Digital transformation projects create the opportunity to deliver value to engineers and their organizations every day. However, like other projects, digital transformation projects routinely face the risk of disasters. With awareness of the source of digital transformation disasters, projects can mitigate their impact by:

• Listing the associated risks in detail in the project charter to educate stakeholders.
• Including specific tasks in the project plan to avoid or minimize the impact of disasters.
• Conducting parallel projects to address these situations before they turn into disasters.

Here is a list of the most common issues that cause digital transformation disasters. Anticipating these issues will position your project for success, allowing the organization to squeeze more value from its data.

Too many data problems

Data problems often overwhelm digital transformation projects. Some typical data  problems include:

• Missing values such as incomplete component specifications or missing product descriptions.
• Incompatible key values for data columns, such as customer and vendor codes across systems.
• Incorrect data such as material and supplier codes, dates and discount percentages.
• Missing transaction history where data such as engineering change history or warranty claims is essential to analyzing trends.
• The absence of data quality standards.

Correcting data problems will add to the project cost and extend the schedule, undermining the project team’s efforts. The significant effort required to make corrections will surprise management and potentially reduce their commitment to digital transformation.

To manage data problems in ways that are helpful to advancing digital transformation, engineers can undertake the following actions:

1. Recognize the risk of data issues in the project charter to set stakeholder expectations.

2. During the feasibility phase of the project, profile all the potential data sources to determine the extent of data issues.

3. Share the data issues identified with the data stewards and encourage them to take action to make corrections.

4. Start the project by focusing on data sources that exhibit fewer data issues.

Lack of data literacy

Employees’ lack of data literacy is impeding the realization of benefits from digital transformation because they are not using the available digital data.

This lack of data literacy means the planned benefits of digital transformation are not a reality in the organization. The absence of visible benefits will reduce management’s commitment to digital transformation.

To overcome employees’ lack of data literacy, project teams can take the following actions:

• Develop a library of data analytic routines that employees can run and modify to suit their needs.
• Offer in-house, instructor-led training for the available data and data analytic tools.
• Offer one-on-one coaching for employees.
• Point employees to specific YouTube videos that will improve their conversancy with the available data analytic tools.
• Develop a library of frequently requested reports that employees can run and export the data to Excel.

Viewing generative AI as a silver bullet

The explosion of generative AI during the past two years has caused some to view this incredibly capable technology as a silver bullet that can be easily applied to many problems, including digital transformation.

Delivering generative AI features as part of a digital transformation project is not trivial and can lead to undesirable consequences, including:

• Poorly constructed prompts that produce erroneous results and then misleading recommendations.
• Leakage of commercially sensitive intellectual property into the hands of others.
• Risk of inadvertently infringing on the copyrights of others.
• Investment in multiple generative AI software packages that increase cost more than value.
• A more reasonable approach to applying generative AI for engineering applications includes implementing these elements:
• Orient your organization on generative AI capabilities, risks, and limitations.
• Architect a data and analytics environment that will include a data lakehouse to manage both structured and unstructured data.
• Design an AI computing infrastructure, including cloud components that is efficient, scalable, well-governed and at least somewhat future-proof.
• Strike a balance between leveraging vendor capabilities that provide little competitive advantage and developing in-house models and related software that will be costly.
• Choose where to deploy open-source and proprietary technologies.
• Identify which of the many AI use cases are suitable for your company and can deliver tangible business value.
• Build trust in AI-driven solutions through detailed verification of results.

Chasing the latest technology

Some digital transformation project teams become excited by or even fixated on the latest vendor announcements about information technology advances. Examples include:

• Incorporating a sophisticated data visualization software package when a simpler and cheaper one is sufficient.
• Including generative AI capability when it’s of limited value to the digital transformation.
• Using a graph DBMS when a relational DBMS is sufficient.
• Building a data warehouse when integrating data from multiple data sources is straightforward.
• Introducing a new integrated software development environment that is unfamiliar to the organization.

Often, teams see the potential benefits of new information technology without considering how the immature technology will add cost, create delays and introduce quality problems.

Changing technologies or adding more and more technologies mid-project will distract and overwhelm digital transformation projects. Impacts will include reworking software, training staff, and building familiarity with the new technology.

Engineers can take a superior approach by carefully selecting a set of information technologies near the beginning and sticking with the choices for the project’s duration. Information technologies do not advance so quickly that older technologies become obsolete within a system’s planned existence. Engineers successfully use software packages and application development tools that aren’t the latest and greatest every day.

Fantasy business case

Some companies approve digital transformation projects based on an unrealistic business case. Engineers can recognize a fantasy business case because it will include one or more of the following elements:

• Estimated future revenue increases that exceed the historical trend.
• Estimated future operating costs will decrease more than the historical trend.
• The project cost estimate is unrealistically low, does not include a contingency amount and does not recognize the cost of likely change orders.
• There is no discounting of the value of future benefits.
• Quantification of intangible benefits such as brand value or customer satisfaction. Intangible benefits can be essential aspects of digital transformation projects. However, quantifying these benefits is not realistic.

Engineers can promote a credible business case based on tangible benefits and a more reasonable project cost. While digital transformation offers companies many benefits, those benefits often indirectly support other goals, such as reduced operating costs, compressed product development work or increased market share.

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Ideally, project managers collaborate with project sponsors and stakeholders to position projects for success, reduce risks and mitigate the impact of various issues that arise during project deployment. Project sponsors are assigned by senior management to ensure the planned business benefits are delivered. Project managers manage the work of the project team and report to their project sponsor.

In reality, however, project sponsors often let down their teams and add risk to projects in many ways. Here are eight common project sponsorship missteps and how project managers can politely and diplomatically resolve them (and as much as expressing anger is tempting, it’s never helpful).

Sits on recommendations

The project sponsor refuses to act on team recommendations to resolve issues. In some organizations, it’s better to waffle than risk being blamed for the wrong decision. But in digital transformation projects, delays in waiting for a decision are always more expensive than correcting a decision that turns out later to be incorrect.

Instead of becoming angry, project managers can address this problem through diplomatic coaching of the project sponsor. Diplomatic coaching involves patiently explaining the adverse consequences on the project’s outcomes the project sponsor’s actions or inactions will cause. Diplomacy is required because the project sponsor is typically a powerful person in the organization and does not respond well to blunt criticism.

Project managers do not let the absence of a decision delay the project schedule. They proceed on the assumption that the recommendation will be accepted eventually.

Refuses coaching

When project managers try to make diplomatic suggestions about how the sponsor could better fulfill their role and support the project, the sponsor claims to be too busy or suggests the project manager can handle the issue independently.

Project managers address this refusal professionally by diplomatically assigning project sponsors small, tactical tasks to gradually increase their involvement, and then thank them when the tasks are complete.

Fails to support the project manager

Suppose the project manager feels the project sponsor doesn’t support them. They sense they will be blamed for project shortcomings. In that case, an experienced project manager will begin to think about how to exit the project quietly. Such an outcome can reflect poorly on the project sponsor’s carefully cultivated reputation, the project’s progress, and the team’s effectiveness.

To avoid this situation, project managers seek assurance that project sponsors will support them and the team in the following ways:

• Communicating and selling the digital transformation project benefits among the project sponsor’s executive peers.
• Publicly supporting project recommendations to stakeholders when complex issues inevitably arise.
• Proactively support the project work.

Pushes scope additions

On multiple occasions, the project sponsor has proposed surprising scope additions for approval by the steering committee. There was no prior discussion with the project manager. These additions would add value but are clearly out of scope as defined in the project charter for the digital transformation project.

The project manager politely reminds the project sponsor of the agreed scope management process and has an analyst on the project team complete the proposed scope addition form for review by the project sponsor. Project sponsors usually never review the form, and the idea dies quietly.

