Model-Based Enterprise for Faster Manufacturing

MBE uses digital models and simulation to connect design, manufacturing, quality, and supply chain processes across the product lifecycle.
Manufacturers are adopting MBE to reduce rework, improve traceability, accelerate launches, and support a single source of truth.
The shift to MBE is driven by Industry 4.0, faster time-to-market needs, customization demands, compliance requirements, and supply chain coordination.
A practical MBE journey starts with assessing maturity, defining standards, connecting PLM to downstream workflows, and scaling validated use cases.

The Transformation Gap

Today, BMW completes the critical digital verification for any new vehicle—ensuring the model fits seamlessly into an existing production line—in three days, a task that once took four weeks of manual testing. This massive acceleration is achieved by building and testing its factories as complete ‘digital twins’ in a virtual world first. With platforms like NVIDIA Omniverse, the auto major simulates every detail, from robotic workflows to the entire factory layout, to perfect manufacturing before a single piece of equipment is installed. It represents a significant shift in how products are brought from blueprint to execution.

Yet, this level of digital integration remains a benchmark, not the norm. The consequences of this foundational gap are stark. A recent BCG survey reveals that 74% of companies are stuck in ‘pilot mode,’ struggling to generate value from their AI investments, as their initiatives lack the clean, enterprise-wide data that a connected digital backbone provides.¹

Without an integrated digital backbone, many organizations struggle to move from pilot initiatives to enterprise-scale value.

That’s where an MBE strategy comes into play.

What is Model-Based Enterprise (MBE)?

BMW’s operational transformation demonstrates the National Institute of Standards and Technology’s (NIST) definition of a model-based enterprise: an organization that applies modeling and simulation technologies to integrate technical and business processes across the complete product lifecycle, using models to define, execute, and manage all enterprise operations while applying simulation tools to optimize every step.

But as revolutionary as it may sound, MBE is an evolution of existing practices: it takes the proven model-based design methodologies from the engineering realm and scales them across the enterprise by integrating production workflows, supply chain operations, and organizational functions. It harnesses the power of engineering models to automate downstream processes, improve efficiency and quality, and lower costs.

MBE extends engineering models beyond design, enabling connected workflows and automation across the entire enterprise.

Manufacturers are seeing measurable improvements:

40% reduction in drafting creation time
60% reduction in manufacturing non-conformances
80% reduction in NC programmer time
90% reduction in SQE time on ballooning

The initial successes of this approach were recorded in the aerospace, defense, and naval sectors, followed by the automotive industry. Defense leader Lockheed Martin is advancing MBE adoption with their playbook for suppliers titled ‘Accelerating Transformation,’ released in 2024 to expedite the transition of their entire supplier ecosystem from drawing-based to model-centric operations by 2028.

Meanwhile, to accelerate new-vehicle launches and maximize efficiency, BMW is creating full virtual replicas of production processes, allowing it to perfect manufacturing in a digital world first.

This strategic shift now extends far beyond the aerospace and automotive sectors. GE Appliances, a Haier company, accelerates product development and problem-solving through a comprehensive digital thread. This thread dismantles fragmented data silos, providing the team with a single source of truth for all CAD models and 3D measurement data. It resulted in a faster, data-driven decision-making process that helped them resolve complex design issues virtually, saving significant time and prototyping costs. This digital approach also enhances team collaboration and traceability, driving faster new product introductions and improved quality.

The Race Toward Model-Based Operations

As Industry 4.0 reshapes the competitive landscape, manufacturers view MBE as a core part of their digital transformation strategy to build more responsive and resilient enterprises.

The urgency is driven by critical business imperatives that legacy systems cannot meet:

Faster Time-to-market: Establish a single source of truth that reduces rework and shortens development cycles, with a NIST benchmark showing a potential 78.4% reduction in the entire design-manufacturing-inspection cycle
Modern Customer Demands: Move beyond 2D drawings to support faster delivery, deep customization, and the AR-ready assets that define modern digital experiences
Regulatory Compliance and Traceability: Create a complete, traceable digital thread that provides the watertight audit trail required in sectors like aerospace and medtech
Global Supply Chain Unification: Enable seamless collaboration through a single, unambiguous master model, reducing friction across partners

These pressures are rooted in changing market dynamics. Today, 62% of manufacturers say consumer demand is pushing them to accelerate product development, according to a Protolabs report². Meanwhile, delays of even a few months can significantly erode profitability, making faster, more efficient product launches a business necessity.

