The Industrial Internet of Things (IIoT), which is also known as Industry 4.0 when applied to the manufacturing industry, is a concept of integrating smart manufacturing machinery, artificial intelligence (AI)-powered automation, and advanced analytics to help make every worker and factory more efficient.

The marriage of advanced manufacturing techniques with information technology, data, and analytics is driving another industrial revolution—one that enables manufacturing leaders to combine information technology and operations technology to create value in new and different ways.

What is the “smart factory?”

The smart factory can help manufacturers improve performance in a dynamic, digital world. The smart factory is an environment in which cyber-physical systems monitor the physical processes of the factory, provide analysis, and automate or support the controls and decision-making. This improves manufacturing efficiency and effectiveness.

Interest in smart factory applications continues to grow because of the significant operational benefits and competitive advantage it can generate for the manufacturers. These include:

• Real-time, on-demand visibility into performance across the production chain
• Information and technologies to improve physical process control
• Flexible, adaptive, and proactive production
• End-to-end integration with suppliers and customers

Industry 4.0—accelerating innovation, automation, and driving operational excellence

As the beating heart of your business, the shop floor is central to your smart factory journey. Industry 4.0 is unleashing new technologies that accelerate automation, connect data, and drive operational excellence on the shop floor, to unlock the true mastery of your makers. The following technologies are major disruptors that will shape the next revolution for Industry 4.0:

• Big data and data analytics
• Geotagging and geographical information systems (GIS)
• AI
• Cloud computing and everything as a service (XaaS)
• Robotic process automation (RPA)
• Blockchain
• Remote sensors and Internet of Things (IoT)
• Drones and unmanned aircraft systems (UAS)

The benefits of smart factory technology

By implementing smart factory technology, organizations can yield many benefits, such as:

• Better, faster, cost-effective production of goods
• On-demand availability, including small-batch production
• Customized designs, materials, and delivery
• Complete traceability of all parts, ingredients, and materials
• The opportunity to develop services and experiences, not just products
• Sustainable models that reuse, repair, and repurpose—moving from linear production to a circular economy
• Becoming a trusted partner in your digital supply chain by implementing cybersecurity as a key quality characteristic of your production process and the goods you produce

Fueled by technology and the Autonomous Digital Enterprise

Disruptive, digital technologies have accelerated and changed dramatically in recent years, driven largely by three key developments: lower computing costs and cheaper storage and bandwidth. Data is at the heart of today’s business, and as more manufacturing organizations face an increase in its sources, volume, and complexity, they will need to learn to leverage it by implementing digital enterprise applications that span on-premises, cloud, and hybrid infrastructures.

Smarter strategies and new, centralized, and scalable AI-enabled operations (AIOps) systems will ascend, replacing conventional IT operations (ITOps) approaches to modern management. By harnessing new operational models and technology to drive agility, customer centricity, and actionable insights, companies can achieve the innovation required to succeed, and evolve to become an Autonomous Digital Enterprise, a future business state driven by automation that works with—not instead of—humans.

Originally published on LinkedIn.

Today’s IT organizations are faced with the daunting challenges of managing a vast variety of specialized IT infrastructures and applications and ensuring that everything operates seamlessly together like a perfectly synchronized dance. And yet, these teams are often siloed, using different tools to support their products.

The Internet of Things (IoT) refers to products that are always connected to the internet, streaming behavior-related information and other data. Vendors of these products then analyze the data and draw insights to achieve multiple benefits.

What is AIOps?

You’ve probably heard the term AIOps (artificial intelligence for IT operations) used repeatedly as the next big thing in IT management, or read something about it having a significant impact on system operations and administration in numerous trade publications. But what exactly is AIOps? And why should you care about it?

AIOps is an umbrella term for the use of big data analytics, machine learning (ML), and other artificial intelligence (AI) technologies to automate the identification and resolution of common IT issues.

AIOps marries big data with ML to create predictive outcomes that help drive faster root-cause analysis (RCA) and accelerate mean time to repair (MTTR). By providing intelligent, actionable insights that foster a higher level of automation and collaboration, IT operations (ITOps) can continuously improve, saving your organization time and resources in the process.

What is observability?

Observability is the ability to measure the internal states of a system by examining its outputs. Observability allows teams to monitor modern systems more effectively and helps them to find and connect effects in a complex chain and trace them back to their cause. A system is considered “observable” if the current state can be estimated solely by using information from outputs, namely sensor data.

How AIOps powers digital transformation and redefines IoT

Digital transformation through AIOps is about improving the user experience, whether it’s IT end users or IoT. AIOps bridges the needs of the business with emerging technologies, from the cloud to IoT to big data and AI.

What’s driving the transformation of the user and the IT experience?