Contradicts agreed decisions

The role of project sponsors includes emphatic support of the agreed decisions in conversations with other executives. If it becomes politically expedient to support the contrary view, some project sponsors are tempted to make a U-turn, claim they weren’t part of the decision, and blame the project team.

The project manager should politely remind the project sponsor of the agreed decision and ask the project sponsor if the original decision needs to be reversed. If so, the project manager assigns an analyst on the team to complete the proposed scope change order with an estimate for review by the project sponsor and as a decision record. The form privately embarrasses project sponsors, who quit articulating the contrary view.

Criticizes the project manager

We’ve all observed project sponsors who are smooth political operators. They are reluctant to accept responsibility for anything. They are experts at deflecting criticism and blame. When minor project problems appear, they quickly criticize the project manager, ignore the team and distance themselves.

In this situation, a project manager will become angry and conclude they have been hired as the convenient scapegoat should a problem occur and not, as claimed, as a project manager with a mandate to deliver the project.

Project sponsors who play these political games cause project team turnover and failure. It’s often best for the project manager to lobby the stakeholders to assign another project sponsor.

Commits to a ridiculous project completion date

Sometimes, project sponsors believe they can impress their peers on the executive team by committing to an overly aggressive completion date for the digital transformation project without consulting the project manager.

Naturally, the project manager is angry about not being consulted and the real possibility that the project will be viewed as a failure when it can’t achieve the unrealistic date.

A solution to this problem that avoids embarrassing the project sponsor is to replan the project to create a release that can be achieved by the aggressive date, declare that a success, and then work on the rest of the project after that date.

Criticizes the project

When discussing the digital transformation project with stakeholders, some project sponsors express hesitancy about the benefits and criticize the performance of the project team.

Instead of becoming angry, the project manager should diplomatically explore the project sponsor’s hesitancy about the business case. The project sponsor’s commitment is typically strengthened if the hesitancy can be resolved.

If the project sponsor and manager cannot resolve the hesitancy, they should cancel the project immediately. Continuing will only waste money and perhaps lead to conflicts between the team and the stakeholders.

Project managers can often improve the performance of project sponsors with diplomatic coaching about how to best fill the role, explaining the role of the project manager and describing the value of collaboration.

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Many companies are pushed into business transformation by changes in their environment. However, companies often struggle with business transformation due to complexity and resistance to change. Business transformation depends heavily on digital transformation because of the pervasive role of information technology (IT) and operational technology (OT) in today’s businesses and manufacturing floors. Whenever companies advance digital transformation, business transformation becomes easier. 

Business transformation is a change management strategy that aligns people, processes, and technology to companies’ future vision, shifting market demands and new opportunities based on technological advances. The evolution of product or service offerings is critical to companies’ continued success. Engineers constantly assess, adjust, and advance offerings to stand out from competitors. 

Multiple disruptive events or opportunities can trigger the need for business transformation. This article describes how digital transformation supports various business transformation events. 

Technology triggers  

Most technologies are advancing due to the research and development of a record number of scientists and engineers funded by their companies and government grants. The research results trigger engineering and operational transformation that impact the business transformation. Examples include: 

• Carbon fibre and nanomaterials transformed the product design and manufacturing of industrial products and many types of vehicles. 
• Advanced glass varieties revolutionized telecommunications and transformed the designs of many consumer products, including smartphones. 
• Generative AI is transforming the work of many professions, including engineers and improving their productivity. 
• Improved software development productivity led to more functional apps that transformed control over manufacturing processes, ensured consistent quality and accelerated research and development. 
• FinTech startups are transforming the banking and insurance industries with digital services and simplified loan approval and payment processes. 
• AI and robotics are transforming manufacturing for reduced cost, consistent quality, and transportation with autonomous vehicles. 
• Rapid molecular screening transformed the selection and testing of pharmaceutical candidates. 

When engineers incorporate technology advances into the business, they typically trigger the digital transformation of: 

• Various processes to reduce elapsed time and improve quality. 
• More and better supporting data with better data management. 
• Collaboration with critical vendors through digital collaboration software. 

The Internet triggered business transformation 

The Web and smartphones enabled the e-commerce sales channel. Many companies digitally transformed their businesses beyond their traditional brick-and-mortar sales channel to sell products through websites and apps. Prominent e-commerce examples exist in every product category. They include: 

• Amazon and many retailers threatened by Amazon and its clones. 
• Industrial and construction products dealers. 
• Computer and electronics manufacturers and their distributors. 
• Automobile manufacturers and their dealers. 

This additional sales channel produces new revenue streams and reaches customer types that were unreachable or ignored before. E-commerce requires significant investments in information technology and digital transformation of: 

• Sales, fulfillment, and return processes with sophisticated web-based applications. 
• Marketing communication through social media marketing. 
• Customer support through a call centre and a smartphone app. 
• Distribution, warehousing, and delivery with automation and robots. 

Competitive threats trigger business transformation 

Often, competitive threats to market share and profitability trigger business transformation. Examples include: 

• Japanese auto manufacturers introducing more reliable cars at a lower price triggered bankruptcies and a massive business transformation among American and European auto manufacturers. 
• Korean household appliance manufacturers introducing more advanced and reliable products led to the demise of various erstwhile competitors. 
• The emergence of Chinese aircraft manufacturers triggered a business transformation at Boeing and Airbus. 
• Brick-and-mortar chains, such as department stores, responded to the emergence of e-commerce-only competitors. 

When engineers respond to competitive threats, they typically transform the business by: 

• Designing new and revised products and services. 
• Building new production and distribution facilities. 
• Designing new and revised business processes. 
• Demanding more and better internal and external data. 
• Expanding the supply chain ecosystem. 

The success of these actions depends on advancing digital transformation. 

Price and performance of computing triggers business transformation 

The continuing improvements in the price/performance of computing components of all types, software packages and Software as a Service (SaaS) offerings enable business transformation. For example: 

• Engineers digitally transform product design processes using more sophisticated CAD, simulations, and digital twins. 
• Apple and Android-orientated manufacturers transform handheld computing devices to be more capable and easier to use. That advance increased consumer and business adoption. Engineers use mobile devices to monitor and improve manufacturing plant performance. 
• Products become both cheaper and more capable. Examples that engineers encounter include industrial control devices, simulation software, IIoT sensors, and video surveillance equipment. 
• Telehealth transforms the productivity of healthcare providers and the experience of patients. Less waiting and less driving improve the patient experience. 

When engineers want to exploit improvements in computing components and software, they typically: 

• Design new and revised products and services. 
• Upgrade facilities. 
• Design new and revised business processes. 
• Demand more and better operational data. 

All these actions depend on a high level of digital transformation. 

Regulatory requirements trigger business transformation 

Sometimes, new regulatory requirements drive business transformation. For example, the determination to address the adverse impacts of climate change is causing businesses to: 

• Measure and report on their environmental impacts more accurately, consistently, and comprehensively. 
• Transform their production practices to reduce energy consumption and related GHG emissions. 
• Transform their waste management practices to increase recycling volumes and reduce disposal volumes. 

When engineers respond to new regulatory requirements, they typically: 

• Trigger digital transformation of various existing and new business processes such as digitizing and quality-assuring the required data gathering. 
• Demand more and better reporting data which digital transformation provides. 

Business transformation challenges 

Business transformation is challenging to implement because of its wide-ranging prerequisites for success. It entails adjustments to a company’s structures, incentives, mindset, processes, habits, core capabilities, and technology. In short, business transformation changes much of a company’s culture. 

Business transformation creates new and revises existing business processes. This reality creates a need to comprehensively perform people change management tasks to ensure smooth implementation. 

Engineers can ensure that digital transformation enables business transformation and reduces implementation risk. Uneven digital transformation can become an expensive impediment to business transformation. 

Business transformation succeeds on the foundation of digital transformation. 

The post Technology advances trigger business transformation  appeared first on Engineering.com.