Organizations that respond effectively are already seeing tangible outcomes. According to TechClarity research, 77% of companies with MBE strategies report revenue growth, compared to 48% without.³

Model-based operations are becoming essential as manufacturers respond to faster cycles, customization demands, and increasing complexity.

In this environment, MBE is a strategic foundation for competing in a market defined by speed, complexity, and continuous change.

Core Components of an MBE

MBE's foundation lies in model-based design: creating sophisticated digital models that combine detailed 3D representations with comprehensive simulations.

From this, a successful enterprise architecture is built on three interconnected components:

Model-based Definition (MBD) for a Single Source of Truth: This practice involves embedding all machine-readable product manufacturing information (PMI) directly into the 3D model. This creates an authoritative technical data package (TDP) that serves as the single, unambiguous set of instructions for design, manufacturing, and inspection.
A Centralized PLM Backbone to Manage and Link Data: As a digital nervous system, it manages and connects the MBD models across the enterprise. It orchestrates the flow of data using interoperable formats, such as STEP AP 242, connecting the master model to all functions: digital product architecture, performance analysis, cost modeling, quality management, and manufacturing processes.
Integrated Model-based Processes to Enable Downstream Automation: These unified digital workflows use the master model to link design, manufacturing, and quality. This seamless connectivity unlocks powerful automation for a wide range of applications, including CNC programming, CMM inspection, and AR/VR experiences.

This is where many MBE initiatives fall short in practice.

Figure 1: The Pillars of Model-Based Enterprise

These interconnected components create the digital infrastructure necessary for digital thread and digital twin implementations, positioning MBE as the backbone of Industry 4.0 strategy.

A Practical 4-Step Playbook to Start

With the architecture defined, the challenge shifts from understanding the 'what' to executing the 'how'. This four-step playbook provides a structured path for moving from concept to practice, designed to reduce risk and deliver tangible outcomes:

Assess Maturity and Pick a Pilot: Establish a clear baseline. Using a framework like the NSC MBE maturity index, an organization can benchmark its current practices. This index maps the journey from a Level 0 (drawing-centric) state, where 2D drawings are the official and legally binding source of truth, to the highest level, Level 6 (extended model-based enterprise), where model-based processes are automated and integrated across the entire supply chain. By identifying its current level, a company can define a realistic target and select a targeted, low-risk pilot program to begin the transformation.

Figure 2: NSC MBE Maturity Index Illustrating the Shift from 2D Drawings to a Model-Based Enterprise

Establish Single-Source Models and Standards: Adopt MBD as the authoritative data source and set consistent practices across teams to create and manage models with rich, machine-readable PMI
Connect PLM and Downstream Workflows: Integrate models within the PLM backbone and extend them to manufacturing, quality, and inspection processes
Validate, Measure, and Scale: Prove downstream use cases, capture KPIs, and use the results to build the business case for enterprise-wide expansion

Why Tech Mahindra: Your Strategic Partner in MBE Transformation

Tech Mahindra is among the few organizations to demonstrate end-to-end MBE adoption, from engineering and PLM integration to downstream use cases such as AR/VR, inspection automation, and digital twins.

Our competency encompasses:

Digital Experience Center (XPDI): Experience MBE live, from MBD and PLM integration to XR-enabled downstream applications
Complete Service Portfolio: Deep expertise spanning engineering design, PLM integration, and enterprise adoption delivers measurable results across pilot programs and global deployments
Proven Cross-Industry Results: Successful MBE transformation in aerospace, appliances, and MedTech, accelerating product launches, improving compliance, and enhancing quality

Case in point: an HVAC manufacturer was facing significant inefficiencies in their current PLM, with disconnected processes stitched together by human glue. Tech Mahindra delivered the data-driven clarity needed to break this critical deadlock. By assessing their maturity against NIST standards and benchmarking PLM vendors across more than 50 criteria, we provided a definitive MBE roadmap to help the client confidently select their future digital backbone.

Building the Future-Ready Enterprise

The road ahead is about expediting new product innovation while building a more agile, intelligent factory of tomorrow. MBE provides the digital backbone for both. It creates the infrastructure needed to innovate New Product Introduction (NPI) programs, enabling predictive analytics and powering digital twin capabilities that define Industry 4.0.