• Unpredictable and intense competition
• Automation
• Business innovation
• Downward cost pressures
• New and changing customer demands
• Globalization
• Changing business models

Final thoughts

Companies like BMC are a trusted leader in AIOps, deploying ML and advanced analytics as part of holistic monitoring, event management, capacity, and automation solutions to help ITOps run at the speed that digital business demands. AIOps can help reduce event noise, incidents, time to identify the root cause, and MTTR. AIOps and IoT are comingled and complementary—to deal with the multifaceted nature of the IoT framework, you need AIOps. To learn more about AIOps, visit our blog section on the subject here.

Originally published on LinkedIn.

Many of the conversations taking place around the Internet of Things (IoT) are incomplete without a mention of big data. Connected devices, sensors, and algorithms all operate in ways that involve massive amounts of data.

As organizations step into IoT, they must understand the symbiotic relationship between it and big data. For IoT deployments to really make an impact, they must provide some sort of useful tool or service, while also collecting relevant data. While much of the IoT conversation focuses on the devices themselves, the true potential of IoT extends well beyond hardware—in the data a device generates, the action it instigates, and the ultimate value it delivers.

As the volume and sophistication of connected technology increase, IT leaders must ensure devices, architecture, automation, and human intelligence are working in harmony to create superior end-user experiences. This is part of becoming an Autonomous Digital Enterprise, a future-state business framework and the evolution of growth-minded organizations looking to deliver value with competitive differentiation enabled by agility, customer centricity, and actionable insights.

As IoT technology becomes more entrenched in our everyday lives, industry-leading organizations understand that the devices are not the end game. Rather, when IoT technology, architecture, automation, and human intelligence work together in harmony, IT leaders can drive operational efficiency, reduce time spent on mundane, administrative tasks, and fortify network security to deliver enhanced end-user experiences. We’ll continue to see this vision come to life in stores, schools, and cities.

Defining IoT edge computing

Edge computing services and IoT are inseparable, with communications facilitated by edge computing services and digital transactions facilitated by IoT. The core of an IoT solution is typically a central IT system for storing, processing, and analyzing IoT data. And much of this IoT data often can be located in the cloud, away from the core.

Edge processing addresses the latency challenges this can present. An edge processing unit is a physical device, typically referred to as an IoT gateway, also called a fog node. It connects to devices that are away from the core (often referred to as devices “at the edge”) via communication protocols like Low-energy Bluetooth^® or ZigBee. At the same time, it also connects to the core directly using high-speed internet.

These gateways also provide security and lifecycle management at the edge, so that the edge is a sustainable, manageable compute unit. The hardware used for gateways ranges from high-powered, rack-mounted servers to smaller devices with embedded ARM processors and everything in between.

An idealized vision of an IoT edge, AI-based computing solution

IoT edge computing describes the capability of processing, storing, and analyzing sensor data as well as decision making at IoT gateways. Edge services that support it should include the following:

• Persistence service: Store IoT data on IoT gateways, with IoT administrators configuring which data should be stored locally and setting a data aging policy.
• Streaming service: Analyze IoT data streams, with IoT administrators defining conditions with adjustable time windows to identify patterns in the incoming IoT data as a basis for automated events. For example, certain conditions can initiate transactions and notify appropriate parties.
• Business transaction service: Execute business transactions at the edge to provide continuity for critical business functions, even when the edge is disconnected from the core.
• Predictive analytics service: Use predictive models for analyzing the IoT data. The predictive algorithm is constantly “being trained” and improved in the core based on all available data. The resulting predictive model is then sent to the edge and applied there.
• Machine learning service: Apply deep learning algorithms at the edge specifically for image and video analysis.
• Visual analytics service: Visually explore IoT data stored on IoT gateways. IoT data analysts can visually inspect the data collected at the edge. For example, after an alert has been sent to the core, an analyst can dig into the details which led to the alert.

This vision offers a distributed programming model for the edge and the core, which allows the solution to be placed where it is optimal for a specific scenario and a lifecycle model for edge services, the edge platform, and the attached devices and sensors.

Conclusion

IoT edge computing is playing an increasingly important role in IoT solutions. The industry trend is to deploy functionality as microservices and use container technology for lifecycle management and other benefits that come from isolation. The Autonomous Digital Enterprise leverages relevant edge computing functionality such as persistence, stream processing, visual and predictive analytics, and other microservices designed for implementation on both existing and emerging edge platforms.

Originally published on LinkedIn.

How can companies provide their customers the services for which they are known—without losing business to smaller, more nimble companies that aim to disrupt the status quo? Many are turning to innovation labs to think outside the box and drive new concepts to maintain their relevance and competitive edge.

Innovation happens when you generate value from ideas. Innovation labs, then, are a framework for creatively exploring new ideas to achieve different outcomes—a necessary skill in today’s era of digital transformation.

