The Principles of DFX in context of DFSS

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The Principles of Design for X in the context of DFSS
The Design for X is an effective technique for implementing Concurrent Engineering. It concentrates on a limited number of critical elements at a time, for example seven plus or minus2. The permit the present resources to be put into effective use. For example, Design for Assembly (DFA) concentrates on the enterprise process of an Assembly that is part of the life cycle production. DFA take into account five to nine key factors associated with the subject product, incorporating part symmetry, weight, fits, size, form features, orientation and so on. It takes into account 5-9 key factors associated with the assembly process such as gripping, special tooling, inserting, equipment, handling and so on (Filippi & Cristofolini, 2009). Careful examination of these issues and their association leads to a better design conclusion with respect of the ease of assembly. Additionally, an atmosphere of teamworking cooperation is formed, thus assembly efficiency is enhanced.
The letter X in the DFX is composed of two sections: performance ability and life-cycle processes x. It is a concurrent approach to DFSS, but DFX concentrates on important business elements of concurrent engineering, that is, maximizing the utilization of the scarce resources to the DFSS team. In addition, DFX offers a systematic technique for analyzing design from a spectrum of perspectives. It empowers teamwork within the contemporary DFSS surrounding.
The principles of DFX can be discussed through the context of the following DFX tools: * Design for Manufacture and Assembly * Design for Reliability * Design for Maintainability * Design for Serviceability * Design for Life Cycle Cost The Design for Manufacture and Assembly technique offers a substantial reduction in parts, leading to a more reliable design with less assembly and subordinate manufacturing costs. The Design for Reliability enhance the DFSS team to acquire insight into how and why an anticipated design may not be successful and determine aspects of design that may require to be promoted. When matters concerning reliability are addressed at an early position of DFSS, project cycle duration is reduced. A basic product can be attained through the sequential application of DFMA followed by the Design for Serviceability, which is the capability to diagnose, replace, eliminate, replenish, or repair a component to innovative specifications with moderate ease. Poor serviceability generates warranty costs, lost sales, customer dissatisfaction and market share due to loss loyalty. In Design for Life- cycle cost, the activity based costing (ABC) technique is utilized for estimating the life-cycle design cost to assist, guide the DFSS team in making decision to attain a cost effective Six Sigma design in the available market and operations uncertainty. In the context of Design for Maintainability, the aim of Design for Maintainability is to enhance the satisfactory operation of the design with minimum utilization of effort and budget. The Design for Maintainability, Design for Service and Design for Reliability are associated because minimizing the maintenance and promoting service can be attained through promoting reliability (Filippi & Cristofolini, 2009)

DFX Tools and Applications

The figure above shows the overall structure of the DFX tools (Chiu & Okudan, 2010)

Design for Manufacture and Assembly
Designs that are made to be simple to manufacture during the initial stage of a product growth are much more possible to avoid redesign later when the system is being approved for production enthusiasm. The optimal way to ensure a concept can be made is to have an active engagement from the production and supply chain institutions during concept selection and production. The Desing for Manufacture and Assembly are an efficient technique that the DFSS team can utilize to carefully analyze each design parameter that can be identified as a section for manual or automated manufacturing and assembly to steadily eliminate products that have no function and should have been removed. The most leverage of DFX beyond the design axioms is attacking those wastes such as; the directions of the assembly that require several extra operations, and the design parameters with unneeded tight tolerances (Huang, 1996).
With the Design for Manufacture and Assembly, important improvements tend to emerge from simplicity thinking, particularly decreasing the number of standalone parts. The Design for Assembly technique offers the following criteria against which area must be analyzed as it is added to the assembly; * During the working on the product, does the part move fairly to all other parts already assembled? * Should the part be a distinct material than or be separated from other parts already assembled? * Should the part be isolated from all other parts already assembled because the required assembly or disassembly of other isolated parts would otherwise be not possible?
