Many large enterprises use one form or another of a supply chain application to help manage their supply chains. Supply chain vendors have been touting their investments in artificial intelligence (AI) for the last several years. Alex Pradhan, Product Strategy Leader John Galt Solutions, told me that "all planning vendors have bold marketing around AI." But the trick is to find suppliers with "field-proven AI/ML algorithms" that "have been delivered at scale." Further, while artificial intelligence helps solve certain types of problems, Jay Muelhoefer - the chief marketing officer at Kinaxis pointed out - optimization and heuristics work better for other types of planning problems. This article, which is focused on the different types of artificial intelligence used and the types of problems they are solving, is aimed at helping practitioners cut through the hype.

Advances in the application of artificial intelligence (AI) are starting to have a significant impact on automation technologies used across industry--most notably with machine vision and analytics. And some of the more impactful applications of AI are happening in the pharmaceutical industries. It shouldn't be too surprising that the pharmaceutical industries are looking to optimize production with AI, considering that single batch values for some drugs can exceed $3 million. Yet, research indicates that this industry lags many others when it comes to using analytics to improve production. David Leitham, senior vice president and general manager, pharmaceuticals, at AspenTech.According to David Leitham, senior vice president and general manager, pharmaceuticals, at AspenTech (a supplier of AI software for industrial manufacturers), while other industries have been applying analytics and predictive capabilities to optimize performance and react rapidly to changes in demand, 87% of pharmaceutical industry executives admit their organizations have a poor digital culture.

Any unnecessary downtime can be expensive for pharmaceutical manufacturing operations. What's more, unplanned stoppages can delay the delivery of much-needed product, potentially causing damage to a company's reputation. David Leitham, senior vice president and general manager at industrial artificial intelligence (AI) technology firm AspenTech, recently spoke with Outsourcing-Pharma (OSP) about how AI can be put to use to help predict when maintenance is needed, and avoid unplanned or over-maintenance. OSP: Could you please share an'elevator presentation' description of AspenTech? DL: AspenTech develops software to help customers in capital-intensive industries (such as energy, chemicals, and pharmaceuticals), address their biggest challenges: delivering increased value to stakeholders, responding to an evolving global population, and reducing environmental impact and waste.

Optimized performance, reliability, availability and safety can be achieved with the automated operation. Digitalization in the mining industry is allowing mines to expand the utilization of technologies being used successfully both at the mine and plant levels, one of the most significant of which is mine equipment autonomy. While an exciting prospect, the mining industry is still having challenges as it works to integrate new technologies onto its mining equipment, such as shovels, drills, and trucks for capabilities like communications and positioning. Consider, too, that just because a mine has become automated does not mean that maintenance programs should be eliminated. On the contrary, they are perhaps more important than ever to support the optimized factors like availability and utilization of mine equipment now-automated workings as customers demand more than ever from their technology.

The focus on developing, embedding, and deploying machine learning (ML) algorithms as fit-for-purpose, domain-specific industrial applications, is bearing fruit as business drivers capable of delivering sustainable value for asset-intensive organisations emerge. Here we look at these drivers and assess what asset-intensive organisations need to do to prepare for the age of industrial AI. First, industrial organisations will start focusing on how AI can be applied to address domain-specific industrial challenges. Second, the barrier to AI adoption will be lowered, as a lack of in-house AI expertise among industrial organisations has historically blocked Industrial AI enablement. More organisations will deploy targeted, embedded Industrial AI applications combining data science and AI with purpose-built software and domain expertise.

Aspen Technology, Inc. recently announced the general availability of the aspenONE V12 software release, which embeds artificial intelligence (AI) across the portfolio, and uses the cloud for delivery of enterprise-wide analytics and insights for increased safety, sustainability, and improved margins. AspenTech's Industrial AI solutions democratize the application of AI where it can deliver most value and is a vital step towards the Self-Optimizing Plant. "Aspen Hybrid Models are a major advance in the field of chemical engineering. Hybrid models are a major step forward in bringing together AspenTech's process models and machine learning and are a game changer in process engineering and plant improvement," said Dr. Karuna Potdar, Vice President, Technology Centre of Excellence, Reliance Industries Limited. Aspen Hybrid Models capture data from assets across the enterprise, and then apply AI, engineering first principles and AspenTech's domain expertise to deliver comprehensive, more accurate models at enterprise speed and scale.

Aspen Technology, Inc. announced the general availability of the aspenONE V12 software release, which embeds artificial intelligence (AI) across the portfolio and uses the cloud for delivery of enterprise-wide analytics and insights for increased safety, sustainability, and improved margins. AspenTech's Industrial AI solutions democratize the application of AI where it can deliver most value and is a vital step towards the Self-Optimizing Plant. Aspen Hybrid Models capture data from assets across the enterprise, and then apply AI, engineering first principles and AspenTech's domain expertise to deliver comprehensive, more accurate models at enterprise speed and scale. The process industries have embraced digital transformation to drive operational excellence and innovation as they respond to meet the needs of growing populations and expectations for sustainability. The new solutions in aspenONE V12 address these unique challenges with better modeling accuracy, greater insights and improved total cost of ownership that can support evolving business needs and take advantage of the new digital native workforce.

Aspen Technology, Inc. (NASDAQ:AZPN), a global leader in asset optimization software, today announced the general availability of the aspenONE V12 software release, which embeds artificial intelligence (AI) across the portfolio, and uses the cloud for delivery of enterprise-wide analytics and insights for increased safety, sustainability, and improved margins. AspenTech's Industrial AI solutions democratize the application of AI where it can deliver most value and is a vital step towards the Self-Optimizing Plant. "Aspen Hybrid Models are a major advance in the field of chemical engineering. Hybrid models are a major step forward in bringing together AspenTech's process models and machine learning and are a game changer in process engineering and plant improvement," said Dr. Karuna Potdar, Vice President, Technology Centre of Excellence, Reliance Industries Limited. Aspen Hybrid Models capture data from assets across the enterprise, and then apply AI, engineering first principles and AspenTech's domain expertise to deliver comprehensive, more accurate models at enterprise speed and scale.

There are a number of young technologies that are getting a lot of buzz. But how mature are these technologies? Which of these technologies offer solid ROI, which are worth piloting, and which should be ignored? There are technologies that are proven and widely adopted. In supply chain management, examples would be transportation management, warehouse management, and other well-known supply chain applications.

Today, a fresh generation of technologies, fuelled by advances in artificial intelligence based on machine learning, is opening up new opportunities to reassess the upper bounds of operational excellence across these sectors. To stay one step ahead of the pack, businesses not only need to understand machine learning complexities but be prepared to act on it and take advantage. After all, the latest machine learning solutions can determine weeks in advance if and when assets are likely to degrade or fail, distinguishing between normal and abnormal equipment and process behaviour by recognising complex data patterns and uncovering the precise signatures of degradation and failure. They can then alert operators and even prescribe solutions to avoid the impending failure, or at least mitigate the consequences. The leading software constructs are autonomous and self-learning.