In this article, we’ll look at innovation labs, including models, benefits, and best practices for establishing yours.

(This article is part of our Innovation in The Enterprise series. Use the right-hand menu to navigate.)

Who uses an innovation lab?

Corporate innovation is critical for established companies looking to stay relevant in the face of disruption. Innovation labs have become so vital to business today that they are embedded in industry verticals from telecom and finance to health and insurance. These small creative teams can focus on anything from establishing new business models, creating new services or products, and improving internal processes or visions.

Almost every large company has an innovation lab these days, but it’s not only large companies who benefit from a focus on innovation and a safe place for creativity. Innovation labs are useful for any company because they can provide training, networks, and insights to help intrapreneurs succeed regardless where they work in the company. This can help alter the business-as-usual DNA of a company, creating pockets of innovation across an organization. Such labs also promote skills and concepts that everyone can learn, allowing them to contribute to the innovation process.

Paths to successful innovation

It seems like a tough ask for companies: how to balance business as usual while allowing the freedom to innovate. Importantly, for innovation to work, companies must recognize that innovation is as much a cultural attitude as it is a business process.

As you encourage and promote innovation across your company culture, you can take a generic approach to innovation. This approach may begin by defining the customer and uncovering their unmet need, formulating a hypothesis on what product or service the company can offer to meet that need, and validating the hypothesis by using customer feedback to rapidly experiment and iterate.

More specific innovation models do exist, but these are the three common paths to innovation:

Creating internal stakeholder-led labs

Many businesses look internally for well-aligned stakeholders to lead and strategize these labs. This is a popular strategy because it doesn’t require as much work to get started. But, this approach can be tricky. In-house groups might rely too heavily on existing business models and services, which might stymy creativity or introduce fear into the process.

Instead, these teams need enough separation from the business to develop their own culture and produce the necessary creativity to redefine the challenges they are charged with overcoming. A best practice for using this model is to establish the team leaders, source ideas from the entire company, and then narrow down to the top concepts for ideation and prototyping.

Acquiring innovative businesses

Another option is to acquire a smaller business with proven innovation abilities without squashing their unique expertise as they are integrated into the vision of the larger company. With a lot of work, resources, and money, this approach can work. But, all too often, this effort goes awry if core members of those innovative teams leave due to the deterioration or discouragement of their original culture.

Partnering with external innovators

Partnerships between internal and external innovation teams can be developed into mutually beneficial alliances. In these alliances, the smaller creative team brings new energy and ideas while the larger business team provides increased access to new markets. It’s in these partnerships and alliances where autonomy for both businesses can be sustained while taking advantage of each other’s own resources.

Creating a successful innovation lab

No matter the innovation model you choose, top innovation labs have specific characteristics and needs that make them successful. Capable innovation teams do a lot more than just think up cool new ideas. They actually manage the activities important to the initial concept phases as well as the many tasks, activities, and cost management necessary to develop the idea from its initial phases through implementation to launch. The best innovation labs:

• Learn, create, and change
• Consist of small, motivated teams able to create something new
• Have the trust and support of senior management
• Embrace customer centricity
• Work towards a clear, business-relevant goal
• Experiment relentlessly, with the desire to learn from failures
• Accelerate the build/measure/learn cycles
• Utilize an agile, iterative approach that encompasses Lean methodologies and the customer development method
• Leverage new technologies and business models
• Maintain their own diverse culture

Effective innovation teams, whether inside or outside of a lab, are purpose-driven and diverse.

Your purpose must be clear: perhaps you are focused on developing new business models or generating the next great product ideas. Or, your team can focus on business strategies like improving internal business systems, entering new markets, and improving company culture, employee performance, and client focus.

The composition of your team members is vital, too. Innovation team members should be diverse, with different backgrounds and experiences, work functions, and even cognitive styles. You might consider adding external personnel who bring an expanded skillset and expertise to your team. When paired with internal personal who have both a deep understanding of the company and a passion for innovation, this diversity and real-world experience can spark creativity.

Functions of an innovation lab

Once you’ve establishing the goal and team for your innovation lab, you’ll need to determine the activities and tasks the team will perform. Here are four activities essential to innovating:

Scanning for trends

Innovation labs should take on time- and resource-saving activities like trendspotting. This helps prevent your organization from merely chasing temporary hype and short-term fads.

Generating value from ideas

Innovation team members are tasked with generating value from ideas. Investing in an innovation accelerator or incubator can speed up the process of value creation. As you explore implementation processes, measure the value of these ideas to provide proof for additional funding if necessary.

Communicating with the organization

Communicate your progress to stakeholders and the organization at large. This helps prepare employees for cultural and procedural changes. Tell stories about ideas that have moved forward. Listen to stakeholders to understand areas where innovation is most needed or welcome.

