Industry
Manufacturing
Future-Ready Manufacturing: Partnering in your journey to a sustainable manufacturing enterprise
Highlights
- Efficiently managing internal operations is key for manufacturers to drive profitability, as it offers more control than external factors.
- Transparent and efficient operations can help improve critical production KPIs, optimize shop floor planning and scheduling activities, and improve the return on assets by increasing capacity utilization and eliminating bottlenecks and redundant processes.
- Manufacturers must convert informational data into intelligence and operationalize insights by adopting a holistic approach to data, including collecting data, establishing data governance and policies, and building uniform data structures.
- Digital technologies enable consistent production quality, responsiveness to customer demands, and increased production capacities. They also help align business goals, accelerate product marketing, meet regulatory compliance requirements, and improve ESG performance.
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The complexities of the manufacturing value chain
Geopolitical tensions and trade sanctions disrupt industrial manufacturing, causing raw material delays, shutdowns, and inventory bottlenecks.
Manufacturers must effectively manage the complexities of their value chains to mitigate disruptions and protect profit margins. This demands seamless integration of various critical systems such as lean manufacturing, demand planning, and product life cycle. For these systems to operate smoothly across the value chain, manufacturers require intelligent operations powered by advanced technologies, streamlined processes, and robust infrastructure. By adopting intelligent operations, manufacturers can enhance efficiencies, optimize processes, and ultimately improve key performance indicators (KPIs), thereby elevating customer and employee experiences while boosting sales and profitability.
The need for transforming manufacturing operations
For industrial manufacturers, operations form the key internal value-add or transformation process that impacts all major business KPIs.
The industrial value chain embodies several external and internal stakeholders, complexities, challenges, and dependencies. Efficiently managing internal operations plays a crucial role in positively impacting the bottom-line margins as manufacturers have more control over internal conditions than volatile external factors.
Evolving megatrends such as sustainable business outcomes, skilled workforce shortages, consumerization of manufacturing, rising demand for product customizations, and emerging business models like equipment-as-a-service (EaaS) require a standard lever that organizations can use to keep pace in a dynamic business environment. For example, customer satisfaction in product manufacturing depends on quality, cost, and timely delivery.
Transparent and efficient operations can improve critical production KPIs, optimize shop floor planning and scheduling activities, and enhance the return on assets by increasing capacity utilization and eliminating bottlenecks and redundant processes.
Figure 1 presents the top operational challenges that manufacturers can promptly address with an intelligent operations approach:
Key operational challenges
Key operational challenges
Manufacturers face operational challenges such as processes operating in isolation, small production batches, supply-demand volatility, inefficient processes, tightening operational ESG requirements, digital disruption, and data explosion. When enabled by digital technologies, intelligent operations would provide a much-needed buffer against market volatility, rising material and labor costs, and price reduction pressures. This would enable manufacturers to respond to market demands with innovative technology-embedded products, allowing them to differentiate in a cost-competitive and efficient manner.
The approach to adopting intelligent operations
Given the complexity of industrial manufacturing, a strong foundation is essential for intelligent operations.
Transforming traditional manufacturing architecture requires a top-down approach, from governance and business planning to operations management and process control. This shift demands a balanced and holistic approach of digital, automated, and smart manufacturing architecture, which would enable industrial manufacturers to realize their vision of intelligent factory operations.
Intelligent operations need an amalgamation of strategic enablers—flexible and integrated processes and systems—along with technology enablers, such as AI, digital thread, cybersecurity, and more, built on core building blocks of IIoT, analytics, and automation (see Figure 2).
Enabling intelligent operations
Enabling intelligent operations
Manufacturers can be more agile and responsive to changing business requirements with data-driven intelligent operations. Digitally enabled integrations allow connected systems to share data seamlessly, enhancing operational effectiveness, such as unbalanced load detections and automated fault predictions.
Implementing intelligent operations: How can manufacturers begin?
Plant managers deal with multiple priorities, including efficient shop floor operations, optimizing processes, reducing opex costs, and managing external factors.
Considering this, manufacturers must start by transforming informational data into intelligence and operationalizing insights. Adopting a holistic approach to data – including collecting data, establishing data governance and policies, and creating uniform data structures – is key to implementing intelligent operations.
This would enable exploratory manufacturers to move from fragmented monitoring strategies with disjointed technologies – that result in low visibility across systems – to a first intermediate stage where organizations have well-defined data structures and use data marts or data lakes to automate data collection. Data can be used to not only identify production line faults and quality non-compliance but also create KPI dashboards to monitor critical parameters at all levels and take necessary actions to improve them.
Figure 3 shows a glimpse of what all industrial manufacturers can do with data-led intelligent operations.
Leveraging data for intelligent operations
Leveraging data for intelligent operations
In the second intermediate stage of digital maturity, organizations can apply analytics on captured data to generate insights. AI models can predict faults, downtime, and quality issues before systems fail, improving production efficiency, reducing waste, and maintaining product quality. These insights help plant managers take necessary actions to improve their KPIs through automation capabilities.