From the above, it means that the part must be isolated from Desing for Assembly terminology or an important part. All parts that are not important can hypothetically be eliminated with other important parts. Thus, hypothetically, the figure of critical parts is the lowest figure of isolated parts of the design. The DFSS team approximates the assembly duration and put forward its efficiency rating in terms of assembly hardness. This chore can be carried out when each part is analyzed to determine how it will be oriented and inserted into the product. For this practice, the design is rated and from this rating standard times are identified for all workings needed to assemble the part. The Design for Assembly duration standard is a categorization of the design properties that impact the assembly process. The entire assembly duration can then be estimated and utilizing standard labor rates, the assembly efficiency and cost can also be assessed (Huang, 1996). At this position, manufacturing costs are not taken into account, but assembly efficiency and time offer benchmarks for new restatements.
After all likely simplification chores are established, what follows is the analyzation of the individual manufacture parts. The goal of Design for Manufacture and Assembly is to enhance the DFSS team to measure alternatives, assess the cost of manufacturing and establish trade offs between increased manufacturing cost and physical coupling. The Design for Manufacture technique offers experimental data for estimating cost of multiple process. The Design for Manufacture and Assembly technique usually benefits error proofing approaches, which may be utilized when components are taking form and manufacturing and assembly matters are taken simultaneously. Error proofing is an approach for eliminating human error (Huang, 1996). It was developed to attain a zero defect. The approach begins by analyzing the process for probable problems, identifying parts by the features of dimensions, weight, shape and detecting processes deviating from nominal norms.
Advantages of Design for Assembly and Manufacture
The primary advantages of this tool is that it reduces the production cost per unit by easing the manufacturing unit as well as limiting tool per each step. This means that utilizing this tool helps the organization to maximize its cost and minimizes its production costs. Through this way it also helps to save time. The tool also has the potential of identifying errors during the design stage and assist avoid problems occurring.
Disadvantages of Design for Assembly and Manufacture
This tool incurs organization upfront time and cost to develop a good design that fits into the manufacturing surroundings. Thus, organizations cannot see past the upfront investment.
Design for Reliability
It is the likelihood that a physical object delivers its serviceable requirements for an anticipated duration under defined working conditions. The duration can be estimated in various ways. For example, duration in mileage and in service is both tolerable for automobiles, while the figure of open-close cycles in switches is sufficient for circuit breakers (Huang, 1996). The DFSS team should utilize the Design for Reliability while limiting the life-cycle cost of the design. The examination of reliability usually concerns analysis and testing of stress power and surrounding factors and should always incorporate improper utilization be the end user. A reliable design should expect all that can go wrong.
The Design for Reliabily divide into two categories, namely: * Knowledge based engineering * Variation control
The Design for Reliability utilizes the law of probability to project failure and adopts: * Approaches to lower the rates of failure in the physical object through employing design axioms and reliability science concurrently * Approaches to measure reliability of critical parts and design ways to eliminate coupling and other design weaknesses * Derating * Design failure mode-impact and criticality assessment, which is utilized to look for alternative ways to correct the failures. * Robustness exercise through making the design insensitive to all uncontrollable sources of variation * Redundancy, where needed, which seeks for a parallel system to back up a critical part or subsystem in case it fails
Reliability involves a broad range of spectrum of matters that incorporate human mistakes, technical malfunctions, surrounding factors, insufficient design practices and material variability. However, the DFSS team can promote the reliability of the design through the following: * Minimizing the destruction from service, and repair * Counteracting the surrounding and degradation factors * Lowering the complexity of the design * Optimizing the utilization of standard components * Identifying all the causes of defects through utilization of DFMEA * Regulating the important and critical factors using statistical process control * Tracking all defect rates from both in house and external suppliers and establishing measures to handle them.
