Standardizing AI–System Connectivity in Manufacturing with Model Context Protocol

By: Nikhil Makhija

Reviewers: Gowrisankar Krishnamoorthy, Ravi Soni

The rapid adoption of AI and large language models (LLMs) in industrial settings demands robust, secure, and standardized interfaces to real-world data and tooling. The Model Context Protocol (MCP) is an emerging open standard designed to facilitate seamless integration between AI agents and external data sources, tools, and systems. This article presents a detailed overview of MCP’s architecture, explores its specific relevance to manufacturing operations, and discusses opportunities, challenges, and recommended practices. It aims to equip manufacturing professionals, AI engineers, and operations leaders with insights to evaluate and adopt MCP-driven solutions in the factory environment.

1. Introduction

1.1 Motivation: AI in Manufacturing

Manufacturing organizations increasingly deploy AI for predictive maintenance, quality assurance, process optimization, supply chain forecasting, and human–machine collaboration. However, the value of AI depends heavily on access to timely, contextual data: sensor streams, MES (Manufacturing Execution System) logs, ERP databases, CAD models, control systems, and more. Traditional integrations often involve point-to-point adapters or bespoke middleware, which can become brittle, costly to maintain, and hard to scale.

1.2 The Integration Challenge

AI agents (especially LLM-based assistants or automated decision systems) need to query data, invoke procedures (e.g. control APIs or workflows), and maintain context of operations across different systems. Without a unified protocol, each new data source or tool may require custom integration, leading to “N×M” integration complexity. Moreover, consistency, governance, security, and auditability become major obstacles. The Model Context Protocol (MCP) addresses precisely this gap by offering a universal standard for connecting AI agents to external systems.

2. What Is MCP? Architecture and Principles

2.1 Definition and Origins

The Model Context Protocol (MCP) is an open-source, vendor-neutral standard introduced by Anthropic in late 2024, intended to create a standardized interface by which AI clients (e.g. LLM-based agents) can access external data, perform actions, and manage context. 

MCP abstracts away low-level plumbing so that AI agents can request “tools” or “resources” in a uniform way. It supports operations such as reading files, executing functions, querying databases, and calling APIs.

2.2 Architecture Overview

A simplified MCP architecture comprises:

• MCP Client (Agent Host): The AI application (or agent) that issues requests in the MCP protocol.
• MCP Server(s): Components that expose particular external tools or data sources via the MCP interface, translating requests from the AI into system-native operations.
• Resources / Tools: The underlying systems (databases, APIs, file systems, machine controllers, etc.) that the server mediates.
• Transport Layer & Protocol: MCP is typically carried over JSON-RPC 2.0, via HTTP or standard I/O (stdio) channels. 

In practice, multiple MCP servers may run in parallel, each responsible for a domain (e.g. MES data, quality systems, equipment controllers). The agent composes context from various servers to make informed decisions.

MCP also supports tool discovery, permissions, metadata tagging, and contextual memory to help agents operate more intelligently. 

Diagram 1: MCP Architecture

2.3 Key Properties and Design Goals

Some of the core design goals of MCP:

• Standardization & Interoperability: Provide a common interface so AI agents can interoperate across varied systems without bespoke glue code.
• Modularity / Composability: Enable modular “skills” or “tools” that can be plugged in or extended.
• Contextual Integrity: Maintain a consistent context (metadata, provenance, state) across tool usage to avoid data drift or misuse.
• Security, Access Control & Auditability: Ensure that only authorized agents access systems, and actions are traceable.
• Scalability & Maintainability: Reduce the integration burden and simplify long-term evolution of AI-enabled systems.

3. Relevance of MCP in Manufacturing

While MCP is general-purpose and widely discussed for software and AI use cases, it has resonance in manufacturing, where bridging AI to real-time systems is crucial. Below is core ways MCP can add value on the shop floor and in manufacturing IT/OT landscapes, along with illustrative use cases.

