Siloed or Integrated? Two Approaches to Operational Excellence

Sustained Operational Excellence (OpEx) remains foundational in the journey toward competitive advantage. But not all OpEx programs are created — or sustained — equally. Across the industry, we are transitioning from the old standard project-based approach to one that is integrated and embedded in the company's operating model.

1. 1. Project-Based and Siloed: The Legacy Model

This traditional model is grounded in human glue-centric, policy-driven activities often championed by siloed Continuous Improvement (CI) teams. These programs:

• • • Rely heavily on special knowledge and experienced practitioners.

    • Feature long learning curves and even longer cycle times to results.

    • They are often disconnected from digital transformation or business strategy.

    • Suffer from diminishing returns, turnover, and loss of momentum (Figure 1).

    • Lack a holistic view of the entire value chain and focus on only one aspect of OpEx, such as waste reduction.

Figure 1: Traditional siloed approaches defined by loss of momentum

The Result?

A patchwork of projects — many of which lose steam after initial training — leaves organizations vulnerable to operational rigidity and skill drain. Project-based Operational Excellence models are viewed as “another thing to do” in addition to the everyday work.

2.  Integrated and Aligned: The Metasystem Approach

Integrated Operational Excellence represents the next evolution. It’s not a set of siloed projects — it’s an integrated, digitally embedded system fully aligned with business strategy, risk, and transformation architecture. The integrated approach aligns OpEx as one delivery system for the company’s Operating Model (Figure 2).

Figure 2: Integrated Operational Excellence

This model:

• • • Integrates with value chain processes at the data layer, not just plant floor tactics.

    • Leverages AI/ML, Advanced Analytics, and Digital Twins to reduce reliance on “hero” problem-solvers.

    • Integrates and aligns OpEx with the Operating Model, driving agility, flexibility, and resiliency.

    • Aligns with change management, leadership culture, and digital adoption to enable real-time insights and self-sustaining improvement.

What is an Operating Model?

Operating Models (Figure 3) are principle-based definitions of how work is done at the company. The most commonly understood version of an Operating Model is the Toyota Product System. The relationship between Operating Models and Operational Excellence is that OpEx is a core delivery system for some aspects of a defined operating model. Models and OpEx can be defined and operated in a non-digital method or digitized to a large extent to bake in the culture change.

Figure 3: LNS Research Operating Model Framework

The Result?

Digital OpEx Leaders are twice as likely to report double-digit KPI improvements. Our research on Industrial Productivity shows that Pathfinder companies, those companies bending the curve for declining productivity, are more than 1.5 times more likely to ADAPT an operating model from an “off-the-shelf” version.

Why It Matters

As we near 2030, companies are still navigating OpEx through siloed, manual systems, which are struggling to deliver value through resource-intensive efforts at OpEx. Embedded and aligned excellence is not just a best practice — it’s a competitive necessity.

Whether you’re an Operations P&L owner, a Quality leader, or driving the digital transformation office, the shift is clear: It’s time to stop managing projects and start architecting systems.

Architecting Integrated Operational Excellence:

The architecture that supports Integrated Operational Excellence (Integrated OpEx) is a layered, composable ecosystem designed to embed performance improvement into the operational fabric of industrial organizations. It spans data, applications, analytics, and change management, enabling agility, resiliency, and scalability.

Here’s a breakdown of the Integrated OpEx architecture based on LNS Research’s OpEx and Industrial Transformation (IX) frameworks (Figure 4):

Figure 4: LNS Research IX Reference Architecture

1. Foundational Layer: Digital Infrastructure

This includes the underlying technologies that enable data capture, transfer, and storage.

• • • Industrial Cloud & Edge Infrastructure – Ensures scalable compute and storage across enterprise and plant levels.

    • Industrial Networking & Compute – Enables connectivity across machines, sensors, and control systems.

    • Cybersecurity Mesh – Protects interconnected digital assets and workflows.

2. Data & Integration Layer

Critical for ensuring seamless data flow from multiple sources:

• • • Data Lakes / Industrial Data Hubs – Aggregates structured and unstructured data from MES, SCADA, ERP, quality, EHS, and sensor networks.

    • DataOps & Integration Platforms – Ensure clean, contextualized, real-time data movement across applications. Some to consider here are: Databricks, Cognite, and Snowflake.

3. Collaboration Layer

The core operational toolset where Integrated OpEx is executed:

• • • LC/NC Platforms (Low-code/No-code) – Enable rapid deployment of custom apps to digitize Op Ex tools (e.g., 5S, VSM, Kaizen, A3).

    • Industrial Application Platforms – Host quality, safety, maintenance, and continuous improvement applications. Some examples of data and application platforms to consider are: Tulip, Siemens Xcelerator, and Dassault Systèmes 3DExperience.

    • Connected Frontline Worker Applications – Bridge the “last mile,” delivering real-time insights and instructions to the shop floor. Some vendors in this space are doing interesting things to look out for, including L2L, Augmentir, and QAD Redzone.

    • Operational Excellence Applications – This is a growing software space of purpose-built applications specifically pointed at Operational Excellence activities. These applications typically cross over the boundary with Connected Frontline Workforce Applications in terms of their openness and collaboration enablement. Still, they may not have all the features of a CFW application in the LNS Research definition. There are some legacy vendors and some recent entrants into this growing space. Some to look at include: PTC’s Digital Process Management, Dassault Systèmes Digital Lean, Solvace, and Fabriq.

Advanced Analytics & AI/ML Layer

Delivers decision intelligence and automation to Integrated OpEx efforts:

• • • Advanced Industrial Analytics (AIA) – Enables rapid identification of improvement opportunities. Some applications offer far more advanced prescriptive analytics launched from model-based insights and previous actions taken in a decision intelligence support role. Some vendors here include Oden Technologies, TwinThread, and Braincube.

    • AI/ML Models – Predict risks, recommend actions, and learn from performance data to optimize operations continuously. Most industrial applications today have some form of AI integrated into their platforms. There are also standalone approaches to Industrial AI that can be deployed independently from any particular app. Names you have undoubtedly heard of here are: OpenAI, Anthropic, and Nvidia, but many others associated with the industrial space now have AI offerings as well, including: Amazon Web Services, IBM, and C3.ai.

    • Digital Twins – Used to simulate, test, and validate process improvements before physical implementation.

Key insight: Over 50% of OpEx Leaders use Digital Twins and are 70% more likely to use AI for opportunity identification.

Recommendations for Chief Operating Officers to build a sustained, culture-changing, integrated approach to Operational Excellence:

1. 1. 1. Pivot from the project approach to OpEx as an accelerator for how work is defined at your company. Integrated Operational Excellence is a delivery vehicle for the Operating Model.

      2. Adapt your own Operating Model that represents how work is done at your company, with Integrated Operational Excellence as a core pillar. Adopting a standard Ops Model will only get you so far. To scale, you must adapt it to your unique situation.

      3. Digitize for sustainment and accelerated Decision Intelligence. Decision speed is crucial in the battle for operational performance improvement and competitive advantage. By leveraging integrated Advanced Industrial Analytics and AI models to deliver actionable insights at the time of need, less experienced workers can perform more like experienced workers who have retired from the workforce.