What ROI can be expected from well-conducted industrial digitalization?

Introduction

Industrial digitalization is far more than a fleeting trend: it stands as a concrete lever for competitiveness and modernization in the industrial sector. Behind the concepts of Industry 4.0, companies seek tangible benefits:

• Measurable increase in productivity,

• Significant reduction of unexpected downtimes,

• Improved operational profitability and optimized resource management.

This blog analyzes detailed return on investment (ROI) by technology, relying on recent studies and practical cases, to demonstrate that double-digit gains are accessible with a structured approach to industrial digitalization. You will also discover how data mastery, intelligent automation, and the integration of innovative solutions sustainably transform industrial performance, while addressing economic and environmental challenges.

1. ROI of Industrial Digitalization by Technology

MES / MOM (Manufacturing Execution Systems / Operations Management): Pillar of Industrial Digitalization

MES/MOM systems are one of the major pillars of industrial transformation. They provide complete visibility into every stage of the production process, improve overall equipment efficiency (OEE), reduce unplanned downtimes, ensure refined batch traceability, and optimize planning.

In France, industrial companies that have invested in these information systems consistently record double-digit increases in productivity and quality, as well as enhanced adaptability to unexpected events. Implementing a MES/MOM notably enables refined organization of operational data: identifying constraints is quicker and decisions to optimize profitability are facilitated. Successful integration boosts line ramp-up, aligns actions with strategic directions, and secures every stage of the industrial digitalization process.

Returns on investment are documented and reproducible. According to a MESA/LNS analysis relayed by Critical Manufacturing (👉 Source: Critical Manufacturing – LNS/MESA Study), industrial digitalization via MES/MOM generates:

• +22.5% on the total cost per unit produced, reflecting better resource control and reduced waste;

• +19.4% on net margin, the result of optimized planning and fewer production defects;

• +22% on on-time delivery, due to a more synchronized and reliable supply chain.

Additional frequently observed benefits include double-digit improvement in OEE, enhanced real-time visibility, optimized flows, and increased production capacity. Strengthened traceability simplifies audits and batch management, while instant access to operational data enables anticipation of deviations to prevent incidents and sustainably increase site productivity. (👉 Source: LNS Research)

Compiled studies show up to –30% stock cost reduction and –40% defect reduction thanks to MES. These results stem from finer supply management and anticipation of needs, confirming that industrial digitalization via MES limits overstock, reduces material losses, and improves final quality. (👉 Source: Industry EMEA – ROI MES)

Predictive Maintenance (PdM / IIoT + AI) and Industrial Digitalization

Predictive maintenance ("PdM") is revolutionizing industrial asset management by leveraging IIoT and artificial intelligence. This approach anticipates failures before they impact production, transforming traditional reactive maintenance into a proactive, structured process. Connected sensors collect real-time data on equipment condition (temperature, vibration, pressure, cycles, etc.). These data are analyzed by AI algorithms capable of detecting warning signs, identifying deviations, and recommending targeted interventions. 

Precise tracking of KPIs before and after deployment justifies investment, objectifies ROI, and secures operational ramp-up. Beyond cost savings, predictive maintenance promotes operational reliability, optimizes resource planning, and supports more responsible infrastructure management. This methodology, based on data and transparency, becomes a true lever to maximize availability, reduce breakdowns, and strengthen industrial competitiveness over the long term.

Analyses conducted by McKinsey and relayed by IIoT-World confirm the power of predictive maintenance in the realm of industrial digitalization. Thanks to smart sensor integration and advanced data analysis, it is now possible to anticipate machine failures well before they occur, thus avoiding production losses and costly stoppages.

• –18 to –25% reduction in maintenance costs
  
  Industrials investing in predictive maintenance observe a significant reduction in expenses related to equipment upkeep and replacement, while extending the lifespan of their assets. This resource optimization allows budgets to be reallocated to innovation or capacity ramp-up.

