Recent pandemics have highlighted vulnerabilities in our global economic systems, especially supply chains. Possible future pandemic raises a dilemma for businesses owners between short-term profitability and long-term supply chain resilience planning. In this study, we propose a novel agent-based simulation model integrating extended Susceptible-Infected-Recovered (SIR) epidemiological model and supply and demand economic model to evaluate supply chain resilience strategies during pandemics. Using this model, we explore a range of supply chain resilience strategies under pandemic scenarios using in silico experiments. We find that a balanced approach to supply chain resilience performs better in both pandemic and non-pandemic times compared to extreme strategies, highlighting the importance of preparedness in the form of a better supply chain resilience. However, our analysis shows that the exact supply chain resilience strategy is hard to obtain for each firm and is relatively sensitive to the exact profile of the pandemic and economic state at the beginning of the pandemic. As such, we used a machine learning model that uses the agent-based simulation to estimate a near-optimal supply chain resilience strategy for a firm. The proposed model offers insights for policymakers and businesses to enhance supply chain resilience in the face of future pandemics, contributing to understanding the trade-offs between short-term gains and long-term sustainability in supply chain management before and during pandemics.

Integrating Internet of Things (IoT) technology inside the cold supply chain can enhance transparency, efficiency, and quality, optimizing operating procedures and increasing productivity. The integration of IoT in this complicated setting is hindered by specific barriers that need a thorough examination. Prominent barriers to IoT implementation in the cold supply chain are identified using a two-stage model. After reviewing the available literature on the topic of IoT implementation, a total of 13 barriers were found. The survey data was cross-validated for quality, and Cronbach's alpha test was employed to ensure validity. This research applies the interpretative structural modeling technique in the first phase to identify the main barriers. Among those barriers, "regularity compliance" and "cold chain networks" are key drivers for IoT adoption strategies. MICMAC's driving and dependence power element categorization helps evaluate the barrier interactions. In the second phase of this research, a decision-making trial and evaluation laboratory methodology was employed to identify causal relationships between barriers and evaluate them according to their relative importance. Each cause is a potential drive, and if its efficiency can be enhanced, the system as a whole benefits. The research findings provide industry stakeholders, governments, and organizations with significant drivers of IoT adoption to overcome these barriers and optimize the utilization of IoT technology to improve the effectiveness and reliability of the cold supply chain.

While the U.S. is making strides in the advancement of AI use cases across industries, we have a long way to go before AI technologies are commonplace and truly ingrained in our daily life. What are the missing pieces? Better data access and improved data sharing. As our ability to address point applications and solutions with AI technology matures, we will need a greater ability to share data and insights while being able to draw conclusions across problem domains. Cooperation between individuals from government, research, higher education and the private sector to make greater data sharing feasible will drive acceleration of new use cases while balancing the need for data privacy.

New York, NY, May 26, 2021 (GLOBE NEWSWIRE) -- Facts and Factors have published a new research report titled "Artificial Intelligence in Retail Market By Type (Offline, and Online), By Technology (Natural Language Processing, Machine Learning, and Deep Learning, and Others), By Solution (Customer Relationship Management, Payment Services management, Price Optimization, Product Recommendation, and Planning, Supply chain management and Demand Planning, Virtual Assistant, Visual Search, Others) By Service (Managed Services, and Professional Services), By Deployment Model (On-Premises, and Cloud), and By Application (In-Store Visual Monitoring and Surveillance, Location-Based Marketing, Market Forecasting, Predictive Merchandising, Programmatic Advertising, and Others): Global Industry Perspective, Comprehensive Analysis, and Forecast, 2020 – 2026". "According to the research report, the global Artificial Intelligence in Retail Market was estimated at USD 2.7 Billion in 2019 and is expected to reach USD 20.05 Billion by 2026. The global Artificial Intelligence in Retail Market is expected to grow at a compound annual growth rate (CAGR) of 39% from 2020 to 2026". Digitalization in retail is much more than just linking objects. It's about turning data into observations that guide decisions that produce better market results.

The role of artificial intelligence in vaccine distribution will be very critical in vaccinating the global population against COVID-19. Vaccine distribution is one of the biggest logistical challenges humanity has faced so far and I think AI can be leveraged to help us with the equitable distribution of the vaccine. In the United States, as of now the rollout of the vaccine has been painfully slow with a lot of logistical issues from distribution to inoculations. Worldwide, the progress is even more sluggish, with some countries yet to start the journey of inoculations. The role of artificial intelligence in vaccine distribution involves the following challenges that AI can help with provided we have quality and accurate data.

Artificial intelligence (AI) will transform the pharmaceutical cold chain -- not in the distant, hypothetical future, but in the next few years. As the president of a company that has been actively involved in the creation of an application that will utilize machine learning to generate predictive data on environmental hazards in the biopharmaceutical cold chain cycle, I've seen firsthand the promise of this technology. When coupled with machine learning and predictive analytics, the AI transformation goes much deeper than smarter search functions. It holds the potential to address some of the biggest challenges in pharmaceutical cold chain management. By aggregating and analyzing data from multiple sources -- a drug order and weather data along a delivery route, for example -- AI-based systems can provide complete visibility with predictive data throughout the cold chain.

The main objective of any supply chain remains to be the management of inventory, from procurement to supply the right product at the right time in the right place. And, for traditional supply chain companies, it has always been a challenge to achieve it as they focus majorly on optimizing a particular segment of the supply chain, rather than optimizing the entire value chain. This limits their operational efficiency to meet the need for granularity in customers' unique expectations. Artificial Intelligence (AI) can help supply chain companies in breaking the silos to reinvent their operational models. AI in the supply chain helps companies in procuring and processing large datasets and provides better visibility within the supply chain.

Bottom line: Machine learning makes it possible to discover patterns in supply chain management data by relying on algorithms that quickly pinpoint the most influential factors to a supply networks' success, while constantly learning in the process. Discovering new patterns in supply chain data has the potential to revolutionize any business. Machine learning algorithms are finding these new patterns in supply chain data daily, without needing manual intervention or the definition of taxonomy to guide the analysis. The algorithms iteratively query data with many using constraint-based modeling to find the core set of factors with the greatest predictive accuracy. Key factors influencing inventory levels, supplier quality, demand forecasting, procure-to-pay, order-to-cash, production planning, transportation management and more are becoming known for the first time.

Global Artificial Intelligence in Retail Market was valued US$993.6 Mn in 2017 and is expected to reach US$8314 Mn by 2026, at a CAGR of 30.41% during a forecast period. The report is majorly segmented into types, technologies, solutions, services, deployment modes, applications, and region. Further, Artificial Intelligence in a retail market based on type includes online and offline retail. Technology segment is sub-segmented into machine learning and deep learning, Natural Language Processing, and others. Solution segment in the report comprises product recommendation & planning, customer relationship management, visual search, virtual assistant, price optimization, payment services management, supply chain management & demand planning, and others which include website and content optimization, space planning, and fraud detection.

First came the promises and hype about artificial intelligence (AI) in healthcare. Then came its practical application in select areas such as imaging and population health. Now, machine learning (ML), a type of AI in which algorithms are continually refined as additional data makes them more predictive, is taking hold in supply chain management. The supply chain in hospitals hadn't received much attention from healthcare executives until recently, when narrowing margins has made driving efficiency in all aspects of hospital operations essential. Because it's highly complex, running both wide and deep, it can be hard for executives to grasp its importance and understand how to better manage it.