The question is how quickly organizations can implement it to secure their competitive position in the digital manufacturing era.

Transform faster, compete smarter. Connect with our Digital Experience Center to explore how a strategic MBE pilot can accelerate your journey from traditional manufacturing to Industry 4.0 excellence.

TAGS: Engineering Services

Frequently Asked Questions

Our FAQ section is designed to guide you through the most common topics and concerns.

MBE is an approach that uses models and simulation to define, execute, and manage technical and business processes across the product lifecycle.

Model-based design focuses on creating digital models for engineering. MBE extends that approach across the enterprise by linking design with manufacturing, quality, and supply chain processes.

Manufacturers are adopting MBE to improve speed, reduce non-conformances, strengthen traceability, and create a single source of truth for product data.

The core components are model-based definition, a centralized PLM backbone, and integrated model-based processes that enable downstream automation.

They should assess their current maturity, choose a low-risk pilot, establish standards, connect PLM to downstream workflows, and then validate and scale.

End Notes

About the Author

Anshu Malhotra

Program Manager, Tech Mahindra

With over two decades of experience in Product Lifecycle Management (PLM), Anshu has spent the past eight years at Tech Mahindra. He plays a key role in driving digital continuity initiatives by leveraging PLM and engineering technologies to enable integrated, end-to-end enterprise solutions.

Prashant Deshpande

Delivery Head, Tech Mahindra

With over 22 years of experience at Tech Mahindra, Prashant has held roles across project, program, and general management. He holds an Executive MBA from United Business Institutes, Brussels.

