LCC method that allows the designer to make comparative LCC estimation ... The product attributes at the conceptual design phase and LCC factors are ...
Approximate Product Life Cycle Costing Method for the Conceptual Product Design J.-H. Park’, K.-K. Seo’, D. Wallace’, K.4. Lee3 (1) CAD/CAM Research Center, Korea Institute of Science and Technology, Seoul, Korea 2 Department of Mechanical Engineering, MIT, Cambridge, MA, USA 3 School of Mechanical and Aerospace, Seoul National University, Seoul, Korea
1
Abstract Although the product life cycle cost (LCC) is mainly committed by early design stages, designers do not consider the costs caused in subsequent phases of life cycle. The estimating method for the product life cycle cost in early design processes has been required because of both the lack of detailed information and time for a detailed LCC for a various range of design concepts. This paper suggests an approximate LCC method that allows the designer to make comparative LCC estimation between the different product concepts. The product attributes at the conceptual design phase and LCC factors are introduced and the significant product attributes are determined by statistical analysis. Neural network algorithms are applied to estimate LCC by considering the identified product attributes as inputs and the LCC as output. Trained learning algorithms for the known characteristics of existing products will quickly give the estimation of LCC for new product concepts. The estimation for maintenance and energy costs of electronic appliances is shown as an example. The proposed method provides the good estimation for the LCC and gives the guidelines leading to cost-effective design decision-making at the early design stage. Keywords: Life Cycle, Cost, Artificial Neural Network (ANN) 1 INTRODUCTION The ability of a company to compete effectively on the increasingly competitive global market is influenced to a large extent by the cost as well as the quality of its products and the ability to bring products onto the market in a timely manner. In order to guarantee competitive pricing of a product, cost estimates are performed repeatedly throughout the life cycle of many products. In the early phases of the product life cycle, when a new product is considered, cost estimate analyses are used to support the decision for product design. Later on when alternative designs are considered, the best alternative is selected based on its estimated life cycle cost (LCC) and its benefits. Manufacturers usually consider only how to reduce the cost the company spends for material acquisition, production, and logistics. In order to survive in the competitive market environment especially resulted from the widespread awareness of global environmental problems and legislation, manufacturers now have to consider reducing the cost of the entire life cycle of a product. The costs incurred during life cycle are mostly committed by early design decisions. Studies reported in Dowlatshahi [ I ] and by other researchers in design suggest that the design of the product influences between 70% and 85% of the total cost of a product. Therefore, designers can substantially reduce the LCC of products by giving due consideration to life cycle implications of their design decisions. The research on design methodology for minimizing the LCC of a product also becomes very important and valuable [2,31. The need for sustainable development has begun to change the way many companies design products. Generally, product designers are being asked to judge the cost of the products to be developed. Not only is this an additional task for designers, but it is also necessary something they are qualified to do. Therefore, the cost models created by cost estimators should be integrated with traditional design models, making the parametric cost results available on demand. However, the use of detailed parametric models is not well suited to early conceptual design, where ideas are diverse and
numerous, details are very scarce, and the pace is swift. This is unfortunate because early phases of the design process are widely believed to be the most influential in defining the LCC of products. This paper suggests an approximate LCC method that allows the designer to make comparative LCC estimation between the different product concepts. The novel approach provides the approximate and rapid estimation of product LCC based on high-level information typically known in the conceptual phase. The product attributes at the conceptual design phase and LCC factors are introduced and the significant product attributes are determined by statistical analysis. An artificial neural network (ANN) is trained on product attributes and the LCC data from pre-existing LCC studies. This approach does not require a new LCC model. The paper is organized as follows. In section 2 the proposed methodology is overviewed. The product attributes that are used to estimate the product LCC in conceptual design are defined and idetified in section 3. In section 4, the approximate LCC method based on the ANN model is developed. Finally, some concluding remarks and future works are described in section 5. 2 OVERVIEW OF THE METHODOLOGY In this paper, the possibility of an approximate LCC method is investigated. The proposed method provides useful LCC estimation of products in terms of product attributes related with LCC fators. The LCC factors and high-level product attributes are introduced and identified by correlation test between them. The approximate LCC method based on ANNs is a different approach from other LCC methods. It does not require any LCC modeling on a product basis. Learning algorithms train ANNs using the identified high-level product attributes and the corresponding LCC factors. Through this training, the ANN is adapted to emulate existing LCC studies and generalize trends between products. This is illustrated in Figure 1.
the firm developing the product in the LCC analysis. In this paper, the total enegy cost during life cycle and maintenance cost in usage phase are shown as examples.
