1 Introduction
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Customized module: Customized modules are designed and manufactured by the original equipment manufacturer (OEM) using mass customization methods for customer choice during the purchasing process [11].
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Personalized module: Personalized modules are designed and manufactured to satisfy the personalized requirements of the customers. They can be designed by customers and/or purchased from a third-party vendor.
Type of product | Definition | Example | Included modules and interfaces | |||
---|---|---|---|---|---|---|
Common module | Customized module | Personalized module | Adaptable interface | |||
Mass produced product | Product obtained through large-scale production for meeting standard requirements | Standardized bearings | Yes | No | No | No |
Mass customized product | Product designed and manufactured to meet customized requirements with approximate efforts of large-scale production | Electric vehicles that provide customized features for user selections | Yes | Yes | No | No |
Reconfigurable product | Product that can meet different requirements through rapid reconfigurations of components can be reconfigured to meet different requirements | Cameras can be reconfigured with different lenses to meet various photographing requirements | Yes | Yes | No | Yes |
Upgradeable product | Product that can accommodate new requirements or technology advancement by upgrading or replacing components | Computers that can be upgraded by replacing their memory modules | Yes | Yes | No | Yes |
Open architecture product | Excavators that can be connected to different front execution devices provided by third-party vendors | Yes | Yes | Yes | Yes | |
Adaptable product | Product that can be easily adapted in the operation stage to meet different requirements [6] | Blades of a wind turbine can be adapted to meet different operation conditions | Yes | Yes | Yes | Yes |
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The dataset used is not large enough because only a limited number of customers can participate in the interview, and only parts of the customers provide useful feedback or comments on the product. This results in an insufficient reflection of customer preferences in the marketplace.
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Product specification correlations originating from both conscious and subconscious customer preferences were not fully considered. Because most existing methods focus on analyzing customer preferences for certain functions and/or features of a product, few studies have investigated specification correlations originating from customer preferences and their combinations.
2 Proposed Method
2.1 Data Collecting and Preprocessing
2.2 Correlations among Product Specifications
2.3 Specifications and Components Relations
Scenario | Design objectives | Processing | Condition of application |
---|---|---|---|
1 | Minimize the number of changing components while satisfying the specification changes | 2&4→I 1&3→N | Satisfy changeable requirements of product specifications |
2 | Minimize the number of specifications influenced by required changes of components | 3&4→I 1&2→N | Facilitate required component upgrading or adaptation |
3 | Maximize the adaptation capability to satisfy unknown changes of specifications and components | 2&3&4→I 1→N | Satisfy potential changes of both specifications and components |
2.4 Correlations among Product Components
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It is a symmetric matrix, which indicates non-direction in the component correlations.
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The values of the diagonal elements are equal to one, which reflects the correlations between the components themselves.
2.5 Components Clustering and Frequency Analysis
2.6 Rules for Supporting Decision-Making of Modular Product Design
3 Case Study
3.1 Data Collection and Preprocess of EVs
ID | Sales in 2018 | Specifications | ||||||
---|---|---|---|---|---|---|---|---|
Price (×1000 CNY) | Electric mileage (km) | Total power (kW) | Total torque (N·m) | Energy density (W·h/kg) | Quick charge time (h) | Battery capacity (kW·h) | ||
1 | 46213 | 23.07 | 400 | 160 | 310 | 160.8 | 1.5 | 60.48 |
2 | 43634 | 27.41 | 350 | 160 | 310 | 140.97 | 1.5 | 61.9 |
3 | 35699 | 9.99 | 305 | 160 | 310 | 146.27 | 0.5 | 43.2 |
4 | 31426 | 23.83 | 400 | 120 | 250 | 135.4 | 0.5 | 52 |
5 | 27870 | 17.19 | 200 | 41.8 | 150 | 140.91 | 0.5 | 23.6 |
6 | 16102 | 20.29 | 318 | 80 | 230 | 122.68 | 0.5 | 48 |
7 | 15336 | 5.98 | 255 | 30 | 90 | 150 | 0.5 | 27 |
8 | 10329 | 18.98 | 401 | 90 | 276 | 144.05 | 0.5 | 54.3 |
9 | 8852 | 24.65 | 410 | 132 | 290 | 212 | 0.5 | 54.75 |
10 | 7484 | 14.58 | 353 | 120 | 250 | 142.07 | 0.5 | 52 |
ID | Specification | Values | ID | Specification | Values |
---|---|---|---|---|---|
S1 | Price (×1000 CNY) | [0,10]; (10,20]; (20,30]; (30,40]; (40,+∞); | S2 | Electric mileage (km) | [0,200]; (200,300]; (300,400]; (400,500]; (500,+∞); |
S3 | Engine type (horsepower) | [0,90]; (90,120]; (120,150]; (150,180]; (180,210]; (210,+∞); | S4 | Weight (kg) | [0,1000]; (1000,1300]; (1300,1600]; (1600,1900]; (1900,2100]; (2100,+∞); |
S5 | Motor power (kW) | [0,50]; (50,100]; (100,150]; (150,200]; (200,+∞); | S6 | Top speed(km/h) | [0,100]; (100,120]; (120,140]; (140,160]; (160,+∞); |
S7 | Total motor torque (N·m) | [0,100]; (100,200]; (200,300]; (300,400]; (400,500]; (500,+∞); | S8 | Electricity of 100 km (kW·h) | [0,11]; (11,13]; (13,15]; (15,17]; (17,19]; (19,+∞); |
S9 | Energy density(W·h/kg) | [0,135]; (135,145]; (145,155]; (155,165]; (165,+∞); | S10 | Quick charge time (h) | [0,0.5]; (0.5,1]; (1,1.5]; (1.5,+∞); No; |
S11 | Battery capacity (kW·h) | [0,25]; (25,40]; (40,55]; (55,70]; (70,+∞); | S12 | Size of central console screen (inch) | [0,5]; (5,7]; (7,9]; (9,11); (11,+∞); |
S13 | Cell model | Cylindrical; Square; | S14 | GPS | Yes; No; |
S15 | Power sunroof | Yes; No; |
3.2 Calculation of Specification Correlations
