such holistic products and supporting services is currently ... PROMISE Demonstrators in the Automotive, Railway, .... technician could intervene only when a.
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CLOSING THE PRODUCT LIFECYCLE INFORMATION LOOPS
C.C. Røstad, O. Myklebust, B. Moseng Productivity and project management, SINTEF Technology and Society, S.P. Andersens vei 5, N-7465 Trondheim, Norway
Abstract This paper presents the new possible business practices and possibilities that are enabled by the product lifecycle management and information tracking using smart embedded systems (PROMISE) technologies, product lifecycle models, product embedded information devices (PEIDs) with associate firmware and software tools for decision making based on data gathered through a product’s lifecycle. The aim is to enable and exploit the seamless flow, tracing and updating of information about a product after its delivery to the customer and up to its final destiny. The main focus of this paper is therefore to present the background of PROMISE and the new business possibilities arising from PROMISE, as this can give important input to how e.g. production systems, ERP-systems and value-chains must incorporate the expected changes in order to stay competitive. Keywords: Application scenarios, product lifecycle, new business possibilities
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INTRODUCTION
1.1 Cost, quality and time-to-market is no longer sufficient to gain market advantage In the globally changing business environment, companies are seeking new ways of providing additional value to customers and gain a competitive edge over their competitors. Past initiatives aimed solely at product cost, quality, or time-to-market are no longer sufficient to gain market advantage. The focus today is on innovation: products that differentiate themselves from others while also being affordable, reliable, and early to market. Total management of the product lifecycle is critical to innovatively meet customer needs throughout its entire life cycle without driving up costs, sacrificing quality, or delaying product delivery. The ability of industry to provide such holistic products and supporting services is currently limited by the information gap in the products life cycle (i.e. the flow of information between the design/production phase and middle and end of life phase of the products life cycle). 1.2 The PROMISE project In order to focus on the identified challenges, the PROMISE (Product lifecycle management and information tracking using smart embedded systems) project was initiated and started in 2004. PROMISE is an endorsed IMS project (IMS project no. 01008) and brings together a large international partnership involving five IMS regions: EU, Switzerland, Japan, Australia and USA. This kind of integration of research efforts with common or similar objectives contributes to develop synergies at four levels: private, national, EU and international. The PROMISE consortium consists of 23 partners from 8 EU member states and 3 Swiss partners including endusers, world leaders in their respective domains, a world leader in PLM software, 6 high-tech SMEs, reserving about 30% of the requested funding, in the domains of transponder applications and associated software development and well known Universities and Research Institutes. In addition to that, it integrates research activities performed at private (project M-LAB), national (one German) and EU (one approved FP6-IST-IP) level.
1.3 Scope and focus of the PROMISE project PROMISE focuses on developing appropriate technologies, including product lifecycle models, Product Embedded Information Devices (PEIDs) with associated firmware and software components and tools for decision making based on data gathered through a product’s lifecycle. This is done to enable and exploit the seamless flow, tracing and updating of information about a product, after its delivery to the customer and up to its final destiny (deregistration, decommissioning) and back to the designer and producer. The breakthrough contribution of PROMISE, in the long term, is to allow information flow management to go beyond the customer, to close the product lifecycle information loops, and to enable the seamless eTransformation of Product Lifecycle Information to Knowledge. The PROMISE R&D implementation plan includes fundamental and applied research activities in the disciplines of information systems modeling, smart embedded systems, short and long distance wireless communication technologies, data management and modeling, statistical methods for preventive maintenance, End Of Life planning, adaptive production management and Design for X. PROMISE integrates Research Cluster activities covering the main research challenges of the project and resulting in a prototype PROMISE PLM (Product Lifecycle management) System, Application Cluster activities covering applications of the PROMISE concepts with 11 PROMISE Demonstrators in the Automotive, Railway, Heavy Load Vehicle, EEE and White goods sectors, Innovation Cluster activities covering Integration & Standardisation and Business Development issues and Training Cluster activities covering development and delivery of specific training packages for an extended trainee audience involving potential PROMISE technology developers as well as end-users. 1.4 Business proposition to the Product Lifecycle stakeholders PROMISE offers the following business proposition to the Product Lifecycle stakeholders: to create value by
