Role of Controls in Product Lifecycle Management

Those with responsibility for control engineering have a lot to gain by collaborating with design engineers, saving considerable resources through product lifecycle management (PLM). Don't think you don't have something to contribute to the process. Have you ever identified a product design modification that could significantly streamline manufacturing, then thought better of suggesting a chang...

06/01/2004


This article includes an online extra side bar on digital manufacturing applications.

AT A GLANCE

 

  • PLM defined

  • Digital manufacturing

  • Design and automation collaboration

  • PLM and MES intersect

Sidebars:
Digital manufacturing: a definition


Those with responsibility for control engineering have a lot to gain by collaborating with design engineers, saving considerable resources through product lifecycle management (PLM). Don't think you don't have something to contribute to the process. Have you ever identified a product design modification that could significantly streamline manufacturing, then thought better of suggesting a change because there's no time?

Because the PLM arena is fraught with a variety of interpretations, let's begin with a definition: PLM is a strategic business approach that applies a consistent set of business solutions in support of the collaborative creation, management, dissemination, and use of product and plant definition information across the extended enterprise from concept to end of life—integrating people, processes, business systems, and information. PLM creates and manages the digital product or plant and provides an information backbone for a company and its extended enterprise.

PLM had its inception in product data management (PDM) applications developed during the mid 1980s. Early implementations primarily wrapped around engineering design data. But as the industry evolved in response to customer requirements, the scope has expanded far beyond engineering departments.

Share information, processes

As part of this evolution, the scope or definition of the 'product lifecycle' has also changed. Fifteen years ago, 'lifecycle' focused on the design engineering activity, as the tools concentrated on CAD data management. In the late '80s, that perspective began to expand to include workflow and processes across the product lifecycle—sharing information and processes among different design activities.

That expansion continues today with PLM solutions touching many different business functions and organizations beyond traditional engineering and design departments. Companies want to leverage product and plant definition information in every area of the business to improve ability to design, manufacture, and service products and plants. One primary initiative is to more tightly integrate design and manufacturing engineering processes.

The concept of developing a product and its production processes at the same time was introduced with concurrent engineering. These concurrent activities are typically two distinct, but related, development threads. Concept and product engineering are primarily concerned with the definition, design, and analysis of a product or plant, and manufacturing engineering deals with development of the processes used to produce that product. In many cases, manufacturing processes outlive product cycles, and the manufacturing engineering task becomes one of adapting processes to a new product.

From a software perspective, CAD/CAE (computer aided design/engineering) tools are employed to help define 'what' is to be built and manufacturing process managementtools are used to help define 'how' it is to be built. This is then delivered automatically to a manufacturing execution system (MES) to manage actual production. PLM backbone solutions provide the integration and management of these tools and processes.

Digital manufacturing

As a key component of PLM, digital manufacturing software (see 'Digital manufacturing' sidebar) first were tightly integrated with product-development applications, enabling companies to more effectively optimize product design and production process plans early in the development cycle. Today, these solutions are being integrated with MES to quickly and automatically bring the process plan to the manufacturing floor. Digital manufacturing concepts have been around for many years and have roots in groundbreaking initiatives from the 1980s and 1990s—including lean manufacturing, agile manufacturing, just-in-time manufacturing, design for assembly, design for manufacturing, and concurrent engineering.


How PLM and MES are integrated in a unified enterprise system environment.

Integration of digital manufacturing into PLM provides an excellent opportunity to improve the link between engineering and manufacturing by sharing critical data about product configurations and associated production processes between PLM and ERP (enterprise resources planning), thus supporting dissemination of product knowledge. Moreover, as an enabler for working collaboratively in the early stages of product development, digital manufacturing enables design and automation engineers to work together to quickly develop designs that can be manufactured most readily and economically within the available production facilities. Such integrated systems also support greater manufacturing agility, giving companies greater flexibility in changing operations according to shifting demands of competition and customer preference.

MES provides real-time manufacturing data collection based upon the latest manufacturing bill of materials (BOM) and process routing, providing visibility into the manufacturing plant and monitoring of production activities. Since MES collects data on every shop-floor activity, these data can be analyzed and shared with the various planning systems including ERP and PLM for problem resolution, planning, and reconciliation (see 'Integrated PLM and MES' graphic).

Many companies are now developing and refining digital manufacturing solutions, including IBM/Dassault Systemes (with its DELMIA suite), Polyplan, Tecnomatix, Visiprise, and UGS PLM Solutions (with E-Factory and its relationship with Tecnomatix).