The complexity of digital transformation requires significant multi-disciplinary collaboration to understand, analyze and resolve problems. But, historically, information technology (IT) and operational technology (OT) teams have worked in isolation of each other. They have different priorities, often don’t speak to each other at all, and, when they do, they speak a different language when it comes to technology terms.

Bridging this IT/OT divide is an essential aspect of a successful digital transformation. But the need to collaborate to innovate also extends to other areas of the company, including the partner ecosystem.

For some clarity on strategy, this article will help identify ideas you can implement to enhance collaboration in your organization.

Value of collaboration

When engineers and others shift mindsets from a hyper-individualistic approach to a more collaborative culture, then teams make digital transformation progress and foster innovation.

Collaboration causes individuals on digital transformation teams to challenge each other. Those creative interactions lead to:

• Innovative designs.
• Practical solutions to problems.
• An improved level of team performance.
• Reduced risks.
• Avoidance of the common reasons for failure.

When organizations enhance their collaboration culture with software that aids collaborative work, digital transformation is more efficient.

Impact of collaboration

Cross-functional collaboration during digital transformation:

• Breaks down silos and fosters a culture that drives innovation.
• Engages stakeholders, employees and external partners for a holistic transformation approach.
• Incorporates digital technologies more efficiently into products, services and processes.

A collaboration culture connects teams that may be geographically dispersed in complex business and multi-cultural settings to promote corporate goals, foster shared values, and build personal relationships. This collaborative work environment helps lower costs, shorten timelines, and improve productivity. These outcomes increase return on investment (ROI), market share and customer satisfaction.

Conversely, a lack of collaboration coupled with communication breakdowns lead to misunderstandings, delays, errors and inefficiencies that negatively impact productivity and quality. These trends will lower sales, ROI and customer satisfaction, threatening the organization’s survival.

Typical issues requiring collaboration

Digital transformation projects frequently encounter the following issues that require collaboration among various professionals, including engineers:

• Correct data quality lapses such as incompatible dates, inaccurate product codes or incomplete material descriptions due to inadequate quality control of data entry work.
• Acquire missing data such as product designs not converted after an acquisition.
• Design, build and test complex data and systems integration, such as multiple BOM structures managed by different inventory or external supplier systems.
• Acquire new data sources from data vendors such as weather data, standards documents or real-time shipping status data.
• Design digitally-enabled business processes such as fabrication or logistics.
• Encourage reluctant stakeholders including the finance or facilities departments.

Benefits of collaboration

Collaboration in digital transformation produces these benefits:

• Accelerates the pace of innovation by sharing collective knowledge and expertise to ensure first-mover advantage.
• Improves decision-making processes by involving diverse perspectives.
• Enhances agility and adaptability to respond to evolving market dynamics.
• Encourages employee engagement.
• Improves business execution.
• Reduces rework.

Collaboration strategies

Collaboration strategies that engineering leaders can use to encourage the team, and which lead to successful digital transformations, include:

• Building a collaborative ecosystem by fostering strong relationships across departments and with technology and data partners.
• Encouraging knowledge-sharing.
• Establishing clear communication channels.
• Cultivating leadership qualities.
• Championing transparency that allows team members to reach out for help as soon as they hit a snag instead of worrying about the reactions of their seniors and team members.
• Fostering trust so team members can freely share their opinions, suggestions and criticisms.
• Recognizing team achievements.

Collaboration challenges

Successful digital transformation project teams work deliberately to overcome these collaboration challenges:

• Resistance to change processes and adopt new technologies.
• Achieving a consensus among leaders on digital transformation priorities.
• Interpersonal and political conflicts.
• Conflicting departmental objectives such as the IT/OT divide.
• Dated organizational structures and leadership styles.
• Focusing on technology potential at the expense of business value.
• Mistaking diplomatic communication for a willingness to collaborate.

Communication is centered around knowledge-sharing, while collaboration applies this knowledge to problems, opportunities and tasks.

Roles in digital transformation

Effective digital transformation collaboration requires a project team. The typical roles on the team include the following:

• The project sponsor is the senior executive who approves the digital transformation project and is committed to the project goal and objectives as stated in the project charter.
• The project manager leads the work of the project team. That includes developing a detailed project plan, supervising the deliverables, and resolving issues that invariably arise.
• Business analysts describe the business pain points, opportunities, customer experience issues and technology strategy. They regularly interact with business staff across the parts of the organization involved with the project.
• Data stewards manage an organization’s digital assets to provide engineers and other end users with high-quality data that is quickly and consistently accessible. They help the digital transformation team understand available corporate data.
• Business process experts, often engineers, understand the company’s current workflow and technological environment deeply. They identify processes that digital transformation can improve and confirm the accuracy of project deliverables.
• Designers expand the work of business analysts and process experts into detailed designs. The designs will be configured in software packages by business analysts or coded into custom software by software developers.
• Security and compliance specialists contribute to the project’s technology, architecture and design choices to ensure compliance.
• Software developers code and test the software required to integrate data from diverse datastores included in the digital transformation scope.
• Change management specialists have the communications skills to generate commitment and help end-users adopt the digital transformation.
• Implementation staff lead the implementation of process changes and new applications.

Digital transformation projects may not have to fill all these roles depending on their size and goals.

Collaboration software selection criteria

Software that can improve the effectiveness of collaboration is essential for successful digital transformation. Software enables digital and virtual collaboration and remote work among engineers and other employees regardless of their physical location.

Consider the following digital collaboration software selection criteria:

• Real-time file sharing: Enable team members to access, edit and retrieve the latest files with version control. Enable specific file and folder sharing to authorized individuals outside the team.
• Multiple device support: Enable team members to access the collaboration environment using laptops or mobile devices.
• Cloud-based: Allow end-users to access the collaboration environment anytime and from anywhere to stay productive in the office, at home, or on the road.
• Search: Allow end-users to use keywords, tags, and search filters to locate content, files, and previous conversation threads quickly.
• Meetings: Support audio and video conferencing.
• Personal and group calendars: Simplify scheduling using shared calendars to coordinate team meetings and activities.
• Ease-of-use: How easy is the user interface to learn and productive for ongoing use?
• Chat or direct messaging: Available?
• Workflow automation: Ability to define and operate multi-step business processes.
• Onboarding and training: Provide sufficient training and support so end-users can maximize the productivity value of the collaboration environment.
• Integration: Support integrating other apps into the collaboration environment.
• Security: Safeguard sensitive data with encryption to stay compliant with industry regulations.
• Uptime and backup: Ensure the collaboration environment has built-in redundancy and a comprehensive backup and recovery plan to minimize downtime.
• Access control: Only authorized end-users can view, edit, or share specific files.
• Usage analytics: Collate and analyze usage data to improve the collaboration environment.

The differences across the major collaboration software packages are insignificant for most digital transformation projects or digitally enabled work. Don’t acquire another software package when your organization is already using one.

In summary: Digital transformation integrates new technologies and digital data into business processes. To succeed, engineers must acquire new collaboration habits, skills and knowledge to use data-driven digital applications productively.

The post Digital transformation requires a collaborative culture appeared first on Engineering.com.

Too often, these projects are organized in ways executives find exhausting or downright scary.

If you find yourself struggling to build and maintain executive commitment to your digital transformation project, it’s time to start speaking the executive language. Follow these tips to win the support you need.

Release functionality updates often

Executives are demanding and impatient people. Organize your digital transformation project to respond to those high expectations.

Plan to release visible digital transformation improvements for production use about four times per year. Communicate and recommunicate the new functionality you’re delivering to remind your executives of the associated benefits. Always include data visualization among the functionality you’re releasing to illustrate progress unequivocally.

For example, add digital transformation functionality in every release with:

• A new data source, such as external supply chain information, IIoT data from another manufacturing facility or simulation data.
• New data visualizations of manufacturing performance against plan, product quality measures or demand forecasting accuracy.
• Data quality improvements for customer, shipment or materials inventory data.