In the final stage of digital maturity, manufacturers should have autonomous and self-healing systems that require minimal human interference. Advanced machine learning algorithms and digital twins will not only predict failures but also self-adjust operating parameters to balance and optimize performance, ensuring maximum efficiency with minimal breakdowns and quality issues.
The figure below shows the various stages industrial manufacturers must navigate through to implement truly intelligent operations.
Digital maturity stages
Digital maturity stages
A deep dive into the role of technologies in intelligent operations
Digital technologies ensure consistent production quality, responsiveness to customer demands, and increased production capacities.
Digital technologies (see Figure 5) like cloud computing, IoT, analytics, AI, and blockchain can significantly improve industrial operations by enhancing collaboration, connecting disparate systems, providing real-time visibility, and optimizing processes. They help align business goals, speed up product marketing, meet regulatory compliance requirements, and improve ESG performance, while creating new revenue streams and facilitating ecosystem orchestration.
Key technologies used by manufacturers
Key technologies used by manufacturers
With the rapid adoption of digital technologies, manufacturers can create new norms of efficiency and flexibility by connecting assets, processes, stakeholders, and information streams.
Let us deep dive into a few of the technologies that manufacturers use across production plants:
Industrial Internet of Things (IIoT): IIoT enables shop floor-to-top floor integrations, forming the foundation for smart, connected enterprises. IIoT applications provide real-time visibility, reduce downtime, improve utilization rates, and enhance material and energy efficiency, reducing opex cost significantly.
Big data and analytics: Big data and analytics applications are improving operational decision-making by enabling plant managers to adjust parameters, identify concerns, and plan corrective actions.
Cloud computing: Cloud computing allows manufacturers to capture, store, and access operational data anytime, anywhere, increasing asset life and enabling pay-as-you-go workload provisions.
Industrial automation: Recent innovations in control and robotic technologies have made implementing automated systems at scale easier and cost-effective. Automation reduces operational costs and production lead times, while optimizing resource utilization.
Immersive technologies: Augmented and virtual reality technologies have revolutionized plant operations by enhancing safety, efficiency, and cost-effectiveness. Augmented reality (AR) aids in remote maintenance and employee training, while new interaction models reduce iteration and time spent on product design, prototyping, and production lines.
The figure below shows a few use cases across various functions within a manufacturing plant.
Intelligent operations use cases
Intelligent operations use cases
What outcomes can industrial manufacturers expect?
Intelligent operations can lead to notable improvements in overall factory operations.
Industrial manufacturers can significantly enhance KPIs like throughput, cycle time reduction, and production changeover times by improving visibility, coordination, smart controls, material and energy efficiencies, data analysis, and the understanding of equipment and process bottlenecks.
The graphic below lists a few vital KPIs that can be positively impacted through intelligent operations:
The impact of intelligent operations on key KPIs
The impact of intelligent operations on key KPIs
Industrial operations generate numerous data points related to processes, equipment, environmental conditions, operating parameters, and input and output quality requirements. Production teams can use advanced analytics and plant simulation tools to automate process variable decision-making by adopting machine learning algorithms to monitor, detect, analyze, compare, classify, and optimize decision variables. Integrating information from single data repositories can lead to smarter workflows.
By effectively leveraging data with AI within operations, industrial manufacturers can experience the following benefits:
- Inventory and network optimization
- Accurate demand management and forecasting
- Effective risk management and process control
- Product quality monitoring and prediction of failures
- Warranty and services liability prediction
- Networked buildings and infrastructures
- Efficient performance of assets and facilities, and optimal energy consumption
- Richer customer experience
Intelligence and beyond
From being machinery suppliers, industrial manufacturers are evolving to become experienced outcome providers.
To thrive in the ever-changing business landscape, manufacturers must rely on technology to improve operations and drive breakthrough innovations. As manufacturing technologies evolve in the form of adaptive and smart manufacturing systems, imbibing dynamic, cognitive, smart, and flexible integration capabilities, the prospects for innovative products, business models, and value creation for consumers will rise significantly. However, these technologies require significant investments and manufacturers must assess their current information technology (IT) and operational technology (OT) landscape to align with their strategic business goals.
The actual benefit realization, however, depends on executing organizational change management functions, as disparate systems and processes require collaboration among various departments. With due diligence, manufacturers can pilot deployments across limited controlled processes to validate their cost-benefit analysis. With acceptable results and ROI justifications, operational improvement initiatives can be rolled out in a phased manner to other critical processes.
A factory running on intelligent operations will have no downtime, wastage, and quality defects. There will be smooth material flow, zero safety incidents, and an efficient workforce, which would provide unparalleled experiences for employees and consumers, leading to improved sales and bottom-line performance.