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Anshu Malhotra

Program Manager, Tech Mahindra

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Prashant Deshpande

Delivery Head, Tech Mahindra

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The Biopharma Industry: The Story Thus FarOver the past five decades, biopharma has enjoyed a remarkable run, delivering robust growth (~7% CAGR) and demonstrating a unique resilience. By developing defensible intellectual property in pursuit of innovative therapies, the industry has withstood economic downturns and sustained margins that few other sectors can match.This resilience is a core feature of the business model, established long before the modern digital transformation wave. While ‘digital’ has helped the industry amplify scale and efficiency, it hasn't fundamentally altered the scientific core. Competitive advantage remains ‘baked in the kitchen’—discovery and development—where scientific insight is poured into carefully guarded experiments (recipes), perfected over years of effort and masterful human judgment.In observing the technology trajectories of top biopharma players, I have noticed a high responsiveness to digital modernization (Cloud, Data, SaaS) among CIOs and CDOs. However, business leadership—CEOs and CMOs—has rarely seen digital technology as a fundamental pivot. The organizations were always data-savvy, becoming increasingly literate in harnessing external data sources. Traditional AI (classical ML and simulations) was commonplace, yet the impact was felt only on the margins. The process of developing therapies remains famously slow (10–15 years) and expensive ($1B+). The ‘Big Solve’ remains elusive because the fundamentals of scientific decision-making have remained materially unchanged.The Generative AI DifferenceGenAI represents a tectonic shift. The combination of generative algorithms, novel training techniques, and accelerated compute costs creates a compelling business case for AI at the core of the business. In our conversations with industry leaders, as reflected in analyst surveys, executives are excited about GenAI for good reason.The impact is broad-ranging, moving well beyond incremental automation. McKinsey & Co. estimates that GenAI could unlock between $60M and $100M in annual value across drug discovery, clinical trials, and commercialization. With deep proprietary data and scientific know-how, the sector is structurally well-positioned to harness GenAI into a durable advantage.With GenAI, for the first time, AI-augmented digital systems can participate inside the scientific workflow—capable of not just analyzing outcomes but influencing what gets tested next. AI is moving from the margins to become a co-pilot in the kitchen. This creates a massive opportunity, but with a warning sign: proceed with caution. An industry’s reputation is only as good as its clinical safety. Every step requires extraordinary vigilance to uphold clinical standards.The mandate is twofold: accelerate the kitchen with AI—without compromising the recipe that delivered decades of resilience.Accelerating The KitchenExtending the metaphor from the chef’s fire to the table, acceleration must span the full value cycle—from bench to bedside. Speed in one function alone does little if bottlenecks persist elsewhere. Accelerating the kitchen means improving every station: discovery, clinical development, and commercialization.Discovery: Self-Learning PipelinesA closed-loop learning model is the most significant opportunity to accelerate discovery. GenAI is already showing signs of success, particularly in AI-augmented decisions around predicting experiments or suggesting sequencing. The traditional linear pipeline is transforming into a true learning cycle, where the twin engines of lab experimentation (human-centered) and computation (algorithm-centric) work in a continuous loop. The goal: reduce wasted cycles, eliminate low-probability paths sooner, and reach viable leads faster.The marketplace for AI-native platforms like AlphaFold (Google DeepMind) or BioNeMo (Nvidia) is maturing rapidly. With these integrated capabilities, there is potential to compress target-to-lead timelines by 3–12 months and save tens of millions of dollars. Our discussions with R&D teams confirm the value of AI in eliminating targets that looked promising on paper but failed under simulation.Development: Cycle-Time And Early