To eliminate the likelihood of failure, it is important to determine all likely modes of failure and the mechanism through which these failures happen. Thus, detailed examination of Design for Reliability is established after physical and process structure, growth, followed by prototyping, but consideration concerning reliability should be considered in the conceptual phase. The DFSS team should take benefits of the present knowledge and experience of similar objects and any development modeling approaches that are present. Failure avoidance, especially when associated to safety is important. Various dangerous analysis techniques are present. In general, this technique begins by illustrating dangerous elements and then proceed to determine all events than may change these elements into dangerous conditions and their symptoms. Thus, the DFSS team has to determine the corrective actions to remove these conditions (Huang, 1996). One of these techniques is the fault-free analysis, which utilizes deductive logic gates to put together events that can generate failure or the fault of interest. Other tools that can be utilized with fault tree analysis incorporate FMECA and the Fishbone diagram.
The advantages Of Design For Reliability
The tool for design for reliability analyzes the potential failure mechanism and remove them. It also provides past data, thus becoming an important tool in retrieving the suppliers history. As a matter of fact, this tool analyzes the load strength and association. Thus, it is a relevant tool for the organization in determining the progress of the organization from the beginning.
Disadvantages Of The Design For Reliability
This tool wear-out. When this tool wear out, the product fails. Moreover, this tool encounter coding errors.

The Design for Maintainability The goal of Design for Maintainability is to guarantee that the design work satisfactorily throughout its projected life with the lowest expenditure of budget and effort. The Design for Maintainability, Design for Reliability and Design for Serviceability are associated because minimizing maintenance and enhancing service can be attained through promoting reliability. An efficient Design for Maintainance minimizes the following: * The user and technical maintenance time * The downtime for maintenance * The cost coming from maintainability characteristics * The downtime for maintenance * The logistics needs for replacement parts, personnel, and backup units.
Actions concerning maintenance can be corrective, overhaul and preventive. The Design for Maintainability incorporate control and access, handles, displays, labels, testing, and mounting and positioning. Thus, the DFSS team require to follow the following guidelines in order to go in line with this: * Design for minimum hand tools * Utilize common fasteners and attachment techniques * Provide for safety devices * Lower the number of serviceable design parameters with simple skills and procedures
The DFSS team should also establish the strategy for repair decisions within the framework of life cycle costing. The key maintainability cost factors to take into account incorporate transportation, handling, repair logistics and training of maintenance people, which include the design of service, distribution, production, and installation of repairable components.
Advantages of the design for maintainability
This tool helps in reducing maintenance cost through its maintainability features. This is in line with the aim of the design for maintainability. The tool also establishes the user or and technician maintenance time. It does this through providing access and control features, which are important in maintaining the tool. This tool also streamlines the production processes and remove delays that may lead to cost overrun, which can impair organization competitive edge.
Disadvantages Of The Design For Maintainability
The maintainability action may be preventive of the occurrence of a process that is important to the development of the product.

Design for Serviceability
It is the capability to diagnose, replenish, repair or remove any component to original specifications with ease. Poor serviceability generates warranty costs, lost sales, customer dissatisfaction and market share due to loss loyalty. The DFSS team may check their voice of the customers for any voiced serviceability attitudes. Simplicity of serviceability is a performance quality in the Kano analysis. Thus, the DFSS team should strive to have serviceability personnell concern in the initial stages as they are taking a customer segment (Huang, 1996). This is enabled through focusing on the following Design for Serviceability: * Labor duration * Safety * Diagnosis * Customer service attributes * Special tools * Ease of service * Unique tools * Frequency of repairs and occurrence
Advantages Of The Design For Serviceability
This tool provides a base for planning for the needs of the customers. It is highly concerned with customers needs, thus, it derive the product requirements. It also puts forward the serviceable items as well as the required procedures during the product development.
Disadvantages Of The Design For Serviceability
This tool may lead to generation of warranty costs if the tool is poorly serviced. As a result, customer dissatisfaction occurs, which may lead to loss of sales and market share due to loss of customers loyalty.
