Smart factory software: where to start your implementation?

Smart factory software is a set of systems that connect machines, people, data and production processes. It covers MES, SCADA, IIoT, ERP, QMS, APS, analytics and a data platform, among others. Implementation is best run in stages: maturity assessment, pilot, scale-out and optimisation.

For many manufacturers the question is no longer whether to digitalise the plant, but how to do it in an orderly way. A smart factory is not created by buying a single tool. It is an ecosystem – sometimes described as intelligent manufacturing software – built step by step, in which shop-floor data is available, understandable and useful for production, quality, maintenance, planning and management.

This article explains what smart factory software is, which categories of systems make up the smart manufacturing solutions stack (sometimes called the smart manufacturing software stack), how to map them onto factory maturity levels, and where to start an implementation.

What is smart factory software?

Smart factory software is a group of industrial and business systems that make it possible to collect production data, monitor processes, manage order execution, analyse results, support decisions and automate operational activities.

In practice, smart factory software is not one product but an entire technology stack. The core components of smart factory software include:

MES, or Manufacturing Execution System:

• SCADA, or Supervisory Control and Data Acquisition;
• HMI, or Human-Machine Interface;
• IoT and IIoT platforms, meaning the Internet of Things and the Industrial Internet of Things;
• ERP, or Enterprise Resource Planning;
• QMS, or Quality Management System;
• CMMS, or Computerized Maintenance Management System;
• EAM, or Enterprise Asset Management;
• APS, or Advanced Planning and Scheduling;
• data platforms;
• analytics and BI tools, meaning Business Intelligence;
• workflow applications and digital work instructions;
• OT cybersecurity solutions.

Smart factory software connects the physical layer with the information layer. Sensors, PLCs, machines and SCADA systems generate process data. MES organises production execution. ERP provides the context of orders, materials and planning. QMS supports quality control. CMMS or EAM handles maintenance. APS supports scheduling. A data platform consolidates information from different sources, and analytics helps to interpret it.

SAP describes the smart factory as a network of machines, sensors, systems and processes that use data, automation and AI to optimise production. Autodesk points out that smart manufacturing software connects data and the physical world in areas such as the supply chain, quality, factory layout and production operations.

In the context of the Smart RDM cluster, this topic belongs to the Knowledge Platform area, because a smart factory requires not only data but also the retention of operational knowledge, work standards, decision context and digital ways of working.

Smart factory vs a traditional factory – the differences

A smart factory differs from a traditional factory in the way it uses data: production information is collected, combined, analysed and used for day-to-day decisions, not only for periodic reporting.

Area	Traditional factory	Smart factory
Production data	Often scattered, manual, available only after the fact	Collected automatically and available close to real time
Operational visibility	Based on shift reports, spreadsheets and local know-how	Based on dashboards, alerts, events and data from source systems
Response to problems	Reactive, usually after the loss has occurred	Faster thanks to monitoring, rules, prediction and workflows
System integration	Limited, point-to-point, often dependent on manual exports	Designed as a data flow between OT, MES, ERP, QMS, maintenance and analytics
Role of people	Knowledge often stays local and informal	Knowledge is supported by instructions, workflows, data context and activity history
Automation	Mainly machines or individual workstations	Covers data, decisions, actions, planning, quality and maintenance
Improvement	Based on periodic analyses and projects	Based on continuous monitoring, root cause analysis and iterative improvement

A smart factory does not mean a factory without people. It means an environment in which operators, engineers, planners, maintenance teams and managers work on more consistent data and have better access to the context of a situation.

Nor does it mean that traditional automation disappears. PLC, SCADA and DCS systems and machines still control the processes. What changes is the way data from those systems is shared, interpreted and connected to business processes.

Categories of smart factory software

Smart factory software spans several layers: control and monitoring, production execution, planning, quality, maintenance, data, analytics and operational applications.