3.3 Relation Modeling of Specifications and Components
ID | Component | ID | Component | ID | Component |
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C1 | Chassis | C2 | Drive motor | C3 | Motor controller |
C4 | Central control unit | C5 | BMS | C6 | Gear lever |
C7 | Accelerator pedal | C8 | Brake pedal | C9 | Battery pack |
C10 | Front mounting | C11 | Front wheel | C12 | Front wheel brake |
C13 | Steering gear | C14 | Car body | C15 | Body adornment |
C16 | Car door | C17 | Seat | C18 | Body shell |
C19 | Rear Suspension | C20 | Rear wheel | C21 | Rear wheel braking |
C22 | Transmission shaft | C23 | Differential mechanism | C24 | Reducer |
S1 | S2 | S3 | S4 | S5 | S6 | S7 | S8 | S9 | S10 | S11 | S12 | S13 | S14 | S15 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
C1 | 3 | 1 | 1 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
C2 | 3 | 3 | 3 | 3 | 4 | 3 | 4 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
C3 | 3 | 3 | 1 | 3 | 1 | 3 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 1 |
C4 | 3 | 3 | 1 | 3 | 1 | 3 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 1 |
C5 | 3 | 3 | 1 | 3 | 1 | 3 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 1 |
C6 | 1 | 1 | 1 | 1 | 1 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
C7 | 1 | 1 | 1 | 1 | 1 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
C8 | 1 | 1 | 1 | 1 | 1 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
C9 | 3 | 3 | 1 | 3 | 1 | 3 | 1 | 3 | 2 | 2 | 4 | 1 | 2 | 1 | 1 |
C10 | 3 | 3 | 1 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
C11 | 3 | 3 | 1 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
C12 | 3 | 3 | 1 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
C13 | 3 | 3 | 1 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
C14 | 3 | 1 | 1 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
C15 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 3 | 1 | 1 | 1 | 2 | 1 | 2 | 2 |
C16 | 3 | 1 | 1 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
C17 | 3 | 1 | 1 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
C18 | 3 | 1 | 1 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
C19 | 3 | 3 | 1 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
C20 | 3 | 3 | 1 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
C21 | 3 | 3 | 1 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
C22 | 3 | 3 | 1 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
C23 | 3 | 3 | 1 | 3 | 1 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
C24 | 3 | 3 | 1 | 3 | 1 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
3.4 Component Correlations
3.5 Component Clustering and Frequency Analysis
Clusters | Components | Specification groups |
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Cluster 1 | C10, C11, C12, C13, C19, C20, C21, C22 | S1, S2, S4 |
Cluster 2 | C3, C4, C5, C23, C24 | S1, S2, S4, S6 |
Cluster 3 | C1, C14, C16, C17, C18 | S1, S4 |
Cluster 4 | C6, C7, C8 | S6 |
Cluster 5 | C2 | S1, S2, S4, S6, S7 |
Cluster 6 | C9 | S1, S2, S4, S6, S8 |
Cluster 7 | C15 | S1, S8 |
3.6 Modular Design Decision Support of EVs
Clusters | Components | Types of modules |
---|---|---|
Cluster 1 | C10, C11, C12, C13, C19, C20, C21, C22 | Personalized |
Cluster 2 | C3, C4, C5, C23, C24 | Personalized |
Cluster 3 | C1, C14, C16, C17, C18 | Personalized |
Cluster 4 | C6, C7, C8 | Common |
Cluster 5 | C2 | Personalized |
Cluster 6 | C9 | Personalized |
Cluster 7 | C15 | Customized |
ID | Modular product design decision recommendations | Rules |
---|---|---|
1 | Components in the same cluster are recommended to be grouped into the same module (e.g., C6, C7, C8 are recommended to be grouped into the same module) | a |
2 | Components in different clusters are recommended not to be grouped into one module (e.g., C2 and C9 are not recommended to be grouped into the same module) | b |
3 | Components in cluster 4 are recommended to be designed as common components or modules. (e.g., C6, C7, and C8 are recommended to be designed as common components or being grouped into a common module) | c |
4 | Values of S8 are alternatives and limited, thus cluster 7 in which C15 is located should be designed as a customized module | d |
5 | Values of S4 have a large number of alternatives, thus cluster 1 should be designed as a personized module | e |
6 | Adaptable interfaces are proposed to connect components in different clusters | f |
4 Discussions and Conclusions
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The newly proposed specification correlation can reflect customer conscious and subconscious preferences for product specification combinations using big sales data.
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The correlations of components originating from customers/market preferences with product specifications are identified by defining the relationship between specifications and components.
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The proposed method can accurately and comprehensively determine the correlation degree of product specifications and components from big sales data analysis, rather than defining the correlation degree using small amounts of data from experts and/or customers.
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The six rules proposed for supporting design decision marking can potentially facilitate axiomatic design, adaptable design, product family design, product platform design, and open architecture product design.
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Investigation of evolution trends of product specifications in the market using historical data on product sales.
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Calculating functional correlations among product specifications to better facilitate decoupling strategy planning for a complex product or system.
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The effects of physical constraints of both the component features and manufacturer technique capability make the proposed method more specific for design decision support.