Evolved Product Lifecycle Management with IoT

"Traditionally there have been sometimes formidable challenges and pain points for organizations attempting to be digital, agile and responsive. PLM is about to undergo a radical digital transformational change – especially with the growth of connected devices (IoT)."

Welcome to Part 5 of Adaptive Digital Factory (ADF) series. As you may recall, Part 1 covered the Industrial Internet and Industrie 4.0; Part 2 dealt with IoT and Supply Chain Transformation; Part 3 looked at the omni-device customer as well as the impact of these connected devices on manufacturers; and Part 4 focused the OODA loop for manufacturing: Insight to Action. This blog focuses on the digital transformation of Product Lifecycle Management, especially due to IoT.

Product Lifecycle Management (PLM) is an end-to-end process that allows manufacturers to move from innovation, to design, to prototyping, to production, to monitoring, to service, for their products, services, or processes. The objective is to drive profitable revenues while building innovative, sustainable, efficient, productive organizations. PLM aims at satisfying the needs of customers, shareholders, supply chain partners, and employees (sales, marketing, engineering, production, service support, etc.). However, traditionally there have been sometimes formidable challenges and pain points for organizations attempting to be digital, agile and responsive. PLM is about to undergo a radical digital transformational change – especially with the growth of connected devices (IoT).

Traditional PLM solutions address several pain points:

  • Organizational Consistency and Change Management: The organization is not on the same page as to what products are important to the customer and how the value changes over time. Constant change leads to problems with configuration management, controls and compliance.
  • Product Innovation and longevity: A reduction in the longevity of the product lifecycle occurs as poor management inhibits agility and growth. As a result, the product becomes prematurely obsolete and the effort to replace is monumental and expensive.
  • Instability: Business requirements are not captured early or maintained during the chaotic events that occur during product implementation resulting in a product that suffers from an extended instability stage.
  • Sourcing: Alternative sourcing opportunities are difficult to cultivate or consider since it is not clearly understood who does what, who or how it impacts others, and how changes to the process are determined, governed, or managed.
  • Performance Improvement: Key performance indicators (KPI’s) are hard to define, measure, or manage to.
  • Data Integrity: Manufacturers are challenged with data quality problems and lack or effective Master Data Management, whether for suppliers, customers, products or materials master data.
  • Waste and Quality Problems: Difficult to consistently measure and manage waste in the various phases of the production life cycle while controlling the critical quality required of products and services.

The Adaptive Digital Factory of the future will manage the product lifecycle more efficiently than ever by leveraging the value from smart connected assets, and machine to machine communication (M2M), such as machinery, equipment, and assembly tools on the shop floor. Real-time data from physical assets can be integrated with other systems, such as ERP, CRM and data warehouses to provide in-depth understanding of the production flow and quality.

In the adaptive digital factory, IoT data from connected assets provides visibility into new opportunities to improve daily operations. The following are some examples of those potential improvements:

  • Equipment Efficiency: By monitoring the usage and behavior of the equipment, the manufacturer can discover better ways to perform maintenance. By measuring and analyzing critical data points, the factory can improve its overall equipment efficiency (OEE).
  • Pro-Active Resolution: Early detection and notification of issues can trigger proactive resolution of the problem and prevent costly downtime.
  • Warranty and Maintenance: When alerted to a potential issue, the engineers will also be informed of any critical information, such as whether the repair is covered under warranty or not.
  • Insight from IoT Device Data: The equipment manufacturer can use real-world usage data from the devices to enhance or upgrade features and functionality in the planning and design of the next-generation of products.
  • Improve Business and Customer Experience: Reacting dynamically to detected usage patterns and recurring issues will positively impact customer satisfaction and loyalty.
  • Product Design and Innovation: Product design enhanced by using real-time data to improve and streamline product design and innovation: resulting in faster time to market and increased competitive advantage.

In the adaptive digital factory, new products are developed in days, not months. Smart connected devices improve the collaboration and flexibility across the entire value chain from the supplier to the factory to the end customer. Harnessing IoT data allows the plant manager to adapt to changes more quickly, whether it is a new product introduction, or fluctuations in the supply of a particular component or in customer demand.

Part 6 of the Adaptive Digital Factory series will cover “IoT in Dynamic Case Management.” For a sneak peek, check out the entire Adaptive Digital Factory eBook at pega.com.