3.1 From Sensor Streams to Decision Agents

Modern factories deploy myriad sensors (vibration, temperature, pressure, current, throughput counters) and edge computing devices. An MCP server can expose a sensor feed as a resource, allowing AI agents to query real-time or historical sensor data in a structured way. Downstream, the agent may invoke tools (e.g. predictive maintenance model or control command) to adjust operating parameters or flag anomalies.

For example, an AI assistant could issue, via MCP:

• “Fetch last 24 hours vibration data for spindle #3”
• “Apply anomaly detection model on that stream”
• “If vibration exceeds threshold, issue a command to reduce spindle speed by 10%”

This creates a tight loop between insight and action.

3.2 Integrating MES / ERP / PLM Systems

Production planning data in ERP, shop-floor state in MES, design data in PLM, and quality logs reside in structured, legacy systems. MCP servers wrapping those systems let AI agents pull relevant context: e.g. order schedules, material availability, past defect rates associated with parts, or design tolerance specifications. This enables agents to surface recommendations, link issues to root causes, or propose schedule adjustments.

3.3 Quality Inspection & Root-Cause Assistance

Imagine an AI agent assisting quality engineers. Upon receiving a defect alert, the agent may:

1. Query relevant inspection images or measurement logs (via MCP).
2. Request historical defect rates and machine settings.
3. Suggest potential root cause hypotheses (e.g. “tool wear increased after 1500 cycles in similar scenarios”)
4. Invoke a test or inspection tool (via MCP) to run further diagnostic tasks.

By plugging into existing QC tooling and data via MCP, the agent becomes a proactive assistant.

3.4 Adaptive Scheduling, Throughput Optimization & Resilience

When disruptions occur, machinery downtime, supply delays, or quality rejects—AI agents using MCP can dynamically simulate and propose schedule adjustments or reassign tasks across lines. Because MCP provides real-time connectivity to data, control systems, and workflows, the agent can evaluate trade-offs (e.g. minimize delay vs maximize throughput) and execute changes via downstream systems.

Why MCP Matters in Manufacturing

1) Closing the Loop Between Data and Decisions

Factories generate high-volume, multi-format data—sensor streams, machine logs, WIP states, and quality results. MCP allows agents to pull relevant context and trigger actions (e.g., create a CMMS work order or adjust schedules) using a single protocol instead of many bespoke connectors. That makes closed-loop use cases—predictive maintenance, statistical process control, and production optimization—easier to scale. 

2) Simplifying IT/OT Integration

By wrapping ERP/MES/PLM/QMS/SCADA endpoints as MCP servers, teams reduce “N×M” integration complexity. Vendors in the industrial ecosystem are already building MCP servers, indicating practical feasibility for shop-floor deployments. 

3) Governance, Security, and Auditability

Because MCP formalizes resource discovery, permissions, and logging, it provides an enterprise-ready path for RBAC, traceability, and least-privilege access—key for regulated plants and ISA/IEC 62443 programs. Industry commentary highlights that MCP’s standardization strengthens oversight for agent actions.

Where MCP Fits in the Smart Manufacturing Stack

Diagram 2: MCP in Smart Manufacturing Stack

Implementation Pathway

To successfully adopt MCP in manufacturing environments, a phased approach is recommended:

1. Read-Only Pilot: Start by exposing data sources such as production KPIs or sensor logs.
2. Advisory Agents: Let AI recommend but not execute actions (e.g., scheduling changes).
3. Controlled Command Execution: Allow safe operations under human review.
4. Full Closed-Loop Automation: Once validated, permit autonomous actions within strict safety limits.

From a technical standpoint, manufacturers can deploy MCP servers using containerized microservices, each corresponding to a domain—production data, quality data, or maintenance logs. Consistent APIs and schema validation simplify expansion and maintenance.

Benefits and Challenges

Benefits                                                     Challenges

Unified AI–system connectivity             Security and access management

Lower integration costs                             Latency in time-critical applications

Transparent audit and governance             Safety validation for agent commands

Modular and future-proof architecture     Need for cultural and IT readiness

Mitigation strategies include role-based access control, sandbox testing, and human-in-the-loop validation before autonomous actions.

Looking Ahead

The MCP ecosystem is expanding rapidly Open AI, Anthropic, and other AI platform providers are aligning around this open protocol, suggesting it could become a de facto interoperability layer for AI systems.

