This is the third blog in a series of four on the adoption and implications of cost engineering, outlining the impact on people, processes, and technology. In the first, I outlined the compounding volatility in modern industrial markets due, in part, to increased digital hyperconnection, There clearly is a need for a kinetic, self-healing supply chain that shifts from solely backward-looking financial estimating to digital intelligence that enables real-time forward-looking methods, aka cost engineering, based on, for example, physics-based models. The second blog drilled into the implications for the human element, detailing the needed workforce transformation across key roles.

All good and fine. However, embedding the right people with the right knowledge, skills, and abilities (KSAs) into environments with outdated, rigid workflows will just lead to failure. Realizing the potential benefits of cost engineering requires industrial organizations to examine their processes. In this blog, I’ll discuss legacy silos and processes related to procurement methodologies, production practices, downstream logistics, and their connection to executing frictionless, informed supply chain orchestration.

Silo Deconstruction: Inevitable Doesn’t Mean Easy

Generally speaking, industrial supply chains have tended toward the linear and sequential. From a product perspective, design engineers created a product, handed it off to be priced, then procured, produced, and so on. This isolated structure inevitably created silos, with decision making confined to walled-off start and stop points. When processes worked well, it was perceived to be an outcome of strong relationships, experience honed over time, and effective judgment.

When things went poorly, it often resulted in catastrophic misalignment across business units and operating ecosystems. The misalignment often cascaded beyond the silo in which it was created, as it often was not identifiable in the moment. Simple examples range from creating little-to-no margin in design to forcing continuous rework due to unforeseen downstream constraints. In today’s hyperconnected business world, the consequences of misalignment aren’t sustainable.

De-siloing processes, such as cost engineering, and the attendant data is seen as an answer to these challenges. Yet, this tendency toward silos is exacerbated by many compounding realities of today’s work and operational environment. Spreadsheets and paper are still all too common. There remains an affordability and sophistication gap between larger tier-one and medium and small industrial organizations. The aging workforce is having a greater deleterious impact than the solutions designed to offset the retirement of knowledge, skills, and abilities (KSAs). I could go on all day.

Cost engineering processes cannot exist in a vacuum. The rewiring of how business gets done isn’t served by traditional change management. The magnitude and complexity are vast, especially as the work moves beyond the walls and immediate control of the organization. It requires an intelligent vision, value-based performance indicators, commitment to change in work across a diverse set of roles, new incentivization structures, and so on.

Processes must evolve to integrate specific expertise continuously throughout the entire product lifecycle. That change means uniting design, manufacturing, and procurement into a single, cohesive decision loop that accounts for business and operating strategies, material constraints, and regulations, to name just a few process inputs. For businesses reliant on supply chains as critical components of the value they deliver, de-siloing is an inevitability that must be addressed. However, it doesn’t make it easy.

Getting to Dynamic Collaboration

To break down internal barriers and rapidly solve complex manufacturing bottlenecks, leading organizations are abandoning the sequential handoff in favor of highly agile, cross-functional intervention teams. A prime example of this process innovation is the deployment of Supplier Operations Support (SOS) teams, pioneered by high-performing aerospace organizations like Rolls-Royce. When a critical supplier struggled to produce a component, the company didn’t default to an age-old punitive reaction, like simply issuing a contractual penalty every time it occurred. Instead, it deployed an SOS team, with decision-making autonomy, directly to the supplier’s factory floor.

With this deployment, the hard work began, based on what processes needed to improve continuously to achieve the necessary outcome. Despite that, the collaboration increased the value of the relationship for both organizations.

The concept and use of SOS groups, often referred to as tiger teams, certainly isn’t new or novel. However, the need for industrial data fabrics and the push toward autonomous AI have the potential to considerably modify the goals, responsibilities, and authority of these teams. Historically, these teams were constructed and deployed when reactive firefighting was necessary. They consisted of top-tier subject matter experts, so companies were very judicious in taking them away from their normal roles. Now, leaders in innovation view these teams with a very proactive mindset.

It’s the right move, but it’s not without tension. The transparency required both within and outside the organization (especially outside) is inherently uncomfortable, as it suggests sharing proprietary operational data. Uncovering hidden cost premiums and identifying specific inefficiencies won’t initially feel like a win for the supplier. The comfort of static purchasing and forecasting has to give way to continuous, real-time visibility of the entire production process.

This can cut two ways. On one hand, it can be seen as an intrusion on the ability of supplying businesses to generate revenue and maintain margin by the supplying businesses. On the other hand, it can deepen the value, reliability, and differentiation of the relationship. Those that understand that costing needs dynamic updating and employ experts to realign processes with that goal in mind will be able to manage price spikes, geopolitical and economic tensions, weather events, and all of the disruption inherent in competition in hyperconnected markets.

Leadership Via New Process Pathways

It is clear that AI has the ability to reason and execute at scale to help ensure autonomous optimization of many of the mundane, data-heavy tasks that used to consume organizational bandwidth. The question then becomes obvious: what to do with that bandwidth? Some downsizing is a reality, there’s no getting around that, particularly as AI transitions more directly into physical intelligence, with massive implications for supply chains. Processes that are transactional, repetitive, or dangerous will be the first targets. In these situations of human-to-digital migration, leadership needs to be exceedingly careful not to inadvertently get rid of critical expertise, as is an all-too-common mistake.

