Shell partners with Accenture and Ripjar on supply chain AI
With new cyberthreats to supply chains popping up at an alarming speed (one need only look at the likes of the identified by IBM), businesses are increasingly putting AI to work in the risk assessment space.
Securing the supply chain
The UK-based Ripjar offers a platform which utilises AI to detect criminal threats including money laundering, terrorism and crime networks. Its Labyrinth offering analyses across the data sources fed into it and uses AI to then automatically identify risk, with specific solutions for different industries including finance and defence.
Partnering with the energy giant, the latter company is to employ Ripjar’s solutions within its global supply chain - with possible benefits including an 80% reduction in data reporting errors versus existing legacy systems.
“Global energy companies like Shell can better provide the products and services that are essential to our society with the help of a business ecosystem,” Jeremy Annis, CEO of Ripjar. “Ripjar’s technology, enhanced by AI, can help reduce the number of steps it takes to conduct due diligence and detect risks through continuous real-time monitoring of the supply chain.”
AI for risk management
The tool will run on Shell’s own Microsoft Azure cloud-based infrastructure, with Accenture's role including tailoring Ripjar’s solutions, ensuring the product is scalable and ensuring employees worldwide are able to utilise the software as they interact with third party vendors.
“In a proactive move to tackle the growing challenges associated with criminal activity, security and fraud in today’s global business landscape, Shell is reinforcing its risk management capabilities across the supply chain,” Adam Markson, a managing director and lead for Risk and Compliance at Accenture.
“Together, we are taking a time-consuming, manual process and applying state-of-the-art automation with more insights into data to not only improve accuracy, but also give management complete audit capabilities and accountability over the entire screening process. Indeed, Accenture was selected for its deep industry expertise and track record of implementing the next generation of compliance capabilities and enabling transformational change.”
Google launches Visual Inspection AI tool for manufacturers
Google Cloud has launched Visual Inspection AI, a new tool to help manufacturers identify defects in products before they're shipped.
Poor production quality control often leads to significant operational and financial costs. The American Society for Quality estimates that for many organisations this cost of quality is as high as 15-20% of annual sales revenue, or billions of dollars annually for larger manufacturers. Google Cloud’s new Visual Inspection AI solution has been purpose-built for the industry to solve this problem at production scale.
How does it work?
The Google Cloud Visual Inspection AI solution automates visual inspection tasks using a set of AI and computer vision technologies that enable manufacturers to transform quality control processes by automatically detecting product defects.
Google built Visual Inspection AI to meet the needs of quality, test, manufacturing, and process engineers who are experts in their domain, but not in AI.
- Run autonomously on-premises: Manufacturers can run inspection models at the network edge or on-premises. The inspection can run either in Google Cloud or fully autonomous on your factory shop floor.
- Short time-to-value: Customers can deploy in weeks, not the months typical of traditional machine learning (ML) solutions. Built for process and quality engineers, no computer vision or ML experience required. An interactive user interface guides users through all the steps.
- Superior computer vision and AI technology: In production trials, Visual Inspection AI customers improved accuracy by up to 10x compared with general-purpose ML approaches, according to benchmarks from several Google Cloud customers.
- Get started quickly, with little effort: Visual Inspection AI can build accurate models with up to 300x fewer human-labeled images than general-purpose ML platforms, based on pilots run by several Google Cloud customers.
- Highly scalable deployment: Manufacturers can flexibly deploy and manage the lifecycle of ML models, scaling the solution across production lines and factories.
Industry use cases
The demo video shows how Visual Inspection AI addresses use cases to solve specific quality control problems in industries such as automotive manufacturing, semiconductor manufacturing, electronics manufacturing and general-purpose manufacturing.
Kyocera Communications Systems, a manufacturer of mobile phones for wireless service providers, has been able to scale its AI and ML expertise through the use of the solution. “With the shortage of AI engineers, Visual Inspection AI is an innovative service that can be used by non-AI engineers,” said Masaharu Akieda, Division Manager, Digital Solution Division, KYOCERA Communication Systems. “We have found that we are able to create highly accurate models with as few as 10-20 defective images with Visual Inspection AI. We will continue to strengthen our partnership with Google to develop solutions that will lead our customers' digital transformation projects to success.”
Visual Inspection AI has fully integrated with Google Cloud's portfolio of analytics and ML/AI solutions, giving manufacturers the ability to combine its insights with other data sources. The tool integrates with existing products from Google Cloud partners, including SOTEC, Siemens, GFT, QuantiPhi, Kyocer and Accenture.