• Up to 50% reduction in unplanned downtimes
  
  The impact is direct on line availability: the ability to predict failures prevents major production interruptions, ensuring better operational reliability and optimal yield throughout the chain. This level of control strengthens the competitiveness of industrial sites. (👉 Source: IIoT-World – Predictive Maintenance ROI)
  
  

Scientific literature, particularly via NCBI/PMC (2023), highlights the qualitative benefits of predictive maintenance. By optimizing intervention planning and real-time monitoring, PdM anticipates breakdowns and ensures continuity of industrial operations, while enhancing safety and overall cost control. (👉 Source: NCBI – Predictive Maintenance Review)

IoT Analytics analysis reveals a striking figure for industrials: the median cost of downtime exceeding $100,000/hour for critical assets. This amount fully justifies investing in an industrial digitalization strategy that incorporates PdM. The quick ROI results from preventing major incidents and the ability to keep production lines continuously operational. (👉 Source: IoT Analytics – Downtime Cost)

Digital Twins

Digital twins are disrupting how industrials approach daily management and optimization of their plants and supply chains. By creating a faithful virtual replica of the environment and/or physical dynamics of the plant, they allow experimentation, simulation, and adjustment of every scenario without impacting actual production. This ability to experiment accelerates decision-making, reduces risks linked to field trials, and lowers costs associated with equipment immobilization. Thanks to integration (real-time or near real-time) of data from the field, it becomes possible to anticipate deviations, test improvement strategies, and validate the impact of every change before deploying it on site. Digital twins also enable quick identification of bottlenecks, optimized resource allocation, and precise evaluation of process efficiency. This structured approach transforms investment management: executives gain clear visibility into potential productivity gains, capex/opex cost reductions, and carbon footprint optimization. Predictive analysis provided by these tools supports budget planning, proactive asset management, and compliance with sustainability requirements.

• +20 to 30% capex/opex efficiency gain observed by companies integrating digital twins into their industrial digitalization strategy, thanks to real-time simulation and optimization of operations (👉 McKinsey - Digital Twins create value from connected data).

• Reduction of unexpected events and better deadline control, allowing greater responsiveness to market uncertainties and faster ramp-up.

• Optimized resource allocation and risk anticipation, facilitating project team coordination and rapid integration of new technologies.

• Qualitative improvement of industrial efficiency, instant decision-making, and reduced uncertainty through modeling of complex scenarios and advanced flow management (👉McKinsey - The digital twin driving RoI).

Feedback from the transport, aerospace, and space sectors demonstrates better risk management, agility in operations, and significant reduction in time-to-market. In summary, digital twins position themselves as catalysts for industrial transformation, enabling optimization of profitability, accelerated innovation, and alignment of industrial operations with the environmental, societal, and economic ambitions of each company. Their structured adoption becomes a major asset for driving performance, securing investments, and ensuring industrial site sustainability in a constantly evolving environment.

IIoT and Industrial Connectivity

The industrialization of connected objects (IIoT) is deeply transforming the industrial value chain by connecting machines, operators, sensors, and information systems into an integrated digital ecosystem. This connected infrastructure enables continuous data collection and analysis, advanced process supervision, and real-time centralized plant control. Thanks to automatic reporting of field data (temperature, vibration, energy consumption, machine status), decision-makers have a comprehensive and precise overview, facilitating quick, informed decisions. Operationally, industrial connectivity accelerates anomaly detection, allows dynamic flow adjustment, optimizes stock management, and enables remote maintenance. Targeted interventions become possible, limiting production interruptions and significantly reducing unplanned downtimes. IIoT thus brings new agility to teams: they can anticipate risks, act before breakdowns, and ensure reliability of critical equipment. This digitalization of industrial operations comes with enhanced traceability, automatic documentation, and instant reporting, supporting regulatory compliance and product quality.

• 92% of industrial organizations that invested in IIoT report a positive ROI (👉 RTInsights – IoT Adoption ROI).

• 68% of companies report a significant progression in their IoT deployments over the past twelve months (👉 Viasat – State of IoT 2024).

This is explained by the ability to automate alerts, secure critical processes, reduce human errors, and harmonize communication between different plant systems. Intelligent flow automation and data synchronization optimize resource allocation, accelerate decision-making, and improve overall productivity. This modernization of industrial processes goes beyond automation: it facilitates the integration of new technologies such as collaborative robotics, embedded AI, and cloud platforms, which enrich the factory's digital ecosystem, streamline flows, and strengthen data security.

In France, industrials benefit from an environment conducive to connectivity, with high-performance networks and scalable solutions suitable for all types of sites. This connectivity facilitates integration of new sensors, IoT gateways, and cloud platforms while maintaining continuity of existing operations. Companies can therefore deploy advanced supervision tools, control the production chain in real time, and react instantly to unexpected events. IIoT is not reserved for large groups: industrial SMEs now have modular and scalable solutions tailored to their specific needs. These tools help improve competitiveness, optimize energy efficiency, support ramp-up, and accompany the transition to a leaner, more flexible, and resilient industry. IIoT adoption constitutes a genuine lever for transformation to secure investments, anticipate market changes, and position French industry at the forefront of industrial digitalization.