Never plan a Big Bang project. Never promise to release spectacular results after a year or more of work, during which time executives see nothing they can recognize as progress. This approach will kill executive commitment.

Visualize data

Digital transformation improvements become more visible with engaging data visualizations than boring reports of the same data. Charts deliver value to engineers and build commitment with cautious executives because they’re easier to understand and more engaging.

For example, include powerful data visualizations in every release:

• Variance analysis of actual production or defects vs. plan.
• Trend analysis of gross margin or market share vs. competitors.
• Sales analysis to better understand technology preferences or adoption rate.

Powerful data visualizations exhibit these characteristics:

• Show trends, key performance indicators or variances rather than point-in-time data.
• Communicate an immediately clear message while avoiding clutter and ornamentation.
• Invite end-user engagement by supporting input to change the data visualization.
• Use animation, typically to illustrate change over time.
• Minimize legends.
• Avoid pies, donuts and treemaps because they’re challenging to evaluate visually.

Reports with endless rows and columns of data don’t build commitment with executives or communicate well with engineers.

Start small

Digital transformation is a multi-year journey that consists of many projects. Tackle a small digital transformation project where you can deliver a quick win first. A small project involves only two data sources, helps a single engineering workgroup and requires minor data cleanup.

Afterwards, your executives will support a slightly more ambitious, follow-on digital transformation project based on the initial success. Examples of small digital transformation projects could include:

• Improve the demand forecast for critical components for one product line.
• Confirm the accuracy of the physics formulas that drive a critical simulation.
• Simplify data imports for the CAD system.

Being too ambitious initially and then completing late or over budget because of team or stakeholder exuberance does not build executive commitment for digital transformation. Similarly, initial successes can cause executives to push for a schedule speedup. That push will undermine quality and tarnish your hard-won credibility. Push back diplomatically.

For starting digital transformation ideas, consider 5 technologies to quickly kickstart digital transformation in 2024.

Communicate risks

Almost all digital transformation projects include associated risks that could adversely affect cost and schedule. Executives can’t be aware of risks unless you tell them.   Communicate a summary of risks and your project’s mitigation plan to maintain executives’ commitment, trust and transparency during the project.

These frequent digital transformation risks need to be communicated. For example:

• Data quality lapses you didn’t plan for that will need to be remediated.
• Staff turnover will trigger the need to hire replacements.
• Unexpected data integration complexity will increase software development efforts.

When risks that executives don’t know about explode into reality, it will kill their commitment. Don’t exaggerate risks, because that will cause executives to wonder if your project should have been approved in the first place.

Prioritize tangible benefits

Executives value digital transformation projects that deliver tangible benefits that increase revenue or decrease cost. Tangible benefits are numeric and are not subject to challenge or dismissal.

Engineers can track tangible benefits achieved as digital transformation projects progress and regularly report the dollars on a chart to management. These charts maintain executive commitment. For example, the following tangible benefits can be quantified:

• Shortened elapsed time for product development.
• Reduced use of expensive physical prototypes.
• Reduced scrap rates from product manufacturing.
• Reduced warranty claim costs from customers.

Avoid exaggerated benefits that are not credible and intangible benefits where achievement is difficult to recognize.

Sell the project business case

Digital transformation projects require an appealing business case to achieve management approval. Management is paid to be skeptical about the supposed benefits of technological advances, including digital transformation. Build support by communicating your business case succinctly and avoiding exaggerations.

For example, if the business case is about:

• Improving product quality consistency, describe how better IIoT data will narrow variation. Don’t claim you’ll reach close to perfection.
• Accelerating construction work, describe how daily effort reporting will highlight schedule slippage better for intervention. Don’t suggest you can quickly reduce prior slippage.
• Reducing design defects, describe how more sophisticated simulations will reduce those before you build a prototype. Don’t claim you’ll eliminate the defects.

Don’t oversell the business case by overstating benefits or understating costs and risks. Don’t sell the value of advanced technology. Management may become skeptical, and you will set yourself up for failure.

Speak in business terms

All digital transformation projects require technical wizardry to integrate incompatible systems, fill in data gaps and produce systems integrations, data visualizations or simulation results. However, executives are not information technologists. Maintain their commitment by speaking in business terms.

For example:

• Indicate that a new database is improving simulation performance. Don’t discuss how in-memory processing or a graph database produces the improvement.
• Report that better use of supplier data is shortening the supply chain. Don’t explain that a new network gateway and advanced data transformation software underlie this improvement.
• Demonstrate how augmented reality headsets help plant maintenance. Don’t elaborate on the software development difficulties of integrating video and plant drawings.

Technical talk about complex hardware and software will scare executives, unreasonably increase their sense of project risk and reduce their commitment to the project.  Technical discussions are better held with the organization’s CTO or enterprise architect.

Manage change

Include addressing people, processes and behavioural change in your project plan to ensure a smooth implementation of digital transformation releases. If executives hear a lot of employees whining about your digital transformation project, their commitment to the project will wane.

For example, engineers will need time and support to build familiarity with new functionality such as:

• CAD or simulation software.
• Revised inventory tracking and reporting processes.
• An upgraded prototype testing facility.

Believing that engineers and others can simply adopt new processes and software on the fly will lead to undesirable outcomes, such as slow adoption, not achieving expected benefits or even active resistance.

Fail fast and learn fast

Set expectations with executives and stakeholders that small failures will occur on the road to digital transformation and that small failures form the basis for meaningful learning. Adopt agile principles. Be willing to shuffle the feature list significantly as you learn and as priorities and benefits come more clearly into focus.

For example:

• Initial use of data integration and visualization tools reveals serious shortcomings. Select and acquire more suitable tools.
• Design work reveals gaps in requirements statements. Reprioritize the feature list to design based on solid requirements first.
• Efforts to build new data visualizations reveal software defects in the current Excel-based charts. Redevelop accurate charts first.

Always prioritize schedule—jettison scope to achieve on-schedule releases. Don’t let others spin failed experiments as digital transformation failures and unnerve executives.

Keep executives committed to digital transformation by delivering frequent, modest advances and avoiding large, risky projects that can easily fail.

—

Yogi Schulz has over 40 years of Information Technology experience in various industries. He also writes for ITWorldCanada and other trade publications. Yogi works extensively in the petroleum industry to select and implement financial, production revenue accounting, land & contracts, and geotechnical systems. He manages projects that arise from changes in business requirements, from the need to leverage technology opportunities and from mergers. His specialties include IT strategy, web strategy, and systems project management.

The post How to speak to executives and win support for your projects appeared first on Engineering.com.

Digital transformation succeeds when business processes are redesigned to benefit from digital technology. Redesigning processes is best performed using the systems thinking method, a holistic approach that reveals problems, bottlenecks and inefficiencies.

There are many systems thinking techniques that can help you plan your digital transformation initiatives, such as causal loop diagrams, RACI tables, process maps and the iceberg framework. Here are four more systems thinking techniques every engineer should know about.

Agent-based modeling (ABM)

Agent-based modeling (ABM) creates a visual representation of a complex system that models autonomous agents interacting with each other during the modeled process. Agents are individuals, contractors or suppliers.

The engineering applications of ABMs include manufacturing situations where the interacting variables are poorly understood, leading to unacceptable product quality variation. Examples include digital chips, high-value glass and steel, carbon fiber and nanoparticles.

Example of an agent-based model. (Image: AnyLogic.)

Key benefits of ABM for digital transformation include:

• Stakeholders like how simple it is to describe individual interactions
• Realistic model behaviors enable better insights into process dynamics
• Engineers can explore various scenarios and actions by simulating different process outcomes
• ABMs can handle complex, nonlinear interactions among agents to reveal how small changes at the agent level can lead to significant shifts at the process level

Various software packages support ABM. The ones that are more general purpose with GIS and 3D capabilities include AgentScript, AnyLogic and MASON.