Category	Main role	Typical functions	ISA-95 level
SCADA / HMI	Process monitoring and supervision	Visualisation, alarms, trends, equipment states	Level 2
IoT / IIoT Platform	Connectivity and data acquisition	Connectors, MQTT, OPC UA, edge gateways, sensors	Levels 1–3
MES	Production execution	Orders, production tracking, genealogy, traceability, paperless manufacturing	Level 3
ERP	Business planning	Orders, materials, finance, resource planning	Level 4
QMS	Quality management	SPC, CAPA, non-conformities, inspections, audits	Level 3 / 4
CMMS / EAM	Maintenance and assets	Work orders, spare parts, failures, asset lifecycle	Level 3 / 4
APS	Planning and scheduling	Finite capacity, sequencing, constraints, changeovers	Level 3 / 4
Data Platform	Data layer	Integration, contextualisation, retention, data access	Levels 2–4
Analytics / BI	Analysis and reporting	Dashboards, models, alerts, prediction, recommendations	Level 4
Digital Work Instructions	Work execution and knowledge	Digital instructions, work standards, checklists	Level 3
Digital Workflows	Coordination of actions	Escalations, approvals, event handling, tasks	Level 3 / 4

MES – the production operations layer

MES, or Manufacturing Execution System, manages production execution in real time. It covers orders, routings, operation recording, batch tracking, product genealogy, execution reporting, traceability and paperless production.

In the ISA-95 architecture, MES usually sits at level 3, between control systems and enterprise planning systems. It combines data from SCADA, PLCs and devices with information from ERP, such as production orders, materials, plans and the product structure.

MES is often treated as the operational backbone of a smart factory, because it organises what actually happens in production: what is being made, on which line, by whom, from which material, in what sequence and with what result.

SCADA and HMI – process visibility

SCADA, or Supervisory Control and Data Acquisition, provides process monitoring and supervision. HMI, or Human-Machine Interface, lets the operator interact with a machine, line or installation.

SCADA and HMI systems deliver information on equipment states, alarms, trends, process values and events. They are an important data source for MES, historians, production monitoring systems and IIoT platforms.

In a smart factory, SCADA remains the process visibility layer, but its data can be shared more widely – with analytics, reporting, quality systems, maintenance and the data platform.

IoT and IIoT platforms – the connectivity layer

An IoT or IIoT platform connects devices, sensors, machines and edge gateways to the higher layers of the system. In industrial environments, protocols such as OPC UA, MQTT and Modbus, as well as machine vendor interfaces, are essential.

IIoT can cover:

• edge gateways;
• sensors and measuring devices;
• data acquisition from PLCs and machines;
• local buffering;
• data stream management;
• transferring data to a data platform, historian or the cloud;
• monitoring of device and connection status.

The IIoT layer should not be built as a collection of random integrations. Its value grows when data has consistent names, units, timestamps, quality status and asset context.

ERP – the business layer

ERP, or Enterprise Resource Planning, is responsible for planning enterprise resources. In the smart factory context it supplies data on orders, materials, purchasing, costs, customers, inventory, finance and planning.

Integrating ERP with MES enables a two-way data exchange: ERP passes on the plan, orders and material data, while MES returns information on execution, consumption, quality, timing and deviations.

Examples of ERP systems used in manufacturing include SAP S/4HANA, Oracle and Microsoft Dynamics. Choosing an ERP is not the subject of this article – what matters is that the business layer should communicate with the operational layer in a controlled and maintainable way.

QMS, CMMS, EAM and APS – specialist layers

QMS, or Quality Management System, supports quality control, SPC (Statistical Process Control), CAPA (Corrective and Preventive Action), non-conformity handling, inspections and compliance requirements. In a smart factory, a QMS can link inspection results to the batch, machine, operator, process parameters and order.

CMMS and EAM support maintenance and asset management. They cover work orders, spare parts, failures, inspections and equipment lifecycle. Data from a CMMS or EAM can be used by predictive maintenance, although the details of predictive models are a separate topic.