2. Correlations by Sector and Plant Size

Analysis of correlations between industrial digitalization, sector of activity, and plant size reveals nuanced trends.

No universal ROI model prevails: the effectiveness of a technology depends primarily on its adaptation to business context.

For example, in automotive, aerospace, or rail, implementing a MES/MOM designed for the sector’s specific constraints delivers significantly better results than a generic approach. Large plants benefit from economies of scale in data collection, pooling of IT resources, and process standardization, which accelerates ROI. Conversely, mid-sized sites or industrial scale-ups favor modular and scalable solutions that can quickly adapt to growth and diverse flows. Technologies such as predictive maintenance and digital twins demonstrate their value mainly in environments with high asset criticality or continuous production, where each unplanned stop has substantial impact on operational results.

Sectoral studies show that digitalization profitability relies on alignment between chosen solutions, the site's digital maturity, and strategic objectives set by industrial management. In summary, the success of industrial digital transformation is not measured by plant size but by the relevance of technological integration and the ability to drive results via adapted performance indicators.

• LNS highlights that MES effectiveness strongly depends on its sector alignment: a MES designed to precisely meet the needs of a given industrial sector (automotive, aerospace, rail, pharma, etc.) delivers far greater results than a standard solution.
  
  

• Process specifics, flows, and regulatory constraints demand fine-tuned technology adaptation to maximize ROI. Furthermore, the literature does not offer a unique ROI curve by plant size; returns are directly correlated to digital maturity, operational complexity, and field team engagement.
  
  

• IoT Analytics reports a positive ROI in 92% of industrial digitalization use cases across all sectors. This statistic illustrates the relevance of IIoT and industrial connectivity for improving productivity and operational reliability, regardless of site size or type. However, no fine segmentation by plant size is published, confirming that success mainly depends on matching the deployed solution to specific business challenges.
  
  

• Regarding predictive maintenance (PdM), scientific literature indicates a reduction of –18 to –25% in maintenance costs and up to –50% unplanned downtimes depending on sector and level of digitalization. This shows the direct impact on reliability and profitability of industrial operations.
  
  

• Digital twins generate +20 to +30% capex/opex efficiency gains for companies integrating them into their industrial digitalization strategy, through simulation and operational optimization. (👉 Source: LNS Research | RTInsights – IoT ROI | NCBI – Predictive Maintenance Review | McKinsey – Digital Twins)
  
  
  

Conclusion

Adopting industrial digitalization means choosing a calculated transformation, driven by data and focused on measurable performance. Experience proves it: each technology — MES/MOM, predictive maintenance, digital twins, IIoT — delivers a concrete competitive advantage when selected and integrated coherently with industrial strategy.

The key lies in alignment between business needs, site digital maturity, and the ability to precisely measure impact via relevant performance indicators.

To digitalize is not just to automate; it is to give teams the means to anticipate, optimize, and manage their industrial tools in real time, while securing investments and controlling risks.

The observed gains — cost reduction, increased productivity, improved quality and reliability — are not reserved for a specific company type or sector: they are accessible to any organization ready to structure its approach and rely on proven solutions. Successful industrial digitalization is based on transparency, traceability, responsible resource management, and strong commitment to results. This approach enables sustained operational transformation, accelerated growth, and agile response to industrial economic, technological, and environmental challenges.

The figures confirm that industrial digitalization, when targeted and well managed, offers tangible ROI, as illustrated by concrete statistics:

• MES/MOM: double-digit gains in costs, margins, and quality. For example, +22.5% on total unit cost, +19.4% on net margin, and +22% on on-time delivery.

• PdM: –18 to –25% reduction in maintenance costs and up to –50% unplanned downtime, directly impacting availability and operational reliability.

• Digital Twins: +20 to +30% capex/opex efficiency thanks to simulation and operational optimization, enabling better investment and resource control.

• IIoT: 92% of industrial organizations investing in IIoT report positive ROI, and 68% register significant progress in IoT deployments over the past twelve months.

Success relies on two main factors: relevance of the technology choice considering the plant’s digital maturity and implementation of before/after tracking KPIs to objectify results and secure investment.