Design for Life Cycle Cost
It is the exact cost of the design. It incorporates not only the initial cost of manufacture, but also the related costs of defects, buybacks, litigations, distribution warranty, implementation cost and supports of all employed DFX approaches. The activity based cost is a strong approach for determining the life-cycle design cost, especially when coupled with uncertainty provisions. The approach employs process action and sensitivity charts to determine and detect important parameters influencing the Life Cycle Cost. The Activity Based Cost approach asserts that the design, whether a service or a product or a process consumes activities. This assertion differentiates Activity Based Cost from convectional cost estimation approaches that asserts resources consumption. The Activity Based Cost goal is to determine activities in the design life and then allocate reliable cost drivers and consumption intensities to the activities (Huang, 1996). The probability distribution is provided to represent inherent cost uncertainty. The Monter Carlo simulation and other discrete-event simulation approaches are then utilized to model uncertainty and to determine the impact of uncertainty on cost.
Advantages Of Life Cycle Costing
The design for life cycle cost is an important tool because it provides the real cost of the design. This means that this tool covers all the cost employed by the DFX methods. Through this an organization is able to identify the total cost of the product
Disadvantage Of The Life Cycle Cost
It relies on the assumption of the Activity Based Cost, which assumes that the DFX design consume activity. This may not be the case for all the design process because the consumption of activities may not be the same in line with cost drivers.
How Design for X can be Implemented Efficiently
The Design for X can be implemented through the engagement from multiple departments within an entity. At the implementation level, there should be a Design for X personnel reporting to the Board level who can then affect other departments from the top down to assign resources and time and then integrate Design for X into their regular task. The most important thing in the implementation of the Design for X is its goal. The goal of the Design for X should be real and measurable to everyone and the advantages must be justifiable (Shahin, 2010). So as to be implemented efficiently, the Design for X must be part of the organizational culture and must have strong back up from all levels of management.
Therefore, the preparations for implementation for DFX should be driven by the main objective, which should be to gain support of the total organization including the workforce and the management team. This is because each an every protocol is likely to be affected by the changes from the DFX analysis. Without the support, the DFX process will not deliver the benefits promised by the DFX tool and projected by the DFX team. Likewise, the chosen best change package should be turned into a change plan of actions through careful planning. This means that necessary resources should be brought into the actions to effect the optimum sequence events: who should carry out a task, how, when and where.
Moreover, in the execution, implementation plan the change program should assert the status of action. This strive for consideration breaking down the actions into smaller steps so that each step can be tested in a short period. This applies particularly when the problem or the solution is especially complicated or if the results of the solution has a high degree of uncertainty related with it. Furthermore, efficient implementation must be executed in line with the plan. Thus, it is usual to identify expected problems popping up, which may create conflicts and confusion. Therefore, it is necessary to work quickly and diligently with the assistance of the people in other affected departments to revise the plan and remove such conflicts in a manner that works for all involved (Shahin, 2010).

Elements of DFX material Process | X | DFX | Assembly | Boothroyd-Dewhurst (DFA) Hitachi AEM Lucas DFA | Fabrication | Design for Dimension Control Design for Manufacturing Hitachi MEM | Test and Inspection | Design for Dimensional Design for Inspectability | Provision logistics | Design for Distribution and Storage | Recycling and disposal flexibility | Design for Ease of Recycling | Environmental Repair | Design for Reliability and Maintainability |

The above table provides a list of DFX elements to the significant material. These elements are important in all stages of process design. The application of DFX concerns these elements because they act as the product design or processes. The DFX needs a design process or product that permits for flexibility and the ability for negotiation from the beginning (Shahin, 2010). Rather than specifying rigid needs, a design range is determined for each strategy that allow the design to regularly converge to a final concept that meets all needs.
All in all, the efficient implementation of DFX involves the DFX elements in line with the necessary material of the process. DFX emphasizes on all design elements, goals and associated constraints in the design stage. Through taking into account all these, the best product can be generated. Through the implementation of DFX may need extra effort in the design stage or process, with the integration of the process and the product into the design through business practices, technology tools, and management perceptions, the outcome is a more predictable product to better meet the needs of the customers, a smoother and quicker transition to manufacturing and a reduced life cycle cost. Nevertheless, the biggest challenge is not implementing new approached, but overcoming organizational hindrances and resistance to changing the new ways of doing things. It is obvious that DFX plays a significant role in the current manufacturing sectors (Shahin, 2010).
