developed to maximize gains in the process of developing new products. Keywords: SBCE, product development, production processes. 1 INTRODUCTION.
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OPTIMIZING THE SET-BASED CONCURRENT ENGINEERING (SBCE): NEW PRODUCT DEVELOPMENT MANAGEMENT DECISIONS Abstract ID: PDEN10455 H.M. Rocha1, L.M.F. Affonso2, U.R. Oliveira3 1 FER, AEDB & DENP, Rio de Janeiro State University, Av. Cel Prof. Antonio Esteves, nº 01, Resende/RJ, 27523-000, Brazil 2 MECSMA, Centro Universitário de Volta Redonda, Av. G.al Affonseca, 1026/104, Resende/RJ, 27520-173, Brazil 3 ECHS, Federal Fluminense University, R. Des.dor Ellis Hermydio Figueira nº 783, Volta Redonda/RJ, 27215-350, Brazil
Abstract The article discusses the set-based concurrent engineering (SBCE) concept and its application in a Development Center of commercial vehicles. The SBCE differs from the conventional concurrent engineering due to the fact that it pursues the parallel development of various design, manufacturing, and assembly concepts, postponing Design decisions. The additional effort required to develop multiple concept simultaneously is compensated by concepts premature abandonment avoidance, increasing the development process reliability and capability, reducing risks and reworks, shortening development time. The research main contribution was to identify SBCE mathematic rationale, enabling Design Managers to establish optimized development strategies. A computational model was used to identify the optimum amount of concepts to be developed to maximize gains in the process of developing new products. Keywords: SBCE, product development, production processes.
1 INTRODUCTION Companies must adapt to market environment conditions to remain competitive, continuously offering solutions that fulfill customers’ needs with quality, flexibility, logistics performance, reduced costs, and technological innovation. In such challenging environment of rapid change and fierce competitiveness [1] [2] [18] [29], the product development process (PDP) is a critical success factor for companies [27]. The present article discusses the principles of the SetBased Concurrent Engineering (SBCE) as an enabler of a flexible, cost-effective, sustainable, and robust PDP. The research was performed at a Development Centre located in a commercial vehicle manufacturing plant installed in the southern Rio de Janeiro state, Brazil. With approximately 4,500 people and a production capacity of 300 units per day, the unit develops new models and new technologyembedded products. The choice of such unit for the research is justified by its relevance, as subject of study by several authors [8] [33], due to its Modular Consortium model, in which the partners interact directly on the final product assembly line, dividing physical space and responsibilities. Although such production system concept is quite relevant, it will not be discussed in this article. 1.1 Relevance and objectives The new product design phase represents only 5% of the development total cost, but establishes 70% to 80% of the operating costs [22] [24]. Therefore, investing more time and talent during the early stages is highly recommended, when expenses are low [3] [10] [11] [21]. However, mortality, since the basic idea until it becomes a profitable product, is as high as 95% [12]. Out of ten ideas about new products, three will be developed, 1.3 will be launched, and only one will be profitable [3]. Usually, companies are not able to keep production costs within the budget and nor launch their products on schedule [3]. Due to the criticality of such paradox, this research purports to present the use of the SBCE to increase PDP
flexibility, speed, and reliability, establishing the variables involved, expected development productivity gains, and its sensitivity to the variables, contributing to make project decisions more robust. 2
RELATED LITERATURE
Based on Toyota’s lean product development, Morgan and Liker [23] emphasize the front-loading effort, taking advantage of initial flexibility/freedom to change to fully exploit alternative solutions, having in mind that project conditions can change drastically along the development [5] [6] [25]. The SBCE foundations determine that acceptable design solutions are developed simultaneously, at the intersection of product capability, process and solution alternatives, postponing final concept decisions. As product launch deadline approaches, the set of alternatives will be gradually narrowed, eliminating weaker solutions: what appears infeasible and/or too inferior is discarded, while what remains acceptable continues to be studied, overlapping development activities [7] [9] [15] [23] [28] [32]. It contrasts to the traditional design practice, the ‘point-based design’ [26], which pushes for design decisions, closing possibilities as quick as possible, by determining the approximate design solution early in the project. The multiple concept approach is not new: Krishnan and Bhattacharya [17] discussed product development in a technological uncertainty environment, deciding whether using a robust and proven technology or choosing a still uncertain technology capable to leverage competitive product. The authors developed stochastic models to establish the optimum technology innovation level, balancing risk involved with expected value generated. Stochastic models for PDP improvement have also been used by other authors [4] [16] [19]. Under certain circumstances, developing multiple design alternatives in parallel generates significant value, fully accounting for the increase in costs of doing so [34]. Inoue,
