Transform business process automation and people productivity with Microsoft Dynamics 365 and the Microsoft Cloud
Transform business process automation and people productivity with Microsoft Dynamics 365 and the Microsoft Cloud
The focus of every industrial revolution has been increasing the productivity of production systems. The fourth industrial revolution is here, and it’s seeking to improve both production and management systems. Digital transformation driven by smart manufacturing (also known as Industry 4.0) is the basis of this latest one – creating opportunities to achieve levels of productivity and specialization not previously possible.
Combining data generated through the Industrial Internet of Things (IIoT) and analytics creates a new set of capabilities known as predictive maintenance and quality. Fueled by smart manufacturing, these new capabilities are changing the way we do and see business, helping recognizing patterns and predicting failures or product quality issues before they happen.
Most factories are composed of operation technology (OT) assets such as machines, equipment lines and robotic devices that aren’t always connected. The current trend is leaning toward smart manufacturing with a more IT-based factory floor to help save time, labor, cost and maintenance and upkeep. With OT and IT converging, the IIoT platform is emerging as a new, innovative concept for smart manufacturing with artificial intelligence (AI)-based technologies, including analytics, big data and cognitive manufacturing.
Smart manufacturing can spur a new surge of manufacturing productivity.
In order to understand the impact of Industry 4.0 solutions, we must examine the key people involved in all aspects of a factory. True transformation happens when all unique challenges and each pain point is targeted.
Keeping the needs of different types of workers in mind and using our extensive manufacturing experience, IBM developed a three-tiered distributed architecture to implement smart manufacturing more efficiently. The model addresses the autonomy and self-sufficiency requirements of each production site and balances the workload between the three tiers.
IBM offers a suite of enterprise asset management (EAM) solutions to help drive cost savings and operational efficiency across the factory value chain. The portfolio of EAM solutions from IBM analyzes a variety of information from workflows, context and the environment to drive quality and enhance operations and decision making. The portfolio of EAM solutions from IBM helps deliver a smart manufacturing transformation.
Production quality insights use IoT and cognitive capabilities to sense, communicate and self- diagnose issues to optimize each factory’s performance and reduce unnecessary downtime. Insights help reduce unplanned downtime.
We are going to share our vision on the importance of infusing AI into our cloud platform and DevOps process. Gartner referred to something similar as AIOps (pronounced “AI Ops”) and this has become the common term that we use internally, albeit with a larger scope. Today’s post is just the start, as we intend to provide regular updates to share our adoption stories of using AI technologies to support how we build and operate Azure at scale.
There are two unique characteristics of cloud services:
To build and operate reliable cloud services during this constant state of flux, and to do so as efficiently and effectively as possible, our cloud engineers (including thousands of Azure developers, operations engineers, customer support engineers, and program managers) heavily rely on data to make decisions and take actions. Furthermore, many of these decisions and actions need to be executed automatically as an integral part of our cloud services or our DevOps processes. Streamlining the path from data to decisions to actions involves identifying patterns in the data, reasoning, and making predictions based on historical data, then recommending or even taking actions based on the insights derived from all that underlying data.
Figure 1. Infusing AI into cloud platform and DevOps.
AIOps has started to transform the cloud business by improving service quality and customer experience at scale while boosting engineers’ productivity with intelligent tools, driving continuous cost optimization, and ultimately improving the reliability, performance, and efficiency of the platform itself. When we invest in advancing AIOps and related technologies, we see this ultimately provides value in several ways:
Figure 2. AI for Cloud: AIOps and AI-Serving Platform.
Moving beyond our vision, we wanted to start by briefly summarizing our general methodology for building AIOps solutions. A solution in this space always starts with data—measurements of systems, customers, and processes—as the key of any AIOps solution is distilling insights about system behavior, customer behaviors, and DevOps artifacts and processes. The insights could include identifying a problem that is happening now (detect), why it’s happening (diagnose), what will happen in the future (predict), and how to improve (optimize, adjust, and mitigate). Such insights should always be associated with business metrics—customer satisfaction, system quality, and DevOps productivity—and drive actions in line with prioritization determined by the business impact. The actions will also be fed back into the system and process. This feedback could be fully automated (infused into the system) or with humans in the loop (infused into the DevOps process). This overall methodology guided us to build AIOps solutions in three pillars.
