6. Product data management

Requirements and strategies for the retention of automotive product data

Product data modelling and management has become an increasingly feature for enterprises in order to strengthen their competitive position. Modelling can be regarded as a requirement analysis, whilst management is a means to apply powerful application to product data. This data describes the status of a product during the whole product life cycle as well as the transformation of one state to another. Operations performed in one phase of the product will definitely have impact on later phases and the product itself. It is of interest to analyse this impact and to extract methods to assist the designer of a product in order to improve the quality of a product. Based on an internal analysis of European research projects, this paper shows how product data management is used during the product life cycle of a product and how it can improve a products quality. The goal is to derive support strategies for the designer based upon the data structure and knowledge representation used within these projects. It describes an approach to measure product data structures and design knowledge representations. Different levels of complexity concerning the managed product knowledge in design support systems have been identified as well as different design support strategies.

To carry out the share of product data between different PDM (product data management) systems, this paper provides a domain ontology model of PDM system which is based on ontology and constructed by Protege3.1. This model consults the framework of PDM system in STEP PDM schema, uses the UML class figure to describe the relationship between the system classes. Finally by data mining the mass data of PDM, ontology libraries can be fully exploited and used, information and knowledge can be acquired for analyze and decision-making of enterprise

High-variety production like mass customization is facing the challenge of effective variety management, which needs to deal with numerous variants of both product and process in order to accommodate diverse customer requirements. To utilize commonality underlying product diversity and process variation, it has been widely accepted as a practice to develop product families, in which a set of similar variants share common product and process structures and variety differentiates within these common structures. Based on such variety implication, this paper proposes a data structure, called generic Bill-of-Materials-and-Operations (BOMO), by unifying Bills- of-Materials (BOM) and routing data into a single set in order to synchronize multiple perspectives on variety such as customer ordering, product engineering, and operations planning. A generic structure is accordingly developed for characterizing variety effectively. The merits of the generic BOMO for integrated product and production data management are detailed in terms of order processing, engi neering change control, production job planning, cost accounting, as well as integrated material and capacity planning. An implementa tion of the proposed generic BOMO methodology in customized souvenir clock manufacturing is also reported.

Among the well-known enterprise application software modules, product data management (PDM) and enterprise resource planning (ERP) have played major roles in the design and production departments respectively. Among those, PDM helps design engineers to manage the product data and product development process, and ERP acts as the main tool for order, production and inventory related processes. There are a few duplicated functions among these systems such as BOM management. However, very few works addressed building a bridge among the systems. On the other hand, an agent-based approach has been applied in the collaboration of shop floor equipment for some time. Basically, each agent acts like a machine controller that can take charge of the respective equipment and communicate with other agents as well. This research applies similar concepts to bridge the gaps between PDM and ERP systems and to support the collaboration activities of the two systems. A three-stage framework was proposed to develop the agent-based collaboration system. In the concept stage, the modelling tools such as VAD and eEPC were used to capture collaboration requirements between PDM and ERP. In the design stage, a UML-based analysing method MaSE was used to develop the agent-based system. Also, an agent development tool ZEUS was used to generate agent code. Finally, in the implementation stage, a PDM/ERP collaboration system was developed to support the designer in making decisions about the replacement parts requirement analysis.

Product Data Management (PDM) systems and their offspring, Collaborative Product Development and Product Lifecycle Management technologies, aim to bring engineering enterprises together, allowing seamless interoperability between different departments and throughout the extended enterprises. However, there are a number of shortcomings in the current crop of commercially available systems, such as the lack of design knowledge sharing, links with Enterprise Resource Planning systems, knowledge management tools and a generic standard for PDM system implementation. This paper presents a proposed software solution to some of the above problems. In particular, the paper describes methodologies being developed that are aimed at overcoming the lack of analysable product information at the conceptual stage of product design and manufacturing evaluation, along with the integration of such a concept design tool within a distributed environment. A leading PDM system is used to manage all the information and knowledge that is made available to internet/intranet users in a controlled manner. The international standard for exchange of product data model (STEP) is implemented to enable the integration of the design environment with manufacturing and enterprise resource management systems. In addition, the paper also introduces three other recent/ongoing projects, being carried out at Cranfield University, in the application of PDM, knowledge management and STEP standard for integrated manufacturing businesses.