DecisionsAI in clinical development offers massive potential for both time and cost savings. Even minor insights from advanced analytics are vital for improving trial design, site selection, and patient identification. Simulations and stress-testing protocols can meaningfully influence recruitment and safety.The idea is to augment—not replace—the judgment of clinical and biostatistics teams. Fast foresight helps organizations decide what to pursue earlier in the cycle when productivity curves are still manageable. From an implementation perspective, we are increasingly collaborating with clients on ‘bespoke development’ for AI-assisted protocol authoring and the creation of synthetic datasets across the clinical cycle.The idea is to augment—not replace—the judgment of clinical and biostatistics teams. Fast foresight helps organizations decide what to pursue earlier in the cycle.Commercialization: Insights Beyond ApprovalImpact does not stop at approval. Across launch planning, market access, and field engagement, AI enables ‘live’ learning systems. Signals from prescribing behavior and payer dynamics can be integrated in real time. Over time, a feedback loop takes shape in which commercial insight informs development strategy, and vice versa. This is critical in markets where differentiation windows are increasingly narrow.Safeguarding The RecipeAs GenAI moves closer to the core, the organizational risk profile changes. What used to be a question of data protection at the edges now touches the heart of how science is conducted. IP protection remains front and center, but new exposure points for leakage are emerging. There is a need to fortify security mechanisms to factor in AI-related threats as a strategic domain.In an AI-augmented world, reverse inference, supplier risk, and ‘black-box’ fragility are emergent challenges that biopharma must strategically contend with. Furthermore, the specter of stiffer regulations demands carefully crafted compliance. Safeguarding the organization’s assets now includes protecting AI algorithms, models, and data. This necessitates a pivot in organizational design to demonstrate credibility, explainability, and auditability to regulators on demand.Cyber Risk: Scientific Core ExposureWith AI at the core, the attack surface for cyber threats expands to include proprietary biology, training data, and model parameters. These ‘crown jewels’ are now encoded within the process. AI assets become a new class of sensitive IP, often harder to monitor when compromised. Addressing these risks requires AI-native cybersecurity, treating models and data as protected assets by design rather than as an afterthought.Regulatory Lens: Upstream EngagementRegulators are signaling a measured approach—keeping the door open while deep-diving into the scope of AI-augmented filings. They want to know how decisions were reached, requiring verifiable evidence and clear human oversight. In this environment, governance is not a ‘compliance tax’; it is a precondition for speed.AI assets become a new class of sensitive IP, often harder to monitor when compromisedSeizing the Initiative: Proactive Organizational DesignLeaders are not waiting for regulations to force their hand. They are redesigning their organizations to bring AI governance to the forefront. In our client conversations, a pragmatic federated model has emerged as the preferred choice. Much like a sports league, central functions act as rule-setters, while functional teams (Discovery, Clinical, and Commercial) are the players running the plays within those guardrails.Key principles include treating AI artifacts as regulated assets, separating public foundation models from proprietary layers, and aligning cyber, legal, and scientific leadership voices into a unified front.Looking Ahead: Gaining Enduring AdvantageWith GenAI, digital capability moves from supporting science to participating within it. What comes next will be shaped less by technical novelty and more by how deliberately GenAI is integrated into the core.From what we observe at Tech Mahindra, the most durable progress is made when GenAI is elevated from episodic experimentation to an enterprise-wide thread. The early leaders are setting flywheels in motion that will accelerate innovation while preserving the trust that is the lifeblood of this industry. Sustained momentum and small daily successes will define the next phase of global leadership.