transforming information to knowledge at all phases of the product lifecycle and thus improve product and service quality, efficiency and sustainability. The product and service value may be created at various levels, with respect to the above statement, as follows: Technical: optimal accomplishment of the expected functions and user expressed and unexpressed needs, after exploiting “field” knowledge gathered through the product lifecycle. Economical: creation of value for the producer (better products, better CRM), for the service provider (new business opportunities, better CRM), for the product owner (extended product life). Environmental: minimization of pollution, of resources and of energy consumption by applying optimal BOL (Beginning of Life), MOL (Middle of Life) and EOL (End of Life) planning. Social: comfort, safety, security and satisfaction of the product user, either the operator of the product (e.g. the driver of a truck) and /or the user of the service (e.g. the passenger of a bus, the user of an elevator, etc.). 1.5 The development of Product Embedded Information Devices (PEIDs) and new business opportunities The development of Product Embedded Information Devices is expected to progress rapidly and largely used for advanced Product Lifecycle Management and real-time data-monitoring throughout the Product Supply Chain and it will expand greatly and explode into a multi-billion dollar market in 2006 and beyond. This technology will particularly allow producers to dramatically increase their capability and capacity to offer high-quality after-sales services while, at the same time, being able to demonstrate responsibility as producers of environmental friendly and sustainable products. Some examples of new after-sales services and breakthrough improvements that will become possible through PROMISE are: new types of leasing services, closing of the information gap in customer relationship management, proof of producer, damage management, and enhancement of security. New business opportunities and uses of this technology are described through the PROMISE demonstrators in this paper, and a conclusion is drawn on some of the challenges facing manufacturing industries with regard of the PROMISE scope. 2
THE PROMISE DEMONSTRATORS AND THEIR APPLICATION SCENARIOS The basis for identifying how the development of the PROMISE technology and methods can, and will, impact manufacturing businesses is found in the PROMISE Application Scenarios presented in this section. 2.1 The Demonstrators The eleven Demonstrators of the PROMISE project are, in one way or another, all involved in manufacturing. The main focus area of each industrial partner’s Application Scenario is shown in Table 1. This table also shows the main lifecycle phase their Application Scenario focuses upon. These are denoted BOL (Beginning of life), MOL (Middle of life) and EOL (End of life). BOL focuses on design (DfX), development, manufacturing and the use of information from MOL and EOL in order to make better products, processes etc. MOL focuses on the
use, service etc of the product, while EOL is the phase where the product is sent to dismantling / recycling etc. Table 1: Overview of the Demonstrators and their main lifecycle phase focus in the Application Scenario Main focus area
Main lifecycle phase
Production systems Electric locomotives Refrigerators and Proxy devices Construction & mining equipment Light trucks to heavy lorries & buses Milling machines Multi-Service Access Nodes (MSAN) Gas boilers – industrial and home Recycling plants Construction & mining equipment Passenger vehicles
BOL BOL BOL/MOL MOL MOL MOL MOL MOL EOL EOL EOL
2.2 The Application Scenarios The application scenarios cover aspects ranging from technical issues related to the application area, value-chain issues, business/economical issues and environmental/social issues. The main focus of this paper is to present the business issues. This might give indications on how the use of PEIDs will impact the current manufacturing area and give important input to how e.g. production systems, ERP-systems and value-chains through a products whole lifecycle could be designed. 2.3 Method used for collecting data The following method were used when collecting data from the Demonstrators related to their respective Application scenario A suggestion for areas that were needed to be covered were prepared and presented to all Demonstrators in a common kick-off meeting The suggested areas were refined and the use of diagrams etc were included in an Application Scenario Description (ASD) document ready for distribution to all involved Demonstrators All Demonstrators completed the draft version of their application scenario (ASDdraft) and submitted this with comments if any changes to the structure of the ASDdraft needed to be carried out Based on the input to the ASDdraft, some adaptations of the structure of the ASD were carried out. The adapted ASD together with questions and comments from the researchers to the industrial partners regarding their draft version were then distributed to all Demonstrators All Demonstrators completed their final version of their application scenario Final adjustments were carried out by researchers in the PROMISE group based on interviews carried out at the industrial partners (i.e. the Demonstrators)
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NEW BUSINESS PRACTICES AND POSSIBILITIES ENABLED In this section, the individual Application Scenarios are presented. Their main objective, together with the most evident business improvements (e.g. in operations, and practices) are referred. The changes enabled are based on the introduction and availability of the PROMISE technology and related systems as described in section 1. 