How AI can solve supply chain issues


As production comes back up to speed after the initial impact of COVID-19, companies are scrambling for a means of monitoring the inbound flow of product, to figure out how it can be received, stored and shipped

Many businesses have been forced 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 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 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 & 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:

  1. Leverage AI to gain significantly more value out of existing industrial software: SCADA (an acronym for Supervisory Control and Data Acquisition generally refers to industrial control systems) and other types of control systems have become standard practice in most industrial facilities. Real-time and historical data is typically used for trending, reporting, and HMI visualisation. AI allows companies to get much more value and insight from this historian data through state-of-the-art technologies such as multi-variate machine learning and deep learning. By integrating software infused with AI into existing industrial IT infrastructures, businesses can greatly amplify the value and ROI by detecting and solving operational and maintenance issues before they become larger problems that often result in unplanned downtime. This alone can increase uptime by 10% annually, resulting in substantial avoided costs and efficiency gains.
  2. Allow AI to integrate into the core of the supply chain to take advantage of cutting edge capabilities: AI-driven operational scheduling and work process automation can eliminate mistakes and allow industrial companies to get the most out of the resources they have available. Supply chain success is critical to overall business success, and an increase in efficiency can often be the difference between turning a profit or not. AI provides incredible value in this area, and businesses shouldn’t be afraid to leverage its power as an integral part of their supply chain process.
  3. Use the cloud to ease the implementation of AI, allowing companies to scale: Artificial Intelligence is fast becoming the brains behind the cloud. Consequently, companies can quickly deploy and access a variety of industrial software capabilities that are driven by various types of AI technology. The cloud is the delivery mechanism, and SaaS is the commercial model; however, AI drives much of the value gained. Now more than ever before, AI is becoming more easily accessible and more cost-effective to deploy into industrial environments.
  4. Bridge the gap between AI and humans: In order to glean maximum value from AI, companies must ensure that they bridge the gap between AI and human understanding. A significant portion of the workforce today is somewhat distrustful or fearful of AI. It is critical that companies do everything they can to ensure that the benefits from AI-infused software are translated into the vernacular of the targeted worker. The benefits provided by AI must be put in context, be useful and actionable. If this does not happen, then much of the value of AI is wasted.
  5. Be open to continued innovation and change: AI capabilities continue to evolve and improve. Software will become more intelligent through combinations of AI capabilities in order to achieve more sophisticated machine-based thought and reasoning. Amid these changes, companies can reap more and more benefits through deeper insight into cost vs risk decisions, an improved understanding of business processes and associated efficiencies, and better forecasts of future events. By continuing to plan for and incorporate change, companies can take advantage of ever-improving AI capabilities and insight.

Time to reflect and evolve

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. We are also seeing a period of distrust and disinformation while global supply chains are disrupted, data is key to traceability and provenance ensuring that drugs and food come from authentic sources. Better visibility allows us to understand where resources such as food and pharmaceuticals are and how we can get energy efficiently to those who need it.

Digital transformation stands to provide an immediate and compelling competitive advantage for those quick to adopt – and to demonstrate provenance. Businesses require intelligent software to address industrial pain points for value creation, productivity improvement, insight discovery, risk management, and cost optimisation. AI is a key differentiator and a propelling force behind improvements in the supply chain.


Damien McDade
Head of Pacific for AVEVA

AVEVA Group plc provides innovative industrial software to transform complex industries such as Oil & Gas, Construction, Engineering, Marine, and Utilities. AVEVA’s software solutions and platform enable the design and management of complex industrial assets like power plants, chemical plants, water treatment facilities and food and beverage manufacturers – deploying IIoT, Big Data and Artificial Intelligence to digitally transform industries.