For manufacturers, this means AI assistants will increasingly come “MCP-ready,” capable of connecting to on-premises data, IoT networks, and enterprise systems out of the box. When paired with digital twins and edge AI, MCP could power real-time optimization loops—predict, simulate, decide, and act—all through a single interoperable framework.

Key Takeaway

The Model Context Protocol represents a practical step toward trustworthy, context-aware AI in manufacturing. By bridging AI models and factory systems through an open, auditable, and extensible interface, MCP helps manufacturers move beyond dashboards to intelligent, autonomous operations.

Manufacturers exploring AI for operations, quality, or maintenance should watch this protocol closely—and consider pilot projects where MCP can bring tangible efficiency and data cohesion.

References

1. Model Context Protocol Specification
2. “Model Context Protocol (MCP): Everything You Need to Know” – Zencoder.ai
3. “Model Context Protocol for Manufacturing” – Tulip
4. “MCP and Enterprise AI Governance” – Shakudo.io
5. Axios: “Hot New Protocol Glues Together AI and Apps”

Smart Manufacturing Transformation: 5 Key Challenges

By: Murugan Boominathan

Reviewers: Conrad Leiva, Nikhil Makhija, Gerhard Greeff

The automotive industry is at a crucial point, influenced by global competition, supply chain disruptions, and the urgent need to shift toward electrification and sustainability. Central to this change is smart manufacturing, an approach that uses Industry 4.0 technologies like AI, IIoT, advanced analytics, and digital twins to improve efficiency, agility, and resilience.

The Promise and Complexity of Digital Transformation

During a recent panel discussion with leaders from Tier 1 suppliers and OEMs, experts shared their views on the journey to smart manufacturing. The benefits are significant: cost reduction, better quality, and making operations future-ready. However, the path can be complicated. Manufacturers need to evaluate their digital readiness and create a tailored smart factory plan that fits their specific capabilities and operational realities.

A recurring theme from the panel was the challenge of integrating existing manufacturing facilities with legacy equipment and infrastructure that were not originally designed for digital or AI integration meaning “brownfield” environments. Successful transformation requires not only investment in technology but also a cultural shift. This includes bridging the gap between IT, OT, and business teams, along with encouraging collaboration across departments.

5 Key Challenges Identified

Legacy Systems and Brownfield Integration: 
Many automotive plants use infrastructure that is decades old. Integrating modern digital solutions without halting production requires careful planning and strong change management. This process often includes reviewing existing assets, spotting integration points, and ensuring that new systems can work alongside established processes. The complexity of these environments means that every step must be managed meticulously to prevent costly downtime or operational issues.

Skills Gap and Workforce Readiness:
The journey to digital transformation is as much about people as it is about technology. Upskilling and retraining the workforce, attracting new talent, and promoting a culture of continuous learning are vital for success. Panelists stressed the need for targeted training programs, mentorship, and partnerships with educational institutions to ensure employees have the skills needed for a digital future. Creating a workforce that is flexible and open to change is crucial for long-term competitiveness.

Data Silos and Interoperability:
Disconnected systems and siloed data continue to be major obstacles. The panel highlighted the need for open standards and integrated MES platforms to allow seamless data flow and real-time decision-making. Breaking down data silos enables organizations to use information from across the enterprise, leading to better insights and decisions. Interoperability between systems is essential to unlock the full benefits of digital transformation.

Building the Business Case:
Justifying digital investments requires a clear explanation of ROI, balancing cost, efficiency, and scalability. Panelists shared real-world examples of how to create convincing business cases for smart manufacturing initiatives. This includes identifying measurable outcomes, such as reduced downtime, better quality, or increased throughput, and aligning these benefits with organizational goals. Clear communication with stakeholders at all levels is key to gaining support and maintaining momentum.

Cybersecurity and Change Management:
As digital connectivity grows, so do risks. Ensuring strong cybersecurity and managing organizational change are essential for protecting operations and maintaining progress. The panel discussed the need for comprehensive security strategies, including regular risk assessments, employee training, and the use of advanced security technologies such as endpoint protection, network segmentation, and real-time threat monitoring. These measures help safeguard sensitive manufacturing data and prevent disruptions to production systems.