DFSS v's NPD
DFSS is a development of an existing New Development (NPD) process that gives more structure and an efficient way to manage the deliverable resources and trade offs. DFSS is the way by which individuals employes tactics, strategies and tools to acquire entitlement performance (Yang & Haik, 2011). In this case, it integrates quantitative and qualitative tools and major performance measures that permit continuous organizations to manage the NPD process in a better way to optimize various competing key drivers such as cost, time to market and quality. This means that an archetypal NPD process might incorporate various high level development phases such as the seven step systems engineering approach: needs assessment, concept design, preliminary design, detail design, process design and construction, manufacturing and end of life.
Most entities have incorporated management reviews known as checkpoints or toll gates throughout these design phases to permit them to assess risks, ensure transitions from each phase and monitor progress to the following phase. But, since the quantitative measure of performance may not be present until the physical product is prototyped in the later design phases, all that exist early in the NPD process is the risk of future problems. It is the reason progressive entities concentrate on risk management in the initial stages if an NPD process rather than on the applications of other quantitative quality tools. DFSS opportunities categorize the size and effort such as macro opportunities that concern the design and development of the whole new product or the key redesign of an existing one. Therefore, DFSS seeks to eliminate manufacturing process problems through utilizing advanced Voice of the Customer approach and proper systems engineering approaches to avoid process problems at the outset. The DFSS approach obtains the necessary requirement of the customer and derive engineering system parameter requirements that improve NPD effectiveness in the eyes of all people (Yang & Haik, 2011). This yield products that give great customer satisfaction and increased market share. This approach also incorporates tools and processes to predict, stimulate the product delivery system and model as well as the analysis of the NPD system life cycle itself with necessary investigation outcomes and gains to ensure absolute customer satisfaction with the proposed NPD process. In this way, the DFSS is closely associated with systems engineering, operation research, workflow balancing, systems architecture and concurrent engineering.
NPD process is developed to address macro opportunities in most entities based on the seven step system engineering model. The contemporary implementation of DFSS comprises of integrating DFSS deliverables into the checkpoint criteria for the macro level NPD process, training the management group ways of resourcing and guide the effort and utilizing a structured method to the application of DFSS principles at the micro level. This means that at the micro level, there is no regular standard for integrating DFSS into an existing NPD process because NPD processes are as broadly varied as the products and services they produce.
DFSS is an integral part of NPD process and extremely entrenched in its culture. DFSS gives a rigorous structure that is important in the manufacturing phase, though, the sometimes the structure appear to be a huge burden in the ideation and discovery phase of the process. The NPD is a collaborative and concurrent effort towards a cross functional process in nature.
DFSS is an entity process focused on improving profitability. If it is applied properly, it produces the right product or service at the correct time at the right cost. Through its utilization of product and team scorecard, it is a strong program management approach. It is an enhancement to the NPD and not a replacement for it. Thus, a documented, well-comprehended and useful NPD process is essential to a successful DFSS program.
The NPD process gives the roadmap to success. While the DFSS gives tools and teamwork to get the task done efficiently and effectively. Through rigorous applying the tools of DFSS, once can be assured of predictable product quality. Thus, it can help entities overcome many of the problems that cause NPD to fail. Through this, it enables the entity to reduce time, benefit financially and improve quality. Thus, when the NPD concentrates on the roadmap to success, DFSS concentrates on the creation of new value with inputs from customers, business requirements and suppliers. The NPD may use these inputs, making it to focus on improvement and not design for a new product. Nevertheless, the NPD shows the engineering base of DFSS ((Yang & Haik, 2011).