Nahm, and Ishikawa [13] proposed a design approach that obtains a ranged set of feasible design solutions while incorporating the designer's preference for design parameters. Spahi and Hosni [36] have, also, determined the optimal degree of customization from a product’s structural design perspective, establishing an optimal solution to how far an organization should customize a product to best satisfy its own organizational strategic goals. The concept that seems counterintuitive, expensive, and inefficient [20] [37], purposes to prevent good ideas’ premature abandonment, reducing development risks, reworks rates, and development time. The risk reduction on SBCE occurs due to redundancy, robustness, and knowledge absorption [14]. 2.1 Trade-offs Since there must be a compromise between the factors that add value to the product and those that cause cost increase [3], a disadvantage of the SBCE would be the fact that it requires more people to develop multiple solutions [35]. However, Rocha, Delamaro, and Affonso [31] refute such assumption, based on a theoretical model of PDP gain through the use of multiple-concept, comparing the additional manpower required to develop more than one concept at the time versus the project reliability increment (thus design rework reduction), i.e., gain due to design loopings minimization. The authors assert that if the odds of developing a winner project concept were 90%, a product project requiring 50 new concepts would have a 50 0.5% ‘do-it-right-the-first-time’ success rate (0.9 = 0.5145%), whereas, developing three concept simultaneously (for each concept area, i.e. each of the 50 product areas requiring the development of a new concept), the affected project area would fail only if the tree 3 concepts fail, what would represent a 0.1% situation (0.1 = 0.1%). Therefore, since the odds of each successful concept area would be, now, 99.9%, the overall expected 50 success rate would be above 95% (0.999 = 95.1206%). Based on the additional resources required for multipledesign development, the authors deducted the gain provided by the use of multiple-concept (G) shown in Equation (1). c n
n
G = 10 log [(1 – (1 – Ta) ) / (Ta c)]
(1)
where Ta is the Average concept (idea) success rate; c is the Quantity of different concepts for each design area; and n is the Quantity of design areas (areas requiring the development of new concepts). 3 SBCE APPLICATION The Concept Design Team at the studied company develops three concepts (three ideas) for each feature, mechanism or project subsystem. A new product being developed based on an existing 25 Ton 6 x 2 vehicle platform has been selected for the study. Initial figures estimated a total of twelve new concepts (n) to be developed to customize the existing platform to the new product requirements, in areas such as powertrain and injection system, brake system, fuel tank, harness, chassis and suspension, etc. Since the group had no historic data to establish its idea/concept success rate, Baxter [3] figures were used: 1.3 out of three ideas being launched, i.e., this study took the estimate of Ta = 43.3%. Calculating G (with Ta =
0.433; c = 3; and n = 12), the gain of 28.34 confirms the advantage to use multiple concepts on automotive PDP. A question that comes up is: why three concepts? Why not two, four, or one thousand? Since the quantity of ideas to be developed for a given project area is a managerial decision, it is important to identify an optimum number of concepts which would maximize the gain. Therefore, the objective function shall be Maximize G = f(c), i.e.: c 12
Maximize G = 10 log [(1 – (1 - 0.433) ) / (0.433
12
c)] (2)
c has been set as a non-negative, non-zero, and integer variable (positive integer). Using Excel Solver ©, the optimum solution found would be seven concepts (ideas), with G = 34.14. Such result must be analyzed under a conservative prospect, since the common sense indicates that the task of developing seven different ideas for each concept area seems to be excessive. The gain variation, as function of quantity of concepts, shown in Figure (1) and Table 1, provides a better understanding about this scenario. There is a strong gain grow as the quantity of concepts increases to two and three, but the marginal growth decelerates very quickly after that.
Figure 1: Gain variation as function of concept quantity. Table 1: Gain variation as function of concept quantity c
G
Marginal growth
1 2 3 4 5 6 7 8 9 10
0.00 20.39 28.34 31.89 33.45 34.05 34.14
39.03% 12.51% 4.91% 1.77% 0.29% -0.44% -0.79% -0.95%
33.99 33.72 33.40
3.1 Computational analysis Such findings highlight the importance to perform the gain sensitivity analysis to the variables Ta, c, and n. A 5,000run simulation has been performed, adopting the following limits: Ta to be between 20% and 90%, since values out of those limits would be quite improbable; c, between 2 and 10, justified by the fact that there must be at least two different concepts (ideas) for each area, while ten seem to be too much; and n to be between 2 and 30, based on the project complexity level ranges for automotive industry,
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i.e., 2 to 4: low complexity; 10 to 20, medium; and, 20 to 60, high [31]. 3.2 Gain as function of idea success rate
International Conference on Production Research
of three concepts is much better than two, although, going beyond three concepts might require further investigation.
