The globalization wave of the late twentieth century undeniably made the manufacturing world a smaller place through sophisticated global supply chains. In today’s manufacturing scene, an airplane consists of hundreds of thousands of parts manufactured and shipped from multiple countries around the globe. However, raw material and parts suppliers and the final customer are only remotely connected (or more likely disconnected) by a monolithic supply chain that cannot handle sudden changes. For instance, a bottleneck on an OEM’s shop floor doesn’t just impact its direct customer; the effects flow two or three tiers downstream in the demand chain. With every new market entered, or with every additional layer added to a supply chain, the degree of uncertainty increases.
LNS Research data shows that 40% of manufacturing companies have started a smart manufacturing initiative (IIoT, Industry 4.0, etc.). However, only a few have extended the capabilities and benefits of their smart manufacturing effort outside the factory walls. Companies, indeed the entire value chain, has much to gain by collecting, analyzing, and sharing data beyond their enterprise and enabling a smart supply chain. A truly smart supply chain is one that has visibility of data, transparency in sharing that data, extensibility to support change management, and flexibility to accommodate uncertainty.
Most digital transformation advocates might assert that the typical digital twin use case applies to product development stages and is firmly factory-related. However, a company can also benefit tremendously from a digital twin of the supply and demand value chain. It’s an approach to share simulations across multiple stakeholders and apply insights from the simulations to predict otherwise uncertain delays at various points throughout the supply chain. A supply chain digital twin can pull information from the physical world, analyze relevant data from a variety of sources across the value chain, and close the feedback loop by sending insights to internal or external teams that are affected. Insights from this kind of twin can feed prescriptive analytics, providing the user, for instance, possible solutions to remedy or overcome a delivery delay, instead of just a notification.
Just-In-Time (JIT), introduced by Toyota, is one of the most widely practiced lean manufacturing methodologies. JIT dictates that companies should only order what is required right now, to lower inventory costs and reduce production lead times. Due to globalization and complex supply chains, companies have been forced away from JIT with a buffer of safety stock at their disposal.
Today’s industrial revolution offers manufacturers a unique opportunity. By investing in a good manufacturing execution system (MES) plus an advanced scheduling system, companies can position themselves to perform complex analysis on data inside and outside the enterprise. Ultimately, it’s a foundation to provide critical information for more precise decision making, even with shorter lead times. As an example, a plant manager equipped in such a way can even use advanced analytics to make data-driven decisions based on data outside the system, such as weather, logistics and traffic.
Another compelling use case for a smart supply chain is customer engagement in new product introduction (NPI). A holistic platform with a robust data model is a must-have for collecting more data, performing advanced analytics, and sharing insights and information across the value chain. This holistic platform will provide applications for engaging customers earlier in NPI and make concepts like design-to-order and configure-to-order feasible in today’s factory. Both modes require real-time data sharing and several iterations of collaborative simulations.
Companies like Nike are experimenting with design-to-order to provide customers with the option to specify how their shoes are designed. One of the biggest challenges manufacturers face with this approach is change management. A smart supply chain with automated data collection, analysis, and persona-based dashboards can streamline change management and enable more effective customer engagement in NPI.
Yes, most global manufacturers today have a complex supply chain that’s monolithic, rigid, and struggles to accommodate flexibility. The good news is that today’s industrial revolution paves the way for a decentralized supply chain that eliminates common problems. Manufacturers eager to capitalize on a smart supply chain should consider the following actions high priority:
Learn how industrial companies can enrich supply chain performance with manufacturing data in the research eBook, "Smart Manufacturing: Smart Companies Have Made Smart Manufacturing the Center of the Enterprise.”