APS, or Advanced Planning and Scheduling, handles constraint-based scheduling. It can take into account finite capacity, changeover sequences, material availability, staff and equipment. APS works with both ERP and MES, because a business plan has to be translated into a feasible production schedule.

Smart factory architecture according to ISA-95

ISA-95 helps to organise smart factory architecture by showing the relationship between the physical layer, control, manufacturing operations and enterprise systems.

ISA-95 is a standard for the integration of enterprise and control systems. It defines the exchange of information between production control functions and enterprise functions, and refers to a hierarchical model based on the Purdue Reference Model.

Level	Scope	Example systems	Role in a smart factory
Level 0	Physical process	Product, material, equipment, physical reaction	Source of actual events and states
Level 1	Basic control	Sensors, drives, actuators, PLCs	Measurement and direct control
Level 2	Supervision and control	SCADA, HMI, DCS	Monitoring, alarms, trends, area control
Level 3	Manufacturing operations	MES, MOM, historian, QMS, CMMS	Production execution, quality, maintenance, tracking
Level 4	Business planning	ERP, planning, finance, supply chain	Orders, resources, plan, costs, inventory

In practice, a smart factory requires the levels to be connected, but this does not mean connecting everything to everything directly and without control. Integration should respect the boundary between OT and IT and provide controlled data flows.

ISA-95 also helps to explain why MES is not the same as ERP, and why SCADA does not replace MES. Each layer has a different function, different timing requirements and different users.

Technologies that support a smart factory

A smart factory relies on IIoT, edge computing, cloud computing, analytics, AI/ML, the digital twin, OT cybersecurity and a digital layer of operational knowledge.

IIoT and the digital thread

IIoT, or the Industrial Internet of Things, makes it possible to collect data from industrial equipment, sensors, machines and control systems. Typical components include edge gateways, sensors, OPC UA, MQTT and Modbus communication, and device data management.

The digital thread means continuity of information from design, through production and quality, to service and operation. In practice it allows design, production, material, quality and maintenance data to be linked into a consistent context.

Cloud and edge computing

Cloud computing provides scalability, a SaaS model, multi-site management and central data services. Edge computing processes data closer to the source, reduces latency, enables local responses and allows data to be buffered when connectivity is interrupted.

Many industrial environments use a hybrid architecture: edge handles local processing and operational resilience, while the cloud or a central environment supports reporting, cross-plant analytics and data management.

AI, ML and analytics

AI, or Artificial Intelligence, and ML, or Machine Learning, can support quality prediction, maintenance, recommendations, deviation detection and optimisation. In a smart factory they are a layer that consumes validated and integrated data – not a replacement for MES, SCADA or ERP.

Analytical methods, models and dashboards are described in more detail in the article on manufacturing analytics. Here it is enough to stress that AI in a smart factory depends on the quality, context and availability of source data.

Digital twin

A digital twin is a virtual representation of an asset, process or plant. It can support simulation, scenario analysis, change testing and virtual commissioning.

In a smart factory the digital twin can be a scenario-testing layer, but its architecture and implementation are a separate subject.

OT cybersecurity

OT cybersecurity protects every layer of a smart factory: devices, industrial networks, control systems, integrations, data and applications. It covers network segmentation, access control, threat monitoring, patch management and secure communication between IT and OT.

The ISA/IEC 62443 series defines cybersecurity resilience requirements across the lifecycle of industrial automation and control systems. For a smart factory this means that cybersecurity should be designed together with the architecture, not added once the systems have already been integrated.

The smart factory maturity model

A smart factory maturity model helps to plan software rollout in stages, instead of trying to launch every Industry 4.0 technology at once.

