VIEWPOINT
UNLOCKING SUPPLY CHAIN VALUE WITH AI JIM CHAPPELL, GLOBAL HEAD OF AI AND ADVANCED ANALYTICS AT AVEVA, LOOKS AT THE BENEFITS OF ARTIFICIAL INTELLIGENCE WITHIN COMPLEX BUSINESSES
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ver the past four decades, much of manufacturing production world-wide has been organised in what has become known as Global Value Chains (GVCs). COVID-19 has struck at the core of global value chain hub regions, including China, Europe and the US. Now, as production comes back up to speed, companies are scrambling for a means of monitoring the inbound flow of product, to figure out how it can be received, stored and shipped at a time when demand for all but the most critical items has ceased. This has prompted many businesses to review their current supply chain processes and to evaluate how they might build in resilience ahead of any future disruption. Digitalisation is essential for industry in this current climate, both to increase margins and operational performance in good times and to adapt in the bad. AI has reached a key juncture where the real-world benefits are instantly recognisable. Understanding where AI boosts existing processes In the industrial sector, AI application is supported by the increasing adoption of devices and sensors connected through the Internet of Things (IoT). Production machines, vehicles, or devices carried by human workers generate enormous amounts of data. AI enables the use of such data for highly value-adding tasks such as predictive maintenance or performance optimisation at unprecedented levels of accuracy. Hence, the combination 28
CXO INSIGHT ME
OCTOBER 2020
of IoT and AI has begun the next wave of performance improvements, especially in the industrial sector. Furthering this automation, AI uses the historical IoT data to analyse trends which can help in streamlining and improving the supply chain process through cutting-edge solutions such as AI-driven operations scheduling. This provides recommendations to humans as to the optimal scheduling sequence, substantially reducing error and inefficiencies. Further, this AI learns as it goes and tailors its guidance to particular situations, gaining intelligence the more it runs. In addition, AI-driven robotic process automation removes the human element from repetitive tasks
BUSINESSES NOW NEED TO BE INCREDIBLY AGILE TO MANAGE THE COSTS OF TURNING DOWN PRODUCTION, FOLLOWED BY THE WORKING CAPITAL CONSTRAINTS TO THEN REBUILD PRODUCTION LEVELS AS ECONOMIES RECOVER.
in varying levels of complexity, thus furthering increases in efficiency and accuracy. Through integrated workflows, much of the supply chain process can be intelligently automated. These types of AI-driven capabilities have the potential to redefine the business supply chain process. In certain industries such as oil and gas, these types of operational gains are more important than ever in today’s climate. Early adopters of AI have deployed these technologies on-premises, in the cloud, at the edge, and through many types of hybrid architectures. AI itself is not one thing but comprised of several technology types, including neural networks, deep learning, natural language processing, computer vision, unsupervised machine learning, supervised machine learning, reinforcement learning, transfer learning, and others. These various types of AI are applied in different ways throughout the industrial world to create targeted solutions provided as descriptive, predictive, prescriptive, and prognostic analytics. Overcoming the fear of automation Beyond deciding where and how to best employ AI, an organisational culture open to the collaboration of humans and machines is crucial for getting the most out of AI. Trust is among the key mindsets and attitudes of successful human-machine collaboration. Here are some practical steps to consider if a business is looking to explore the implementation of Artificial Intelligence or Machine learning capability into their business process:
