Industry
Manufacturing
Future-Ready Manufacturing: Partnering in your journey to a sustainable manufacturing enterprise
Highlights
- A well-defined, controlled, and secured IT/OT integration is critical to reap the benefits of Software defined Factories (SDF).
- Emerging technologies, including software-defined networking, storage, computing, data centers, and pervasive broadband networks, are essential for envisioning the future of manufacturing.
- By harnessing the power of automation, analytics, robotics and AI/ ML algorithms, manufacturers can optimize operations across the entire value chain.
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Traditional Manufacturing
In traditional manufacturing, production relies on fixed, large-scale machinery and manual labor, making it difficult to adapt quickly to shifts in demand or efficiently introduce new products.
Manufacturing is constrained by rigid, hardware-driven processes that are inflexible and overly complex, which hinders productivity, time to market and requires excessive human involvement at every step of the manufacturing process. As a result, these closed, proprietary technology systems are increasingly unable to meet the changing demands of today's modern shop floor.
In the Industry 4.0 era, the introduction of digitization and automation has streamlined tedious, repetitive, and labor-intensive tasks, paving the way for the development of smart manufacturing systems. Now Industry 5.0 calls for human and machine collaboration in a sustainable way. The emergence of digital technologies like AI/GenAI, IOT and IIOT, has accelerated the evolution of sustainable manufacturing. And we are witnessing traditional control systems like Programmable Logic Controllers (PLC), Distributed Control Systems (DCS), and Human-Machine Interfaces (HMI), which ’control’ various processes that occur on the shopfloor, are now being connected to the internet or larger IOT platforms. This integration allows operational technology (OT) to merge with information technology (IT), enabling better control and coordination of manufacturing processes and supply chain activities. This shift is known as the 'Software-Defined’ approach to manufacturing.
Technological advancements from Industry Revolution 4.0 to 5.0
A Software Defined Factory (SDF) envisions a software layer that oversees machines, processes, workflows, assets across the entire plant, enabling the execution of manufacturing processes with a single click. This centralized system acts as an intelligent, configurable hub for routing, scheduling, and controlling operations, allowing the factory to adapt to dynamic demands. In collaborative SDF ecosystems, manufacturers can boost productivity by 30 to 50%.
The Software-Defined Factory (SDF) approach is gaining widespread adoption because software is easier to modify and maintain than hardware. In this software-centric production environment, control of machines and equipment increasingly shifts from hardware to software platforms. SDF represents a potential alternative to hardware-defined manufacturing, creating a division between the software and physical layers of the manufacturing ecosystem. Its goal is to establish generic, reconfigurable hardware systems that are open and easily adaptable through a software layer. Typically, SDF is characterized by basic building blocks of Industry 5.0. Overall market size for Industry 5.0 is anticipated to reach around USD 820 billion, with an expected CAGR of over 30% from here on till 2033. Generic hardware in an SDF does not rely on vendor-specific software for configuration. Through the software control layer, these devices can be directly programmed as needed, bypassing the limitations of proprietary software that allows only limited configurability.
The Software-Defined Factory (SDF) maximizes the potential of digitization by separating physical production hardware from its control software. This decoupling enables manufacturers to quickly respond to market demands and implement design or production changes through software updates instead of lengthy hardware modifications.
With advancements in software-defined networking, standard protocols, open-source solutions, and software-defined storage, low-cost edge devices now allow OEMs to adopt the SDF approach. The flexible framework of the Software-Defined Factory enhances resource sharing and collaboration, positioning it to transform the traditional hardware-centric manufacturing sector.
Core Components of SDF
To make SDF a reality, manufacturers must upgrade their traditional bespoke systems and decouple tightly integrated software and hardware systems.
SDF can be viewed as a scalable, collaborative, and integrated platform that supports multiple functions while leveraging automation and emerging technologies. In this context, efficiency, scalability, and adaptability are essential elements to drive success.
