Digital Factory

Today’s products as we know are increasingly becoming intelligent with multiple system interfaces within the product and in connected ecosystem they participate -- increasing the complexity. All of this primarily to reboot and provide enriched customer experience.

With raw materials coming at one end from supplier and going through a series of manufacturing process steps in factories delivering finished products to customer addressing, throughput, cost, inventory turns & quality, and at the same time addressing market demand fluctuations, requires strong automation focus and Industrie 4.0 adoption.

? At an Enterprise level, Plant to Plant; Line to Line comparison; drill-down of specific factors for performance management; insights into factors for production losses; and waste are of interest.

However, plant level managers at each plant would like to see:

• ?What’s happening (equipment and process) — Opportunity for Increase tool uptime with predictive or prescriptive maintenance.
• ?How is it happening (Work order routes, schedule, Tracking) — Identify opportunities to be able to Increase Throughput
• ?What should they do to control what is happening (Quality data, Tracking) — Improve Yield and increase adoption of automation

Significant investments around automation and integration have been made during the past two decades in majority of modern factories with MES and ERP systems deployed. MES systems were able to address the monitoring(OEE, Downtime, Equipment SPC), Schedule execution(Work order route, Tracking) , Production control(Quality data and expanded tracking). These systems helped integrate machine data to enterprise systems giving relevant views for plant management, quality inspectors and maintenance engineers. The investments have helped increase in tool uptime, improve schedule performance, routing, genealogy & resource visibility but are yet to address the optimization and capacity maximization.

Optimization of the factory with throughput, bottleneck identification, condition based maintenance, and dynamic routing to achieve capacity maximization is still a long shot for many enterprises striving for maximum yield. The critical questions to be addressed: How and when will the supply chain vary with fluctuating commodity prices driven by impending inflation? How to plan for every part? Without walking on the plant shop-floor, How to get a view of inventory and minimize inventory? How to address inventory churns keeping in view of inbound and outbound logistics? In addition, supply and demand dynamics surfaced up gaping holes in remote operations for enterprises during the pandemic.

To deliver intelligent products that bring personalized experience and be responsive to the customer usage and changing needs, it is critical and important that organizations transform themselves to adopt Connected Digital Enterprise strategy. Connecting assets and products with sensors and collecting data has matured over last decade. This has enabled machine, production efficiency analytics but the challenge continues to be with driving insights from the data collected for product ,plant and production optimization to help drive KPI's like on time delivery and quality. This requires advanced Industrial, Manufacturing analytics, Image analytics, and Connected logistics to not only optimize the supply chain but also to achieve capacity maximization.

Today’s factories, both brown and greenfield plants, have to be agile & flexible and adopt advanced automation- sensorization, adoption of Cobots, AGV systems, New gen robots, M2M communication & industrial cloud adoption while keeping in view of workforce safety and quality. ?Another aspect is being connected we are not only talking about OT system (machine connected to plant network) but enterprise and ecosystem connected without compromising OT cyber security.

Digital Factory initiatives addressing plant optimization, capacity maximization, remote operations, OT security would continue to be a key enablers to deliver intelligent products securely while improving operational efficiency.