3.1 Main lifecycle phase: BOL A total number of three demonstrators have their main focus in the BOL lifecycle phase. Production system The main objective of this scenario is to improve the overall enterprise performance by enabling the adaptation of a production system to the large number of product and process modifications prompted by the availability of feedback information concerning the whole product lifecycle. The main business implications are: Modification needs about the product/process requested by the market. If the enterprise becomes aware of the most probable product/process future modifications, system configuration activities could be carried out evaluating different scenarios. In this way the enterprise can prove the feasibility of new solutions to its production problem, with a nearly complete analysis. Modification “plans” about the product/process, in order to force the introduction of innovative solutions for the next future. In this way the enterprise role becomes proactive, compelling its competitors to react to the new changes. Information to study which new system configuration best accomplishes to the enterprise objectives, and to provide an estimate of the potential improvements in terms of the most important technological factors, such as productivity, manufacturing flexibility, product / process quality Refrigerator and Proxy device Reducing production and maintenance costs on white goods through improving the testing of the complete units after production. Further, to monitor the appliances during use and improve service, but also provide info to the EOL and BOL of behaviour. The main business implications are: Prevent any fault or malfunctioning by monitoring and gathering data from the white goods operation in MOL Possibility of extending warranty and service Based on MOL behaviour, better understand how to design and manufacture better appliances Reducing energy usage in MOL Dismantling / recycling options better understood as lifecycle data from MOL is available Reduced production costs Reduced maintenance costs Electric locomotives The overall objective is to close the loop of information between experience embedded in field data (captured mainly by Service and/or product embedded devices on the locomotives) and knowledge (concentrated on Design for X aspects) needed by the engineers to improve the designs and realize more competitive products. The main business implications are:
Improved and more competitive product designs, mainly by adequate re-use of proven designs Increased customer satisfaction due to improved fulfilment of customer requirements Reduced design effort by allowing engineers to have direct access to discrete and meaningful DfX product data in every design phase Minimized design changes during product service life (respectively warranty period) due to improved component selection during initial design
3.2 Main lifecycle phase: MOL A total number of five demonstrators have their main focus in the MOL lifecycle phase. Construction & mining equipment The application scenario focuses on information that is gained during MOL events on construction and mining equipment (heavy load vehicles) and how rigorous management of the information can improve MOL responsive to events (i.e. to take available information on the vehicle and convert it into an action that improves responsiveness to customer requirements). In addition, this information can provide feedback to the design and manufacturing sources (MOL) as well as waste stream management to make these processes more robust. The main business implications are: For MOL activities, the critical data for the machine could be obtained without the expense (of the customer) of stopping the machine Improved customer satisfaction Allow dealers to better manage their resources and be more profitable. Allow dealers to better service their customer, giving the manufacturer an advantage over its competition Light trucks to heavy lorries & buses Develop usable predictive maintenance strategies during usage of the vehicle in order to optimise maintenance policy. Use PEIDs and wireless communication for complete and real time feedback to company (design, production, after sales, marketing etc) about the mission profile of the vehicle, and the mission profile and reliability of critical components and vehicle systems. The main business implications are: Optimised maintenance with big economical impacts and environmental impacts. Saving of material / spare parts Increased vehicle availability and reliability Increased flexibility in the maintenance plan Product cost reduction (design cost and product cost) Milling machines Reducing milling machine unavailability in production through diagnosis of the machine (prediction of interventions for substitution of mechanical parts, self tuning), and traceability of components. The main business implications are: Increase of production Increase of quality of technical assistance to the End User Minimize the unavailability of the machine because it could prevent sudden interruption
Reduce the maintenance costs because the technician could intervene only when a component substitution is required
Multi-Service Access Nodes (MSAN) Enable registration of information related to HW and SW combinations in order to advance undertaking of reparative actions in order to improve the product’s reliability. Facilitation and improving communication of product misbehaviour from technical support to engineering team. Support maintenance team in diagnosis and solution identification. The main business implications are: Technicians will be facilitated in their everyday work by being able to exploit knowledge gained through previous similar situations Process will be established and facilitated in order engineers to be informed about repetitive faults that occur and could lead to decision-making about improvements to the product Improved product quality and consequently minimise fault occurrence Improved Preventive maintenance Customers will be provided with services of higher quality Customer satisfaction will be increased Reduced environmental impact by extending the life span of the materials (reusability, reparability) Gas boilers – industrial and home Systematically collect and store the data relevant to the application and to apply evolutionary diagnostic and prognostic algorithms over the products lifespan (MOL), thus giving after-sale services a tool to improve the maintenance and repairing operations of wall hung boilers. The main business implications are: Service organizations can be more efficient, thus improving their profitability Possibility to offer users cheaper service contracts The amount of components replaced will be reduced (the service engineer is not always able to find the real problem and changes components that is working fine) Possibility to adjust parameters from remote, making the boiler work in the most suitable condition, thus increasing the life span and reducing energy usage. Through additional sensors, measure the air/gas ratio, the temperature of exhausts and inlet air, making it possible to measure indirectly the efficiency of a boiler, creating knowledge of when it is necessary to clean the heat exchanger to bring the efficiency back to its nominal value. 