Originally Posted - October 20, 2006
Increasing the Visibility of Requirements
For a design team, a loosely stated requirement is often misleading. That's why it is important to capture requirements in a structured manner—in a requirements database maintained by requirements managers—before they are passed along to the product development teams. The role of the requirements manager may not exist in the current scenario, or it may have been fulfilled by the marketing team. But it is essential to analyze requirements properly before taking them up for development.
Companies can use best practice methodologies like an affinity diagram, a Pugh matrix (decision matrix), or quality function deployment (QFD) for analyzing, prioritizing, and mapping requirements to existing features or to new features that they can deliver. These methodologies also require companies to benchmark what competitors can deliver to satisfy a given requirement.
A true product lifecycle management (PLM) system emphasizes the fact that product requirements should be communicated clearly to all stakeholders of product development, including the design, testing, materials, supplier, manufacturing, production engineering, and service teams. Increasing the visibility of product requirements is the first step in implementing any PLM system.
The design starts from a concept generated by a given market need. The design team must be given a highly efficient environment in order to be able to work with maximum productivity. The use of computer-aided design (CAD) and computer-aided engineering (CAE) tools has been common in the industry for the last decade, but the real challenge is storing the generated CAD data in a centralized, secure, and easily accessible place. It's also important for design engineers to be able to check in or check out these files on a daily basis during the design cycle. This gives designers more control over the product design, since the centralized storage of product data makes for an information-rich product development process.
Furthermore, incorporating a strict approval mechanism enables designers to do things right the first time. Precise CAD data is an important element of analysis for the CAE or computer-aided process planning (CAPP) teams, since erroneous or improper CAD data makes CAE and CAPP efforts useless. Also, ensuring that CAD data is precise results in precise engineering bills of materials (EBOMs) for the manufacturing resource planning (MRP) or enterprise resource planning (ERP) systems.
CAD data cleanliness and control is not only important for the design team, but also for the subsequent product development teams down the line. For this reason, CAD data management is considered the heart of PLM.
Bringing people together is the main objective of any PLM system. When product development people are closer (virtually speaking), they can collaborate more efficiently. Due to current globalization trends, as well as the trend toward leveraging competencies and resources that are geographically separated, it has become mandatory to collaborate in a virtual environment. The health of the collaboration can be parameterized with three basic questions:
- How secure is your collaboration environment?
- How efficiently can you collaborate?
- How many resources is your collaboration environment consuming in terms of the network and hardware and software?
Collaboration plays a vital role when your design teams are separated geographically. It also makes lot of sense for organizations that outsource the whole product design to third-party organizations or suppliers. Collaboration enables the host companies to give product design feedback to the design partner companies or suppliers in the early stages of the design, rather than after completion. This is critical, as issues detected early in product design are less expensive to fix than issues that are detected later.
Manufacturing organizations are striving to answer the question of how to reduce time-to-market. Companies can obtain more market share and profit if they introduce a product to market sooner than their competitors do. Thus, they tend to minimize cycle time whenever possible. Product cycle time as a whole can be broken down into cycle times for design, engineering analysis, validation, buying, process planning, and piloting.
Product development involves cross-functional teamwork, as the following functions are generally involved: marketing, design, engineering, purchase, testing, production engineering, manufacturing, and quality. In a conventional product development cycle, these cross-functional teams work serially, one after the other (also termed serial engineering). This conventional method actually ties product cycle time to a certain period, as only one team can work at a time (while the other teams wait for the results). For example, the analysis, testing, and purchase teams will wait for the design to be completed before proceeding.
The idle time of other teams can be used to shrink the overall cycle time of the product. This leads to the concept of concurrent engineering, where cross-functional teams can start their work at a predefined point of the previous step of the product life cycle. For example, the engineering or purchase departments might start the analysis and buying processes when the design is 60 percent complete. And the manufacturing planning team might start once analysis and testing is 50 percent complete. Concurrent engineering can shrink product cycle time phenomenally by leveraging the maximum time resources possible from all stakeholders of the product.