Schematic Structure of a Software-Defined Factory
Hyper connectedness with IT/OT convergence:
A well-defined, controlled, and secured IT/OT integration is critical to reap the benefits of SDF. OT, traditionally associated with the network of manufacturing and industrial control systems, may have vendor specific connectivity to other similar machines and systems but are not connected to the broader enterprise for analysis and control. This integration of OT and IT with centralized data and communication is the first step towards SDF. This allows companies to streamline and centrally manage their IT landscape, reducing administrative burdens while enhancing the flexibility and responsiveness of production systems. It breaks down data silos and decouples interconnected control units. In some cases, physical legacy devices must converge or be retrofitted with newer hardware to integrate IT into traditional operational technology (OT).
IT/OT convergence unifies traditionally separate components like hardware, servers, storage devices, networking, and management tools into a cohesive, centrally managed system. This allows for seamless data sharing, communication across heterogeneous devices, and the creation of integrated insight and control systems within a single, uniform environment.
Democratizing innovation with modular systems:
SDF emphasizes modularity, enabling manufacturers to efficiently adapt production processes and equipment through software adjustments. It shifts focus from traditional linear manufacturing processes to flexible, efficient, and resilient eco-systems by utilizing reusable, reconfigurable modules that enhance production capacity based on business needs. When companies automate production with software, it sets an entire chain in motion, making production more transparent, efficient, and cost-effective. This leads to faster development and the introduction of new products. This also empowers users to play an active role in shaping and improving products. Ultimately, this will drive the “democratization of innovation”, becoming the cornerstone of software-defined manufacturing.
Cloud Computing
Emerging technologies, including software-defined networking, storage, computing, data centers, and pervasive broadband networks, are essential for envisioning the future of manufacturing.
The existing infrastructure requires upgrades to support modern communication and security protocols. The software control layer should be able to connect with any hardware device along with managing, controlling, and computing the data with which it is interfacing. It needs to leverage open source and standard protocols for communication.
For example, users can provide their requirements and schedule a job via cloud-based applications. The software layer can then handle routing and scheduling of the jobs based on priority. Since it has access to the factory global network, it can remotely control Software-Defined Networking (SDN) switches and other virtual entities, perform necessary configurations in an efficient and timely manner while optimizing resources. The software then monitors the data transfer from edge to cloud and stores the data based on defined strategy. The centralized cloud infra, shared across multiple plant sites, units, and production lines, can connect with physical assets via a multi-layered, firewall protected communication system to execute the job. Once the job has been executed, its progress is sent back to the software layer, providing real-time visibility to the user. Going ahead, SDF can even capture business needs automatically and suggest users implement adjustments in workflow to finetune on ground process to align with business need. This calls for self-learning and self-adjusting systems which is an important proponent of future software defined factory.
Manufacturing functions and related software solutions have been working in silos over the years. This has resulted in more compartmentalized systems which are difficult to navigate. Hence in software defined factories we break the silo boundaries by streamlining data sources into a single pane to ensure the right information flow to right stakeholders for adjusting workflow ahead of time for optimum outcomes.
Real Time Analytics
Implement real-time analytics to collect, analyze and process data from sensors and machines delivering rapid actionable insights.
The addition of edge computing capabilities to the devices enables real-time data processing closer to the source. Instead of transmitting data to a network to a centralized location for processing, IoT devices can analyze time-sensitive manufacturing data on-site, delivering rapid insights for immediate monitoring of production conditions before it becomes obsolete.
Automation
By harnessing the power of automation, analytics, robotics and AI/ ML algorithms, manufacturers can optimize operations across the entire value chain.
Virtual Factory Digital Twins can be built to detect potential issues early, even before any hardware has been produced. The software layer, through a closed loop feedback system, can visualize, sense/monitor, predict and optimize operational efficiency, reduce downtime, improve quality, enhance resource utilization, and boost productivity. For example, our customer, a leading global CPG giant, is developing autonomous manufacturing plants to enable real-time decision-making and adapt production to changing customer demands, an ecosystem where humans and machines coexist harmoniously.
