Mercedes-Benz and his partner GAZ chose Siemens to be its maintenance partner at a new engine plant in Yaroslavl, Russia. The new plant offers a capacity to manufacture diesel engines for the Russian market, for locally produced Sprinter Classic. In addition to engines for the local market, the Yaroslavl plant will also produce spare parts. Mercedes-Benz Russia and his partner needed a service partner in order to ensure the operation of these lines in a maintenance partnership arrangement. The challenges included coordinating the entire maintenance management operation, in particular inspections, corrective and predictive maintenance activities, and the optimizing spare parts management. Siemens developed a customized maintenance solution that includes all electronic and mechanical maintenance activities (Integral Plant Maintenance).

Manufacturing plants are running at a much higher capacity utilization than ever before. Often, it is a 24 hour a day, seven days a week operation. With downtime costing plants up to US$20,000 a minute, they cannot afford disruption to their processes. In fact, a single occurrence can cost a plant upwards of US$2 million.FANUC was struggling with lack of visibility into how their customers were leveraging FANUC equipment on the factory floor. The only insight was gained after a problem had already occurred resulting in costly downtime for the customer.

Objectives • Create an innovative IT environment that supports the move toward a solution-provider business model • Enhance existing business processes and leverage the power of Big Data and predictive maintenance to become more proactive, customer oriented, and competitive • Leverage the SAP HANA® platform to transform and simplify the entire SAP® solution landscape

The extrusion and other machines at Mondi’s plant are large and complex, measuring up to 50 meters long and 15 meters high. Each machine is controlled by up to five programmable logic controllers (PLCs), which log temperature, pressure, velocity, and other performance parameters from the machine’s sensors. Each machine records 300–400 parameter values every minute, generating 7 gigabytes of data daily.Mondi faced several challenges in using this data for predictive maintenance. First, the plant personnel had limited experience with statistical analysis and machine learning. They needed to evaluate a variety of machine learning approaches to identify which produced the most accurate results for their data. They also needed to develop an application that presented the results clearly and immediately to machine operators. Lastly, they needed to package this application for continuous use in a production environment.

Like many organizations, Anaren had a homegrown work order application that had basic asset management functionality. “It was menu driven, so quite cumbersome,” explained Bill, “reporting was limited and it still relied heavily on paper transactions and records. We looked at our business needs going forward and decided this was one area that could be modernized.” On launching the Manufacturing CMMS project, Bill, the business analyst of the company identified three major areas for improvement:1. Improve efficiency by eliminating paper.2. Improve the control of preventive maintenance. 3. Improve inventory management.