Effective change management practices are equally critical. This includes clear communication of goals and expectations, early involvement of key stakeholders across departments, and structured feedback mechanisms to monitor adoption and address resistance. By combining robust cybersecurity with proactive change management, organizations can navigate digital transformation confidently and sustainably.

Looking Ahead

The consensus from Tier 1 and OEM leaders is clear: smart manufacturing is not a final goal but an ongoing journey. Success depends on strategic vision, collaboration across different teams, and a consistent focus on both technology and human factors. As the industry continues to change, those who embrace digital transformation as a whole will be in the best position to succeed amid ongoing disruption.

Manufacturers must stay agile and responsive, continually watching industry trends and new technologies. Collaboration throughout the value chain, from suppliers to OEMs to technology partners, will be critical for driving innovation and achieving sustainable growth. By investing in people, processes, and technology, the automotive sector can build a strong foundation for the future.

In summary, smart manufacturing presents significant opportunities for the automotive industry, but realizing its full potential calls for a thoughtful and coordinated approach. By addressing key challenges, encouraging a culture of innovation, and staying focused on long-term goals, manufacturers can navigate the complexities of digital transformation and secure their place in the next era of mobility.

Developing a Cost-Conscious AI Strategy for Manufacturing

By: G Vikram

Reviewers: Nikhil Makhija & Gowrisankar Krishnamoorthy

Artificial Intelligence has enormous potential. But without careful planning, AI projects can quickly become expensive experiments with limited impact. I've learned that building a cost-conscious AI strategy ensures organizations capture real business value while keeping investments sustainable.

Start Small: Validate Before You Scale

Scaling too early is one of the most common mistakes in industrial AI implementation. According to Deloitte’s Manufacturing AI Study, manufacturers that begin with small, validated pilots achieve a 40% higher success rate when scaling to production.

Observation: Manufacturers often commit to large-scale AI initiatives before validating assumptions, leading to sunk costs when projects underdeliver.

Goal: Reduce financial and operational risk while confirming measurable business value.

Strategy: Begin with Proof-of-Concept (PoC) projects using MES or SCADA data tied to one business KPI.

Tactics: Select one or two high-potential use cases (e.g., predictive maintenance, quality inspection); test them in controlled environments; perform go/no-go reviews before scaling.

Outcome: Teams build confidence, leadership sees early wins, and scaling decisions are backed by real evidence.

Example Use Case – Predictive Maintenance for CNC Machines

A tier-2 automotive supplier implemented a pilot predictive maintenance model using existing MES and PLC data to predict spindle motor failures. By starting small — monitoring just five machines — the company validated accuracy before scaling.

The result? A 22% reduction in unplanned downtime and ROI achieved within 8 months.

Once validated, the model was scaled to 60 machines with only marginal cost increases, proving the value of a cost-conscious, stepwise approach.

Focus on ROI: Business Value Comes First

AI is not about innovation for its own sake — it’s about measurable impact. The Cisco AI Readiness Index 2025 found that only 32% of organizations have defined processes to measure AI ROI.

Observation: AI investments often focus on technical novelty instead of delivering measurable operational or financial impact.

Goal: Tie AI initiatives directly to manufacturing KPIs — yield improvement, downtime reduction, or scrap minimization.

Strategy: Use a Cost-to-Value Matrix to rank potential use cases by measurable payback period.

Tactics: Define success metrics upfront (e.g., cycle-time reduction, OEE improvement); prioritize projects with ROI under 12 months; continuously track ROI post-deployment.

Outcome: AI initiatives earn sustained funding and organizational trust by proving quantifiable business value.

Maintain to Sustain ROI

AI ROI doesn’t end at deployment — it depends on continuous model reliability.

Models degrade over time due to data drift, process changes, or seasonal behavior.

To protect ROI:

• Schedule periodic model audits and retraining every 3–6 months.
• Track model accuracy KPIs (e.g., false positive rate, prediction lag).
• Automate retraining pipelines using MLOps frameworks like Azure ML or AWS SageMaker.