Challenges
A clear roadmap outlining the steps for transitioning to an SDF is essential.
This roadmap should address the multiple challenges that could occur in the transformation journey like modernization or upgradation of on ground OT devices. Interconnecting the shopfloor, from the sensor to the cloud is another key step to achieve. Old network devices and control systems in the plants may hinder digitization initiatives due to high latency and lead to incompatibility with modern technologies, and increased vulnerability to security flaws. Merging legacy systems with new SDF technologies can be a complex process and may require significant investment in both time and resources.
The second aspect is to bring homogeneity to different networks and protocols spread across a manufacturing ecosystem. With a variety of hardware devices, machines, equipment on the shop floor along with proprietary software, there could be integration complexities to resolve. For SDF to be effective, all machines must communicate through standardized protocols. An open network protocol is needed for machine-to-machine communication that facilitates the transmission of telemetry data between devices and the cloud. Standardizing devices, utilizing open-source software and standardized protocols with wired and wireless technologies can reduce operational complexity and enhance interoperability for OEMs. In today’s world of hyper connectedness, security of data and cyber systems has become one of the most prominent aspects of any transformation journey. The need to safeguard IT and OT systems from any vulnerabilities and monitoring it to avoid any downtime is of critical importance. Increased connectivity opens systems to potential cyber threats. Hence, manufacturers must prioritize cybersecurity measures to protect sensitive data and operations.
Collaboration between 5 M’s of Manufacturing (Man, Machine, Material, Methods, and Money) is critical to implement software defined factories. This collaboration enables the efficient production of highly customized, personalized products by combining human creativity with data-driven insights from machines. Automating tasks, integrating intelligent workflows, and executing efficient operations should be seen as augmenting human operators on the shop floor, not replacing them. The goal of automation and robotics is to eliminate low value activities. This may necessitate upskilling or reskilling the workforce to ensure efficient collaboration with emerging technologies.
SDF Implementation
The shift from hardware defined to software defined approach poses unique challenges, from technological to workforce adaptation to ethical considerations and meeting sustainability goals.
Along with process change management there needs to be a change in mindset. Manufacturers need to move away from the perception of automation replacing humans, and instead see this technology as a tool that can be used to augment the frontline workforce. For factories to meet dynamic demands for new products or product variants, in existing operating environments without major re-tooling, needs a fresh systematic approach.
Strategy and governance are essential foundational elements at the beginning of the transformation journey. Conducting maturity assessments of the current landscape can be a good way to identify the gaps to achieve SDF for future factories.
We must also outline the roadmap for the various planned milestones to identify areas for improvement, focusing on immediate, medium, and long-term initiatives needed to realize the vision of implementing SDF.
This may involve onboarding various initiatives related to automation, data, and cloud strategies, as well as redefining on-ground processes.
To accelerate the SDF journey, it is important to adopt rapid application factory kind of concept to leverage existing data and related accelerators in overall value stream. We must understand how new technologies like GenAI or Cobots or Co-pilot can be used to improve operational efficiency, and decision-making capabilities by learning from historical data and adapting to changing conditions.
This fusion of technology inevitably transforms processes and work methods for on-ground staff, requiring them to adopt new skills. However, this can be effectively managed through organizational change management initiatives that support process improvements and personnel transformation.
Conclusion
The future of Software Defined Factory is promising.
As industries continue to evolve, SDF will play a crucial role in shaping more resilient and adaptive manufacturing environments. It will foster collaboration across manufacturing units and production sites and improve the throughput and efficiency. SDF, the factory of the future will accelerate OEMs to response time to the changing demands almost in real time with reduced cost and maximized eco-efficiency to deliver on the net-zero targets and sustainability goals.