As shown in Figure (2), gain (G) is reduced as Ta increases. As a matter of fact, a combination of low c and low n leads to a negative G (a loss) when Ta is above a 45-50% range, as seen on the examples took from the simulation, show at Table 2. As n goes up, even with low c, there is a tendency of G going up (compare run samples 4 and 5, and 2 and 9). Lower Ta, by the other side, is usually related to high gains (see run samples 7, 10, and 11), except at the extreme situation of high c with low n (see run samples 12 to 16). Only one occurrence (out of 5,000 runs) of negative gain was noticed with n = 3 when Ta was below 50% (sample 16), indicating, as a general rule, that developing three concepts increases significantly the odds of success. Figure 3. Gain sensitivity to average concept success rate. 3.4 Gain as function of quantity of design areas As seen in Figure (4), as design complexity increases, i.e. the number of areas that require development of new concepts is higher, the development of multiple concepts becomes more advantageous (G increases).
Figure 2. Gain sensitivity to average concept success rate. Table 2: Run samples Run sample #
Ta (%)
c
n
G
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
90 87 85 86 86 74 45 82 87 45 42 33 35 40 44 48
2 2 2 2 2 2 2 2 2 2 2 10 9 6 5 10
2 3 4 5 6 3 2 6 8 6 7 2 2 2 2 3
-2.2 -1.4 -0.6 -0.2 1.3 0.0 0.8 1.3 1.2 8.4 10.3 -0.5 -0.6 -0.2 -0.4 -0.5
3.3 Gain as function developed
of
quantity
of
Figure 4. Gain sensitivity to quantity of design areas. Table 3: Run samples
concepts
As shown in Figure (3), the analysis of G sensitivity to c shows results quite similar to the calculation shown in Figure (1): as c increases, G initially increases quickly, slowing down beyond the range of 4-6 concepts. This finding indicates and reinforces the idea that development
4
Run sample #
Ta (%)
c
n
G
17 18 19 20 21 22 23 24 25 26 27 28 29 30
56 61 54 52 47 47 51 42 49 40 33 44 40 51
10 7 9 9 10 9 7 9 6 8 10 5 6 3
2 2 2 2 2 2 2 2 2 2 2 2 2 2
-5.0 -4.2 -4.2 -3.9 -3.5 -3.0 -2.7 -2.1 -1.7 -1.2 -0.5 -0.4 -0.2 0.0
CONCLUSIONS AND REMARKS
Unlike the PDP common practices, in which one seeks to establish the design concepts as early as possible, so that design can be frozen (usually being established as a
project milestone and/or target), this paper asserts that the development of multiple concepts and decisions postponement might lead to considerable project development benefits, substantially increasing the odds of success, design reliability, reducing time and cost development. The research main contribution was to identify SBCE mathematic rationale, enabling Design Managers to establish optimized product development strategies. A computational model was used to identify the optimum amount of concepts to be developed to maximize gains in the process of developing new products. Based on a real project situation, the optimal concept quantity could be determined through the model presented, based on a known development success hit rate is known. That seems to be a severe pitfall for most companies, since the ones that collect design failure rate usually do so for the whole project, while concept acceptance would be the required information. It has been identified that the use of multiple-concept development is highly recommended, due to cost, time, and design reliability gains. But it has also been demonstrated that such practice has some limits. Further simulations using the model showed that the higher is the quantity of design areas requiring new concepts in a product project, the higher is the potential project gain by developing multiple-concepts. Therefore, simple follow-on products and/or well known concepts and proven technology would better use traditional ‘point-based design’ practice, otherwise, gains will fade away, since the additional effort to develop more than on concept may not worthwhile. SBCE provides great development advantages when used in mid to high complexity projects. Another aspect deducted was that even though the use of multiple-concept can be advantageous, the decision about quantity of concept developed simultaneously affects the potential development gains: based on the study of a commercial vehicle development, three to five seem to be the most appropriated, since an elevated amount of workload to develop multiple concepts impacts negatively the overall project development performance. Further studies are recommended to verify the influence of the learning curve to the marginal design workload for each additional concept to be developed, since, in this article, it has been assumed that additional concepts were independent variables, i.e. that developing one more concept requires the some effort that has been required to develop the previous one. These paper findings may also be used by any organization that seeks to maximize returns on investments in new product development, fulfilling customer needs faster and more reliably. However, further research must be conducted, raising historical data rates of success / error in specific industries, enabling a more robust analysis and definition of the optimum amount of concepts to be developed. It is also recommended to investigate the cost-benefit analysis regarding the effect over product time-to-market. 5 REFERENCES [1]
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