Maturity level	Characteristics	Typical software	Typical outcome
Level 1 – Connected	Machines and systems start to be connected; data is collected from selected sources	SCADA, IIoT gateway, basic historian, connectors	Basic availability of production data
Level 2 – Visible	Data is presented in dashboards and reports; KPIs and operational states are visible	MES, BI, production monitoring, data platform	Current visibility of production and losses
Level 3 – Predictive	Historical and current data support prediction, alerts and risk analysis	Analytics, ML, predictive maintenance, QMS analytics	Earlier detection of deviations and risks
Level 4 – Adaptive	Systems support a closed loop of decisions and optimisation	APS, advanced analytics, workflow, decision support	Faster response and better coordinated action
Level 5 – Autonomous	Selected processes can run with limited human intervention within approved boundaries	Closed-loop control, AI agents, autonomous scheduling	Automation of selected decisions and process corrections

A maturity model is not a “good–bad” assessment. It is a planning tool. A plant may be highly mature in one area, for example production monitoring, and immature in another, such as quality, maintenance or energy management.

Deloitte describes the smart factory as an environment that can evolve as the needs of the organisation, demand, products, technology and ways of working change. In practice this means the roadmap should be based on the current maturity level and business goals, not on a list of technologies to buy.

The smart factory implementation roadmap

A smart factory implementation is best run in stages: assess, pilot, scale and optimise. This model limits risk, confirms value in a selected area and only then extends the scope.

Phase	Scope of work	Typical horizon	Outcome
Phase 1 – Assess	Infrastructure audit, system map, gap analysis, maturity assessment, use case selection	1–2 months	Roadmap, pilot scope, preliminary business case
Phase 2 – Pilot	Pilot on one line, area or process, PoC, testing of data and users	2–4 months	Validated use case and a list of requirements for scaling
Phase 3 – Scale	Roll-out to further lines, standardisation of data, integrations, workflows and reporting	6–18 months	A repeatable implementation model
Phase 4 – Optimize	Continuous improvement, advanced analytics, AI, optimisation and automation	12–36 months	Embedded operational capabilities and further use cases

Phase 1 – Assessment and maturity audit

The first step is to assess the current state. You need to establish which systems are already running, which machines generate data, where the gaps are, how OT/IT integration looks, which reports are produced manually and which decisions rely on incomplete data.

The assessment should cover:

• production and automation systems;
• data sources;
• data quality and completeness;
• the current level of monitoring;
• the degree of integration between MES, ERP, SCADA and maintenance;
• quality and maintenance processes;
• team competencies;
• cybersecurity constraints;
• business priorities.

The outcome should be a roadmap, not just a list of missing systems.

Phase 2 – Pilot deployment

A pilot should cover one well-defined area: a line, a work cell, a quality process, energy monitoring, downtime or event handling. The scope should be small enough to deploy and evaluate within a few months, yet significant enough for the result to matter commercially.

A good pilot needs:

• a business owner;
• a team from IT, OT, production and maintenance;
• a measurable objective;
• defined data sources;
• a limited integration scope;
• cybersecurity rules;
• success criteria;
• a scaling plan for a positive outcome.

For example: instead of “implementing a smart factory”, it is better to start with “automatically monitor downtime and counters on one line, link them to MES orders and provide a dashboard for the shift manager”.

Phase 3 – Scale-out and standardisation

After the pilot, the organisation should decide which elements become the standard: tag naming, the data model, dashboard structure, workflow rules, user roles, the integration approach, edge architecture and security principles.

Scaling without standardisation simply creates more local islands of digitalisation. Every line then has its own dashboards, its own signal names, its own files and its own rules. Before long, maintaining that environment becomes harder than the original problem.

Phase 4 – Continuous optimisation and AI

Only after a stable data layer and operational processes are in place is it worth moving to more advanced applications: prediction, optimisation, recommendations, simulation and automation of selected decisions.

At this stage a smart factory can support process optimization, but process optimisation is a separate area. Smart factory software provides the data, context and mechanisms for action; optimisation methods determine which changes are justified and how to sustain their effect.

