Increased complexity, increased opportunity

Ordinary products are becoming more complex every day. As products become more connected by the Internet of Things (IoT) their available features and functions correspondingly grow, driven by their reliance on software.

A wrist watch used to be a purely mechanical device, powered by gears, springs and levers. As microprocessors became small and inexpensive, consumers could buy wrist watches that were also calendars, schedulers and alarms – all functions still related to time. But as wrist watches become connected, entirely new markets have emerged: GPS devices, health-monitoring devices, and even cell phones.

This growing product complexity has introduced challenges in how engineering teams manage the development process. Product connectivity added one level of complexity, but that same connectivity has mushroomed the functions and features these products can offer. This connectivity enablement increases the width and breath of product requirements, the interaction between these requirements, and the number and scope of tests that must be executed to deliver the product quality that customers demand.

Each successive generation of products offers more features and functions, exponentially increasing their requirements, design, and testing effort. Therefore, engineering teams need to adopt tools that can help improve efficiency, seamlessly manage relationships between development processes, and scale as product complexity increases.

By 2025, it is predicted that there will be 75-billion internet-connected things, and that they will outnumber humans by nearly 9-1¹.

More software is required for more products²

You’re in a place you’ve never been before. No one else has ever been here, either. Because we’re at a tipping point that will change the way we work and live. We’re entering the Era of Engineered Intelligence.

We’re already seeing autonomous vehicles and manufacturing facilities powered by smart buildings and robotic assembly. Engineering teams are handing over the mundane, repetitive tasks to be performed by highly sophisticated machines, using the power of AI. Today’s connected products are required to perform in unprecedented ways, and AI is being used to help manage the volumes of data required to run these products. So companies can innovate faster. The future of engineering looks very different than it did just a few years ago. Does this future sound exciting to you? It should. Because you’re the one who’s expected to create it.

Complexity is inversely related to time

In recent years, the engineering of complex systems has become increasingly complicated, across nearly every industry. The fusion of the IoT, 5G networks and increased consumer expectations has created unrelenting demand for smart, connected products. These new products generate data that must be collected, stored, analyzed and acted upon. Whether it’s a fighter jet or a washing machine, meeting the expectation of flawless performance is the responsibility of everyone involved in the development process.

Organizations everywhere are feeling unprecedented pressure to deliver high quality products. More quickly. More economically. Without compromising quality. Ever.

Quality is everything; time and budget are everything else

When you’re developing products that require millions upon millions of lines of code, engineering lifecycle management becomes critical. Systems engineers know that. And they’re quickly learning that this digital revolution won’t be successful unless they’re successful at integrating their processes, from requirements to modeling and testing. This is a critical step in digital transformation, which allows global teams of teams to collaborate and have shared access to a single source of truth.

Traditional methods that use documents, emails, spreadsheets and white boards to capture requirements and manage changes simply aren’t going to cut it. What’s needed is a streamlined, collaborative process that diminishes cycle times and reduces error.

There are more demands on the engineering and development teams, and old tools and engineering processes aren’t sufficient. Teams need to improve their performance to cope with and indeed win competitive advantage in their industries.

Integrated, end-to-end engineering lifecycle management solutions make it possible for engineering teams to focus on their engineering – not on managing their tools or depending on over-burdened internal IT groups.

Let’s look at the numbers

NASA performed a study on the relative cost of fixing engineering errors during the various phases of a project development cycle. They discovered that an error that is carried through to development and production becomes exponentially more expensive to correct than one found at the requirements stage. Recalls for products are especially costly to manufacturers. This still holds true today, even more so, given the advanced technology and connectivity of today’s products.

Features and behaviors in today’s products are forcing engineering teams to fundamentally change the way they approach product design and development. Companies cannot afford to hire, nor can they find, qualified engineers at the rate products are growing in complexity. Using artificial intelligence (AI) can help bridge the gap and enable existing engineers to do more. AI is being introduced into engineering processes to aid in the analysis of data, and perhaps more importantly, to identify which data adds value. By injecting intelligence early into the process, teams can improve decision making with increased confidence. The industry disrupters already know that AI in engineering will soon become standard.

Before we delve into how AI can help, it may help to define what we mean by AI. Artificial intelligence is not a replacement for engineers. It’s technology being intelligently used to augment their capabilities. There are two defining characteristics that make AI-based algorithms fundamentally different from traditionally programmed algorithms:

Artificial intelligence is the challenge and the solution

While to some it may seem that AI complicates the design and development of software and products, it is also a solution to its own complexity. When AI is added to requirements management, it helps the requirements process in a way that improves quality, decreases cycle time, and reduces risk.

The ever-increasing complexity of products increases the need for rigor and quality in the engineering process. Certainly, engineering and tool techniques for strategic re-use have helped organizations keep up with this complexity explosion, but it is not enough. We need AI techniques to help manage the exponential growth of complexity and volume.

Requirements management is critical to the success of any project. Poor requirements definition at the beginning of an initiative can result in delays, cost overruns and poor product quality. The introduction of AI is bringing the power of intelligence to the development of today’s complex products. Better requirements help those in regulated industries achieve higher standards of compliance more easily, with more traceability and visibility into requirements tied to test cases and designs.