Product data management (PDM) has proven its value as a critical tool in handling the enormous amounts of technical information companies generate. Now the PDM industry is applying this experience to more affordable systems targeted at smaller organizations. Several suppliers have entered the market with economical approaches aimed specifically at midsized companies. Many companies use PDM to eliminate inefficiencies in the engineering change process. Diebold, Inc. reduced engineering change cycle time by 30 percent through process automation with PDM. IBM is using PDM technology in product development for sharing data among groups, designing tools tightly coupled with release and change processes, interfacing with procurement and other services, and establishing real-time communication of data across the enterprise. Engineers increasingly are using PDM viewing features to track subsystems. Virtual-mockup capabilities enable engineers to import all the parts files for a product designed in Solid Edge, regardless of computer-aided design (CAD) vendor or file format.

PurposeThe purpose of this paper is to provide tangible examples of product data management (PDM) practices in large high‐tech companies, and to highlight current challenges.Design/methodology/approachThis paper is a qualitative interview study. First, a PDM system frame was defined to aid analyses. Second, an interview study was carried out in four companies to clarify the practical realisation of PDM, and the current challenges. The interviewees are experts in the field of PDM, currently holding significant related posts in their companies.FindingsOverall, PDM activities are similar in all companies, however, there is some diversity in the realisation of these practices. PDM related challenges identified in this study are various, strongly influenced by company background and current organisational state.Research limitations/implicationsThis paper includes interviews in four companies with different backgrounds, and a workshop, providing a good view on topical issues in the field of PDM. The obtained results could vary to some degree, should the sample size be larger, or especially should the products of the studied companies be less complex.Practical implicationsThis paper provides managers and PDM system developers' with a better understanding over the issues that are affecting PDM solution development and on major system requirements, together with relevant insight on current challenges.Originality/valueThe existing literature is relatively scarce in describing the practicalities of PDM. The obtained results highlight the significance of company background influencing the selection of PDM solutions.

In Memoriam: Dr. Clive L. Dym (1942–2016)

PDMAR (Production Data Management and Reporting) is software to manage oil and gas operation and production data. It supports integration of automation system and business network that providing fast and accurate production information. It includes production database, data acquisition from automation level, data entry modules, oil and gas allocation modules, formula builder, expert system, data validation, security, asset management modules, link to other applications, and integrated reporting and data browser. It provides customizable configuration modules that enable user to create and modify table that appropriate with their field condition. PDMAR uses commercial RDBMS to store data. It uses latest Microsoft .NET Framework technology and web services to keep pace on speed, efficient, and effective data storage and transfer. They would browse daily report that contains daily production, operation, latest well test, HSE status, oil and gas allocation, chemical storage, transportation, utilities, etc through web interface easily. Data interfacing are performed automatically from major commercial historian database as well as oil and gas production software. PDMAR has been installed in many onshore and offshore facilities. With optimized data transfer, operator will transfer data in less time. Data link between control layer and management level automates field operator's job thus give more reliable and precise data capture. For remote and distributed well field, mobile system that incorporated with handheld device help operator to record important data to main database using automatic synchronization. Engineer and management use less time to analyze production and operation data than before. They receive daily production report in precise time every morning through email and website. They now focuses on analyze data than collecting data before. There is only one validated data version that is used for daily report.

Facing increasing product complexity and pressure to reduce time to market, manufacturing process planning (MPP) engineers must be able to quickly access reliable information in order to make swift and correct decisions. Organizations therefore turn to information management tools, such as PDM (Product Data Management), MPM (Manufacturing Process Management) and ERP (Enterprise Resource Planning), to support their product development processes. These various information management tools compete by offering similar features, while MPP engineers have to manipulate multiple tools to access the information they need. This paper aims to take a fresh look at a fundamental question: what are the specifications of an ideal information management tool that would help MPP engineers efficiently define manufacturing work instructions (process plan) from the product definition? This paper thus presents the approach and the results of a research work conducted within the process planning department of a manufacturing company operating in the aerospace sector. The study was conducted so as to direct the effort toward documenting the MPP process and the MPP engineer’s information needs. The approach that is presented primarily relies on a comprehensive documentation and modeling of the MPP development process. Two processes have been modeled, a reference process of the MWI (Manufacturing Work Instructions) development and a change management process impacting the MWI development. These process models offer a sound basis to conduct an analysis of the MPP engineers’ information needs. This analysis next leads to the specifications of a Dashboard solution aimed at MPP engineers.