The business case: ROI, TCO and KPIs

A business case for a smart factory should combine total cost, expected benefits, implementation risk and measurable KPIs for a specific use case.

In practice it is worth assessing not only the licence but the TCO, or Total Cost of Ownership. TCO covers, among other things:

• licences or subscriptions;
• on-premise, cloud or hybrid infrastructure;
• integrations with machines and systems;
• cybersecurity;
• configuration and deployment;
• training;
• maintenance and support;
• development of further use cases;
• data, storage and network costs;
• vendor or system integrator support.

ROI, or Return on Investment, should be calculated for a specific objective. In some projects an organisation may assume a payback horizon of 12–24 months, but it should confirm that assumption with pilot data rather than treat it as a guarantee.

Typical smart factory KPIs include:

Area	KPI	What it measures
Production	OEE, throughput, cycle time, plan attainment	Visibility of production and losses
Maintenance	Downtime, number of failures, MTTR, MTBF	Asset availability and reliability
Quality	Defects, FPY, complaints, non-conformity handling time	Quality stability and response effectiveness
Energy	Energy consumption per unit of product, deviations, losses	Energy efficiency
Work	Response time, tasks closed on time, user adoption	Effectiveness of workflows and operational work
Data	Completeness, timeliness, number of gaps, data quality status	Reliability of the data layer

A smart factory can support OEE improvement, but the metric itself and its detailed interpretation are covered in the article on OEE. Likewise, energy efficiency requires dedicated management methods, which are developed in the article on the energy management system.

Smart factory implementation challenges

The most common challenges concern legacy system integration, cybersecurity, data quality, team competencies, justifying ROI and change management.

Legacy systems and brownfield

Most plants do not start from greenfield, meaning a new factory designed from scratch. More often they operate in a brownfield model, where machines, controllers, local SCADA systems, files, historical databases and informal processes already exist.

In such an environment a smart factory cannot assume everything will be replaced at once. A better approach is gradual integration, modernisation of selected points and building a common data layer on top of the existing infrastructure.

Cybersecurity and IT/OT convergence

Integrating data from machines, production systems and business applications makes cybersecurity more important. Network segmentation, access control, secure communication, monitoring and change management are all necessary.

This area requires close cooperation between IT and OT. Detailed principles for integration between operational and information technology are covered in the article on OT/IT integration; in this context, what matters is that security should be part of the architecture from the outset.

Data silos

A smart factory loses its value if data stays scattered across systems that share no common model of assets, orders, products, shifts and time. A data platform can aggregate information from MES, SCADA, IoT and ERP, creating the foundation for reporting, analytics and workflows.

This topic is developed further in the article on the Manufacturing Data Platform. Here it is enough to note that without a coherent data layer, a smart factory becomes a collection of screens rather than the operating system of the plant.

Competencies and new ways of working

A smart factory requires new competencies: working with data, understanding digital processes, IT/OT collaboration, exception management, interpreting dashboards and taking ownership of data quality.

The change does not affect operators alone. It also affects production managers, process engineers, maintenance, quality, planning, IT, automation and the management board. The implementation team should be cross-functional from the start.

Smart factory software vendors 2026 – the market landscape

The smart factory software market includes vendors of MES, ERP, automation, IIoT, QMS, APS, data platforms, no-code tools and solutions for manufacturing operations.

Vendor	Area	Deployment model / context
SAP	ERP, digital manufacturing, smart factory and enterprise planning	Enterprise systems, often in multi-site environments
Autodesk	Design and manufacturing software, smart manufacturing software	Design, production, engineering data and collaboration
COPA-DATA zenon	Automation, SCADA, HMI and smart factory software	Monitoring, automation and integration of industrial processes
SmartFactory MOM	Manufacturing Operations Management and MES	Management of manufacturing operations
Applied SmartFactory	Factory automation and smart manufacturing	Automation and production digitalisation projects
Caisoft	Smart factory materials and solutions	Transformation context and manufacturing software
SourceForge	Directory of smart factory software tools	Comparing tool categories and product listings
aSmartFactory / Tulip	Operational applications and building production processes	Applications for frontline teams and workflows
Siemens	Software-defined factory, automation, MES, simulation and industrial software	Production environments and industrial automation
Smart RDM	Industrial data, monitoring, workflows, analytics and operational context	On-premise, cloud or hybrid deployments for industrial organisations