3.2 Product attributes
Figure 1: Training process of an approximate LCC model The product designers query this learning model with high-level product attributes to quickly obtain an approximate LCC for a new product concept. Designers need to simply provide high-level attributes of new product concepts to gain LCC predictions based upon trends inferred from real products and LCC studies used as training data. 3
IDENTIFICATION OF VARIABLES IN THE CONCEPTUAL PRODUCT DESIGN There are two key variables that are investigated in order to evaluate the proposed LCC estimation models. Firstly, the feasible LCC factors as output to predict the product LCC are introduced. The LCC factors should be in a form useful to cost estimators and designers. Secondly, a list of reasonable product attributes as inputs is identified and correlated with LCC factors in order to make a set of meaningful attributes. The product attributes must be meaningful to designers and consist of only product attributes typically known during conceptual design.
3.1 Life cycle cost factors The first issue is to identify the feasible LCC factors as outputs for use in an ANN model. In order to introduce the LCC factors, all the costs incurred in the product life cycle are investigated and enumerated. The LCC of a product is determined by aggregating all the LCC factors. The product LCC can be decomposed into cost factors as shown in Table 1 [4, 51. Table 1 provides a list of cost factors for product life cycles that was adapted to the feasible LCC factors as useful outputs for predicting the product LCC in the ANN model. This decomposition is by no means the most comprehensive and representative of all products or any product for that matter. Life cycle
Cost factor
Design
Market Recognition, Development
Production
Materials, Energy, Facilities, Wages, Waste, Pollution, Health Damages
Usage
Transportation, Storage, Waste, Breakage, Warranty/Service, Energy, Materials, Maintenance, Pollution, Health Damages
Disposal 1 recycling
Disposal / Recycling Dues, Energy, Waste, Disposal, Pollution, Health Damages
Table 1: Life cycle stage and LCC factors for use in the ANN model The cost factors considered will depend on the stage in which we want to use the model, the kind of information to be extracted from the model and the product being designed. While the life cycle cost is the aggregate of all the costs incurred in the product's life, it must be pointed out that there are differences between the cost issues that will be of interest to the designer of the product and
In this section, product attributes as inputs for the ANN model are defined and identified. General product attributes The product attributes need to be both logically and statistically linked to LCC factors, and also be readily available during product concept design. The attributes must be sufficient to discriminate between different concepts and be compact so that the demands on the ANN model are reasonable. Finally, they must be easily understood by designers and, as a set, span the scope of the product life cycle. These criteria were used to guide the process of systematically developing a product attribute list. With these goals in mind, a set of candidate product attributes, based upon the literature and the experience of experts was formed [6, 71. Experts in both product design and cost estimation discussed as candidate attributes derived from the literature. The candidate product attributes identified initially are listed in table 2. They are specified, ranked, binary or not applicable according to their properties such as an appropriate qualitative or quantitative sense or typically rank order conce ots. Durability Price Strength Product Liability Process Distribution mass Mas Distribution volume Volume Transport distance Materials Transportation means Performance Lifetime Functionality Use time Assemblability Disassemblability Additional consumable
Energy source Power consumption Upgradeability Serviceability In use flexibility Recycled content Recyclability Reusability Mode of operation
Table 2: The candidate product attributes This study helped us identify attributes that designers could both understand and had knowledge of during the conceptual design. Furthermore, we were able to evaluate which attributes are likely to vary significantly from concept to concept. The maintainability attributes are additionally identified because the purpose of this study is to estimate maintenance cost of usage phase in conceptual design. Maintainability attributes Maintenance is an important aspect of life-cycle concerns and plays significant role during the usage phase of a product. It is the design attribute of a product which facilitates the performance of various maintenance activities. Design characteristics of a product which facilitate maintainability will be effective factors which support product maintenance during usage phase. Maintainability is one of the product design parameter that has a great impact in terms of ease of maintenance. Maintainability attributes for products, in general, can be identified by design, personnel and logistics support properties [8, 91. The maintainability attributes under design property are only considered as the product attributes at the early design stage and presented in table 3. They are also estimated by an appropriate qualitative or quantitative sense. Sampling data with product attributes and corresponding enery and mainternace cost from previous studies are collected for different electronic products. Based upon these data, the candidate attribute set is again refined and then test for first order relationships with the energy and maintenance costs.
