R&D: A Make-Or-Break Investment (Part II)

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Integration of R&D with production transpires not just while finishing the design process, but it is fully involved from the beginning to the end.

by Behnam Adib

** This is part two a two-part article on the value of integrating R&D with production in a manufacturing enterprise. Click here for part one.

Who’s to blame?

The Executive team wants to know what went wrong. Did they not have all the expertise and enough resources in-house for the NPD? Did they not have enough parts and materials? Why did the production crew not finish their preparation and test runs on time? Why did they not manage to fix the problems they encountered promptly during their test run? Engineers blame R&D; R&D blames the production group and so on.

The truth of the matter is that none of these individual groups performed erroneously. All they did within their entity was standard practice and was procedure driven.

As you can see the real problem is that the communication or handoffs between R&D and production team did not occur, as it should have. Managers did not have a robust system with sophisticated bridge between R&D and production in place.

The R&D group was isolated the entire time and they never communicated during their entire design. In this example they had 12 months of no communication and isolation and when R&D finally released the prototype to production group they (production) did not clearly understand how to correctly implement and test the prototype. They asked the R&D team frequently about the issues but they (R&D) were busy with the next prototype and did not have enough time to support production, not to mention that at this point it was not their responsibility anyway.

As you can see, R&D cannot remain isolated from the production team during the entire process of prototyping and design.

Modern R&D Integration Model




























The figure above shows a modern integration model. In this model, marketing and managers communicate as in the classic model. However when it is time for engineers to create specifications and design requirements, the R&D group are also heavily involved to ensure the specs and requirements are feasible based on the available resources. If necessary they can ask for more resources before it is too late. Also, they can inquire and be provided sufficient training and equipment automation that they need in advance.

In addition, when it is time to start the R&D process they are not isolated from the production team as they were in the classic model.

During prototyping and design, they constantly work with production group to ensure that their prototype can be easily used in the production line later on. In addition, all the new support applications, documentation and blueprints are supervised by the production team and the managers according to the company’s standards.

During the design steps, all the necessary support documents that the production crew need later on will be provided with the help of R&D team and can be revised as they progress.

When it is time for the test run, the R&D team is also involved to monitor the process and update their prototype and documents and prepare it for the real production.

As you can see the R&D process takes more time in this model however it is premeditated and planned accordingly in comparison to the previous model, which lacked the anticipated actual process time due to unforeseen incidents.

Advanced R&D Integration Model

In the modern model we have recognized that R&D is integrated heavily with pre-production and test-runs so when test runs are completed, the prototype is ready for production.

In the advanced model, not only is R&D involved with these two departments but also is heavily integrated with all the other departments from the beginning to the end. The product can be shipped to customers from R&D itself and the switching between the R&D line and production line is not happening as you have witnessed in the previous models.

The figure below compares these three models. In the classic model, each department is isolated and there are non-value added tasks in pre-production and testing, the overall performance, which is the slope of each department’s curve, is compared with the other two and has the average lowest efficiency.

In the second model, stage 1, described in the previous section, has a better efficiency in the first two sections since they are heavily integrated. Also better performance can be seen in testing and overall better results.

In stage 2, which is the advanced model, R&D is involved with all the departments and integrated with all the processes from the beginning to the end. Therefore the overall performance is better than the two other models.

In the advanced model let’s assume that in the beginning, the R&D team starts 100 units to produce and 95 units get scrapped at the early stages and just 5% get shipped out of the factory. This number increases over time as R&D morphs into production as it progresses. When all the 100 units get shipped out successfully, the R&D of that product has been completed and production takes over for producing that product for that model. The R&D crew can now use their full resources on their next NPD. As you can see in this model, R&D is heavily integrated in manufacturing processes and the handoff to production is very smooth.




























Design For Six Sigma (DFSS) Approach

The alternative approach to the classic “Build-Test-Fix” methodology for product development is “Design For Six Sigma” (DFSS) which is a proactive approach for identifying requirements systematically. It uses predictive engineering to ensure that requirements get correctly implemented efficiently and appropriately.

The common method of “Build-Test-Fix” has some tribulations such as several iterations of rebuild-retest-repair.

Implementing DFSS is usually harder to justify than conventional Six-Sigma efforts. In Six Sigma for process improvement, there are always before and after facts therefore managers and executives can be easily convinced by comparing the results.

In designing or prototyping of a new product often there are no before facts. Also companies do not have enough resources to launch multiple methods for the same product design to compare the results and choose the best method.

A good example of using DFSS in modern product development is Apple computer’s development of the iPod nano. Most of the companies who have participated in this methodology for launching their new products refuse to share any information globally due to the market competition.

The goal of the lean product development process is to deliver the maximum value in the product by using less resource with the following characteristics:

1. Capturing the voice of customer accurately

2. Using most appropriate technology and design to accomplish the most value and quality in the product

3. Transforming the voice of customer into a high quality design that is low cost, high speed, and uses fewer resources

4. Decreasing the waste in the product development life cycle

In lean manufacturing, the main focus is reducing waste and increasing speed but in lean product development the focus is both on reducing waste, increasing speed and also on increasing the value of the product.

The Lean product development methodology consists of three main management approaches:

1. Lean Task Management

2. Lean knowledge and information management

3. Creating Lean Products

In lean task management, the focus is on increasing “value added” tasks, decreasing “non value added but necessary” tasks and significantly reducing “wastes”. Also the goal on value added tasks is to reduce the interruptions and increasing efficiency.

In lean knowledge and information management, the focus is on producing knowledge and information like a “super market”. It should be fresh and not outdated, easy and fast to access, systematic and organized so one product should not be completely different with another one when it comes to information organization. The information should not be redundant and hard to access. The data and information should be generated in all levels from the beginning of design process and should be ready for integration into production properly with predictive methods.

In lean product design, the focus is to reduce unnecessary functions and parts, loosen up unreasonable or redundant tolerances, using standard parts, outsourcing if it makes more sense, control the technology behind the design to be standard and inclined with companies’ strategy so it can be easily supported and integrated into the production floor, avoid complicated user or operator’s requirement, and avoid complicated interface requirements.

Similar to the “Define, Measure, Analyze, Improve and Control” (DMAIC) phases in the Six Sigma approach, DFSS consists of ICOV steps:

1. Identify Requirements (I): Draft the project charter and identify customer and     business requirements.

2. Characterize the design (C): Transfer customer requirements to technical functional requirements. Also generate design alternatives. Evaluate the alternatives.

3. Optimize the design (O): optimizing the main design by using computer simulation and modeling techniques.

4. Validate the design (V): testing, using DFMEA (design failure mode –effect analysis) and verification and piloting.

A Six Sigma –capable company is a company in which each of the services and products achieves 3.4 defects per million in long-term capabilities. A DFSS – capable company is a company which has less than 3.4 non-conforming decisions per million decisions.

Conclusion

The main goal in a manufacturing company is to make the desirable product, which the customers want immediately, with the expected quality by customers, and with the lowest feasible price.

R&D cannot remain isolated from the production team during the entire process of prototyping and design. The integration between R&D and production is critical and convoluted. Integration of R&D with production transpires not just while finishing the design process, but it is fully involved from the beginning to the end. The predictive engineering methodology should be used to determine what tools, resources and services would be needed to have a successful on-time production with no interruption.

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