The table is not a ranking. Vendor choice depends on plant architecture, maturity level, industry, existing systems, integration requirements, cybersecurity, budget, deployment model, and whether the organisation starts from data, MES, workflows, quality, maintenance or production monitoring.

Smart factory examples – practical applications

Smart factory examples are best analysed through specific use cases rather than through Industry 4.0 as a slogan.

The World Economic Forum runs the Global Lighthouse Network, a network of smart manufacturing companies and value chains showcasing deployments of advanced manufacturing technologies. In June 2026 the WEF reported 238 sites in that network.

Smart factory use cases include:

Real-time production monitoring

Data from machines, counters and SCADA systems is linked to production orders, shifts and products. The shift manager sees the current state of the line, losses, downtime and deviations without waiting for the end-of-shift report.

This area is related to production visibility and OEE, although detailed production metrics are a separate topic.

Paperless manufacturing

The operator receives a digital instruction, a checklist, order data and execution confirmations in one environment. Paper forms are replaced by controlled digital forms, with field validation, status and change history.

This area connects to Digital Work Instructions, but the details of designing digital instructions are described in a separate article.

Digital workflows for operational events

An event, alarm, non-conformity or KPI deviation can trigger a workflow: owner assignment, a deadline, escalation, a decision, an action and confirmation of effectiveness. Smart factory software does not only show the problem – it helps to guide the organisation through the response.

A dedicated description of this area is available in the article on Digital Workflows.

Predictive maintenance

Data from sensors, failure history and service orders can support the assessment of failure risk or asset degradation. A smart factory provides the data context, while model and deployment methods are covered under Predictive Maintenance.

Energy management

Energy consumption data can be linked to production, operating mode, shift, product and downtime. This allows the organisation to analyse consumption in an operational context, rather than only at invoice or main meter level.

This topic is described in more detail under Energy Management System.

How to start: a practical sequence of steps

The safest starting point is data plus one process that has an owner, a measurable problem and a realistic opportunity for improvement.

A suggested sequence:

1. Choose a pilot area.
2. Identify the operational decision or problem.
3. Check the available data sources.
4. Assess data quality and completeness.
5. Define the minimum integration scope.
6. Choose one user group.
7. Launch a dashboard, workflow or monitored process.
8. Measure the effect.
9. Put data and role standards in order.
10. Only then scale to further lines.

Smart factory software should support operational work, not create a separate world of screens and reports. That is why a good starting point is an area where data leads to action: downtime, quality, energy consumption, order fulfilment, plan deviation or a work instruction.

FAQ

What is smart factory software?

Smart factory software is a set of systems used to digitalise and coordinate a factory. It covers MES, SCADA, IIoT, ERP, QMS, CMMS, APS, a data platform, analytics, workflows and digital work instructions, among others.

Its purpose is to connect data from machines, production systems, people and processes in order to support monitoring, production execution, quality, maintenance, planning and operational decisions.

What is a smart factory?

A smart factory is a manufacturing plant that uses connected machines, data, automation, cyber-physical systems, IIoT and software to monitor and coordinate processes.

It is not simply an automated line. It is an environment in which production data is available to people and systems in a way that enables faster response, analysis and improvement.

How does a smart factory differ from a traditional factory?

A traditional factory may have automation, but its data is often scattered, delayed or used only locally.

A smart factory combines data from machines, production systems, planning, quality and maintenance, so the organisation can monitor processes, respond to events and use data in operational decisions.

What software is used in a smart factory?