This ensures AI systems continue to deliver measurable impact — sustaining ROI instead of letting it decay.

Leverage Existing Data: Reveal Hidden Value

Manufacturers already generate vast volumes of data through MES, PLCs, SCADA, and ERP systems. Instead of starting from scratch, companies can accelerate AI adoption by maximizing data they already possess.

Observation: New sensor integrations and data collection are slow, costly, and resource-intensive.

Goal: Accelerate AI adoption using readily available operational and quality data.

Strategy: Conduct a Data Readiness Audit to assess internal datasets, ensuring they’re structured and AI-ready.

Tactics: Map existing data assets; establish secure data pipelines; use tools like Azure Data Factory or AWS IoT Analytics for data cleaning and harmonization.

Outcome: Faster model deployment, reduced data acquisition costs, and higher ROI from existing infrastructure investments.

Use Pre-Trained Models: Don’t Reinvent the Wheel

Developing models from scratch consumes time and compute costs. Manufacturers can reduce expense and speed deployment by using pre-trained models and fine-tuning them for specific production environments.

Observation: Training AI models from scratch requires significant compute, time, and expertise — often exceeding project budgets.

Goal: Minimize development costs while ensuring robust accuracy.

Strategy: Adopt pre-trained AI models from industrial libraries (e.g., NVIDIA Metropolis, Siemens Industrial Edge AI).

Tactics: Fine-tune models using domain-specific production data; integrate via OPC UA or REST APIs; validate with small-batch trials before full-scale rollout.

Outcome: Faster deployment, reduced training costs, and improved consistency in production AI use cases.

Optimize Infrastructure: Choose the Right Tool for the Job

AI workloads can silently inflate operating costs. The Cisco AI Readiness Index 2025 reports that 62% of companies expect AI workloads to increase by over 30% in three years, yet only 34% feel infrastructure-ready.

Observation: Teams often default to high-performance cloud compute resources even for lightweight tasks, leading to overprovisioning and waste.

Goal: Optimize infrastructure cost without compromising performance.

Strategy: Implement a hybrid AI architecture combining on-premises edge systems with cloud scalability.

Tactics: Use industrial-grade edge devices (e.g., NVIDIA Jetson, Dell Edge Gateways) for latency-sensitive applications; leverage cloud GPUs/TPUs only for large-scale training; use Kubecost or Azure Cost Management for continuous spend tracking.

Outcome: Reduced cloud bills, better workload distribution, and infrastructure scalability aligned with real production needs

Integrate Governance, Systems, and Change Management

Cost control in AI isn’t only about technology — it’s about structure, accountability, and adoption.

Observation: Lack of governance and system integration often leads to inefficiency and compliance risks in manufacturing AI projects.

Goal: Ensure sustainable adoption through governance, interoperability, and workforce readiness.

Strategy: Align AI systems with MES, ERP, PLM, and SCADA frameworks while establishing strong governance protocols.

Tactics: Integrate via middleware platforms like Kepware, Ignition, or Azure IoT Hub; Create an AI Governance Council defining retraining cadence, model transparency, and risk ownership (aligned to NIST AI RMF and ISO 27001); Conduct AI literacy and change management workshops for operators and engineers.

Outcome: Secure, compliant, and adoption-ready AI systems that align with enterprise-wide digital transformation goals.

Cultivate a Culture of Experimentation: Fail Fast, Learn Faster

A cost-conscious culture doesn’t discourage innovation — it optimizes it. Manufacturers can enable efficient learning while minimizing financial risk.

Observation: Many organizations persist with underperforming projects due to sunk-cost bias.

Goal: Encourage experimentation while limiting financial exposure.

Strategy: Use innovation sprints and Lean AI principles for short, controlled experimentation cycles.

Tactics: Allocate fixed budgets for rapid PoCs; apply CRISP-DM methodology for structured AI problem-solving; track learnings and stop unviable projects early.

Outcome: Agile innovation with faster learning cycles, reduced waste, and higher long-term ROI.