Process benefits

Manufacturers that have implemented digital manufacturing solutions typically report substantial benefits from improved process and production planning. These benefits include faster ramp-up of volume production, reduction in overall product introduction project time, increased production throughput, reduction in capital costs, greater utilization of facilities, reduction in operating costs, improved product quality, and reduction in continued product support.

Using digital manufacturing technologies, organizations can dramatically reduce the number of design changes. Through simulation, designers and engineers can see how a product is to be produced and assembled prior to actual physical production. Collaboratively they can explore design alternatives and assess impacts on production effectiveness. Since the cost of design changes increases dramatically as a product moves further into production, these early virtual changes can result in significant cost avoidance and significantly reduce the time to start production.

Tool design savings result from using digital representations of the part in the manufacturing environment. This improved visualization permits tool designers and automation engineers to more clearly understand the tools required for production. Moreover, the solution supports effective training of product and tool designers on manufacturing processes and methods.

An inherent benefit of digital manufacturing is to establish proof of concept and validate processes. By digital modeling and process planning, users can define digital manufacturing operations as basis for process simulation. With simulation, a user can visually and analytically verify that a manufacturing operation will perform as planned. Any potential mishaps or inefficiencies can be quickly discerned and corrected.


Digital manufacturing offers a number of time and cost savings throughout operations planning and production.

Lessons learned from process simulations can be incorporated into a set of 'best practice processes' for subsequent reuse in similar situations. Using these verified processes assures that the most appropriate methodology is being employed. Best practice procedures provide consistency and improved product quality, and can significantly reduce costs involved in process and operations planning and production (see 'Operations and production benefits' graphic).

By having access to shared data, information search time can be reduced by as much as 80%. Product and process planners get feedback much earlier, reducing time required for problem resolution.

Digital manufacturing enables control and automation engineers to more fully participate in the overall product definition process, and at a much earlier time. They become collaborative partners with their design counterparts and provide valuable insight and knowledge to improve the manufacturability of parts, assemblies, and products. Their knowledge and expertise are critical intellectual assets needed for companies to improve their products and the manner in which they are produced.



Digital manufacturing applications

Applications encompassed by digital manufacturing generally include tools for the definition, visualization, simulation, and analysis of the step-by-step operations necessary to manufacture a product. Software is used to develop the flow of work through the factory, taking into account speed, capacity, and output of various stations comprising the entire production operation. Simulations are run to study and optimize how work will be performed using available resources, including tooling and equipment, such as machine tools, robotics, and material handling systems as well as the manufacturing personnel. For manual operations, ergonomic packages are used to evaluate human factors such as accessibility for assembly, range of motion, difficulty of lifting and positioning parts, and repetitive operations.

The final output of digital manufacturing efforts is the process plan: the detailed sequence of production steps (along with the required parts, materials, tooling, resources, etc.) required to fabricate a part or assembly from start to finish as it moves from workstation to workstation on the shop floor. The process plan is an essential input needed by ERP systems and by MES.


Author Information

Ken Amman is director of research at CIMdata Inc. (


Digital manufacturing: a definition

Digital manufacturing is an initiative to provide solutions that support effective collaborative manufacturing process planning between design and manufacturing disciplines. Digital manufacturing incorporates integrated tool suites that utilize the product definition and support visualization, simulation, and other analyses necessary to optimize product and manufacturing process design and to support requirements from the different engineering and manufacturing disciplines. It also supports ergonomic and human factor analyses and other engineering analyses tools necessary to optimize the manufacturing process. It facilitates a holistic view of product and process design as integral components of the overall product lifecycle, and enables product design to be sensitive to manufacturing process constraints and capabilities. Digital manufacturing includes software support for functional areas such as:

Translation of design data to manufacturing;

Process planning;

Production operations and machining process planning;

Assembly definition and sequencing;

Detailed line, cell, station, and task design;

Quality measurement and reporting;

Manufacturing documentation, shop-floor instruction, and collaboration; and

Simulation and analysis of shop-floor activities, facilities, resources, and capabilities.

It is typically implemented to mitigate risk, provide virtual plant tours, establish proof of concept, deploy machinery sooner, validate processes before release to manufacturing, reduce floor space and redesign of equipment, identify bottlenecks, collisions, and worker issues before they happen, improve resource utilization, program machines and cells offline, eliminate prototypes, and reduce rework or scrap.



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