Network analysis

Network analysis illustrates relationships between nodes and the ties between nodes. Typical nodes are individuals, processes or resources, such as equipment or facilities. Ties or edges describe the type of relationship and the data exchanged between two nodes.

Network diagrams and program evaluation and review technique (PERT) charts are used for network analysis and planning larger and more complex projects. Data modeling tools that support graph databases are also used to capture network analysis work.

Example of a PERT chart. (Image: Lucidchart.)

The value of network analysis for digital transformation is that it:

• Offers the ability to examine quantitative network properties
• Reveals how different steps or components influence each other and contribute to the overall behavior of the process

The engineering applications of network analysis include manufacturing complex products such as:

• Electrical and electronic components
• Diesel-electric locomotives
• Supercomputers
• Rockets, satellites and vehicles for space flight

Various software packages support network analysis. Examples include Adobe Express, Lucidchart, Miro, Visme and Visual Paradigm Online.

Scenario planning

Scenario planning analyzes many possible future events and alternative possible outcomes. Creating a scenario often uses political, economic, sociological, technological, environmental and legal (PESTEL) and strengths, weaknesses, opportunities and threats (SWOT) analysis for quantitative projections and qualitative judgments about the many time series variables interacting to determine alternative outcomes.

Example of a scenario planning dashboard. (Image: Anaplan.)

The value of scenario planning for digital transformation lies in the following:

• Learning about the business process through the planning process itself
• Identifying and revising implausible assumptions in the scenarios
• Identifying and correcting missing variables and errors in the relationships among variables

The engineering applications of scenario planning include digital construction planning for major projects such as roads, bridges or skyscrapers. The technique is also applied to enterprise and macroeconomic modeling.

Scenario planning software packages are often included with financial planning and analysis software. Software packages for scenario planning include Adaptive Insights, Anaplan, Cube, Jirav, Mosaic and Planful.

Systems dynamics modeling

Systems dynamics modeling seeks to understand the behavior of complex systems over time. Many variables, nonlinear relationships among variables and multiple components characterize complex systems. The modeling:

• Focuses on the concepts of stocks and flows, time delays and internal causality or feedback loops
• Supports complex equations
• Addresses the problem of multiple variables interacting simultaneously by changing variable values over small periods of time
• Supports various interactions among subcomponents

Stocks are entities that can accumulate or be depleted such as parts inventories. Flows are entities that make stocks increase or decrease such as purchasing, manufacturing or sales.

Example of a stocks and flows diagram. (Image: Vensim.)

Systems dynamics modeling provides value for digital transformation by:

• Enabling insights into the structure and behavior of complex systems and processes
• Revealing how processes are affected by changes in various factors or variables

Engineering applications of systems dynamics modeling include digital simulation of complex products such as turbines, aircraft assembly and petrochemical plants.

Software packages for systems dynamics modeling include ExtendSim, Powersim Studio, Insight Maker, iThink, Simcenter, Stella Architect and Vensim.

The value of diagrams for systems thinking

Most systems thinking techniques produce highly visual deliverables, often diagrams, as they illustrate the work for review and stakeholder communication better than text.

Often, engineers employ more than one systems thinking technique. The challenge is to select the best techniques for the situation. If the technique is too simple, the value is limited. The team bogs down if the technique is too complicated. Overwhelming stakeholders with complex techniques risks not completing the analysis.

Specific systems thinking techniques are not linked to particular diagram types. Teams will gravitate to preferred diagram types based on experience and relevance to process characteristics. The more frequently used diagram types include:

• Bill of materials – A hierarchical list of the raw materials, parts, components, sub-assemblies and related quantities needed to manufacture an end product or deliver a service
• Cause and effect diagram – A “fishbone” diagram used to explore and display the possible causes of a particular effect or observation
• Conceptual model – A diagram of the logical activities required to achieve a system purpose
• Context diagram – A high-level diagram of a system that defines the boundary of the system of interest and its environment
• Influence diagram – A diagram that illustrates significant relationships or influences that exist between the elements of a system
• Input-Output diagram – A high-level representation of the inputs and outputs of a system
• Functional model, process model or sequence diagram – A diagram that illustrates system functionality through its processes and their logical interconnections
• Systems map – A high-level diagram of the situation under investigation showing the system boundary and its major components

Selecting the best diagrams to describe the planned digital transformation work depends on the characteristics of the business processes and the analytical maturity of the stakeholders involved in the analysis. It’s common to begin with a high-level conceptual diagram and then develop more detailed, structured diagrams.

Systems thinking diagrams assist engineers with a wide range of engineering tasks, including root cause analysis, conceptual prototyping and process improvement.

Diagramming software ranges from simple electronic whiteboards or sketching software that captures unstructured graphics to more structured modeling software. Software packages for diagramming include Fresco, Illustrator, Paint, Sketch and Visio.

Example of a functional model, process model or sequence diagram. (Image: Burge Hughes Walsh.)

Best practices for performing systems thinking

Apply the systems thinking method to your digital transformation work by following these steps:

• Introduce a current process problem with a story
• List the actors involved in the current process
• Draw the actor names and show their interactions as arrows using the most appropriate systems thinking techniques
• Explore the issues in the current process in more detail
• Craft a digital transformation opportunity statement
• Identify and diagram or simulate potential digital transformation solutions using the most appropriate diagram(s)
• Select the optimum digital transformation solution
• Plan to implement the digital transformation solution
• Perform the implementation
• Evaluate the performance of the digital transformation solution

While these steps look linear, the systems thinking method is more iterative in practice. Expect to cycle through these steps or a subset of them multiple times.

Systems thinking techniques and related diagram deliverables advance your digital transformation by better understanding current process problems and crafting comprehensive solutions that improve processes with digital systems.

—

Yogi Schulz has over 40 years of Information Technology experience in various industries. He also writes for ITWorldCanada and other trade publications. Yogi works extensively in the petroleum industry to select and implement financial, production revenue accounting, land & contracts, and geotechnical systems. He manages projects that arise from changes in business requirements, from the need to leverage technology opportunities and from mergers. His specialties include IT strategy, web strategy, and systems project management.

The post 4 more systems thinking techniques to advance your digital transformation appeared first on Engineering.com.

Too many companies operate with immature BOM management processes, often supported by Excel, the most misused tool in engineering. That situation risks delayed time in the market, quality problems, poor productivity, excess cost and disappointed customers.

By digitally transformation your BOM management processes, you can significantly improve the maturity of BOM-related business processes and related data management practices. Here’s how.

What is a BOM, and how have they evolved?

A bill of materials lists the raw materials, parts, components, sub-assemblies and related quantities needed to manufacture an end product or deliver a service. It may include instructions for manufacturing, maintaining or repairing the product or service.

A BOM can evolve from a simple list of parts into an extensive hierarchical, multifaceted collection of information and relationships for a complex product. For example, a BOM first defines products as they are being designed to create Engineering BOMs (EBOMs), then as built using Manufacturing BOMs (MBOMs) and later as maintained with Service BOMs (SBOMs).

Some companies digitally transformed their BOM data and related processes as they introduced more and more computer-based digital applications. Engineers manage digital BOMs using licensed or custom software and digital data management tools.

BOM versions support multiple business processes in addition to engineering design work. These processes include:

• Purchasing to order parts and components on time
• Supply chain to manage inventory levels
• Production planning to ensure machine capacity and configurations are available
• Manufacturing to ensure products are built to the BOM specification
• Technical publication writing to produce accurate end-user manuals
• Maintenance or service to perform this work accurately

Multiple digital BOM versions for various processes. (Image: OpenBOM.)

The wrong way to manage BOMs

Too many companies manage BOMs through inadequate methods, including unstructured documents, Excel workbooks or embedding the BOM into CAD drawings. The central problem is that an effective BOM requires a hierarchical structure that these methods cannot support. Also, these approaches are challenging to maintain and share, not visible to the rest of the organization, and difficult to search.

These approaches ignore that the BOM is a critical resource for many activities along the product lifecycle. It can’t be locked up in Engineering.