Empower the Workforce: AI Adaptation in Manufacturing Teams

Successful AI adoption depends as much on people as on technology.
Operators, engineers, and planners need visibility and trust in AI decisions.

Practical Steps:

• Conduct AI literacy workshops and cross-functional training for production teams.
• Include shop-floor users in PoC design to align AI outputs with real operational goals.
• Establish “AI champions” within each plant who act as change agents and feedback bridges between IT and OT teams.

The result is a culture where AI is not a black box but a trusted assistant — making adoption smoother and more value-driven.

Conclusion

A cost-conscious AI strategy is not about cutting corners — it’s about maximizing value with precision. By validating early, focusing on ROI, leveraging internal data, using pre-trained models, optimizing infrastructure, and embedding governance, manufacturers can scale AI sustainably.

Call to Action: Start with an AI Readiness Audit to align your data, infrastructure, and teams. Manufacturers who blend discipline with innovation will lead the next wave of Industrial AI Pacesetters.

The ISA-95 Part 1 Update: A Modern Foundation for Industrial Integration

By: Chris Monchinski 

The ISA-95 committee, in collaboration with Joint Working Group 5 in IEC and ISO, continues to evolve the ISA-95 standard (IEC 62264) to meet the demands of today’s digital industrial landscape. Originally developed to address the challenge of integrating monolithic systems like ERP, MES, and SCADA, ISA-95 remains a foundational framework for reducing integration risk and maximizing information exchange in manufacturing environments. 

Much has changed in the 20+ years since its inception. Today, we see cloud-native ERP, MES, and historian systems, along with distributed control and telemetry. Systems are more modular and containerized, and the appetite for rich, contextualized plant-floor data is being fueled by demands for advanced analytics and AI. 

The core purpose of ISA-95 remains: to standardize the interface between enterprise and control systems. This latest release of Part 1 reaffirms that mission—providing robust model and terminology for modern integration challenges, while recognizing that the landscape has fundamentally shifted.  A major update in this release is an acknowledgment that integration technologies have evolved—from point-to-point connections to message buses, and now to Industrial DataOps and iPaaS platforms. These modern architectures help enforce a semantic consistency and are facilitated by strong data governance.  ISA 95 has become a de-facto ontology with a complete, normalized set of models and relationships to represent industrial data.  This new class of applications will ensure that enterprises represent their valuable manufacturing data in a common semantic context from its very creation and simplify data exchange and integration. 

Another key recognition is the need to clearly understand the value and flexibility of the ISA 95 models when applied to new digital architectures.   A common misconception is that ISA 95 can only be applied where clear demarcations exist between systems (often represented as levels).  The concept of levels is a valuable way to simplify the explanation and understand the scope of an integration challenge.  However, these levels should be considered more as logical spheres or boundaries around system(s) that need to exchange data.  ISA 95 does not need these logical spheres in a specific hierarchy or subordination.  ISA 95 defines the first level of these key manufacturing spheres as functions.   Our ISA 95 Part 1 Figure 7 has been revised to demonstrate these functions and their generalized information exchange.   This figure is a combination of a function view and an information view of the enterprise, as defined in ISO 15704.  This new representation simplifies information exchanges allowing for flexibility and scale when implemented in any current or future technology. 

From ISA 95 Part 1 Ed 3, 2024, Figure 7, Copyright © ISA - USED WITH PERMISSION 

ISA 95 allows for the boundaries between systems and their represented information exchanges to be established to match the challenge of each unique integration.   In this way, ISA 95 can represent data from an individual sensor on the plant floor with its dataset pushed to the edge and cloud; but with the ability to transmit not just raw data but the metadata which puts that sensor's data in context (what site, area, work center, etc.).  The full set of ISA 95 data models allow for multiple dimension of context to be represented for this sensor's data point across a physical, production, maintenance and quality dimension.   Enhancements in this release also include more precise modeling of role-based equipment vs. physical assets and the application of the ISA-95 capacity model for accounting and planning purposes. 

