A global manufacturer of heavy machinery was suffering increasing supply chain complexity due to globalization and vehicle digitization. The complexity increased tooling cost to 25% of vehicle costs and tooling lead times to over 24 weeks. This reduced the competitiveness of the company by hampering new product development and aftermarket responsiveness. They had over 35 assembly plants and parts distribution centers globally affected by the tooling problems.

​

            The company envisioned a digital supply network for manufacturing tooling that encompassed part design, distributed tooling design and fabrication, and distributed use of tooling to produce parts close to manufacturing and assembly points. They wanted to employ machine learning and automation in their vehicle part design process to predict the design features and fabrication processes that minimized total landed costs.  They aimed to establish a supply network that consisted of rapid tooling design firms, fabricators, and users. The envisioned an automated sourcing and procurement process in which they could obtain real-time prices for design, fabrication, and use. Using this information, they could better predict the cost of their machinery and adjust designs. The supply network was connected through a Blockchain through which members shared commercial information and technical data packages. The rapid tooling fabricators completed the tooling and shipped them to users prior to their production runs. The use of rapid tooling avoided storage and tooling maintenance costs which were substantial.

​

            The company and its suppliers needed significant support across all levels of technology and manufacturing readiness for digital manufacturing technologies. They needed to build competencies in digital manufacturing and rapid tooling technologies, in predictive analytics and machine learning, in model-based engineering, and in managing distributed engineering and manufacturing networks through a Blockchain.  Their existing suppliers, users of tooling, also needed support in adopting these new technologies. They lacked the full set of competencies and the new technologies had implications on the expectations from the company.

​

            NOVA MACHINA led a team of engineering consultants and company staff in the creation of a digital engineering, manufacturing, and supply chain strategy that increased EBIT substantially, decreased tooling costs to fractions of product cost, and decreased lead times significantly. Through machine learning, the company identified tooling applications for legacy and new machinery components, identified candidate digital technologies, and defined supply chains. Members of the supply network were connected through a Blockchain to facilitate the exchange of proprietary technical data packages. The strategy addressed the time-phased roll out digital technologies and the knowledge transfer by creating mini-supply chains that would grow and cross-pollinate across other supply chain members.