches in multi-stage manufacturing supply chains to explicitly account for non-linear transportation costs. In this article, we have explored this problem by considering a Two-Stage Production-Transportation (TSPT). A two-stage supply chain that faces a deterministic stream of external demands for a single product is considered. A finite supply of raw materials, and finite production at stage one has been assumed. Items are manufactured at stage one and transported to stage two, where the storage capacity of the warehouses is limited. Packaging is completed at stage two (that is, value is added to each item, but no new items are created), and the finished goods inventories are stored which is used to meet the final demand of customers. During each period, the optimized production levels in stage one, as well as transportation levels between stage one and stage two and routing structure from the production plant to warehouses and then to customers, must be determined. The authors consider “different cost structures,” for both manufacturing and transportation. This TSPT model with capacity constraint at both stages is optimized using Genetic Algorithms (GA) and the results obtained are compared with the results of other optimization techniques of complete enumeration, LINDO, and CPLEX.

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