Key takeaways
Manufacturing configuration management
Digital manufacturing optimization
Reliable data validation
Why manufacturing needs to become more adaptable
The global market has become increasingly competitive, causing manufacturing and production networks to develop at an unprecedented pace, altering quality and productivity. The demand for the ability to deliver customized products quickly and effectively, while also combining human centricity, sustainability, and resilience explains the need to combine smart manufacturing with reconfigurable manufacturing (Li et al. (a) 2026).
Reconfigurable Manufacturing: Adapting production systems for changing requirements
Reconfigurable Manufacturing (RM) focuses on designing manufacturing systems that can be adjusted quickly in response to changes in product requirements, production volumes, or market conditions. Instead of relying on a fixed production line designed for a single product or output level, RM uses modular and adaptable resources that can be rearranged, added, removed, or modified when new requirements arise.
A key principle of RM is that manufacturing systems should be designed with the capacity to change their functionality and production capacity when necessary.
This approach can support greater flexibility, scalability, and cost efficiency. By reusing existing resources and modifying only the necessary parts of a production system, manufacturers can reduce the need for extensive redesigns or entirely new equipment. RM can also help businesses respond to unpredictable market conditions, making it particularly relevant in industries where customer requirements and product specifications change frequently.
However, reconfiguration can be technically complex. Changes to equipment, control systems, and production processes may require considerable engineering work, programming, testing, and validation. Nevertheless, the ability to reconfigure a manufacturing system does not eliminate the need for considerable engineering work, programming, testing and validation (Li et al. (a) 2026).
Smart manufacturing: Using digital intelligence to improve production
Smart Manufacturing (SM) uses connected digital technologies, automation, and data-driven decision-making to improve the monitoring, coordination, and control of manufacturing operations. It enables manufacturers to collect and analyze production data, identify problems, predict potential disruptions, and make better-informed operational decisions (Li et al. (a) 2026).
Technologies such as artificial intelligence (AI), the Industrial Internet of Things (IIoT), big-data analytics, cloud and edge computing, and digital twins play important roles in this approach. Sensors installed on machines can collect information about equipment condition, production output, and process performance. This information can then be analyzed to identify inefficiencies, detect deviations from expected performance, and support decisions concerning maintenance, scheduling, and quality control. Smart Manufacturing therefore improves visibility across manufacturing operations and can help companies respond to problems more quickly. The effectiveness of SM also depends on reliable data, compatible technologies, appropriate analytical models, cybersecurity, and sufficient human oversight (Shao et al. 2024).
Combining Reconfigurable Manufacturing (RM) and Smart Manufacturing (SM)
Combining RM and SM means integrating modular hardware and software with AI, automation, data analytics, and cloud computing to optimize processes in real time. This combination is called Smart Reconfigurable Manufacturing (SRM).
SRM’s core capability is making dynamic, precise changes to manufacturing resources, systems, and processes via these technologies. Smart manufacturing addresses this by using AI, IoT, big-data analytics, digital twins, and cloud systems to connect and integrate systems and enable dynamic reconfiguration decisions (Li et al. (a) 2026).
This approach also relates to Industry 5.0, which emphasizes human-centricity, sustainability, and resilience alongside productivity. SRM can help manufacturers use resources better, respond to disruptions, and keep humans involved in key operational decisions. These outcomes depend on design and implementation, not on the technologies alone.
Smart reconfigurability effectively bridges the gap in addressing the challenges posed by modern manufacturing by complementing the strengths of both smartness and reconfigurability.
Key characteristics of Smart Reconfigurable Manufacturing
SRM combines traits from both approaches to create a cost effective, collaborative, proactive solution for market changes, quality assurance, and maintenance. Key characteristics include (Li et al. (a) 2026; Shao et al. 2024):
Characteristics
Description
Modularity
Modularity means manufacturing systems consist of functional modules that can be added, removed, replaced, or reorganized to meet new requirements.
Scalability
Scalability lets production capacity increase or decrease with demand, enabling manufacturers to match resources to production needs.
Networkability
Networkability means machines, controllers, and information systems communicate and exchange data to coordinate operations.
Digitalisation
Digitalisation collects and analyzes production data to improve visibility, support decisions, and identify reconfiguration opportunities.
Interfacing
Compatible interfaces and communication standards connect and coordinate hardware and software components.
Intelligent systems
Intelligent systems detect deviations, suggest corrective measures, and, when allowed by design and safety, adjust operations or configurations.
Interdependence
These features are interdependent: modularity provides the physical means to alter a system, while connectivity and digital intelligence determine when and how to change it.
Autonomy factors
Autonomy depends on system design, data reliability, and the level of validation and human supervision required.
Role of data-driven systems, automation, and digital technologies
The value of SRM depends on connecting manufacturing data to the ability to change production operations. Digital technologies help manufacturers see what’s happening on the floor, anticipate likely outcomes, and evaluate responses. But data alone isn’t enough, it must be reliable, relevant to the decision, and connected to equipment and control systems that can implement the chosen changes.