Did you know that 95% of corporate AI initiatives show no return on investment?1Most large organizations struggle to scale AI initiatives and translate promise into sustained business impact. The reason is a combination of failures in strategy, operating model, and leadership alignment.But this is not an unsolvable puzzle. The solution lies in a practical, experience-backed approach that drives AI success at scale and speed. It must be anchored in clear business outcomes and empirical evidence. I have based this approach on my doctoral research and conversations with CXOs across industries. Let’s break it down.The Scaling Challenge: Why AI Stalls in Large OrganizationsResearch across consulting firms and industry bodies consistently highlights a sobering reality: while most enterprises run dozens, sometimes hundreds, of AI pilots, only a small fraction succeed in scaling them across the organization. The findings from ‘The State of AI in Business 2025’, which surveyed over 300 publicly disclosed initiatives across 4 major industries, are even more surprising: less than 5% of AI pilots have made it to production and delivered measurable value!1In my experience, some of the factors on why AI Stalls in large complex transformation are synthesized as follows:AI initiatives are often launched in silos, disconnected from end-to-end business processes and without clear business owners.Use cases are either defined too broadly, slowing delivery and diffusing benefits, or too narrowly, failing to address ROI expectationsTeams lack clear ownership, budgets, measurement frameworks, decision rights, and sustained executive sponsorship.Early wins fail to translate into scalable models that can be replicated across business units, thereby increasing overall investment requirements and reducing ROI.As a result, AI is often portrayed as a valuable tool, but it has yet to yield tangible business value in organizational contexts. This is a dangerous situation, as competitors who get it right will build a significant competitive advantage.So how can CXOs drive AI success at speed and at scale in their organization?Start with the Right Problem and be Realistic: Not Too Broad, Not Too NarrowThe first and most critical decision is selecting the right problem to solve.A useful set of questions to measure if you are making the right choices would be:Has your AI initiative taken too long to deliver tangible value over the last 12 months? It is likely too broadly defined.If your AI initiatives delivered only limited or localized benefits without addressing the underlying scale expectations? It is likely too narrowly defined!Organizations that succeed in turning AI pilots to create enterprise-wide impact strike a balance between the two extremes. They focus on well-defined business problems within a broader end-to-end process transformation. Rather than optimizing isolated tasks, they ask how AI can reimagine an entire customer journey, operational workflow, or decision-making loop.For example, instead of deploying AI solely to improve forecasting accuracy, leading organizations examine how demand signals, pricing, data pipeline, inventory, marketing, and customer engagement interact, and how AI can orchestrate decisions across that system.Identify Early Wins that can Produce Visible Business ImpactScaling AI without early success is almost impossible. However small the outcome, it matters to keep top management from shutting down the project or cutting critical foundational investment that builds momentum.Typical high-impact and quick areas in the consideration set could include:Customer experience transformation through personalization, leveraging a unified data fabric and insights produced by ML algorithms.Bottom-line improvement via cost optimization, automation, and smarter resource allocation by identifying inefficiencies and automating them through AI agents.Employee experience enhancement through intelligent AI-embedded workflows and decision support to reduce manual and repetitive tasks in various internal processes.Revenue uplift through better targeting, pipeline management, dynamic pricing, and product-market alignment recommendation engines.The key is not novelty, but ‘impact’. Even simple SLMs can enable such use cases, and today, many product vendors offer out-of-the-box capabilities that simply need to be unlocked. Early success builds organizational confidence, unlocks funding, and creates the belief that AI is not theoretical but practical and can deliver short-term impact.Choose Interconnected Domains to Maximize Value DeliveryAI delivers disproportionate value when applied to interconnected domains rather than isolated functions. The increase in the number of interconnected processes and accompanying activities enables AI to analyze and suggest real-time improvements that would be very hard for a manual operating system to address.Because AI thrives on complex data relationships, it is particularly powerful at identifying inefficiencies that span boundaries from marketing and sales, operations and supply chain, to service and product development. These intersections are difficult to optimize using traditional methods but lend themselves naturally to AI-driven insights and value unlocks.Organizations that focus on such interconnected domains often uncover value that is both larger in magnitude and harder for competitors to replicate. As Michael Porter pointed out in his seminal paper ‘What is Strategy?’, it is the set of interconnected reinforcing activities that forms the bedrock of a firm’s competitive advantage. AI acts as a catalyst by improving the fit between activities, reducing inefficiencies and unlocking new value.Build the Right Team: Squads with Shared CapabilitiesTechnology teams working in silos will never be able to scale AI. A collaborative cross-functional team and enabling structures are critical to success.Successful organizations adopt a squad-based model anchored in business ownership, supported by shared AI capabilities. Each squad typically includes:A business champion with a clear mandate to improve top line, bottom line, or customer experience. This will ensure that the line function takes charge of outcomes, leveraging AI.A product owner accountable for scoping, features, and the overall roadmap helps avoid defining initiatives that are either too narrow or too broad.A change-leader to drive adoption and behavioural shifts, including a UX/ UI expert to support.AI practitioners such as data scientists, engineers, designers, and business analysts support the right technology and tool choices.A scrum master to ensure execution discipline, collaboration, and value delivery in agile sprints.Critically, shared capabilities, such as data engineering, analytics platforms, and AI tooling, are pooled centrally and reused across squads. This goes a long way toward improving ROI and, more importantly, enabling agility by sharing resources and best practices. Reuse may include common metadata definitions, shared machine-learning libraries, standardized data-visualization components, and API-based access to legacy systems.This balance between decentralization (business ownership) and centralization (capability standardization and reuse) is a hallmark of AI maturity and scaled execution among top organizations.In conclusion, AI at scale is not about doing more experiments. It is about doing fewer things exceptionally well and then replicating them relentlessly for scale.Organizations that succeed combine sharp problem selection, visible early wins, empowered teams, strong leadership, and a repeatable operating model. When speed and scale are addressed together, AI moves from promise to performance—and becomes a true engine of enterprise transformation.

