Smart manufacturing for heavy equipment

An essential component of smart manufacturing for heavy equipment is digital information sharing on different levels. These include between the design and manufacturing organization, teams within the manufacturing organization, and across the broader ecosystem with partners and suppliers. This is enabled by connecting all stakeholders via a common product lifecycle management environment that features a powerful, flexible and open data infrastructure where all information can be stored and kept up to date. This data sharing is the basis for better communication and collaboration across the entire manufacturing organization, which greatly improves overall efficiency and flexibility, and allows companies to swiftly respond to change requests without risk.

Achieving the highest quality standards every single time without failures is difficult when working in a global organization with a large and diverse set of suppliers and partners. When making highly customized or even unique products in an Engineer-to-Order (ETO) context, zero-defect manufacturing comes down to doing it right the first time. This is only possible if you analyze everything that can happen upfront and anticipate by virtually validating the process before implementing it. During manufacturing execution, it is also key to closely monitor all steps and decisions that are being taken, including those that are made by partners and suppliers around the globe, and to take corrective action based on the collected data. This requires capabilities for making a digital twin of your production process, as well as a comprehensive closed-loop approach to manufacturing operations across the entire global manufacturing ecosystem.

Smart manufacturing for heavy equipment is about deploying tools and processes that help manufacturing organizations make smart data-driven decisions, thereby greatly improving all aspects of their operations, including productivity, sustainability and quality. Enabling continuous communication and collaboration based on shared data, will allow you to achieve synergies between stakeholders to spot issues and opportunities earlier, for example regarding the manufacturability of designs. By creating a digital twin of the manufacturing process, organizations can virtually assess the impact of decisions before actually implementing them and run what-if scenarios to study various options. Finally, by deploying feedback loops based on data collected during execution, manufacturing organizations can continuously refine their processes, improve their operations and find the optimal balance between KPIs.

Lean manufacturing is a production methodology that focuses on improving productivity, quality, and reducing waste in all forms, including materials, time and resources, by analyzing processes and adapting them retrospectively. Lean manufacturing approaches have been very successful in optimizing manufacturing processes by streamlining the value chain and improving the operational flow. Today however, products and their associated manufacturing processes have become more complex while some new requirements for manufacturing operations are not covered by the lean manufacturing principles. One example is that today’s customers have greater expectations regarding personalized requests and the ability to change those along the way. Another is that the sourcing strategy can no longer be based on the just-in-time principle and on cost but should also consider less quantifiable aspects like supply chain resilience and sustainability. Such manufacturing challenges require more data that is instantly available for continuous real-time decision-making and improvement and a greater degree of integration across the manufacturing ecosystem. As opposed to lean manufacturing, a smart manufacturing approach exploits today’s new digital technologies like cloud, data analytics, machine learning, automated feedback loops, digital twins, and more.