On the other hand, DFSS is a parallel activity with all important parts of an entity represented within a cross-sectional team. There is a vast psychological variation between carrying out a task within a functional group and performing it as a cross-sectional team member. When individuals from distinct functional groups and with distinct experiences and skills work together towards groups and goals that affect large areas, there is a synergy that maximizes what each individual brings to the project. Moreover, all important knowledge and information is made present to the teams so they can ground their decision on data rather than on judgement. Therefore, NPD process can be optimal relative to each other taking into account that the DFSS provide a structure for managing the NPD, thus adding value and improved customer satisfaction.
As a matter of fact, DFSS improves the quality of NPD because it concentrates on determining customers needs, expectations and priorities from the beginning. This is the initial form of quality from the perspective of the customer. The DFSS prioritizes other quality factors in design decisions and find creative ways to make decisions.
The primary goal for DFSS is to reduce life cycle costs. These costs may not be eliminated, but when first applying DFSS in the NPD process. While some costs will decrease, others will rise because of the requirement to determine what the customer needs or expect. But, these costs are recovered as the NPD process is improved (Yang & Haik, 2011). The NPD process concentrates on getting products right as efficiently as possible. When there are established priorities and designs and development activities, iterations and verifications are fewer, less cost in incurred. This happens with the help of DFSS.
The NPD process begins from the perspective of developers and moves through designs and prototypes through iterations of testing and building. It later causes various design changes and wasteful rework and therefore incurs losses in terms of the cost of poor quality. With DFSS, in contrast, the design begins from the perspective of customers and design is assessed through modeling and simulation before prototypes are created and tested. Moreover, the later design errors are determined in the manufacturing operations or process problems in transactional operations, the higher the cost and the variation can be dramatic. It makes sense and saves costs to detect problems at the earliest point and to prevent them if possible. This is the logic of DFSS, that is reducing variations is relevant at any point, but the further upstream the better. Sigma | Defects in millions | Cost | 6 | 3.4 | <10% | 5 | 230 | 10%-15% | 4 | 6200 | 15%-20% | 3 | 67000 | 20%-30% | 2 | 310000 | 30%-40% | 1 | 700000 | >40% |

The figure shows how DFSS in the initial stage of NPD process can save in the operational stages. It shows that, though the design typically represent the smallest actual cost elements in NPD, it leverages the largest cost influence (Yang & Haik, 2011). Thus, any improvements in the design have a large direct impact on cost. For example a 30pecent savings through design simplification would lead to over 21percent costs saving overall.
DFSS gives an integral part of the NPD process incorporating: changing voice of customer data into customer needs, functionality and first principles modeling, concept brainstorming and selection, robust design, design for manufacturing and assembly, tolerance and ability analysis, and other analytical tools.
How DFSS Can Be Integrated Into NPD
Drawing from the preamble to 21CFR820, device manufactures can utilize DFSS as a tool for creating quality into NPD (Hasenkamp & Olme, 2012). DFSS is a business management approach and an NPD process. It utilizes data, metrics, statistics, risk management, team dynamics as well as project management instruments to take products from theory to commercialization. The information driven decision making process delivers a sigma six able products through concentrating on design and process parameters grounded on customer and market needs. The DFSS includes the six sigma information drive principles in the NPD process. NPD processes are designed, established and distributed to six sigma quality and ability for those critical to quality needs specified by the market and the customer. It has become successful because DFSS utilizes a systematic technique. Manufacturers drill down from the customers needs to detailed product features and specifications. These drill downs determine acceptance strategy, specifications and tolerance for the completed product and facilitate documentation.
Grounded on corporate initiative from the parent holding corporation, DFSS training can be introduced to the various enterprise units. This happens when six sigma has already been successfully illustrated on the manufacturing side of the enterprise in projects concentrated on waste reduction. The DFSS approach adds a roadmap and specific tools for the upstream development process that concentrate on understanding the relevant parameters in the design and how these can interact with the production processes. DFSS stresses on the significance of using cross-sectional teams and bridging the production side of the enterprise into the development process as early as possible. One of the emphases of DFSS is on acquiring the voice of the customer using instruments like customer surveys, quality functional deployment, and KJ analysis (Hasenkamp & Olme, 2012). Statistical tools such as the design of experiments, analysis of variance, measurement system analysis, hypothesis testing and ability studies should be taught in the DFSS training with the focus placed on how these tools can be utilized in the NPD process to proactively create products that have high capability to launch successfully.