3.3 Main lifecycle phase: EOL A total number of three demonstrators have their main focus in the EOL lifecycle phase. Recycling plant Tracking and tracing of plastics (production waste, e. g. from the automotive industry) foreseen for a reuse in this sector or in other sectors needing a high plastic material quality. The goal is to totally eliminate each kind of paper and human interacting related to the data transfer between the different steps in the process from receiving the plastics, through sorting, milling, extrusion to final recycled materials. The main business implications are: Less paper and manual information tracking
Reduced costs for information handling and transfer Higher degree of transparency in all processes Optimised decision making in the recycling plant
Construction & mining equipment Focuses on information that is gained during EOL events for heavy load vehicles and how rigorous management of the information can improve EOL responsive to events as well as provide feedback to BOL and MOL functions and tracking of total life cycle information. The primary objective is to manage the waste stream from MOL activities. In addition this information can provide feedback to the design and manufacturing sources as well as management to make the PLM processes more robust. For EOL activities, the critical data from the machine could be obtained by using MOL data from actual operations and service Improve the manufacturers ability to fulfil recycling obligations Better decision making related to reuse options Passenger vehicles Assess the use of PEIDs/RFIDs for improved decision making in EOL (delivery dismantler to transport of parts etc to their final destination) (based on information concerning parts status and history stored on RFIDs), materials tracking and for testing the achievement of recycling and reuse targets as stated by European directives. The main business implications are: The implementation of the European directive will probably load the automakers with extra costs, especially if the target of 95% of recovery must be implemented. The use of valuable lifecycle information will however streamline and optimise the decision making and open some opportunities in the area of used parts 4 SOME IMPORTANT CHALLENGES In section 3, new business practices and possibilities enabled by the technology and systems found in section 1, were presented. However, there are some very central challenges that need to be addressed and handled in order to achieve the positive results. These issues tend to be common for all lifecycle phases, BOL, MOL and EOL. The lifecycle chain (from BOL to EOL) implications are evident as the gathering and structuring of “field”-data in the MOL-phase will in many cases require the cooperation of manufacturers, users, service organizations and so forth. In some of the industrial sectors, rather large investments will be required in order to fulfill the needed requirements for closing the informational lifecycle loop. These investments will both be on the manufacturers side, but also in service providers (e.g. technicians, maintenance, call-centers and so forth), and at EOL parties. The investments relate to e.g. sensors and systems incorporated in a product, the data gathering methods, long-distance/short-distance transfer of data between the product and some centralized (or decentralized) database(s), decision making / decision support systems. These cost aspects are especially important to address in low-cost products or for products that compete in a price-pressured industry sector. The challenges in ensuring data validity and that all data is collected that is relevant for the lifecycle, is another issue that can increase lifecycle costs. There is, for example, a need for incorporating various computer and sensor systems (both onboard and in the participating companies in the lifecycle chain) in order to gather and ensure data
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validity. This might also incur costs. Service organizations, users and personnel in the MOL phase can also in some cases be required to use additional efforts in order to register all that are happening to a product. There are also important issues related to protection of personal privacy in the MOL phase. Will the user/owner of e.g. a car want to have his/hers driving profile be made available for all lifecycle participants? The same is equally true for companies using products that utilize the technologies and systems described in this paper. Data security is therefore of vital importance in order to create trust and interest in the possible upsides of the technology. In some industry sectors, the customers/users will welcome the possibilities presented, while in others, the new technology might be perceived as a threat. 5 CONCLUSSION There will be a shift in business practices based on the closing of the product lifecycle loop. Especially will the seamless e-transformation of product lifecycle information into knowledge be important for manufacturing companies as this allows for greater insights and control of the lifecycle of products. Further, the process implications, e.g. product development, manufacturing, use of a product and the end of life options will be greatly impacted and will need new methods for utilizing the available information. Manufacturing companies not closing the lifecycle loop will most possibly experience reduced competitive power compared to companies that are able to utilize the new technologies, product lifecycle models, PEIDs and supporting decision making tools. 6 ACKNOWLEDGMENTS The authors of this paper wish to thank all the contributors in the PROMISE project for making this paper possible to write.
