**3.2 Product manufacturing sheet and figure**

As stated previously, all process/product monitoring and the consequent PDCA cycle should be data driven.

A Product Manufacturing Sheet is useful from which a Product Manufacturing Figure (PMF) can be calculated. The sheet and figure are living documents and figures, in the sense that they must be periodically updated to monitor the improvement of a certain production product/process.

The Product Manufacturing Sheet contains structured information regarding its three macro-topics: design, manufacturing and purchasing.


The Product Manufacturing Figure (PMF) is calculated, as indicated in Eq. (1), by summing the three previously mentioned factors, each having a weight (α, β, and γ) proportional to the importance the company gives to each factor.

$$\text{PMF} = \alpha \ast M\\\text{NFR} \gets \emptyset \ast D\\\text{DP} \gets \gamma \ast \text{SC} \dots \alpha = \frac{1}{2}, \emptyset = \frac{1}{4}, \gamma = \frac{1}{4} \tag{1}$$

The PMF is computed in the following way:


**19**

**Author details**

Engineer.

Ernesto Limiti and Patrick E. Longhi\*

provided the original work is properly cited.

\*Address all correspondence to: longhi@ing.uniroma2.it

Department of Electronic Engineering, University of Roma Tor Vergata, Rome, Italy

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

*Design for Manufacturing of Electro-Mechanical Assemblies in the Aerospace Industry*

PMF close to 100% indicates the part can be fully produced on-time, in-spec and on-quality. Lower values indicate that you should expect some contained derogation of one of the three parameters. PMF < 40% indicates that the product is not enough for mature for an Industrial-grade production and important improvements have to be applied to one or more of the three parameters. Furthermore, PMF is a *living* index, since it can be computed periodically to register changes in the three parameters. For example, MNFR could improve after a set of tooling is made available or

Evidence so the Industrial engineering team can proactively contribute to designing parts and address manufacturing issues during the design follow is provided. In this chapter, the starting point is deep knowledge and understanding of the critical technologies that apply to each manufacturing process and their impact on product assembly and performance. Once the technologies have been considered, the key-points Industrial engineering team must engage are: involvement from the early stages, definition of rules and guidelines for

Occasionally, the prior activities are not sufficient and some product improve-

ment must be carried out during the production process. Specific continuous improvement activities (PDCA cycle) and also detailed tools and figure to quantify

Patrick E. Longhi would like to thank friends and colleagues at Elettronica Group in Rome (ITA) for the many fruitful and insightful technical discussions during his time spent in the company as a Microwave Design Engineer and Industrial

"design quality" in manufacturing have been provided.

*DOI: http://dx.doi.org/10.5772/intechopen.90098*

SC worsen if a component becomes obsolete.

**4. Conclusions**

manufacturing.

**Acknowledgements**

3.Consequently PMF is calculated.

*Design for Manufacturing of Electro-Mechanical Assemblies in the Aerospace Industry DOI: http://dx.doi.org/10.5772/intechopen.90098*

PMF close to 100% indicates the part can be fully produced on-time, in-spec and on-quality. Lower values indicate that you should expect some contained derogation of one of the three parameters. PMF < 40% indicates that the product is not enough for mature for an Industrial-grade production and important improvements have to be applied to one or more of the three parameters. Furthermore, PMF is a *living* index, since it can be computed periodically to register changes in the three parameters. For example, MNFR could improve after a set of tooling is made available or SC worsen if a component becomes obsolete.

### **4. Conclusions**

*Design and Manufacturing*

also useful.

tive and populated database.

cycle should be data driven.

of the key components.

**3.2 Product manufacturing sheet and figure**

ment of a certain production product/process.

three macro-topics: design, manufacturing and purchasing.

major engineering changes ongoing, if any.

In this context, systems for tracing non-conformities are vital so the create an effec-

W. Edwards Deming's famous quote is therefore a cornerstone of this problem

Another practice that contributes to improve product/process performance are manufacturing and engineering organizations periodically reviewing quality non conformities to determine if engineering changes are required. Creating dedicated interdisciplinary teams to perform a specific improvement project is

As stated previously, all process/product monitoring and the consequent PDCA

A Product Manufacturing Sheet is useful from which a Product Manufacturing Figure (PMF) can be calculated. The sheet and figure are living documents and figures, in the sense that they must be periodically updated to monitor the improve-

The Product Manufacturing Sheet contains structured information regarding its

• DDP (design data package): specification, engineering drawings, data libraries, SW code, design rationale documentation, test planes, are available. List of

• MNFR (Manufacturing and workmanship): are all the Tooling/machinery available? Personnel has been trained for the specific product? Automatic test equipment – if necessary – is available? Screening procedures are in place?

• SC (SUPPLY CHAIN) quantifies on-time and on-quality purchasing of the major "buy" items that constitute the product, any obsolescence, vendor rating

The Product Manufacturing Figure (PMF) is calculated, as indicated in Eq. (1), by summing the three previously mentioned factors, each having a weight (α, β,

1.At first, the weight is set for each e parameter (the sum of the weights must be unitary). In Eq. (1), for example, α = 0.5, β = 0.25, and γ = 0.25. These weights

2.A figure between 0 and 100%, according to a checklist, is computed for each parameter (MNFR, DDP and SC) in Eq. (1). This figure changes in time as the three topics improve (or worsen). Checklists become handy to substantiate the figure—between 0 and 100%—associated to each parameter. Moreover, Quality Notifications can be used to obtain useful information of product non-

2 , <sup>β</sup> = \_1 4 , <sup>γ</sup> = \_1 4

(1)

and γ) proportional to the importance the company gives to each factor.

*PMF* = α ∗ *MNFR* + β ∗ *DDP* + γ ∗ *SC* … α = \_1

shall remain constant all over the production process.

The PMF is computed in the following way:

solving technique: *"Without data you're just another with an opinion".*

**18**

conformities.

3.Consequently PMF is calculated.

Evidence so the Industrial engineering team can proactively contribute to designing parts and address manufacturing issues during the design follow is provided. In this chapter, the starting point is deep knowledge and understanding of the critical technologies that apply to each manufacturing process and their impact on product assembly and performance. Once the technologies have been considered, the key-points Industrial engineering team must engage are: involvement from the early stages, definition of rules and guidelines for manufacturing.

Occasionally, the prior activities are not sufficient and some product improvement must be carried out during the production process. Specific continuous improvement activities (PDCA cycle) and also detailed tools and figure to quantify "design quality" in manufacturing have been provided.

#### **Acknowledgements**

Patrick E. Longhi would like to thank friends and colleagues at Elettronica Group in Rome (ITA) for the many fruitful and insightful technical discussions during his time spent in the company as a Microwave Design Engineer and Industrial Engineer.

#### **Author details**

Ernesto Limiti and Patrick E. Longhi\* Department of Electronic Engineering, University of Roma Tor Vergata, Rome, Italy

\*Address all correspondence to: longhi@ing.uniroma2.it

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
