A major, integrated oil & gas company was experiencing significant production disruptions due to equipment failure. The company had over 30 production facilities across the world experiencing significant turndowns and shutdowns. Obsolescence of equipment parts or their long lead times exacerbated the costs of the disruptions. The objectives of the company were to predict the failure of equipment accurately and to establish a distributed, engineering and manufacturing network to supply replacement parts quickly to the production facilities prior to failure.

​

The company envisioned employing machine learning technologies to identify candidate process equipment parts and to determine their optimal replacement time across their production locations. They wanted to embed sensors into the replacement parts to better predict performance of key equipment. They also wanted to automate inventory management and the procurement of engineering, manufacturing, and logistics services using robotics (e.g., Task Bots, Meta Bots, and IQ Bots). The company aimed to use digital manufacturing technologies (e.g., 3D printing) to increase economies of scope, increase robustness of supply, and to fabricate near consumption. They envisioned a Blockchain to exchange automatically commercial and technical data across the supply network. The network would consist of engineering firms, materials suppliers, fabricators, testing/inspection organizations, OEMs, and logistics service providers. In particular, the network would use the Blockchain to share existing and create new technical data packages for replacement parts. Bots would first determine whether validated technical data packages existed in the network. If not, then Bots would initiate their creation within the network using the Blockchain. In both cases, Bots would hold a real-time bidding process to allocate the demand across members of the supply network. Engineering firms would optimize part designs for the best digital manufacturing technology.  Testing and inspection organization would validate and certify the technical data packages and submit all information necessary for regulatory compliance through the Blockchain.  Fabricators would create the replacement parts according to the technical data packages. Logistics services providers would deliver the finished parts to the production facilities prior to equipment failure. All of these steps were automated with the only human intervention at the receipt and installation of the replacement part in the field.  

​

            The company required extensive support across all levels of technology and manufacturing readiness for digital manufacturing technologies. Specifically, they needed to build competencies in artificial intelligence, digital manufacturing, model-based engineering, and broad scale automation.  The company also needed expertise in predictive analytics for equipment performance to create the models and training algorithms for artificial intelligence. The parts were complex, requiring conformity to engineering standards for corrosion, fluid flow equipment, and pressurized equipment. Additionally, the parts were required to conform to safety regulations across the world, such as OSHA 1910 and ATEX.

​

            NOVA MACHINA led a team of engineering consultants and company resources to develop the vision and strategy for the global, digital engineering and manufacturing network. They also led this team in the execution of the strategy. This responsive network supplied replacement parts to the production facilities that met specifications, standards, and regulations. They established the models for identifying candidate parts based on their likelihood of failure, production impact, part characteristics, and global supply chain responsiveness data. NOVA MACHINA created the architecture for the Blockchain used in the engineering design process to develop certified technical data packages for every part. They also led the establishment of the model-based enterprise to manage the part information. NOVA MACHINA also led the engineering team in identifying and qualifying manufacturers to be part of the supply network. The company and the members of the network were connected by a shared PLM system through the Blockchain architecture. NOVA MACHINA also led the engineering team in setting up an advanced planning and optimization system, a manufacturing execution system, and a quality management system to manage the network. These systems were fully automated.