Prior to the completion of the DFSS training, the management should identify the tools and approaches taught required to be integrated into the NPD process so as for the organization to gain benefits of DFSS. In conjunction with this, a structural change should be made to the organization be introducing a separate NPD group entirely that concentrate on creating new products as well as integrating DFSS into the NPD process. Other engineering group outside of the NPD group should become the applications group that concentrates on taking existing designs that has already been validated and customtailroing these designs for particular customer utilization. However, following the guidelines of DFSS and other approaches, the development group should be structured as two product teams with a project manager, technician and the design engineer. In order to support the NPD group, a full time quality engineer, staff engineer and manufacturing engineer should be added into the group.
Moreover, besides the job of integrating DFSS into the NPD process, the NPD group should project the development time to a certain value say 36months. This is to allow the tools for DFSS to complete the NPD process swiftly which still attaining a successful product launch. Indeed, the model for DFSS should be a phase process that review each phase separately. Some of the phases that can be included in the DFSS model include; concept, design, optimize and capability (Hasenkamp & Olme, 2012). Through this way, special tools should be related to each phase and a generic roadmap be given in the DFSS training through which the NPD team can follow the methodology. The NPD team should only take relevant tools from each phase and include them into the existing development process effectively preserving the phases of the existing development process. The key distinction between the old process and the NPD process is that the NPD process concentrate on acquiring and understanding the voice of the customer incorporating both external and internal customers like manufacturing. In addition, the DFSS tool place emphasis on using process data early in development in order the products being designed to have a high likelihood of success at the start product launch. In particular, tools such as Monte Carlo simulation and design ability studies concentrate on utilizing the actual variability data to introduce tolerancing and design strategy.
After integrating DFSS into the initial stage of NPD process, the only input manufacturing has into the design is first to create a review after important decisions concerning what concepts to take forward is made. It should be noted that DFSS, like any concurrent engineering methodology stresses the engagement of manufacturing and quality as well as other disciplines during the initial development of the NPD concepts.
The integrated development process should maintain the entire phase format of the NPD with particular tools added in where applicable. The initial phase of the process should be definition of concept, which should also be subdivided into stages such as customer input, customer needs, prioritization, generation of concept and concept down selection. The initial stages concern gathering the voice of the customer through customer surveys and then documenting the data into a raw response table. Next, the data should be translated into what the particular responses mean to the organization in terms of design strategy. On the prioritization stage, the system level house of quality should be constructed, which identifies the most relevant design constraints grounded on the customer input and a comparison of the existing product offerings to the rival products. In the generation of concept stage, the NPD team should start the initial design concepts that can potentially satisfy the needs of the customers. However, the concepts should be evaluated in terms of cost grounded on the preliminary bill of provisions and functionality grounded on technical advantages and disadvantages. Nevertheless, a phase review should exist with the NPD team recommendation to the management as to which design concepts should be taken to the next development phase (Hasenkamp & Olme, 2012). This is the level at which the management should determine the efficiency of the process.
The following phase of the development should be the proof of concept phase whereby the NPD team should concentrate on understanding and establishing different concepts including the nominal performance anticipations. This phase should further be subdivided into; sub-system selection, analytical examination, design, lead time determination, control compliance and concept validation. The sub-system selection stage subdivide each concept into separate sub-systems in order the hybrid concepts be evaluated and established. During this stage, a formal design for manufacturability examination for each concept is finished to feature the relative strengths and weaknesses of each concept. This examination puts emphasis on the manufacturing side of the enterprise early in the design process and should assist with the launch of the product. An analytical examination of the anticipated nominal performance is also carried out for each concept. The design stage concentrates on establishing a preliminary drawing package of the different components and concepts to assist analyze potential assembly matters and also back up the initial cost estimates for various components through preliminary supplier quotations. Moreover, the concentration of the lead-time determination stage should be on understanding the timing effects on any new capital tool purchases on the program schedule so long lead-time can be ordered sufficiently in advance, and as the product being developed relates to safety restraints in the automotive sector, numerous regulatory approvals must be acquired in one of the development stages. At the final stage of the development process, the phase should end with a concept validation test series. The down selected concepts and manufacturing process are first assessed through producing a limited test series of preliminary prototype products. Nevertheless, a review should be carried out in order to determine the efficiency of the NPD process.