However, the impact of rework in concurrent engineering is heavy compared to serial engineering. For example, when there is a design change after 60 percent completion of the design, it will impact the work of the analysis and testing teams, since they have already started their work. But this impact can be easily managed in a digital workplace.
If analysis and testing is being conducted in a digital environment that is seamlessly integrated with the CAD environment, then the impact of design change is minimal: this is the power of digitization. It is strongly suggested that the product development environment be digitized as much as possible in order to attain successful concurrent engineering.
Nowadays, there are CAD tools that are tightly integrated with native CAE and CAPP tools. Concurrency can be easily achieved with the kind of digital environment that allows for designing, analyzing, simulation testing, and product planning, since these activities do not necessarily have to be conducted physically. This also reduces the cost of building physical prototypes.
Engineering Data Control
Engineering data is the output of the design team, which releases the design to other teams in order to get their feedback. The design team then incorporates the collected feedback on the engineering parts. There should be a revision control mechanism in this process. Even after the design is completed, there can be a design change due to failure in product development steps down the line. And even after design, analysis, and testing, there may be difficulties in the assembling process only during the piloting stage. In this scenario, the part has to be revised, and the design change will be incorporated into the new revision. The product will once more undergo reviews, analysis, testing, and validation before it comes back to piloting.
Engineering data control is more cumbersome when design is outsourced to a third-party organization or to a supplier. In this case, the host company should notify the design partner or supplier that a design change is necessary. When companies do not have a healthy collaborative environment, this design change process increases cycle time drastically. This is why collaboration and integration between the host company and its design partners and suppliers play an important role in reducing time-to-market.
Preserving Product Knowledge
Introducing more new products every year is becoming a trend in the manufacturing industry. Research shows that the companies introducing more products per year will be more profitable than those that do not. Within this trend, it has become vital for companies to preserve product knowledge in a well-defined IT system, instead of keeping it only inside the brains of various employees. In other words, product knowledge is an asset to companies—if they have a proper system for storing product data, they do not need to worry about human resource attrition. Product knowledge can consist of any one of the following elements:
- engineering data
- product requirements or specification sheets
- lists of suppliers that can supply the product
- lists of technical specifications such as CAD drawings, CAD models, process specs, etc.
- engineering analysis reports on parts
- the number of configurations that can be generated from product aggregates, and their compatibility rules
Modular Product Development—Design One, Configure Many
Organizations striving to introduce more new products per year should concentrate on modularizing their product structures and on generating more variants from the structures. The concept of modularization helps phenomenally in saving time-to-market. All organizations need to do is break down the product structure into meaningful sets of aggregates, and create compatibility rules between the aggregates under each product line. Basically, they need to make a product portfolio rationalization. If they make sure their aggregates can work with each other across different models under a single product line, they can easily generate new product configurations on the fly (based on product need), and launch the new product in just weeks. This eliminates the time barrier for conventional product development, which involves starting from scratch. It takes time for organizations to come to the level of reusing and leveraging product aggregates to generate many configurations, and only a few organizations around the world have achieved this so far.
PLM: Manage Your Innovation Process
In general, PLM helps manage the product innovation process in many ways. It enables companies to directly map product requirements to features and to obtain control over product data. It also helps them to preserve their product knowledge assets, and allows companies to enter into the new paradigm of modular product development.
About the Author
R. Nagarajan is a consultant with wide PLM implementation experience, having been involved in successful implementations around the world. He has worked with international customers, including Honeywell, GE, Medtronic, and Venture, and also provides consultancy to Indian companies such as Eicher Trucks, Ashok Leyland, and ELGI Equipments. Now in the process of helping ELGI Equipments (India) to obtain quicker return on investment from their PLM implementation, he can be reached at email@example.com.
[Editor's note: this information was current as of the original publication date.]