If you’re new to ISA-95, this is a great time to engage with a standard that enables modular, interoperable system architectures. For seasoned practitioners, this update reflects an evolution—retaining the foundation while preparing us for what’s next.   As someone who has been with ISA-95 since its early days and now serves as chair, I can confidently say: the best is yet to come. 

You can find the released versions of ISA 95 here. https://www.isa.org/standards-and-publications/isa-standards/isa-95-standard 

And as always, we welcome all forward-thinking contributions and participation in the standards community that maintains, adapts and promotes this important industrial standard. https://www.isa.org/standards-and-publications/isa-standards/isa-standards-committees/isa95 

What Is the Hidden Factory?

 By: G Vikram, Digital Consultant, Architect, Accessor, Technology Adoption, Partnerships, Maxbyte Technologies Services Private Limited

Understanding the time concealed in your hidden factory can help unlock your full production potential, providing significant gains without any new expenditures.

What Is the Hidden Factory?

The hidden factory represents the untapped capacity of your manufacturing plant – the maximum amount of additional production that can be unlocked without capital investment. Fully utilizing your hidden factory means around-the-clock perfect production – manufacturing only good pieces, as fast as possible, with no downtime, every hour of every day.

The term “hidden factory” was popularized by Armand Feigenbaum in the late 1970s . Feigenbaum’s concept of the hidden factory was primarily focused on quality, specifically the waste and costs caused by “bad work”, much of which is “hidden” below the surface of day-to-day operations.

Over time , the hidden factory concept has broadened to include all waste in manufacturing. In this page we explore the hidden factory from that broader perspective, specifically focusing on the four areas of lost (or hidden) production potential from an equipment perspective:

• Schedule Loss (time where production, could  be running – but is not scheduled)
• Availability Loss (time where production should be running – but is not)
• Performance Loss (time where production is running – but not as fast as it should)
• Quality Loss (time where production is running – but one or more pieces are not good the first time through)

How Big Is Your Hidden Factory?
The untapped production potential in the hidden factory is typically very significant. Many manufacturers are surprised to learn that they have more capacity in their hidden factory than they are using in their actual factory.

The fastest way to discover how much potential is in your hidden factory is to perform two very simple calculations:

First, calculate your Fully Productive Time by multiplying Good Pieces by Ideal Cycle Time. Fully Productive Time represents how close you are to perfect production - manufacturing only good parts, as fast as possible, with no downtime. Good Pieces are pieces that pass through the manufacturing process the first time without needing any rework. Ideal Cycle Time is the theoretically fastest cycle time your process can achieve under optimal conditions.

Second, calculate your Hidden Factory by subtracting Fully Productive Time from All Time (24/7). This Hidden Factory time represents the untapped capacity of your manufacturing plant.

Where Is Your Hidden Factory?

To unlock the potential of your hidden factory, it’s important to understand your losses – where they occur in production. First, make sure you are measuring losses that affect your manufacturing constraint. Then understand how each loss factor impacts your hidden factory.

Some important tools are:

• TEEP (identifies losses due to time that is not scheduled for production)
• OEE (identifies losses during scheduled production time)
• Six Big Losses (provides more detail on losses during scheduled production time)

Benefits of Tapping into Your Hidden Factory

The most significant benefit of tapping into your hidden factory is that you can increase throughput without additional capital expenditures. Simply put – making more with what you already have.

When you increase throughput, this enables three big benefits:

• Decreased Conversion Cost: Fixed costs are spread over more output (increasing profitability).
• Increased Flexibility: Shorter production runs are possible, improving lead times and reducing inventory.
• Deferred Spend: Increase throughput on existing assets and defer spending on new equipment or facilities.

At the factory-floor level, tapping into your hidden factory can decrease overtime or eliminate outsourced production. This benefit, though, is more in the realm of traditional OEE.

What is specifically unique about the hidden factory is that in addition to OEE Losses it also takes into account Schedule Loss, which makes it an excellent tool for capacity planning:

Improved Capacity Planning: Understand and take into account the untapped capacity of your manufacturing plant when doing long-term capacity planning.

My Take on the Hidden Factory
Start with strong processes to reduce losses, then enhance results with digital solutions. Real transformation is about making more with what you have. 💡