Figure 3. AIOps methodologies: Data, insights, and actions.
Today, we’re introducing several AIOps solutions that are already in use and supporting Azure behind the scenes. The goal is to automate system management to reduce human intervention. As a result, this helps to reduce operational costs, improve system efficiency, and increase customer satisfaction. These solutions have already contributed significantly to the Azure platform availability improvements, especially for Azure IaaS virtual machines (VMs). AIOps solutions contributed in several ways including protecting customers’ workload from host failures through hardware failure prediction and proactive actions like live migration and Project Tardigrade and pre-provisioning VMs to shorten VM creation time.
Of course, engineering improvements and ongoing system innovation also play important roles in the continuous improvement of platform reliability.
AI can boost engineering productivity and help in shipping high-quality services with speed. Below are a few examples of AI for DevOps solutions.
Figure 4. Incident management procedures.
As shared in AIOps Innovations in Incident Management for Cloud Services at the AAAI-20 conference, we have developed AI-based solutions that enable engineers not only to detect issues early but also to identify the right team(s) to engage and therefore mitigate as quickly as possible. Tight integration into the platform enables end-to-end touchless mitigation for some scenarios, which considerably reduces customer impact and therefore improves the overall customer experience.
For an example that has already made its way into a customer-facing feature, Dynamic Threshold is an ML-based anomaly detection model. It is a feature of Azure Monitor used through the Azure portal or through the ARM API. Dynamic Threshold allows users to tune their detection sensitivity, including specifying how many violation points will trigger a monitoring alert.
To improve the Azure customer experience, we have been developing AI solutions to power the full lifecycle of customer management. For example, a decision support system has been developed to guide customers towards the best selection of support resources by leveraging the customer’s service selection and verbatim summary of the problem experienced. This helps shorten the time it takes to get customers and partners the right guidance and support that they need.
To achieve greater efficiencies in managing a global-scale cloud, we have been investing in building systems that support using AI to optimize cloud resource usage and therefore the customer experience. One example is Resource Central (RC), an AI-serving platform for Azure that we described in Communications of the ACM. It collects telemetry from Azure containers and servers, learns from their prior behaviors, and, when requested, produces predictions of their future behaviors. We are already using RC to predict many characteristics of Azure Compute workloads accurately, including resource procurement and allocation, all of which helps to improve system performance and efficiency.
We have shared our vision of AI infusion into the Azure platform and our DevOps processes and highlighted several solutions that are already in use to improve service quality across a range of areas. Look to us to share more details of our internal AI and ML solutions for even more intelligent cloud management in the future. We’re confident that these are the right investment solutions to improve our effectiveness and efficiency as a cloud provider, including improving the reliability and performance of the Azure platform itself.
Note blog reference: https://azure.microsoft.com/en-in/blog/advancing-azure-service-quality-with-artificial-intelligence-aiops/
In the not too distant past, efforts in manufacturing to optimize productivity and increase customer satisfaction were viewed as separate endeavors. Today, the convergence of physical and digital trends is disrupting these kinds of assumptions.
Manufacturers today care about integrated digital and physical systems, improved visibility, increased efficiency, additional flexibility, and lower costs. They want to connect equipment and factories, leveraging data from the factory floor to the customer call center to improve every aspect of their operations.
But this is just the beginning. Digitization is fundamentally changing the way manufacturers do business, enabling a customer-centric approach while optimizing operations. Digitally empowered manufacturers engage customers throughout the product lifecycle from design to field service. They sell value-add services to complement the product sales, opening new revenue streams and strengthening their customer relationships. And they are revolutionizing delivery of these differentiated services, using technology like augmented reality to combine the eyes of a technician in the field with the insights of an expert back at headquarters.