Engineers initially develop BOMs to estimate product costs and analyze engineering tasks using an engineering-centric view. Later, many departments will copy the BOM data into their own spreadsheets or systems to add their own data and perspective. This enrichment and transformation creates data anomalies, leading to outdated and misleading BOM information.

These issues can be overcome by managing BOMs using a suitable software package. Then, engineers can increase the visibility of the BOM for other essential processes, such as concurrent design for manufacturing, service procedures, product documentation, sales collateral and vendor communication.

Many companies use Excel to support BOM-related business processes much longer than is prudent. Engineers can spot one or more of the following indicators that their  company is suffering the negative consequences of overusing Excel:

• BOMs are hampering digital transformation progress
• The growing number of BOMs has become too difficult to manage
• “Where used” queries for specific BOM parts or components have become unreliable
• BOM data summarization through pivot tables and rollup formulas requires frequent developer attention
• The inability to associate file attachments with particular BOM parts or components is becoming a data management problem
• The lack of support for BOM versioning has led to awkward workarounds
• Integrating BOM data sources and sinks requires too much effort or has become unreliable

For a more extensive discussion of Excel limitations, read Engineers are World Leaders in Misusing Excel.

Recognizing when to adopt a digital BOM

Companies with inadequate BOM-related business processes will encounter various business issues. Engineers can champion the implementation of a digital BOM when they observe one or more of these symptoms:

• Errors in manufacturing
• High scrap rates and rework effort
• Wrong parts are being ordered
• Production delays due to part shortages
• Inability to confidently implement engineering changes
• Challenges in personalizing products
• Poor traceability of parts
• Slow review and approval processes for design changes
• Inability to share product and production information with downstream departments and their supporting systems

Benefits of digital BOMs

Mature digital BOM management processes focus on accurate and timely information. Companies that implement these processes experience many operational benefits, including the ability to:

• Manage product complexity to improve customer satisfaction without reducing quality
• Offer increased product variation and personalization without losing control or sacrificing margins
• Improve efficiency to boost staff productivity and reduce costs
• Automate processes as a result of digital transformation
• Prevent mistakes and associated rework to reduce costs and avoid warranty claims
• Ensure thorough change management and impact traceability
• Support multiple views of the BOM for various stakeholders and not just the engineering view
• Enable the use of digital twins to accelerate product development and improve problem resolution
• Improve parts, components and vendor management for consistent quality and more predictable pricing
• Enhance BOM integration, communication and collaboration across departments and with supply chain vendors to smooth product delivery

At the strategic level, companies with mature digital BOM management processes exhibit increased innovation to respond to market changes, agility to minimize the impact of shortages and supply chain disruption, and faster time to market to build market share. These benefits lead to increased revenue, profitability and market leadership.

Selecting a digital BOM SaaS

Once engineers recognize the shortcomings of their Excel-based BOM management processes, they must select a replacement BOM Software-as-a-Service (SaaS). Consider the following selection criteria as you embark on a digital transformation of your BOM practices.

Software functionality

Superior BOM software will offer functionality for:

• Developing complex product structures
• Managing BOM revisions and changes
• Supporting product variability
• Managing structured data and documents associated with BOM elements
• Integrating BOM data with other systems
• Transforming BOMs to meet the needs of other stakeholders
• Visualizing products defined by the BOM
• Analyzing the impact of part and component changes across BOMs
• Reporting and documenting BOMs
• Ease-of-use for end-users

Vendor

Does the BOM software vendor score highly on the following criteria?

• Financial viability
• Knowledge of engineering, manufacturing, service and product lifecycles
• Customer references and reviews
• Implementation services
• Support services

Cost

Make sure you understand the initial and ongoing costs of implementing any BOM software:

• Acquisition cost
• Implementation cost
• Operating cost

Implementing digital BOMs

Even the best BOM SaaS doesn’t implement itself. Below is a list of the high-level deliverables your BOM implementation project should include, organized by project phase.

Feasibility

• The project sponsor has been named
• A project manager has been engaged
• A consensus on the project goal has been achieved with stakeholders
• The project objectives support the project goal
• A consensus about the project scope statements has been achieved
• The project charter has been approved

Planning

• The project team has been organized
• A reasonably complete project plan exists
• The BOM SaaS has been selected and contracted
• The project steering committee has been organized
• A risk management plan exists
• A BOM system cutover plan exists
• The end-user training plan exists

Design

• A BOM system requirements document exists
• A BOM reference data standards document exists
• The stakeholder engagement plan exists
• A BOM integration software design document exists
• A BOM data import design for existing BOMs document exists
• Risks are being managed

Build

• The BOM SaaS environment is accessible to the project team
• BOM integration software exists
• BOM data import software exists
• End-user training is occurring
• The BOM reference data has been created

Test

• The BOM SaaS software has been tested
• The BOM integration software test has been completed
• BOM data import software has been tested
• The BOM reference data has been tested

Deploy

• An operations plan exists
• The BOM data import software has been executed and checked for accuracy and completeness
• The BOM integration software is running routinely
• End-users are routinely using the BOM SaaS system

Close

• The project sponsor has accepted the project
• Lessons learned have been documented

Implementing digital BOM management as part of a digital transformation initiative delivers a foundational element that serves engineering, manufacturing and service activity well.

The post Digital BOMs are essential for digital transformation appeared first on Engineering.com.

Digital transformation aims to move organizations to a data-driven management culture that increases data-driven decision-making. This culture reduces operational risk, increases revenue and reduces costs.

Most software vendors are adding generative AI features to their software and services. Examples include well-established vendors, such as Google, IBM, Microsoft, Oracle, and SAP, as well as new entrants, such as Databricks, Dataiku, Open AI and Snowflake. These AI features add value for engineers working on the following aspects of digital transformation.

Data management

All digital transformation advances and data-driven decision-making depend on data management improvements such as more systems integration, data lakehouses, data warehouses and data restructuring. The software vendors supplying these capabilities are rapidly incorporating generative AI features into their products. For example, Oracle Autonomous Database Select AI integrates large language models (LLMs) with generative AI data management applications.

Data management includes acquiring, validating, storing, protecting and processing organizational and external data. The goal is to provide engineers and other disciplines with timely access to comprehensive, integrated data for analysis and data-driven decision-making.

Widely implemented data management software includes AWS Redshift and EMR, Databricks, Dataiku, Google Cloud Platform, Microsoft Azure Data Lake Store and Synapse Analytics, Oracle Exadata Cloud Service, SAP HANA Cloud and Snowflake.

Data quality

The leading risk engineers experience during digital transformation initiatives is inadequate data quality. The software vendors that help organizations improve their data quality processes are rapidly incorporating generative AI features into their products. For example, Informatica Data Quality applies AI-driven models to recognize many invalid or missing values and correct these to improve data quality.

Data quality management includes data preparation, enrichment and quality monitoring. The value is to assure end-users that data is accurate, consistent and reliable for data-driven decision-making.

Widely implemented AI-assisted data quality software includes Alteryx, IBM InfoSphere, Informatica Data Quality, Oracle Enterprise Data Quality (EDQ), Precisely Trillium, SAP Data Intelligence, SAS Data Quality and Talend Data Fabric.

Software development

Many digital transformation projects include some custom software development. Handcrafting software is expensive and subject to annoying defects.

Many AI-enhanced coding assistants have emerged that support various programming languages and integrated development environments (IDEs). For example, Amazon CodeWhisperer uses AI to suggest source code ranging in size from snippets to complete functions. The benefit of coding assistants is significantly improving software development productivity, which hasn’t improved much in many years.

Widely implemented AI software development products include Amazon CodeWhisperer, Blackbox, CodiumAI Codiumate, GitHub Copilot, Google AlphaCode 2, Google Cloud’s Duet and Tabnine. Their capability, use of AI and number of supported programming languages vary considerably.