If done correctly, this expertise is retained and can then be shifted, proactively delivering competitively differentiating value by driving performance change aligned with modern market demand. Those experts become orchestrators, and there is a stark difference in progress between leaders and laggards. The latter continue to be unable to demonstrate the value of modernization. In contrast, leaders are gaining ground that they likely will never cede by proactively designing the business for autonomous operations.

Once this structure is in place and teams proactively drive business value using new processes underpinned by autonomous tools, the use cases for improvement are many. An example is building supply chain digital twins to integrate self-healing design and optimize network performance. Predictive maintenance, optimized energy/delivery, and sourcing/production risk management are also employed by leaders.

This move to collaborative teams proactively rewiring the process flows of the organization also allows critical, very complex, use cases to be tackled. Use cases related to sustainability, still a critical supply chain concern, can be addressed in new, highly effective ways. According to ARC’s annual survey, in 2026 energy transition and decarbonization are second only to cost reduction in industrial investment driver priorities. Turning this prioritization into actual business value has been challenging at best for industrial organizations, leading critics to continually and accurately raise the concern of greenwashing.

The combination of expert orchestration and autonomous tools, backed by access to and use of contextualized data, could ensure the execution and proof of regulatory compliance for something specific like a Corporate Sustainability Reporting Directive (CSRD) mandate or product carbon footprint lifecycle tracking requirement. When implementing cost engineering, the relationship between cost, risk, and benefit factors becomes much more transparent to all stakeholders. In turn, those elements can be factored more readily into decision lifecycle processes. The business, and its customers, can better understand what truly moves the needle. AI tools can be used to ensure that the movement occurs. In this way, sustainability becomes a crucial and appropriately weighted variable in every relevant decision. In addition to sustainability, these examples collectively demonstrate that the definition of high-value work is shifting away from traditional approaches such as manual intervention, reactively fighting fires, or leveraging financial threats.

Shift Left

The innovation modern cost engineering delivers entirely upends procurement and sourcing processes, of course, aiming to reduce and/or automate the transactional and minimize adversarial or negative ecosystem behavior. In industries where this is established, such as aerospace and defense, the benefits are in plain view. When effectively implemented, workflows inevitably move toward deep, transparent collaboration. As an example, conversations can occur with a mathematically defensible, highly granular baseline model of component cost at the center.

This injects fact-based transparency into the negotiation process in a way that can be beneficial to both parties. If the cost is out of line with expectations, the appropriate SMEs can enter conversations with the supplier to identify specific constraints or inefficiencies and move more quickly toward how they can be solved. The levers that can be pulled are more obvious to both parties. Approaches to volume uncertainty and other disruptions can be implemented so that, prior to or as they occur, they can be dynamically modeled, understood, and contractually accounted for without production whiplash.

In fact, cost engineering shifts the analytical processes as far upstream as they can go. And that is well beyond procurement and sourcing. At its most effective, cost engineering gets beyond “autopsy” thinking limits and even “what if” intelligence (though it does retain those principles) to “exactly what now” autonomous decision making. Nowhere is this more evident than product design.

After all, the redesign loop is reactive, sequential, and ripe for inaccuracy to find its way in. Cost engineering sits at the front of design so that cost is a property of R&D. When these processes are also then informed by autonomous agents monitoring the real-time environment, across its expanse no matter how large the footprint is, implications are made transparent and decisions obvious. Constraints and impractical product tolerances are actively baked out of design. By starting from an optimized design state, all downstream decisions begin from that raised product lifecycle floor.

Integrity is a Process, Too

Of course, the discussion isn’t complete without raising the specter of trust issues inherent in adopting cost engineering that is heavily reliant on digital methods and AI. Integrity isn’t born; it is behavior-based and nurtured over time. Let me try to phrase it another way. If one is a supply chain, engineering, product, or other SME professional, many forms of today’s AI must seem like forms of tribal knowledge. After all, AI is positioned as consisting of KSAs that human experts can’t really match. It informs its KSAs via whatever information sources it can access, whether they are good or bad. Over time, that is, as it gains experience, its expertise will surpass those SMEs. While it can be designed with the mission to share, its most valuable and efficient state is thought to be autonomous action. It’s a keeper of knowledge, and based on the ability applied to a task, its assessments are often unexplainable. It can also be inconsistent or dreadfully wrong while confidently certain in its misinformation. It also has motive, or at least the keepers of the revenue who deploy it do.

Listen, I’m not trying to say they are the same things, but the point makes itself, I believe. Integrity of output requires trust, and AI is no different. That doesn’t just mean behavior guardrails and cybersecurity. Going back to where I started in this blog, inevitable is not the same as easy. As traditional estimating processes are increasingly automated via various digital and AI techniques, organizations must build new processes to ensure human operators can trust the machine’s output, and that’s not a straightforward task, no matter what the selling market says. For cost engineering, this means ensuring AI is only scaled into production processes where it demonstrably improves and explains outcomes, rather than simply adding layers of technological complexity and obfuscation.

In the fourth and final blog, I’ll explore the technology aspect of cost engineering. The discipline has a massive impact on systems, particularly as it flows downstream into the supply chain needed to take cost engineering from concept to reality.

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