AI can analyze large amounts of data in order to identify patterns and hidden correlations, which would otherwise take humans considerably longer to comprehend and decipher. It can also be fed with a wide range of variables, which gives the engine more flexibility in its analysis. AI is revolutionising the overall healthcare landscape Automating healthcare is then, perhaps, a result of this changing healthcare landscape and the growing use and rapid adoption of technology. The healthcare sector is experiencing a dramatic shift in how goods and services are delivered. There are several driving factors in this transition, including a shift away from treating shorter episodes of illness towards a greater focus on long-term wellness and prevention. In addition consumers and patients alike show the deviation from and changing expectations.

Kevin Beasley, CIO at VAI, oversees the corporation's overall technology strategy. As the supply chain stabilizes, many manufacturers are returning to normal operations with more robust technological capabilities. In fact, nearly half of supply chain leaders increased spending on innovative technologies and systems during the pandemic -- including predictive analytics. Predictive analytics uses statistical algorithms combined with internal and external data to forecast future trends, which enables businesses to optimize inventory, improve delivery times, increase sales and ultimately, reduce operational costs. When paired with artificial intelligence (AI), the insights gleaned from these advanced systems are the key to more accurate and timely forecasting going forward.

Money saved is money earned. This is true more so today than ever for procurement and supply chain leaders. Increasingly they're expected not just to reduce costs, but also fund innovation and future business growth. Procurement leaders must increase savings - without sacrificing supplier performance and delivery risks. Supply chain leaders need to manage costs, but also ensure continuity and responsiveness of their complex, multi-tier supply chains.

Artificial intelligence (AI) has the power to transform the way business is done and could contribute up to $15.7 trillion to the global economy by 2030, according to PwC. Among the industries to benefit most from AI adoption, supply chain management is in the top three, per a recent McKinsey global survey, and 76% of the survey respondents at supply chain companies have already reported moderate to significant value from deploying AI. When implemented and used correctly, artificial intelligence can enable exceptional agility and precision in supply chains, regardless of the industry. It can also ignite a transformational increase in efficiencies and decrease in costs where repetitive manual tasks can be automated. There are many applications of artificial intelligence in the supply chain, including AI-enabled robots like inventory-taking drones or automated-guided vehicles such as driverless warehouse carts.

For manufacturers, managing the supply chain from beginning to end has been like traveling two superhighways interrupted by a long stretch of dirt road. Manufacturers have benefited from increasingly powerful tools for demand planning and logistics management – the first and last parts of their supply chains – but tracking the performance of manufacturing production across the supply chain has remained stuck in the era of clipboards, whiteboards, spreadsheets and manually assembled reports. For most companies, understanding machine capacity, throughput, efficiency, and quality across the supply chain remains a black box. Companies that rely heavily on contract manufacturers have even less visibility – challenged by partners with different systems, processes, and levels of willingness to collaborate. Today's supply chain monitoring systems lack the ability to look at machine and part/batch-level data across the supply chain, limiting a global manufacturer's ability to manage their supplier base as an integrated platform.

In 10 years, fewer workers will be tasked to support a growing population of nonworkers, and labor supply will shift to undeveloped regions of the world that are less educated and less technically skilled. Planning for this talent shift is just one area on which supply chain leaders will need to focus to build a successful future supply chain. Supply chain leaders want to make the right business decisions and invest in the right technology to prepare their organization for the future. However, there are so many factors to consider and so many unknown variables that "getting it right" seems almost impossible. "Understanding trends and impacts is a challenging task for supply chain leaders responsible for identifying and putting in place strategies to build the right set of capabilities," says Steven Steutermann, Managing Vice President at Gartner.

Companies are cutting supply chain complexity and accelerating responsiveness using the tools of artificial intelligence. Through AI, machine learning, robotics, and advanced analytics, firms are augmenting knowledge-intensive areas such as supply chain planning, customer order management, and inventory tracking. What does that mean for the supply chain workforce? It does not mean human workers will become obsolete. In fact, a new book by Paul Daugherty and H. James Wilson debunks the widespread misconception that AI systems will replace humans in one industry after another.

Olay Skin Advisor is a mobile app that relies on machine-learning algorithms to analyze skin care needs. The app performs a facial analysis from a consumer's no-makeup selfie and recommends products based on personal data and best practices from skin care experts. The artificial intelligence (AI)-enabled app also collects buying behavior data directly from the consumer and uses that data to determine the demand for and recommend specific products. Supply chain leaders must assess their company's risk culture to determine their readiness to explore and adopt emerging offerings Similarly, FlavorPrint, an AI-based platform introduced by McCormick spinoff Vivanda, determines what is called a "flavor DNA" -- a digital taste identifier that matches consumers to food items. Through this direct customer engagement, FlavorPrint is sensing demand by better understanding customer preference.