The objective of this chapter is to provide an introduction to Product Data Management (PDM) systems in the PLM environment. The chapter will help readers working in PLM Initiatives understand the importance of PDM systems and participate in activities related to PDM systems. The chapter also aims to give students who are studying PLM a basic understanding of the components of PDM systems.

Conventional Product Data Management (PDM) supports aspects of information management but don’t promote the application of target oriented design methodologies like ‘Axiomatic Design’. For Manufacturing System Design PDM has to manage the actual targets, to direct a target-oriented procedure, and to manage objects for evaluation. Targets result from customers and manufacturers. The definition of ‘Functional Requirements’ and ‘Design Parameters’ has to be organized between different core competencies at different times and places. The goal is the configuration of a PDM framework supporting distributed collaborative manufacturing system design in a ‘Axiomatic Design’ oriented way. Based on an axiomatic approach, the implementation of this methodology in a distributed PDM framework is presented. For this, the necessary procedure and controls of ‘manufacturing system design’, information architecture and data interfaces to other software tools are considered and then the paper describes necessary metamodel.

Production Data Management and Surveillance in Shale Operations presents both the benefits and challenges of field data management in shale production operations. The examination of the lifecycle of field production data the paper offers will lead us to explore issues of surveillance, predictive analytics, enterprise data availability, data quality, integration, and regulatory compliance from the perspective of unconventional resource operators. The paper also includes case studies that detail the implementation and lessons learned from five shale oil and gas producers. Factory drilling in unconventional plays leads to highly paced operations and a large number of wells, which in turn leads to the generation of an enormous amount of field data that must all be captured, processed and turned into actionable information for surveillance, operational accounting and HSEQ compliance. This data, much of which is collected by field personnel at the well site, includes fluid volumes and operating conditions as well as run tickets, tank battery inventories, sales volumes, equipment status, and chemical usage. Also, increased sensitivity to environmental issues around shale plays requires operators to closely manage water, emissions and other environmentally impactful measurements, all of which must be collected, monitored and reported. Maintaining the quality of the gathered data is paramount as poor data can lead to costly consequences, from under- optimized production to fines for inaccurate reporting. Integrating surveillance processes with the field systems will promote consistency and accuracy. In conjunction, tools that provide asset-specific variances, alerts and visualizations will help identify operational issues immediately, allowing for swift alignment of the field team with corporate goals. Raw production measurements collected in the field are processed to produce allocated production volumes at each well and zone. These allocated volumes are then put to use by different departments across the operator’s enterprise, in their planning, forecasting, operations management, revenue accounting, marketing, regulatory and partner reporting. Field information and production data lie at the heart of operations management for all operators. Production Data Management and Surveillance in Shale Operations submits multiple case studies on the collection, analysis and distribution of this data, along with the best practices employed in shale plays.

The housing industry faces challenges in product data management, resulting in negative effects on productivity, efficiency, and quality. The purpose of the study presented in this paper is to elaborate on requirements and functions of a support system to mitigate challenges with product data management in the wooden single-family house industry. As a support system, a pilot product lifecycle management (PLM) system was developed with functions to fulfill the requirements. The support system was installed and tested in a real-world setting, a wooden single-family house company, Company T. The paper contributes with the insights that a PLM system for industrialized house building can be developed, fulfilling the requirements in this specific industry. The functions developed were perceived to have value and could, for Company T, mitigate their challenges with product data management. The support system facilitate integration between the pilot PLM system, the building information modeling tool and the enterprise resource planning system at Company T. Based on the study carried out, the paper concludes that a PLM system is feasible, and that it might be a way to mitigate challenges with product data management in the wooden single-family house industry.