Reimagining the Air Travel ExperienceWhat if traveling in flight felt effortless? What if airport experiences were designed to improve our comfort and save time at every step? Answers to these questions would have appeared to be a dream until a few years ago. But, right now, this is no longer a dream, it is slowly becoming a realityAir travel has the potential to shape our lifestyles. The pace of connecting with people and experiencing life could be greatly influenced by air travel. Gen Z understands the impact of air travel at this level more than any other generation, and they are actively acting on this understanding.Travelers, especially Gen Z, no longer want trips that merely ‘work’ from point A to item B on the itinerary. They want journeys to feel simple and designed for them.As expectations evolve, air travel systems must deliver seamless experiences without compromising safety, reliability, or compliance.Planning and AnticipationLet’s imagine a world where the trip (might as well be a ‘life-changing trip’) feels as if it were particularly designed for us. From the beginning of planning to the time we reach our destination and return home after the journey, there is no friction. The flow of information, decisions, and actions throughout the journey is effortless and continuous.Or let’s say we’ve missed planning for a long weekend and only realize it on a Wednesday. We turn to Cary, a Digital Experience Manager, and simply say, “Hi Cary, could you please plan my trip?” It will already know the preferences, from window-seat choice to the time it takes to reach the airport, along with every other little detail.Using this information, Cary curates the trip and takes charge from the planning stage, alerts about the roadblocks on the way to the airport, and even ensures the coffee order is ready in time for takeaway.Effortless Movement Through the AirportWhen we reach the terminal, the experience is straightforward; there are no buttons or touchscreens to look for in haste. It simply involves a check-in through a conversational, voice-activated kiosk, followed by biometric luggage check-in. These moments are supported by TechM’s Engineering Services, built over decades through large, integrated programs in which design and manufacturing engineering operate together, backed by millions of engineering hours with global aerospace leaders. Personalization at scale is possible when the underlying platform is built with the same discipline and rigor that goes into building an aircraft that operates safely in the sky.After the check-in is complete, we move on to the ID verification step. The process was perhaps a hassle, but that’s all in the past. Now, a chip-enabled e-passport stores our biometric data, which helps us glide through the verification processes. At larger hubs, automated corridors verify our identities as we pass through the gates. Exceptions are seamlessly routed to officers who take over from that point. If all this seems so easy and effortless, well, that’s the point!Under the hood, this level of automation needs the same engineering integrity used in avionics, which includes requirements traceability, tool qualification, and validated software life cycles. These are familiar territories for TechM, where regulated engineering is the norm, not the exception. And that’s the beauty. Engineering excellence doesn’t take a pause here. It continues to show up in places we might not have generally thought about.Crafting a Thoughtful In-Flight ExperienceWhen we step into the aircraft and take our seat, the cabin environment adjusts automatically to accommodate our preferences. Cabin lighting aligns with the destination’s time zone, and the low-level ambient noise, the kind that usually lingers on and contributes to fatigue, in the area diminishes. These experiences are supported by capabilities that airports and airlines execute consistently. Full-lifecycle engineering across cabin interiors, connectivity for seamless transits, stress analysis, embedded electronics, certification, and retrofit programs is executed across fleets. This is where TechM’s cabin interiors practice thrives, with hundreds of our engineers turning thousands of creative ideas into reality, all guided by a single vision: for a better tomorrow.A highly customized and connected cabin experience is pivotal to the overall journey, and a significant means to deliver customer delight.Now, back at the cabin, we would normally pick up a magazine with content irrelevant to our interests. In the future, however, we can select shows and movies and even get recommendations for places and activities at our destination, based on our preferences. This level of personalization is possible through seamless integration of machine learning capabilities within the systems. So here’s the thing: Gen Zs expect surprises in experiences and not in task-oriented activities. There’s a fine line of difference between them. If the service feels smooth and seamless and there’s fun and adventure in those actual experiences, Gen Z is all in for experimenting.Seamless Arrival and Baggage ExperienceAs the aircraft begins its descent, we get the next set of instructions for the exit gate and walking route, and a heads-up on baggage timing. After reaching the destination, we can collect our bags on time through Cary’s first-bag and last-bag notifications.Airports modernize baggage infrastructure and apron operations using engineering expertise that TechM applies in manufacturing, including NC/CMM programming, PPAP/FAI, non-destructive inspections, and should-costing techniques to eliminate inefficiencies and streamline operations. Passengers experience these improvements as a smooth, routine journey, similar to ‘just another day, another thing’ moments.Navigating the Complexities in Airlines and AirportsWhy do these travel experiences matter at all?While airports and airlines face increasing pressure, paying attention to the changing shifts creates better passenger experiences and keeps operations efficient and safe. Understanding these shifts comes down to two key factors:The traveler persona is changing fast. GenZ and Millennials, who will soon control about 80% of travel spending, prioritize simplicity, transparency, and experiences that understand their requirements.Airports and airlines are juggling multiple operational constraints such as labor availability, regulatory requirements, on-time performance, and sustainability goals.The answer to addressing all these issues is simplifying processes, beginning with the minute details and eventually improving the whole experience exponentially. And this meticulous attention to detail is what TechM’s Engineering Services is all about. Our decades of experience in aerospace & defense, global delivery centers, certified design organizations, and large program ownership help us combine engineering discipline with human-centric experience design.Building Trust to Enable Personalization at ScaleAnd what does it take to achieve this dream further? The answer is simple but crucial: trust.Personalization must be built on explicit consent. We bring aerospace-grade quality systems and safety-critical engineering to ensure privacy and security aren’t afterthoughts. With trust as the foundation, we bring the end-to-end connectivity to the journey. Our engineering capabilities span identity check-in systems, baggage infrastructure, cabin connectivity, technical publications, Maintenance, Repair, and Overhaul (MRO), and aircraft health monitoring.Making Effortless Air Travel a RealityAviation is more of an experience than waiting in long queues. Airports and airlines around the world are already piloting the building blocks of this experience. The next step is to bring them together securely, empathetically, and at scale with the travelers who will soon define the market.Imagine the next air travel trip being easy to plan, smooth at the airport, comfortable in the air, and stress-free upon arrival. It’s a future within reach, one we can shape together.That is the magic of not just engineering, but TechM’s engineering.

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Blueprint for Transformation: The Model-Based Enterprise Explained

The Transformation Gap

What is Model-Based Enterprise (MBE)?

The Race Toward Model-Based Operations

Core Components of an MBE

A Practical 4-Step Playbook to Start

Why Tech Mahindra: Your Strategic Partner in MBE Transformation

Building the Future-Ready Enterprise

Frequently Asked Questions