The most commonly used categories are MES, SCADA/HMI, ERP, QMS, CMMS, EAM, APS, IoT/IIoT platforms, a data platform, analytics/BI, digital work instructions and digital workflows.

The choice depends on the plant’s maturity level and on whether the problem concerns data, production visibility, quality, maintenance, scheduling, energy or operator work.

What is MES in smart manufacturing?

MES, or Manufacturing Execution System, manages production execution. It handles orders, operations, production tracking, traceability, product genealogy, execution reporting and paperless production.

In the ISA-95 architecture, MES usually sits at level 3 and connects the operational layer with ERP and automation systems.

What is the best MES?

There is no single best MES for every plant.

The choice should depend on the type of production, the industry, integration with ERP and SCADA, traceability requirements, the level of regulation, the deployment model, the number of sites, budget and team competencies. For one organisation full product genealogy will be critical; for another it will be fast production reporting or changeover handling.

Which ERP is best for manufacturing?

There is no single ERP that is best for every manufacturer.

The assessment should consider manufacturing functionality, integration with MES, material planning, finance, locations, industry requirements, the deployment model, partner availability and fit with the existing IT landscape. Examples of ERP systems used in manufacturing include SAP S/4HANA, Oracle and Microsoft Dynamics.

What is the role of IoT in a smart factory?

IoT and IIoT connect devices, sensors, machines and edge gateways to higher-level systems.

Their role is to collect shop-floor data, provide connectivity, pass data to a data platform, historian or analytical systems, and monitor device status.

Which protocols connect smart factory devices?

The most common protocols and technologies are OPC UA, MQTT, Modbus, AMQP, PROFINET, EtherNet/IP and machine vendor interfaces.

OPC UA is often used for interoperable industrial data exchange, and MQTT for lightweight publish/subscribe communication in IIoT and edge architectures.

How do you start a smart factory implementation?

The best starting point is a maturity assessment, a system map and the selection of one measurable use case.

A practical sequence is: assessment, a pilot on one line or area, scaling the approved pattern and continuous optimisation. At the beginning, do not try to implement every technology at once.

How much does smart factory software cost?

The cost depends on scope, the number of lines, the type of systems, the deployment model, integrations, cybersecurity, data, training and maintenance.

You should analyse TCO, the total cost of ownership, rather than the licence alone. In small projects the cost may cover a pilot and one selected line; in enterprise organisations it covers multi-site architecture, standardisation and integration with central systems.

What is a smart factory roadmap?

A smart factory roadmap is a plan for moving from the current maturity level to the target operating model.

It should cover an assessment of the current state, priority use cases, data architecture, integration scope, cybersecurity requirements, the team model, KPIs, the pilot, scaling and subsequent development phases.

Which KPIs measure smart factory success?

Typical KPIs include OEE, downtime, throughput, cycle time, on-time delivery, quality, FPY, response time, energy consumption, the number of closed tasks and data quality.

KPI selection should follow the goal of the implementation. Different metrics will be appropriate for a production monitoring pilot than for maintenance, quality, energy or digital workflows.

Brownfield or greenfield – how do the approaches differ?

Greenfield means designing a new plant or line from scratch. Brownfield means modernising an existing factory in which machines, systems, procedures and constraints are already in place.

Most smart factory implementations take place in a brownfield environment, which is why legacy integration, phasing, cybersecurity and the gradual build-up of a common data layer are so important.

Sources and further reading

1. SAP – materials on the smart factory and connected manufacturing systems.
2. Autodesk – materials on smart manufacturing software and Industry 4.0.
3. International Society of Automation – ISA-95, Enterprise-Control System Integration.
4. International Society of Automation – ISA/IEC 62443, Industrial Automation and Control Systems Security.
5. Deloitte – materials on the smart factory and connected manufacturing.
6. World Economic Forum – Global Lighthouse Network.
7. McKinsey – analyses on connected insights and scaling digital manufacturing operations.
8. Gartner – materials on industrial IoT platforms.