Skip navigation
smart factory concept Alamy

The Vital Assets Needed for Smart Factory Initiative Success in 2023

Organizations looking for rapid scalability of smart factory initiatives can realize the enormous value of cloud-enabled ERP solutions.

Although global manufacturing has made tremendous strides in optimizing labor productivity and asset efficiency, the next leap in operational performance can only be unlocked through optimizing operations from manufacturing to supply chain.

This notion of optimizing processes within manufacturing and supply chain functions to achieve operational excellence is broadly defined as smart manufacturing. The term arose in the mid-2000s, prompted by the advent of new technologies such as the industrial internet of things (IIoT) and artificial intelligence (AI).

Similar concepts like digital and cyber manufacturing converge under the smart manufacturing umbrella. Global standards to further define smart manufacturing are still a work in progress. However, a vast majority of large manufacturers are accelerating investment in smart factories.

How Does a Smart Factory Work?

Smart factories combine human creativity, digitally connected machines, assets, and AI-powered analytics. This augmentation of human intelligence with machine intelligence helps fuel adaptability and speeds up the capacity to customize outputs based on real-time data and insights. The visibility, agility, and resilience of smart manufacturing make it vital for more efficient supply chain models and overall business operations. Today, dozens of so-called smart factory technologies have given birth to a myriad of smart manufacturing use cases.

Smart factory technologies can be categorized into three buckets:

  • Cloud-scale data management and analytics — Implementing predictive intelligence and forecasting capabilities, these digital technologies will also enable IT-OT convergence to support end-to-end digital continuity from design to operations, such as digital twin, closed-loop engineering design.
  • Connectivity — Leveraging IIoT to collect data from existing equipment and new sensors
  • Intelligent automation — Traditional automation (plant control systems, MES, distributed control) and IIoT-enabled automation (machine vision, drones)

Another way to describe a smart factory is the ability to create a closed-loop, data-driven optimization of end-to-end operations. This closed-loop smart factory undergoes continuous procedural improvement to self-correct and self-optimize — it can self-learn (and even teach humans) to be more productive, adaptive, and safe. Generally, the first milestone of the smart factory is to use advanced analytics to drive decision support. However, the ultimate goal is to reach a stage of operations where the smart factory can self-optimize performance across a broader network, self-adapt to and learn from new conditions in real- or near-real time, and autonomously run entire production processes.

The closed-loop optimization paradigm of a smart factory is based on three iterative steps:

  • Data acquisition — Leveraging IIoT and modern database technologies to acquire and curate disparate sets of valuable data across manufacturing and supply chains. Through a network of edge devices and connected portals, AI-powered systems can compile data sets related to operations, market trends, logistics, or any other relevant source.
  • Data analysis — Leveraging machine learning and AI to analyze the gathered disparate data. This data analysis enables varied used cases, from predictive maintenance of assets, pricing intelligence, and customer experience to supply chain planning and scheduling. The iterative nature of the optimization allows the study of workflow efficiencies over time to find the global optimum for any operational decision.
  • Intelligent factory automation — After the data acquisition and subsequent data analysis, optimized workflows are determined, and instructions are sent to the machines and humans within the system. These assets and workers can be anywhere within the smart factory or downstream in logistics or aftermarket services. The data sets that can be compared and analyzed present almost infinite possibilities of combinations to inform digital factory optimization and supply chain forecasting.

Getting Started: The Power of Modernized ERPs

For any organization, enterprise resource planning (ERP) is the central location where all operational and financial data is collected, maintained, and shared — making ERP the de facto single source of truth. When you consider ERP in conjunction with its other components, such as WMS, MES, and PLM, it has a strategic vantage point that provides end-to-end visibility of any organization's operations. Hence, the modernization of ERP and transition to the cloud is the most vital step to bring the smart factory to fruition. A cloud-based ERP makes applying AI/ML to operational data possible. The sheer span and reach of an ERP system make it an obvious first choice for organizations looking to achieve scale in smart factory initiatives.

Organizations looking for quick wins and rapid scalability of smart factory initiatives can realize the enormous value of cloud-enabled ERP solutions with these vital use cases:

  • Kickstarting a smart factory — Integrating systems such as ERP, CAD, PLM, MES, and CRM can enable holistic business decision making.
  • Integrated business planning in a smart factory — The ability to conduct scenario-based planning with an overarching objective of maximizing profit, reducing resources, and minimizing risks
  • Making ERPs self-learning knowledge system the smart factory backbone — A closed-loop self-learning ERP system can transform business and manufacturing processes and help optimize decision making.
  • Improving OEE of capital equipment in a smart factory — Through AI/ML, IoT, and cloud, an ERP serves as an always-learning knowledge system that can monitor equipment data in real time, recognize failure patterns, and help organizations transition from time-based to a predictive maintenance paradigm of equipment.
  • IoT, AI/ML, and ERP integration — When an ERP, with the integration of AI and ML technologies, has access to IoT data, the system is empowered to bridge the intelligence gaps many businesses face in pursuing new business models such as servitization.
  • Optimized product quality — With ML, an ERP system gets tracking/tracing capabilities that can help businesses predict which product or process characteristics cause failure as well as enable closed-loop product design based on the product's entire lifecycle.

Smart factories are a journey, not a destination. The capabilities of a smart factory will expand with the digital maturity of its owner. Manufacturers embarking on smart factory initiatives must consider long-term scalability. Cloud-based modern ERP is the starting point for creating an integrated platform that allows a smart factory to combine fast incremental innovation with deployment at scale.

Chirag Rathi is Industrial Manufacturing Strategy Lead for Infor.

Hide comments


  • Allowed HTML tags: <em> <strong> <blockquote> <br> <p>

Plain text

  • No HTML tags allowed.
  • Web page addresses and e-mail addresses turn into links automatically.
  • Lines and paragraphs break automatically.