Identification, Standardization, Diagnosability, Modularity,Tribo-features
Table 3: Identified maintainability attribute of design property Bivariate correlations are computed and correlation tests to 95% statistical significance are performed between quantitative attributes and the data of energy and maintenance costs for various products.
The architecture for a BP neural network was developed for the estimating the product LCC. More than 50 experiments were performed to determine important design decisions for BP network including the encodig scheme, network topology and parameter tuning. Figure 2 shows the structure of the developed BP neural network, which consists of an input layer with 21 or 24 nodes, a hidden layer with 16 or 20 nodes, and an output layer with one node. Input layer 21 nodes 24 nodes
Hidden layer Output layer 16 nodes 20 nodes
1 node for energy wst 1 node for maintenance cost
XI X2
Inputs: Identified product attributes
-#
,Life cycle energ)I wst or Maintenance wst
outputs: Product LCC
Figure 2: Structure of the BP neural network to estimate the product LCC Table 4: An example of correlation coefficients: product attributes vs. energy 8, maintenance cost The product attributes strongly correlated with the energy and maintenance cost are used to predict the product LCC in the approximate LCC model. Finally, 21 product attributes for energy cost and 24 product attributes for maintenance cost are chosen and used as inputs in ANN models.
4
DEVELOPMENT OF THE APPROXIMATE LIFE CYCEL COSTING METHOD
4.1 An approximate product life cycle costing using ANNs Sampling data collection As mentioned earlier, the feasibility test of the proposed method was conducted focusing on the total enegy and maintenance cost components of the LCC factors. Sampling data with product attributes and corresponding energy and maintenance costs from the past studies [ l o ] were collected for 40 different products. The energy cost was obtained by total energy consumption during the life cycle of products. The maintenance cost in usage phase was calculated by equation (1). The equation is composed of labor cost, part replacement cost, and failure rate. The equation is useful in determining maintenance cost. MC = [(LcF,x& + (TL x RJ + C d ] x RF (1) Where: LCFlxed= Fixed labor cost such as the fixed cost when a maintenance representative visits a customer ($) TL = Labor time such as mean actual repair time(MART) or mean actual maintenance time(MAMR) (hour) RL = Labor rate ($/hour) C R = Mean replacement cost of parts or materials when maintenance occurs ($) RF = Failure rate The examples of sampling data for the ANN model are shown in table 5. Artificial neural network (ANN) with backpropagation A backpropagation (BP) neural network is a multi-layer, fully connected, and feed-forward neural network.
4.2 Results and discussions In order to test the capability of the trained ANN, ten other samples are used as the test set which have not been used in the training. The results of the product LCC predicted by the ANN model for ten products are provided in table 6. In table 6, some observations from the testing results are summarized: (1) Among ten testing samples, it can be found that the percentage errors of the samples are within ?6% except for energy cost of the heater (12%). This is considered as a very good LCC estimation at the early design stage. (2) The maximum deviation of LCC estimate from the actual LCC is about 12% which is acceptable for practical use. (3) The performance of the trained ANN is consistent in the training, validation and testing samples. During the early conceptual design stages of product development, available data are limited and the cost analyst must depend primarily on the use of various parametric cost estimating techniques in the development of cost data. The accuracy of a LCC model deviated from an actual LCC is typically between -30 and +50% [ I l l , so the proposed method based on ANN shows better product LCC estimation and gives the guidelines for the cost-effective design decisions in conceptual design phase. The two advantages of the approximate LCC method are summarized: (1) The product attributes include the cost aspects related to products. Extracting such product attributes can be easily done by a product designer. Detailed information for cost estimation is not required. (2) The ANN based method can help better conceptual design through evaluating the costs of different design alternatives. 5 CONCLUSIONS AND FUTURE WORKS It has been recognized that the design process needs cost models that: 1. Take into account the complete life cycle of products 2. Can be used at the very early stages of design. 3. Can provide information to designers in a timely manner and in a form that can be understood and used. The product LCC is mainly determined by early design decisions. But, at early conceptual design stages
Manufacturing System, Annals of the ClRP 4711: 353-356. Alting, L., 1993, Life cycle design of products: a new opportunity for manufacturing enterprises, In Concurrent Engineering: Automation, Tools, and Techniques, A. Kusiak (ed.): 1-17. Alting, L., and Legarth, J., 1995, Life cycle Engineering and Design, Annals of the ClRP, 4412: 569-580. Park, J.-H., Seo, K.-K., and Wallace, D., 2001, Approximate Life Cycle Assessment of Classified Products using Artificial Neural Network and Statistical Analysis in Conceptual Product Design, Proceedings of EcoDesign 2001: 321-326. Sousa, I., Eisenhard, J. L., and Wallace, D., 2001, Approximate Life-Cycle Assessment of product Concepts Using Learning Systems, Journal of industrial Ecology, 414: 61-81. Takata, S., Hiraoka, H., Asama, H., Yamoka, N., Saito, D., 1995, Facility model for lifecycle maintenance system, Annals of the ClRP 4411: 117121. Tarelko, W., 1995, Control model of maintainability level, Reliability Engineering and System Safety; 4712: 85-91. Eisenhard, J. L., 2000, Product Descriptors for Early Product Development: An Interface between Environmental Experts and Designers, M.S Thesis, MIT. Creese, R. C., and Moore, L. T., 1990, Cost modelling for concurrent engineering, Cost Engineering, 3Z6: 23-27.
