Corporate Level Standards, BPM Creates Value, Predictive Engineering Maintenance
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2PLM NewsletterJohn Stark Associates May 24, 2010 - Vol13 #4 |
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Welcome to the 2PLM e-zine This issue includes :
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| Standards at the Corporate Level by Roger Tempest |
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| Readers of 2PLM will be familiar with the progress of the PLM standards initiative at the concept level (April 12) and the detailed level (April 26); and of the potential benefit for the SME marketplace (May 10).
The next phase is to establish a collaborative environment for user companies and their suppliers to develop standards that will help PLM implementation in multinational corporations. If your company produces 10 million complex product units per year from 17 countries, for example, you need standards that let you work at that level. Participants at the PLM User Forum events in 2008 came from companies with an average sales value of around $3bn, and found extensive common ground when talking about the problems they faced. |
Sharing experiences is good, but structuring them into effective working material is even better. What are the standard techniques and best practices that will simplify the work of PLM managers of large corporations?
Vendors and systems integrators have long been evolving as providers of solutions rather than software, and need to be part of this process. Cooperation on standards increases the depth of business relationships; allows the users to retain control as they follow the new best practices; and benefits the suppliers by speeding implementation progress. Roger Tempest is co-founder of the PLMIG. You can request more information or find out how to participate via standards@plmig.com |
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| BPM Creates Value by Scott Cleveland, Coldwater Technology |
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| More and more organizational leaders are realizing that business process management [BPM] creates a significant competitive advantage. According to Gartner, BPM is one of the fastest growing segments in software, and is predicted to remain so during the next 5 years.
BPM software reduces costs and increases efficiency by providing:
My Thoughts.... Reduced Costs - Business Process Management does more than just create efficiency. Knowledge sharing and collaboration improves decision making. Process performance reports help optimize workflows. Notifications and triggers help reduce errors and eliminate waste. An intelligent rules engine helps enforce best practices. Not only does Business Process Management increase workforce productivity, but it improves product quality and reduces corporate risk. |
The result - companies will see a substantial costs savings within months of deployment.
Increased Revenue - The efficiency created through effective Business Process Management will increase product output, shorten cycle times and improve customer service. Over time, you will see faster time-to-market and an improved company image which should increase sales. Improved Agility - Effective Business Process Management will make your company better equipped to change gears to respond to your changing business environment [faster than your competitors]. In this uncertain economy, effective process management is a key ingredient to success that allows you to break out as a market leader. Your Thoughts.... What steps has your company taken to be a market leader?
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| CL2M Case Study 4 : MOL Predictive Maintenance for Engineering Machines by David Potter |
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| This case study is based on one of the 10 commercial applications developed during the EU PROMISE Project. It briefly describes how common PROMISE technologies were applied in order to enable a predictive maintenance strategy that would increase performance and availability of complex integrated machining systems.
The equipment in question is the product of a world leader in the design, construction and marketing of integrated systems for the machining of complex forms for the moulds and dies industry, comprising high-speed milling systems as well as their numerical controls and servo drives. The primary objective of the demonstrator was to implement a predictive maintenance strategy for the mechanical components of a machine tool which have a long and slow degradation, as opposed to electronic components that break down suddenly. This would minimise production stoppages and sudden interruptions, and consequent heavy economic impact on the user. Thus the main benefits are: For End Users:
For Service Departments:
The mechanical and electronic components of the milling machine are identified by a combination of barcode and RFID systems (local PEIDs - Product Embedded Information Devices). Their identities are registered when the machine is first assembled, and during its life they also enable component traceability whenever part substitutions are made during service operations. Integrated sensors enable the collection of operational data which is evaluated on the machine's CNC module. It is this module which also performs the role of the top-level PEID and may also contain a distributed component of the PROMISE Decision Support System (DSS). Both standard and customised machines are produced, and preliminary data will be written onto PEIDs placed on selected critical components. This data will include for each component: manufacturer, serial number, size, weight, batch number, relationships with other components of the machine, etc. The new machine is then shipped to the customer to start its working life.
Once installed at the customer site, a software component on the CNC of each machine periodically (e.g. 3-6 months, 10,000-20,000 working hours, etc.) executes dynamic tests in order to evaluate the "health state" of the machine and of its subcomponents. |
This Predictive Maintenance component triggers different machine motions, and sensors installed on the machine (i.e. position transducers, current sensors, etc.) record useful measurements that can be used to identify defective behaviour or malfunctions of the machine. After executing the test, the DSS component, the so-called "Ageing Module", evaluates these test results and estimates the ageing of each monitored part.
This ageing module may be installed both on the CNC at customer (local) and on the DSS server at the manufacturer (central). When the module is running on the CNC, it calculates the estimated date of next breakdown and a percentage representing the ageing of the component. If the latter exceeds a threshold (e.g. 60%), a warning message will be displayed on the customer screen inviting contact with the service department no later than the date suggested. Whenever the need for service is indicated, the service technician will start a data file transfer between the CNC at the customer site and central system, the DSS and the PDKM (Product Data Knowledge Management) Server. These data are analysed by the central DSS "Ageing module" component, which has a graphical interface that allows item oriented management, more detailed analysis, and use of different algorithms.
The DSS "maintenance cost management" module then determines the costs of maintenance actions and suggests the most appropriate. The service technician can retrieve further information from the PDKM system including the maintenance history of that machine (i.e. previous repairs, similar occurrences, etc.). If repair is needed, the service technician updates the maintenance history, writing new data on PEIDs, and updating the PDKM tables with the description of the problems encountered, the solutions provided, the components changed, etc. After a repair intervention, the Service and Design departments are able to analyse the additional data available on the PDKM system. If a tagged component has been replaced and returned, the technician can examine its entire history. In this way the production department is enabled to identify the presence of defective stock, etc. The design department is able to identify any weaknesses in the product and to improve it. The exchange of information between departments about malfunctions and breakdowns makes the identification of failures easier. This element represents the exploitation of cumulative PROMISE knowledge to improve design. Registered users who are logged in to cl2m.com will be able to access the full public text of this PROMISE demonstrator case study by following this link: Case Study 4: MOL Predictive Maintenance of Engineering Machines. There is no charge for registration.
In the next issue of the 2PLM newsletter, I will present the fifth in this series of case studies, dealing with the application of PROMISE technologies to condition-based predictive maintenance of heavy goods vehicles (trucks). |
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