In the last few years, we’ve all learned how to become more agile. In the face of unplanned events like a global pandemic and various geopolitical events, we had to change and pivot on demand. 

This holds true for individuals and businesses alike, and notably so in the manufacturing environments where I spend much of my time.

That’s why the promise of a new artificial intelligence (AI)-powered technology, called generative AI, is so promising for manufacturers – for today and the future. 

As Industry 4.0 establishes a strengthening foothold in manufacturing, the transition is facilitating greater accuracy and speed in areas where technology supports and improves human intervention. Generative AI will be an integral part of manufacturing’s technology transformation.

In manufacturing, new generative AI technology can be applied to the creation of an iterative, strategic business plan. 

In many ways, strategic business planning is ideal for AI’s capabilities. It can use disparate workflows and data siloes, allowing new levels of data-driven insight and precision. At Dell Technologies, we call this 

Production Assurance AI. 

What is Production Assurance AI?

Global economic conditions, labor shortages and supply chain issues are putting pressure on manufacturers’ profitability. 

In a market that’s competing for skilled workers and balancing unexpected outside disruptions, production efficiency is not the main goal for the digitization of manufacturing—it’s resiliency. 

While manufacturers confront many different challenges, the need for manufacturing organizations to adapt to changes on demand has never been greater. 

However, the most significant challenge involves the ability to plan and ensure profitability in the future. Addressing this challenge requires the creation of a strategic, iterative business plan. 

Capabilities of Production Assurance AI

Production Assurance AI uses enterprise-wide data to train the first pass and second-pass models. The generative AI solution then applies intelligence and inferencing to analyze that data. Insights from that data produce a business plan that predicts future production capacity and profitability and a way to track ongoing progress against the plan over time.

In support of strategic business planning, Production Assurance AI creates reports, forecasts and recommendations from the model for analyses. The generative AI solution has tools that allow “what-if” plans and analytics to track performance using inputs such as market analysis, production rates, maintenance costs and demand forecasts. 

Further, Production Assurance AI helps shape future profitability by enabling production risk mitigation and intervention processes, predicting future costs and product pricing to estimate future profitability. Production Assurance AI executes at the speed of each organization’s business. 

For example, Production Assurance AI can predict future maintenance costs and timing if manufacturers need new equipment for upgraded production lines or require major repairs to bring production lines back up to speed. 

In addition, the solution can evaluate the distribution system, channel partners and the current service network to estimate when expansion will be required. Production Assurance AI also examines and recommends any necessary logistics and supply chain changes, such as shifting from trucks to rail. 

Benefits of Production Assurance AI 

Production Assurance AI can bring a multitude of business-changing benefits to manufacturing organizations. Each benefit on its own is significant. Together, they can be revolutionary.

Benefits include: 

• Create the M&A strategy for the new enterprise.

• Eliminate siloed calculations with no data interdependencies. 

• Reduce reliance on massive spreadsheets and subjective, labor-intensive and hard-to-change manual analyses.

 • Enable weighted interdependencies among departments and the ability to change them easily. 

 • Produce an iterative, multiyear business plan using technology designed to maximize shareholder value. 

• Predict outcomes by using Production Assurance AI “what-if” tools to predict outcomes.

• Monitor how the organization is tracking with the business plan by using Production Assurance AI. 

Moving Forward

Whatever the time horizon — next week, next month, next quarter, or five years from now — Production Assurance AI is a powerful solution. It upgrades a less accurate, time-intensive, disconnected approach with one that creates a fast, accurate, holistic business plan with analytics that deliver insights into future profitability while tracking ongoing progress against that plan. Production Assurance AI is applying new generative AI technology to help manufacturers answer the question: Can we be profitable in the future?

Dell Technologies. Dell Technologies helps manufacturers around the world transform their business outcomes by applying technology to manufacturing practices and processes.

Intel. The compute required for GenAI models has put a spotlight on performance, cost and energy efficiency as top concerns for enterprises today. Intel’s commitment to the democratization of AI and sustainability will enable broader access to the benefits of AI technology, including GenAI, via an open ecosystem. Intel’s AI hardware accelerators, including new built-in accelerators, provide performance and performance per watt gains to address the escalating performance, price and sustainability needs of GenAI.

Want to learn more about Production Assurance AI? Click here to read a white paper for a deeper discussion HERE


To help organizations move forward, Dell Technologies is powering the enterprise GenAI journey. With best-in-class IT infrastructure and solutions to run GenAI workloads and advisory and support services that roadmap GenAI initiatives, Dell is enabling organizations to boost their digital transformation and accelerate intelligent outcomes. 

