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Integral Plant Maintenance

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).

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  • SUPPLIER
  • Siemens
    Siemens is the largest engineering company in Europe. With their positioning along the electrification value chain, Siemens has the knowhow that extends from power generation to power transmission, power distribution and smart grid to the efficient application of electrical energy.Featured Subsidiaries/ Business Units:- Digital Factory- Siemens Technology to Business (TTB)
  • INDUSTRIES
  • Automotive
  • FUNCTIONS
  • Discrete Manufacturing
  • CUSTOMER
  • Mercedes-Benz Russia and GAZ

  • CONNECTIVITY PROTOCOLS
  • SOLUTION
  • The Integral Plant Maintenance concept from Siemens was tailored to production specific needs. The primary goal was to meet the customer's strict availability requirements. This was accomplished with an optimized maintenance management system. Through predictive maintenance, line downtime is selectively avoided, and planned downtime is used to perform the necessary maintenance work. The service solution from Siemens is also responsible for other tasks, including: - Inspections - Preventive maintenance - Corrective maintenance - Optimizing the supply and stocking of spare parts As they were working out the concept, the local maintenance experts were able to draw on the extensive knowledge base of Siemens' global expert network. They received optimal support in implementing the Siemens Integral Plant Maintenance standard through coaching sessions and process mapping workshops. Siemens maintenance experts realise planned and unplanned maintenance activities on two shift operation geared with a performance based contract. Performance Indicators includes plant availability and per-unit maintenance costs

  • DATA COLLECTED
  • Maintenance Requirements, Parts and material pricing, Parts Type and Number , Production Efficiency, Quantity Of Parts Produced
  • SOLUTION TYPE
  • SOLUTION MATURITY
  • Emerging (technology has been on the market for > 2 years)
  • OPERATIONAL IMPACT
  • Impact #1
    [Efficiency Improvement - Maintenance]
    A single point of contact for service activities for the customer that secures the optimal performance of their equipment and systems for years to come.
    Impact #2
    [Efficiency Improvement - Maintenance]
    A precise adaptation of maintenance activities to the plant’s processes ensures a comprehensive collaboration between the maintenance team and all levels of customer’s organization.
    Impact #3
    [Efficiency Improvement - Productivity]
    Maximum availability of the customer’s production equipment, the ability to optimally schedule production, and the means to calculate costs over the long term.
  • QUANTITATIVE BENEFIT
  • USE CASES
  • Predictive Maintenance
    Predictive maintenance is a technique that uses condition-monitoring sensors and machine learning or rules based algorithms to track the performance of equipment during normal operation and detect possible defects before they result in failure. Predictive maintenance enables the reduction of both schedule-based maintenance and unplanned reactive maintenance by triggering maintenance calls based on the actual status of the equipment. IoT relies on predictive maintenance sensors to capture information, make sense of it, and identify any areas that need attention. Some examples of using predictive maintenance and predictive maintenance sensors include vibration analysis, oil analysis, thermal imaging, and equipment observation. Visit our condition-based maintenance page to learn more about these methods.
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