Software unit testing

Software unit testing is a tedious task that’s an inescapable part of software development for digital transformation. Unit tests verify that the software works as intended, catch defects early and ensure that code is maintainable.

For example, Diffblue Cover uses AI reinforcement learning to create accurate and maintainable unit tests more efficiently than manual test development. The benefit of automated unit testing tools is significantly improved software testing coverage. Many software projects shortcut testing due to the pressure to complete projects.

Widely implemented AI-assisted automated unit testing tools include Bito, DeepUnitAI, Diffblue Cover, EvoSuite, Functionize, Mabl, Symflower, Testim, Squaretest and Testsigma.com. These AI-assisted unit testing tools vary in degree of automation.

3D design

Generative AI software assists engineers in 3D design by exploring a vast design space, identifying novel solutions, and testing them. It sometimes generates unexpected design alternatives, leading to more efficient, innovative designs and data-driven decision-making.

Engineers benefit from rapidly exploring innovative product designs that can be developed or manufactured. For example, Blender supports the AI art generator Stable Diffusion through Stability for Blender, a Stability AI text-to-image generator add-on.

Widely implemented design software includes Blender, Catia, Creo, Fusion 360, Solid Edge and Solidworks.

Content creation

Engineers working on digital transformation and data-driven decision-making will produce lots of content. Examples include manuals, project deliverables like requirements and designs, ongoing reports to management and websites. They’ll also perform data analysis and summarize lengthy documents created by others.

Generative AI software understands and responds in contextually relevant ways to content requests while supercharging the productivity of engineers. The benefit of AI for content creation is lower effort for text.

Widely implemented AI content creation software includes ChatGPT-4 from Open AI, Google Gemini (which replaced Bard), Claude 3 from Anthropic, Microsoft Copilot (which replaced Bing AI) and Poe.

Challenges still arise due to the potential for misinformation, bias and limitations in comprehending intricate contextual engineering nuances.

Research

Engineers frequently research specific topics to understand technical developments or to diagnose manufacturing problems. AI-driven generative software for research combines the strengths of ChatGPT for a summary of the information on a topic and a search engine for web links engineers can follow to explore more detailed information.

For example, Perplexity is an AI-chatbot-powered research and conversational search engine that answers queries using natural language predictive text. It provides sources with links in its responses.

The benefit of research software for data-driven decision-making is the assurance that most potentially useful sources have been included in the result set. This assurance is difficult to achieve with more manual research.

Widely implemented AI research software includes Consensus, Liner and Perplexity.

Breaking through the barriers of generative AI

Applying generative AI to digital transformation can feel both daunting and risky. The common concerns that make engineering leaders who are reluctant to adopt generative AI for data-driven decision-making include the following:

• Inadvertently introducing security and data privacy risks
• System integration challenges
• Surprisingly high development and operating costs
• Lock-in and technical dependencies on specific vendors and products
• Lack of specialized AI software talent
• Lack of auditability
• Insufficient regulatory compliance
• Lack of end-user trust in the reliability of generative AI work products
• Lack of control over ungoverned shadow AI usage

Actions that can reduce these inhibitors to digital transformation include starting with specific applications for a single department, such as engineering design or a business function, such as supply chain. Successful digital transformation avoids an enterprise-wide rollout. Focusing on business value rather than technological wizardry is more likely to lead to the successful adoption of data-driven decision-making.

Generative AI has ushered in a new era of possibilities, where software mimics the creative processes of engineers and actively contributes to digital transformation. From generating code snippets to improving engineering data access to crafting visual content, AI software demonstrates the remarkable progress of technology to support the data-driven management philosophy.

Engineers recognize both the potential and limitations of generative AI. They increasingly embrace its power while understanding the need for oversight and refinement. Engineers who explore these cutting-edge generative AI tools are well on their way to achieving unprecedented efficiency, creativity and innovation.

We can expect these AI tools to advance in functionality rapidly in the near term, continuing to improve engineering productivity and quality for digital transformation and data-driven decision-making.

For more, check out 6 ways engineers can use generative AI in 2024.

—

Yogi Schulz has over 40 years of Information Technology experience in various industries. He writes for IT World Canada and other trade publications. Yogi works extensively in the petroleum industry to select and implement financial, production revenue accounting, land & contracts, and geotechnical systems. He manages projects that arise from changes in business requirements, from the need to leverage technology opportunities and from mergers. His specialties include IT strategy, web strategy, and systems project management.

The post These generative AI apps can rescue your digital transformation appeared first on Engineering.com.

It’s not entirely a marketing buzzword. AI PCs, which are now being offered by major PC makers including Dell and Hewlett-Packard, have at least one new part to justify the new name: the neural processing unit, or NPU. Coexisting alongside the CPU—or integrated within it—and complementing the AI-friendly GPU, the NPU is meant to accelerate the machine learning calculations that are becoming increasingly important to modern software.

Engineering software is no exception. CAD and simulation providers have been testing the waters of AI, and it seems inevitable that it will play an increasingly prevalent role for engineers, architects and other professional users.

The Dell Precision 5690 AI PC. (Image: Dell.)

So, should engineers rush out to buy a new, NPU-equipped AI PC, or wait for the NPU to mature? Here’s what you need to know about the emerging tech and where it can make an impact.

CPU, GPU and NPU: The evolution of computing processors

All computers are built around a central processing unit (CPU) that can handle a wide variety of instructions sequentially. Over time, CPU chips have become faster and more powerful. However, our appetite for crunching numbers grew and continues to grow even faster.

Chip designers responded by offloading specific CPU-intensive instructions, such as graphics and video rendering, to a second processor called a graphics processing unit (GPU). Through parallel processing, GPUs significantly improved the performance of graphics and computational tasks in demanding applications such as simulation and 3D rendering.

The recent advent of AI software dramatically increased the demand for processing data yet again. This time, chip designers have responded by offloading more CPU-intensive instructions, such as the mathematics for neural networks, to a third processor called a neural processing unit (NPU). The NPU specializes in AI computations such as matrix multiplication and convolution.

An overview of three computer processors—CPU, GPU and NPU—and how they complement one another. (Image: Qualcomm.)

How do NPUs accelerate AI applications?

Unlike CPUs that sequentially process instructions, NPUs are optimized for parallel computing, making them highly efficient at machine learning algorithms, massive multimedia data transformation and neural network operations.

NPUs can achieve significant performance improvements for certain instructions compared to CPUs and GPUs. Intel, for instance, claims that its new NPU-equipped CPU achieves 1.7 times more generative AI performance compared to the previous generation chip without an NPU. The extent of performance improvement depends on many factors, including how well the application software is optimized to take advantage of the NPU, but the potential for speedup isn’t the only benefit: reassigning instructions to the NPU can also reduce power consumption and improve device battery life. The same Intel benchmark revealed a 38% power reduction on Zoom calls thanks to NPU offloading.

Major chipmakers have developed their own versions of NPUs meant to accelerate AI applications on a wide variety of devices. While the NPU architectures vary along with their speed, processing capacity, power consumption, thermal performance and other characteristics, these neural processors all share the goal of improving AI and machine learning performance. Apple’s custom A-series and M-series processors, which power its Mac computers as well as iPhones and iPads, include an NPU that Apple calls the Neural Engine. AMD’s Ryzen 7040 and 8040 series processors include NPUs based on the chipmaker’s XDNA architecture. Qualcomm’s Hexagon NPU brings AI acceleration to its mobile Snapdragon SoCs.

Overview of Intel’s NPU. (Image: Intel.)

The list goes on, but for engineering users the most significant NPU may be Intel’s. The chipmaker has integrated an NPU into its latest generation Intel Core Ultra processors, which power most of the new generation of engineering workstations. Intel says that its partnered with more than 100 independent software vendors (ISVs) for AI PC optimization, with more than 300 AI-accelerated features to come throughout 2024. Unfortunately, no engineering ISVs are listed as partners on Intel’s website—not yet, anyways.