Liaising with third-party partners

Engage with additional outside partners such as universities, trade groups, governments, and startups. These partnerships can offer vital experience and resources for accelerating ideas and innovations.

BMC Innovation Labs

BMC believes that if you anticipate market changes, then you can act on those changes. BMC Innovation Labs bring together customers, partners, and employees to accelerate the development of new and relevant solutions. It is truly a place to explore all ideas—from those that may fall outside the traditional enterprise IT box to the radical changes that support the future of work. BMC Innovation Labs work on tomorrow’s innovations today by:

• Creating a “fail fast” organizational culture by allowing our employees to experiment on ideas and to let them know it is OK to fail at first. Employees should take these initial failings and use them as learning opportunities, pushing the envelope towards the desired positive future state.
• Harnessing new ideas by creating experimentation spaces and advancing disrupting technologies
• Enabling intrapreneurship with expert counsel and support for fast development and prototyping
• Co-innovating with customers and partners to solve industry-specific challenges and shape the next generation of business technology

BMC has a history of delivering products to the market which help companies run their businesses more efficiently. By focusing on innovation that services customers better, we reimagine and reinvent how we use technology to unlock hidden potential. BMC Innovation Labs provide a place to create, nurture, and test ideas to find out if a promising concept could turn into a product or feature. It’s a safe place to fail fast and keep iterating, as we explore more of what’s possible.

Companies of all stripes need to innovate to stay ahead. What does that look like? Imagine these three scenarios:

1) Wouldn’t it be extraordinary to simulate plans or build what-if scenarios for the products, facilities, and processes you wished to change before you put real-world resources behind actual implementations?

What would it be like if we could take the monitoring power of BMC Helix and tie it to a digital twin of a hydroelectric power plant that overlays the plant’s control room operations center on a live video feed of the water dam to model what-if scenarios?

2) A company needs to understand inventory levels to plan and project shipments. What if we could use machine learning to project the inventory? The use case could be that IoT sensors monitor the current inventory and this data is used to project future inventory needs.

3) If you’re experiencing technical difficulties in a conference room, wouldn’t it be nice to simply click a button that creates a ticket in your service management system to report the issue?

What do all of these scenarios have in common? You likely don’t associate them with traditional ways of working in your new digital enterprise. But these are just some of the kinds of innovations we’re thinking about here at BMC.

One of my passions is looking at new technologies and innovations that can help our customers – and their customers – succeed. And even more broadly, looking at how technology can improve and change lives for the better. That’s why I am excited to introduce BMC Innovation Labs, a place to explore all ideas – from those that may fall outside the traditional enterprise IT box to the radical changes that support the future of work – and work on tomorrow’s innovations today.

BMC has a strong history of delivering products to market that help companies run their businesses more efficiently. We’ve always focused on innovating to service our customers better and we know that there is even more innovation possible that we haven’t yet addressed. BMC Innovation Labs will help to reimagine and reinvent how we think about technology – using cross-domain solutions that would bridge the functional silos of IT and beyond, unlocking hidden potential within our customers’ organizations and lives every day.

BMC Innovation Labs is a place to create, nurture, and test ideas to find out if a promising concept could turn into a product or feature. It’s a safe place to fail fast and keep iterating. Initial ideas include fast prototyping of algorithms to address use cases such as AIOps and incubating machine learning (ML) frameworks implemented within BMC AMI, BMC Helix, and other products as we explore even more of what’s possible.

BMC Innovation Labs will also provide a venue for customers and partners to bring ideas that go beyond traditional enterprise IT thinking.

While we’re in the early days of this effort, some fantastic ideas are already percolating:

Enterprise Digital Assistant: An enterprise-grade digital assistant that simplifies everyday activities such as scheduling a meeting, retrieving information from disparate sources, searching for documents, or getting in touch with colleagues using natural language processing (NLP) – an Alexa or Siri at work that provides automation, operations, or IT self-service via simple channels, giving your users complete visibility, control, and responsiveness in a personalized environment.

IoT and Telecommunications: A solution for trend analysis and alerts based on metrics produced by IoT sensors that are derived from cell towers. We’re co-innovating with a large European customer to bring this to life today.

Puppy Robot: Yes, it’s a real idea. There are already a lot of ways to generate trouble tickets in the traditional enterprise, but we think there are more autonomous ways to get problems solved more quickly.

These are just some of the things we’re already working on in BMC Innovation Labs.

We believe that innovation isn’t a one-time thing; it must be continuous. BMC employees, customers, and partners are encouraged to submit innovative ideas they have to BMC Innovation Labs for possible development. When you take the approach that “no idea is a bad idea,” innovation can flourish.