The compute required for GenAI models has put a spotlight on performance, cost and energy efficiency as top concerns for enterprises today. Intel’s commitment to the democratization of AI and sustainability will enable broader access to the benefits of AI technology, including GenAI, via an open ecosystem. Intel’s AI hardware accelerators, including new built-in accelerators, provide performance and performance per watt gains to address the escalating performance, price and sustainability needs of GenAI.

Read more: Taking on the Compute and Sustainability Challenges of Generative AI

Artificial Intelligence

This post is brought to you by NVIDIA and CIO. The views and opinions expressed herein are those of the author and do not necessarily represent the views and opinions of NVIDIA.

CIOs seeking big wins in high business-impacting areas where there’s significant room to improve performance should review their data science, machine learning (ML), and AI projects.

A recent IDC report on AI projects in India[1] reported that 30-49% of AI projects failed for about one-third of organizations, and another study from Deloitte casts 50% of respondents’ organizational performance in AI as starters or underachievers.

That same study found 94% of respondents say AI is critical to success over the next five years. Executives see the AI opportunity for competitive differentiation and are looking for leaders to deliver successful outcomes.

ML and AI are still relatively new practice areas, and leaders should expect ongoing learning and an improving maturity curve. But CIOs, CDOs, and chief scientists can take an active role in improving how many AI projects go from pilot to production.

Are data science teams set up for success?

A developing playbook of best practices for data science teams covers the development process and technologies for building and testing machine learning models. Developing models isn’t trivial, and data scientists certainly have challenges cleansing and tagging data, selecting algorithms, configuring models, setting up infrastructure, and validating results.

Leaders who want to improve AI delivery performance should address this first question: are data scientists set up for success? Are they working on problems that can yield meaningful business outcomes? Do they have the machine learning platforms (such as NVIDIA AI Enterprise),infrastructure access, and ongoing training time to improve their data science practices?

CIOs and CDOs should lead ModelOps and oversee the lifecycle

Leaders can review and address issues if the data science teams struggle to develop models. But to launch models and ensure success, CIOs and CDOs must establish a model lifecycle or ModelOps.

The lifecycle starts before model development and requires educating business leaders on their roles in contributing to AI projects. It also requires steps for planning the infrastructure at scale, instituting compliance and governance, creating an edge security strategy, and partnering with impacted teams to ensure a successful transformation.

Here are several factors to consider:  

Educate business leaders about their roles in ML projects. Have business leaders defined realistic success criteria and areas of low-risk experimentation? Are they involved in pilots and providing feedback? Are they ready to transform business processes with machine learning capabilities, or will they slow down investments at the first speed bump?Adopt a build, buy, or partner when developing models. Sometimes, developing proprietary models makes sense, but also evaluate frameworks such as recommendation engines or speech AI SDKs.Think a step ahead regarding production infrastructure requirements. The lab infrastructure used to develop models, and the lower scale required to pilot an AI capability, may not be the optimal production infrastructure. For example, AI in healthcare, smart buildings, and industrial applications that impact human safety may require edge or embedded computing options to ensure reliability and performance.Plan for large-scale AI applications on the edge. Where there are thousands of IoT devices, there are opportunities to deploy AI applications to run on the devices. For example, fleets of vehicles, including delivery trucks, construction tools, and farming equipment, can use device-deployed AI apps to provide real-time feedback to their operators that improve productivity and safety. An edge management solution that deploys the apps to the devices, supports communications, and provides monitoring capabilities is critical.Establish MLOps, ModelOps, and infrastructure-monitoring capabilities. The data science teams will need MLOps to automate paths to production, while compliance should require ModelOps and want model updates to address model drift. Infrastructure and operations teams will want monitoring to help them review cloud infrastructure costs, performance, and reliability.

IT teams don’t just deploy apps. They participate in planning to deliver business outcomes and then institute DevOps to ensure delivery and ongoing enhancements. Applying similar practices to data science, machine learning, and AI will improve successful pilot and production deliveries.

[1] IDC FutureScape: Worldwide Artificial Intelligence 2021 Predictions — India Implications

Artificial Intelligence, Data Science, Machine Learning

Dow is reaping the benefits of a year-long program to roll out new digital technologies to one of its largest manufacturing sites, resulting in improvements in performance, reliability, and employee experience.

It’s over five years since Dow Chemical merged with DuPont to form DowDuPont — and three since they split up again to form a new agricultural supplies company, Corteva; a specialist chemicals manufacturer, Dupont; and a supplier of commodity chemicals, Dow.

Melanie Kalmar was Dow’s CIO through all of that. “It was one of the largest spinouts ever that I’m aware of, and it was one of the most complex projects I’ve worked on,” she says.“ As we spun out as a new company, we also wanted to be a more digital Dow.