Capitalizing on these trends isn’t limited to large, well-resourced manufacturers. Across all kinds of manufacturing operations, the opportunity to digitize and transform your business has never been more accessible.
The Microsoft vision for supporting digital manufacturing embraces the seismic shifts in the industry today. We’ve created solutions that provide a unified and flexible approach across front office and production floor processes. Our approach enables transformation in six ways:
By collecting, integrating, and visualizing global supply chain data worldwide, manufacturers gain better visibility into their operations from production to sales. For example, one of the world’s largest industrial automation firms found that by automating the collection and analysis of data from remote installations across the petroleum supply chain, they strengthened their competitive advantage with a faster time to market. Improved access to supply chain data is also the basis for better collaboration across production, supply, service, and sales.
With a consolidated view that unifies process oversight and provides real-time insight, manufacturers can institutionalize efficiency gains and use connected devices to monitor and resolve issues remotely. One leading manufacturer of industrial robots enabled 24-hour continuous uptime using this approach. The additional insights into production and customer usage also allow manufacturers to provide value-added services like ongoing monitoring and proactive support.
To deliver personalized and contextual engagement across any channel, manufacturers must provide customers with more visibility and build trust through fast and convenient responses. This engagement approach is built on a combination of predictive analytics, the ability to deliver value-added services at scale, and guided or self-directed service that’s relevant to customer needs. With the implementation of a connected platform for sales through service, a leading home technology manufacturer not only solved potential problems remotely before customers ever felt the impact, but provided custom differentiated offerings based on unique customer usage and purchasing history.
Thanks to the ever-decreasing cost of IoT sensors, sophisticated mobile devices, and cloud-based data aggregation, manufacturers can improve service quality and margins by offering remote monitoring and proactive maintenance services that supplement break/fix support. By more intelligently coordinating technicians equipped with mobile and virtual reality tools, companies can leverage existing expertise and minimize costly engagements. A leading tire service and manufacturing company found that by combining customer records, technician availability, and back-end inventory in a single mobile-friendly system, it could provide a seamless user experience as well as improve its service delivery.
understand their business more deeply, from customer usage through supply chain sourcing and production. With IoT-enabled parts, assets, and products, manufacturers can gain the insights needed to innovate. Data from connected products and equipment can empower developers, engineers, and technicians to collaborate. For example, teams can identify overengineered or faulty components and track product usage in the field to improve future designs. When a leading information and communication technology company implemented remote monitoring, they decreased time to production as well as accelerated the cycle of innovation using a data-driven approach.
When a company can provide 360-degree views of customer assets and work order history, technicians are empowered by a better understanding of not only the job in front of them, but of other similar and successful field service engagements. This goes hand in hand with empowering service agents to provide instant feedback, using machine learning to find and follow similar cases for successful troubleshooting, and scheduling a visit or evaluation. A leading auto manufacturer wanted to save money by unifying their siloed customer service and customer engagement while providing employees with better tools to understand customer sentiment. To accomplish this, it implemented a system to connect production and project management with their customer relationship management systems in order to deliver personalized service and recommendations to their customers.
For manufacturers, Microsoft Dynamics 365 ends the artificial divide between CRM and ERP systems and supplements necessary capabilities with rich analytics, embedded intelligence, and the convenience people expect from consumer apps on their phone or tablet. Dynamics 365 unites the front office and the back office with a single end-to-end system for managing every aspect of your business, at the pace and scale that’s right for you. Digital transformation isn’t accomplished overnight and leveraging current investments is a key component of any successful approach. With Microsoft, you can optimize across all your processes while laying the foundation for connecting advanced technology in the future.
4 ways technology can help businesses thrive in a digital world. The good news is that the tools that help businesses capitalize on this
digital transformation are more accessible than ever before. The cloud is removing barriers like high up-front costs, ongoing maintenance, and IT dependency.