Should engineers upgrade to an AI PC?

Engineers already ready for a workstation upgrade have no reason to avoid the latest generation of AI PCs, coming as they do with all the standard generational improvements. However, at this point the NPU itself is not a reason for engineers to upgrade. There isn’t yet enough AI incorporated in popular engineering software to make an appreciable difference for those workloads. But that could change, and quickly.

As with CPUs and GPUs before them, it’s only natural to assume that NPUs will evolve in capability as their utility increases. Predicting the future is always tricky, but a few trends are visible in the future of AI-related software that will drive NPU hardware developments.

CPU designs and architectures are approaching various limits. This suggests that GPUs and NPUs will experience most of the advances needed to keep pace with AI improvements. Advances in machine learning algorithms will likely reduce the demand for computing resources, but increases in model complexity may overshadow the efficiency gains. NPUs could serve a key role in making up the difference.

As AI features become increasingly popular in applications, NPUs may become essential to everyone’s PC—even engineers.

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Systems thinking is an analytical methodology that makes sense of digital transformation complexity by viewing the world as a hierarchy of systems and interconnections, rather than splitting complexity into multiple smaller parts. Thankfully, this mindset is easy to learn—and it could pay dividends for any digital transformation project.

What is systems thinking, and how can it help digital transformation?

Systems thinking is a holistic way for engineers to investigate digital transformation opportunities, problems and alternative solutions.

First, engineers use systems thinking techniques to analyze as many factors, variables and interactions as possible that could contribute to the problem.

Then, they continue to use the same techniques to identify potential improvements and solutions.

Finally, they evaluate the improvement alternatives to reach a recommendation for action. Systems thinking is more a mindset than a thoroughly prescribed practice or software.

An overview of systems thinking techniques

The most frequently employed techniques that apply the systems thinking methodology are as follows:

CLDs produce qualitative visualizations of mental models of business processes focused on highlighting causality and feedback loops. CLDs are often developed using a collaborative approach, because no one understands all aspects of the business process being analyzed. CLDs reveal process problems that are used to develop intervention strategies.

The iceberg metaphor reminds us that what we see initially is only the surface of the digital transformation problem. Engineers ask questions like these to explore what might be below the surface:

• Can we improve the problem definition?
• Is the problem statement derived from what we see in reality only a symptom of a more significant system performance problem below the surface?
• What are the laws, policies or information that affect the problem we’re trying to solve?
• What issues, impediments or concerns, such as poor application operation or missing digital data, might lead to what we see above the water?
• Are information technology performance issues or constraints, such as insufficient computing capacity or unreliable data, contributing to the problem?
• Are people performance issues, such as a lack of data management training or poorly written software, contributing to the problem?
• What is the risk that one or more of the proposed solutions will worsen the problem, cost too much to operate or be problematic to implement?

Always start with what you know or are highly confident is accurate. Apply the iceberg metaphor to ask lots of questions to reveal what might lurk below.

Process maps provide a pictorial representation of a sequence of digital transformation actions and responses. Process maps first confirm the current process diagrammatically and then identify bottlenecks, gaps or inefficient steps. Finally, a future-state process map illustrates the recommended process.

RACI stands for Responsible, Accountable, Consulted, Informed. Completing a RACI table with these four columns and the names of the involved individuals or groups as rows ensures that everyone with a role in the digital transformation problem or process has been recognized and consulted.

What are the benefits of systems thinking?

When engineers apply systems thinking to their digital transformation work, they reap the following benefits:

• Comprehensive and defensible recommendations to advance digital transformation.
• Reduced time, effort and money spent on advancing digital transformation.
• Active involvement of affected stakeholders, especially those who can slow digital transformation by not participating.
• Low risk of missing factors, such as vendor involvement or process understanding, that should be considered in the analysis of a digital transformation.
• Stakeholder buy-in to the digital transformation.
• Lower organization silos that are often a prerequisite to effective digital transformation.

When should you use systems thinking?

Systems thinking becomes more necessary and valuable as digital transformation increases in complexity, because it has to navigate the intricate relationships among technology, processes, people and the external environment. For example, engineers should employ systems thinking to attack business situations like the following:

• Globalization of manufacturing is making companies more interdependent and creating more supply chain complexity. The associated data exchanges are sufficiently complex that they become difficult to manage without digital transformation.
• Information technology continues to advance. In addition to on-premises applications, organizations are adding cloud-hosted, Software-as-a-Service (SaaS) and AI-based applications. These multiple hosts for applications increase the complexity of integrating data sources for digital transformation.
• Incompatible data structures and column values create extract, transfer and load (ETL) complexity when integrating data sources for digital transformation.
• Missing and inaccurate data lead to complex rules for choosing the best available data values when integrating data sources for digital transformation.
• Designing the digital transformation of complex business processes. Most organizations previously completed the digital transformation of simple business processes.
• Increasing societal expectations for the performance of companies, governments and individuals are adding to the legal and regulatory thicket. This trend creates reporting requirements that organizations can handle more efficiently through digital transformation.

What types of problems can be solved with systems thinking?

Systems thinking provides the most value when the digital transformation opportunity or problem is:

• Important – Solving the problem will be valuable to advancing the business plan. It’s not trivial or inconsequential.
• Chronic or persistent – It’s not a one-time or infrequent event.
• Familiar – The organization has experienced a long and likely costly history with the problem.
• Difficult to resolve – Previous attempts to solve the problem failed. The organization must learn from the failures, and new ideas are needed.
• Large – The solution will require collaboration and cross-functional cooperation.

How can engineers encourage best practices for systems thinking?

Most digital transformation problems that can be solved within one group or department have been solved previously.

However, the digital transformation opportunities that produce the most value require cross-departmental cooperation and data sharing. Implementing solutions to these opportunities requires multi-disciplinary collaboration and cross-departmental cooperation.

Engineers can create an environment that encourages experimentation and prototypes. A thorough analysis of the situation often does not reveal the optimum solution. Creating a culture that supports experimentation and accepts failures without assigning blame produces superior solutions.

Apply the iceberg framework. It suggests comprehensively describing the digital transformation problem from the angles of events, patterns and structure. When engineers facilitate the use of the iceberg framework, it:

• Arouses the group’s curiosity about the problem.
• Focuses on aspects of the problem that others seem to gloss over or ignore.
• Stimulates discussion about aspects of the problem that are not well understood.

What are the alternatives to systems thinking?

The commonly observed alternative to systems thinking is called event thinking. It’s a shallow, quick, tactical response to the situation and risks an inadequate solution.

Another alternative to systems thinking is traditional analysis, which studies systems by breaking them into discrete elements. This approach is limited in value because it focuses on individual systems but neglects interconnections.

Systems thinking invites engineers to look deeper at patterns of events and related data, structures and mental models we all use, often unconsciously.

How can you kill systems thinking?

Engineers or other team members can kill systems thinking and thereby leave the digital transformation problem unresolved by:

• Assigning blame for the problem.
• Criticizing the potential solution ideas developed by others.
• Aggressively promoting their solution as superior compared to what others are proposing.
• Sweeping the problem away by insisting the event that triggered the discussion is extremely rare and inconsequential.
• Failing to collaborate with stakeholders.

Systems thinking is a disciplined approach to examining digital transformation opportunities and problems more completely and accurately before acting. It encourages engineers to:

• Consider the bigger picture by looking well outside their current box.
• Ask better questions before jumping to conclusions.
• Explore ideas, alternatives and the experiences of others on the road to developing a superior solution.

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Yogi Schulz has over 40 years of Information Technology experience in various industries. He writes for IT World Canada and other trade publications. Yogi works extensively in the petroleum industry to select and implement financial, production revenue accounting, land & contracts, and geotechnical systems. He manages projects that arise from changes in business requirements, from the need to leverage technology opportunities and from mergers. His specialties include IT strategy, web strategy, and systems project management.

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