But it’s not about being digital for its own sake: “It’s about changing how people do their work to be more effective, more efficient, and to drive growth for the company by being able to focus on higher value activities,” she says.

One of the first fruits of that new approach was a program to get IT out of the office and accelerate the deployment of digital technologies across manufacturing and maintenance areas. It’s a project that has earned Dow a CIO 100 Award for IT innovation and leadership.

The goal was to improve the productivity, safety and reliability of manufacturing “assets” — Dow’s term for the vast expanses of concrete and steel and the sprawling outdoor networks of pipes and controls that characterize its production facilities.

Melanie Kalmar

“It really all starts from a drive to be the most reliable supplier in our industry,” she says. That’s a big deal for Dow’s customers after two years of lockdowns, worker shortages, extreme weather events and other supply chain disruptions.

Rather than begin with a small site as a pilot, Kalmar and colleagues went big — 7,000 acres big — choosing Dow’s largest production site in Freeport, Texas, for their first deployment.

“We wanted to show the payback of doing something like this, and if you go to a smaller site, you’re going to get a smaller payback,” she says.

On a site like Freeport, says Kalmar, most of the employees will be found in the control room, monitoring which valves are opening and closing from a dashboard. Previously, if they had to go out to perform maintenance, or do a round of physical checks, they’d walk back and forth between the assets and the control room to pick up work orders or print off documentation. Now, they access that information in a secure cloud via a safety-hardened device connected to a site-wide private 4G wireless network.

“We’ve put all that information at their fingertips now on these mobile devices,” she says. “The speed to maintain, correct or fix something out in the plant has tremendous impact on the overall reliability.” The employees are ecstatic about it as well, having what they need on site in an instant and and not having to print paper, especially if it’s raining or if it’s windy and the papers are blowing around,” Kalmar says.

IT workers who have struggled to pull Ethernet cables through ducts across an office or even a small factory will understand the appeal of wireless on a site the size of Freeport — but Kalmar says it has its own challenges: “At a manufacturing site, you don’t just go in with a backhoe and dig anywhere to put a pole up. All that planning and execution was very new for my team.”

This would have been a difficult undertaking in a lot of companies, where IT and operations technology (OT) organizations don’t work together, but that’s not the case at Dow, says Kalmar.

“One of the early things I did was partner with the VP of manufacturing,” she says. “We agreed that we could do this better for Dow if our teams work together.”

Part of Dow’s largest production site in Freeport, Texas.

Key to that collaboration, she says, was recognizing that it wasn’t about learning one another’s skills but leveraging them: collaborate, but leave certain tasks to the experts.

“We’ve gone through the whole RACI process and identified who’s responsible, who’s accountable, for all aspects of what happens with technology at our sites. We have a much stronger partnership by working through that together, and are even looking at career ladders across the two organizations.”

Such department-spanning career ladders already exist in other specialist areas at Dow, such as data science, where experts might find themselves working in IT, manufacturing, R&D or supply chain management. “You’ve just got to get in there and start breaking down those silos that have historically and traditionally existed across organizations,” she says. “If everything you do is driven by improving the customer and employee experience, those opportunities really start presenting themselves. It’s really changed the mindset for us at Dow.”

There are still boundaries, however. Manufacturing, for example, is still responsible for the control systems that run the manufacturing assets, although IT is starting to get more involved in the network those control systems run on, she says.

The IT organization also had to skill up for the project, adding cloud, networking, and security expertise.

The biggest need, though, was for change management skills. Kalmar expanded her leadership team to include someone specifically accountable for enterprise change. Their remit included identifying who to onboard first to drive adoption rather than merely offer training on the new systems. “Who has cycles in our run-the-business organization to even consume and accept this new stuff when it comes out?” Kalmar says. “We’re looking at change very differently.”

Ready to step up to 5G

For now, Dow’s private wireless network uses LTE, a 4G technology, but it will be ready for 5G when 5G is ready for it.

“Everybody’s hyped up about 5G networks, but you have to have devices and applications that work on 5G, so we’re not going to jump there yet — but we’ll be prepared to go there and make that switch. The infrastructure that we’ve put in place will be able to transition to 5G,” she says.

Using an older and slower wireless technology hasn’t hurt the project, though. According to Dow, more than 3,600 employees have been trained on the new tools, enabling them to perform in less than a minute data-related tasks that once took half an hour or more.

In deploying to a giant site such as Freeport, says Kalmar, it’s important to recognize early on that one-size-fits-all solutions don’t always work, and to spend time up-front speaking with colleagues and getting the right inputs. “Adjust your traditional program approach and be open to large-scale pilot implementations,” she says. “Learn as you go, communicate your wins, and continue to listen to your stakeholders.”

Unified Communications