Organizations are re-thinking traditional methods of requirements management. They’re seeking new processes and tools to help them develop faster, better products, rather than the old solutions that don’t meet today’s needs:

The next generation of requirements management begins with an ecosystem of qualified, collaborative participants

Successful requirements management in today’s complex environment demands the ability to capture relationships and manage dependencies. The most successful initiatives engage more people during the initial stages so that requirements are complete, accurate and better defined. Therefore, systems engineers need to expand their requirements ecosystem to include:

Users: Who better than the user to know how a product should work and what the customer experience should be? This essential constituency is often overlooked by requirements managers.

Business managers: These are the people who understand the business goals and how product performance will impact them. They know their markets and their preferences. Break down the siloes and urge your business managers to participate right from the outset.

Retiring staff: The gray-washing of industries is becoming a problem as senior employees retire and take valuable information and experiences with them. They should be included in the requirements process so the next generation of requirements authors can benefit from their expertise. This is a unique value of AI, allowing senior engineers to translate their expertise for the next generation.

Today, systems engineers need a comprehensive requirements management solution that enables continuous collaboration

Most Chief Information Officers expect projects to be completed quickly, on budget and without problems. To satisfy that expectation, and to create complete, accurate requirements, systems engineers need a sophisticated requirements management program that provides advanced features and functions:

Systems engineers are demanding new engineering lifecycle management solutions to deliver innovative products in a timely manner while ensuring industry compliance

Cloud-based systems engineering tools can have a positive impact on an organization’s technology, process and culture:

• Expanded product development and services ecosystems
• Real-time collaboration across global user networks
• A holistic approach to using data and tapping all internal expertise

At IBM, we understand the challenges faced by software and systems engineers, because we are engineers ourselves. And we’re committed to improving the engineering lifecycle with enhanced product development tools and services.

We know that an exceptional requirements management program will help organizations do much more than reduce failures. So we examined the current and future needs of engineers, identified the deficiencies of existing requirements management systems, and created solutions that help ensure a wholly better outcome. With our methods, innovative, digitally-driven products can be designed, developed and quickly taken to market to meet increased consumer demand and pre-empt the competition.

Requirements management will also help:

• Reduce risk
• Improve predictability
• Satisfy customers
• Improve decision-making

Toward a more knowledgeable ecosystem

In addition to improving workflow, integrating IBM Engineering Lifecycle Management has a beneficial effect on people. It builds confidence in the teams doing the work, showing them the value of their efforts in real time. By bringing Watson AI into the mix, senior engineers can dedicate some of their time to training Watson with best practices from their tenure experience. Not only does this facilitate the creation of a shared and retained knowledge base, it helps mitigate the risk of losing valuable existing knowledge as a result of a retiring workforce.

IBM Requirements Quality Assistant (RQA)

IBM delivers the next generation of requirements management through the power of IBM Watson artificial intelligence.

In a single-tool environment that extends IBM Engineering Requirements Management DOORS® Next, Watson AI helps mitigate risk and ambiguity early in the authoring phase using rapidly integrated AI functionality.

The Watson AI capability of IBM Requirements Quality Assistant includes several features that improve the process by helping improve requirements quality and accelerating the requirements review process:

1. Data management: IBM RQA provides a knowledge-driven requirements process that sorts through data to extract key insights, accelerating data management and improving requirements quality.

2. Writing guidelines: IBM RQA is designed to be consistent with industry best practices, and is pre-trained to detect a number of quality issues based on the INCOSE Guidelines for Writing Good Requirements.

3. Machine learning: The more your teams use IBM RQA the smarter it becomes. Users can provide direct feedback to teach Watson and continually improve the model over time.

4. Natural Language Processing (NLP): IBM RQA removes risk and ambiguity in the requirements authoring phase out-of-the-box. It finds and pinpoints problems and provides expert guidance for correcting them.

IBM Requirements Management solutions enable you to maintain complete control of your projects from beginning to end. IBM clients have used our knowledge driven requirements management tools to:

1. Achieve significant reduction in defect costs

2. Reduce review time and costs

3. Share engineering expertise

Following the deployment of the IBM Requirements Management solution with RQA, some IBM clients have reported:

We’re in the midst of a convergence of ingenuity and expectation. Throughout the world, consumers have set us on a trajectory toward the domination of innovative, digitally connected products. But there’s a caveat: these products must meet the performance expectations of a highly demanding marketplace.

Engineers are under greater and greater pressure to develop more complex designs, and to get them to market more quickly. And they must do so with economic efficiency. The market demands it. And thus these companies are forced to embrace a digital transformation. Seventy-one percent of organizations cite consumer experience as a competitive differentiator⁹, and we can safely assume that 100 percent of organizations want a competitive advantage.

IBM can help you improve your engineering lifecycle management processes. Our solutions are integrated, intuitive and powerful. They give you total control. And the addition of Watson AI through our Requirements Quality Assistant offering gives you increased confidence to move swiftly into the next chapter of engineering innovation.

Surprisingly, moving from your traditional methods of documents and spreadsheets will be easier than you think. And IBM is here to help get you there.

To learn more about IBM Engineering solutions, contact your IBM representative or IBM Business Partner.
Or visit us at: https://www.ibm.com/products/engineering-lifecycle-management