Digital technologies let manufacturers link production performance data to decisions about resource use; their value lies not only in monitoring operations but also in enabling timely, informed changes (Li et al. (a) 2026).
Sensors in the Industrial Internet of Things (IIoT) gather data on equipment condition, output, and process performance. This data lets operators detect deviations and decide whether maintenance or production changes are needed (Shao et al. 2024).
AI and data analytics examine operational data to find patterns, predict problems, and support scheduling, maintenance, and capacity decisions. Their effectiveness depends on data quality, appropriate models, and human supervision (Li et al. (a) 2026).
Digital twins allow simulation and assessment of alternative configurations before production changes. Their usefulness depends on reliable data, interoperability, and validation (Shao et al. 2024).
Automation and integrated control systems coordinate approved changes across equipment, software, and processes, while cloud and edge computing support data exchange and processing to meet operational and security needs (Shao et al. 2024).
Together, these technologies enable a more informed reconfiguration process: data signals the need for change, analytics assess options, and reconfigurable equipment implements the solution (Shao et al. 2024).
Benefits of smart reconfigurable manufacturing beyond efficiency
Combining RM and SM offers benefits beyond boosting productivity or automating tasks. By pairing adaptable production resources with data-driven decisions, SRM helps manufacturers respond to market changes, manage resources, maintain quality, and recover from disruptions. The degree of these benefits depends on implementation cost, technical compatibility, system complexity, and how often production requirements change (Li et al. (a) 2026).
Digital technologies let manufacturers link production performance data to decisions about resource use; their value lies not only in monitoring operations but also in enabling timely, informed changes (Li et al. (a) 2026).
Sensors in the Industrial Internet of Things (IIoT) gather data on equipment condition, output, and process performance. This data lets operators detect deviations and decide whether maintenance or production changes are needed (Shao et al. 2024).
AI and data analytics examine operational data to find patterns, predict problems, and support scheduling, maintenance, and capacity decisions. Their effectiveness depends on data quality, appropriate models, and human supervision (Li et al. (a) 2026).
Digital twins allow simulation and assessment of alternative configurations before production changes. Their usefulness depends on reliable data, interoperability, and validation (Shao et al. 2024).
Automation and integrated control systems coordinate approved changes across equipment, software, and processes, while cloud and edge computing support data exchange and processing to meet operational and security needs (Shao et al. 2024).
Together, these technologies enable a more informed reconfiguration process: data signals the need for change, analytics assess options, and reconfigurable equipment implements the solution (Shao et al. 2024).
Faster responses to changing customer and market conditions
Modular equipment, together with data driven decision making, can enable manufacturers to deal with varying production volumes, new product versions, and changing customer needs.
The primary benefit lies in the ability to alter certain parts of the production system rather than having to redesign the whole line.
Actual response times will depend on the complexity of the change, the engineering requirements, and the validation procedures (Li et al. (a) 2026).
Improved cost efficiency
Reconfiguration could enable manufacturers to adjust their existing resources instead of having to buy completely new equipment.
It is also possible for data driven monitoring to assist with maintenance planning and to help identify production bottlenecks.
Yet the business case will depend on the implementation costs, the equipment’s compatibility, its level of utilization, and the frequency with which requirements change (Li et al. (a) 2026).
Greater operational resilience
Manufacturing systems are affected by both external disturbances, such as material shortages, and internal difficulties, such as equipment failures.
The use of connected monitoring and diagnostic features can help detect emerging problems, and reconfigurable resources can offer alternative production methods.
Such capabilities can expand the range of recovery options, but resilience also depends on the availability of resources, coordination within the supply chain, and having contingency plans (Li et al. (a) 2026).
Challenges manufacturers need to consider
To integrate RM and SM it is necessary to do more than simply introduce new digital tools, manufacturers have to decide whether the new equipment and software can communicate with existing systems, whether the operational data is reliable, and whether the proposed changes satisfy safety and quality requirements. All factors such as existing equipment, cybersecurity risks, the cost of investment, and the lack of skilled personnel can make implementation difficult. Furthermore, digital twins and AI based recommendations also need to be properly validated before they are used to guide important production decisions(Shao et al. 2024; Li et al. (a) 2026).
Conclusion towards more adaptable manufacturing
To achieve manufacturing adaptability, manufacturers need both the ability to change production systems and the intelligence to decide when and how to change them. Reconfigurable manufacturing provides physical and structural capability. Smart manufacturing provides connectivity, data analysis, and decision support. Combined, these approaches enable production systems to respond to changing requirements, use existing resources more effectively, and strengthen operational resilience. The goal is not only greater automation or efficiency but systems that adapt to business needs, supported by reliable data, interoperable technologies, and human decision-making(Shao et al. 2024; Li et al. (a) 2026).