Prior to the phase review, the NPD process development and design validation should follow. This phase is usually subdivided into: design, design failure, quality planning, design validation and application. This phase concentrates on the factors that contribute to the nominal performance of the product as well as the factors that contribute to the variability of the product. The design stage form a total drawing package with the necessary tolerance studies carried out and specifications incorporated in each component. Moreover, optimization studies grounded in design of experiments should be carried out to feature each design concept. The quality planning stage focuses on the quality system for design prototypes that ultimately serve as the base for the product quality system, and the design for failure stage empathizes on managing the perils of the design through completing the exact design for failure mode document that drives the necessary testing and examinations for design reliability and robustness. At the design validation stage the final down selected designs are tested stringently through a complete test series grounded on customer and sector regulations. This test series is much more in depth than the concept validation test series and ensures that the design satisfy the generic customer needs and specifications. Finally, the application stage handles the planning needs for the handoff of the design from the NPD group to the applications groups where the product is tailored for particular customer utilization (Hasenkamp & Olme, 2012). However, a review is held to determine the efficiency of the process.
The majority of the task happens in the application group. The DFSS tools and approaches that are integrated into the NPD process applies mostly in the first phases of the process, which is the primary task of the NPD group.
Benefits Associated with Integration of DFSS into NPD
Integrating DFSS into NPD is important because it improves the product fitness though understanding the voice of the customer. DFSS focuses on the importance of acquiring and interpreting the voice of the customer at the onset of the development process. In particular, tools are taught such as KJ analysis and quality function deployment that assist characterize and quantify the intangible nature of some of the respondent information typical from customer surveys. Constructing the quality function deployment is a tiresome task, but KJ analysis disseminates all of the raw data into a useable format whereby client's priorities are well understood (Chowdhury, 2003). The inclusion of this tool also help the case company to develop products that meet the expectation of the customers.
DFSS concentrates on the analysis and proper utilization of data. It offers a logical roadmap that demonstrates where and how different tools can be utilized to improve the NPD process. It is clear that the tool are not new and have been in existence for a long time. The variation add benefit of DFSS is the organization of the tools and how they can be applied to the development process. Specifically, understanding the voice of manufacturing process plays a major role in the development of new designs. In particular, the product can be designed to be robust in light of the inherent distinction of the proposed manufacturing process and utilizing tools like the Monte Carlo simulation.
Problems Associated with integration of DFSS into NPD
DFSS must be assessed in terms of difficult data to determine whether the investment in training pays for itself in terms of bottom line savings. The challenge with DFSS is in determining which metrics to assess the deployment against because the metrics can all be compared against the strategy as well as historical deployments to determine whether there is existence of actual improvements (Chowdhury, 2003).
In comparing DFSS to its manufacturing associates, the return on manufacturing, Six Sigma is extremely easy to quantify. However, DFSS does not have a luxury until the products enter production later. Though it is not a big issue, there is need to have a regular metric to assess DFSS implementation during the process rather than waiting for the whole process to mature.
Moreover, besides the issue of assess DFSS implementation, DFSS is nothing new and does not add anything to the existing body of knowledge (Chowdhury, 2003). Furthermore, the tools in DFSS have been in existence for a long time as well as the techniques. However, it adds something to the development process, mainly through providing structure and a roadmap of how, when and where to utilize the tools in the development. It collectively combines a lot of ideas and tools that are available in the literature and industry.

References
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