designers do not know the costs incurred in subsequent life cycle phases. This paper has suggested an approximate LCC method for predicting the product LCC, especially focused on enegy cost and maintenance cost, in conceptual design stage. Three areas critical to the preliminary validation of the approach were developed: model outputs in the form of the LCC factors; model inputs in the form of a compact, meaningful, and understandable set of product attributes; and the ability to predict the product LCC using the ANN model. It is apparent that the approximate LCC method based on ANN is feasible for estimating the cost incurred in subsequent phases of the product life cycle based on design decisions at early conceptual design stages. The proposed method can be used to estimate the product LCC and gives the guidelines leading to cost-effective design decisions in the conceptual design phase. In future, the various product attributes for LCC factors are identified and further tests using more data are needed to determine to what extent the ANN model can provide reasonable predictions for product attributes and to test for another LCC factors. 6
REFERENCES Dowlatshahi, S., 1992, Product design in a concurrent engineering environments an optimisation approach, Journal of Production Research, 3018: 1803-1818. Westkamper, E., Alting, I., Arndt, G., 2000, Life Cycle Management: Approaches and Visions Towards Sustainable Manufacturing, Annals of the ClRP 49/21 501-522. Westkamper, E., Osten-Sacken, D.v.d., 1998, Product Life Cycle Costing Applied to Inputs Produci
Mass (kg) 8.17 1.04 0.18 0.64 1.93
Ferrous M. Plastics Lifetime Use time(hrs) (%mass) (%mass) (hours) (hours) 32.62 61.58 61320 3041 16.19 77.65 26280 13 43800 13688 45.77 32.86 22.16 71.09 2160 45 43800 487 2.85 65.54
... ... ... ... ... ...
Power consump. Modularity Energy cost($? (watt) (0-4) 1064 1 596.21 20.53 58 1 0 1 1.65 11.47 13 1 616.44 4 83.54
49.78 67.07 27.64 87600 87600 ... 13 40.46 8.83 25.81 87600 11680 ... 616 ... 19 35.01 24.24 51.75 121764 121764 * Energy 1st is total cost of energy consumption during product's life cycle. ** Maintc mce cost is mean cost of product maintenance during usage phase of a product.
3 3 4
38 39 40
Maintenance cost($)** 6.00 1.79 8.25 1.85 2.75
1467.29 2935.2 313.41
18.00 6.54 12.57
Table 5. Examples of sampling data for the ANN model Product
Actual LCC($) ., Energy Maintenance 596.21 6 1.79 20.53 9.46 24.15 3.23 2893.56 2.83 464.37 22 6358.84 12.38 31 3.41 16 2221 . I 9 6.54 2935.2 2895.1 7 12.57
Predictina L C C ($) ., Energy Maintenance 570.99 6.02 1.75 19.36 9.44 23.24 3.22 3241.37 2.81 488.61 22.01 6365.83 12.38 306.3 16.01 21 93.87 6.68 3020.91 2880.69 12.59 I
1. Vacuum Cleaner 2. Mini Vacuum 3. Radio 4. Heater 5. Coffee M a k e r 6. W a s h i n g Machine 7. Refrigerator (S) 8 . Refrigerator (L) 9. T V 10. L C D T V Ave. absolute error Max. absolute error Training sample size is 30, * * Test sample size is 10
Table 6: The predicted results of product LCC by ANN
Relative error(%) Energy Maintenanc 4.23 -0.38 2.08 5.72 0.1 2 3.75 0.1 5 -1 2.02 -5.22 0.97 -0.1 1 -0.02 2.27 0 1.23 -0.02 -2.92 -0.06 0.5 -2.1 1 3.79 0.59 12.02 2.1 1 .
I
