A Process-based Cost Modeling Approach to the Development of Product Families

Transcription

1 A Process-based Cost Modeling Approach to the Development of Product Families Michael D. Johnson* 3M Corporate Research Laboratory 3M Center Building 235-3F-08 St. Paul, MN Randolph E. Kirchain Department of Materials Science & Engineering and Engineering Systems Division, Massachusetts Institute of Technology Cambridge, MA *Corresponding Author Running Title: Development Using Process-based Cost Modeling

2 A Process-based Cost Modeling Approach to the Development of Product Families Dr. Michael D. Johnson is a senior product development engineer in the 3M Corporate Research Laboratory in St. Paul, Minnesota. He received both his S.M. and Ph.D. in mechanical engineering from the Massachusetts Institute of Technology. He is an adjunct faculty member at the University of St. Thomas, also in St. Paul, Minnesota. His research focuses on the interaction between materials and manufacturing process selection and product development. Dr. Randolph E. Kirchain is an assistant professor at the Massachusetts Institute of Technology, Cambridge, MA with a dual appointment in both the Department of Materials Science and Engineering and the Engineering Systems Division. He received a Ph.D. in materials science and engineering from the Massachusetts Institute of Technology. His research focuses on the environmental and economic implications of materials selection. 2

3 A Process-based Cost Modeling Approach to the Development of Product Families The goal of most firms is to deliver products that satisfy customer needs. Delivering a variety of differentiated products allows firms to satisfy the broadest range of customers. There is, however, a fundamental tension between this product differentiation and product cost. The use of product platforms allows a firm to reduce this tension, offering variety while also benefiting from the economics of mass production for shared components. The selection of components and subassemblies for platforming can have wide ranging effects on both product performance and cost. Ordinal metrics are presented to assess the performance of product families. The methodology of processbased cost modeling is used to estimate product fabrication, assembly, and development costs. Two case studies are presented. The first examines two alternative automotive instrument panel (IP) beam families. The second analyzes two alternative automotive body-in-white product families. Cost breakdowns in both the standalone and shared situations are given for each of the cases. Product family metrics are compared to cost savings resulting from part sharing. The results indicate that product families with a greater percentage of variable costs have lower cost savings, even if they share a greater number of parts than product families with a lower portion of variable costs. In some cases development costs, even when a small percentage of total costs, can account for a large percentage of cost savings resulting from part sharing. Simple metrics that do not take reflect which type of parts are shared were shown to perform very poorly as predictors of cost savings (adjusted R 2 close to zero). Metrics that included weighting by part mass or part cost performed moderately well (adjusted R 2 of 0.26 and 0.53 respectively). The best performing metric included a weighting of fabrication investment and took production volume into account (adjusted R 2 of 0.87). 3

4 Introduction The goal of most firms is to deliver products that satisfy customer needs. In recent history (the early twentieth century onwards), a large number of these products have been mass-produced. This allowed for economies of scale in both manufacturing and development. This is rapidly changing; there is an undeniable trend towards increased product variety (Pine, 1991; Pine, 1993). This is being driven by multiple factors. Individual consumers are demanding variety in their consumption as well as variant products to appeal to their individual tastes (Lancaster, 1990). Retailers are demanding customized products to prevent commoditization and price competition. This trend is requiring firms to offer a wider range of products to compete in the marketplace. Even in the automotive industry, often cited as the birthplace of mass production, there is a trend towards increased product variety and decreased volumes. The number of car models offered in America has increased five times between 1947 and 2005 (2005). The ability to meet the increasing demand for product variety can also have a marked effect on the performance of the firm. Broader product lines have been shown to lead to increased market share and profitability (Kekre and Srinivasan, 1990). In addition to increased product variety, firms also have to compete in an environment with decreased product lifetimes. From cosmetics to toys to tools and pharmaceuticals, product lifecycles have decreased markedly (von Braun, 1990). Products and technologies are becoming obsolete at an increasing rate (Schonberger, 1987; Pine, 1993; Uzumeri and Sanderson, 1995). This puts pressure on firms to develop an increasing array of products that are only viable in the market for a shorter and shorter period of time. Given a limited amount of product development resources, this further increases 4

5 the importance of increased product development efficiency. While faster product development is not always better (Meyer and Utterback, 1995; Cohen, Eliashberg et al., 1996); it is without debate that better utilization of product development resources is required to be successful in today s environment. One method for counteracting both the increased demands of product variety and decreased product lifetimes is the use of product platforms and product families. A product platform is a set of subsystems from which derivative or variant products can be developed and manufactured (Meyer and Lehnerd, 1997). These variant products combine both shared and unique components to perform a given function. The product family is the group of variants that are derived from a given platform (Gonzalez-Zugasti, Otto et al., 2000). Platforming has been shown to increase development effort efficiency, and reduce both development time and product cost (Ulrich, 1995; Meyer and Lehnerd, 1997; Gupta and Souder, 1998; Muffatto, 1999; Gonzalez-Zugasti, Otto et al., 2000). Thonemann and Brandeau use a cost model to show that one of the main trade-offs when determining which components to share among variants is between production cost and complexity cost (2000). For variants where demand is high, customized components can be used (high demand provides economies of scale); for variants where demand is low, commonality of components can produce cost savings (aggregate demand across variants can provide economies of scale). However, their model is limited to determining the optimal number of wire harnesses for a group of vehicle variants. The increased importance of product platforms and product families (in their ability to counter trends that lead to increased stress on development resources and costs) enhances the value of understanding and measuring the performance of these platforms and 5

6 families. Jiao and Tseng note the importance of an analytical measure of commonality when assessing product families (2000). As mentioned above, the relationship between commonality and cost is very important when formulating a product platforming strategy. Measures of commonality when compared with costs can be used to assess alternative product platforms. This work presents a set metrics for measuring the amount of component sharing (or commonality) among variants in a product family. These commonality metrics are then compared to projected product cost differentials between platformed (Shared 1 ) and unplatformed (Standalone 2 ) instances. The relationships between these metrics and the cost differentials are analyzed. The determination of inventory holding and complexity costs is outside the scope of this work. While these are important considerations, the purpose of this work is to determine which metrics best capture the economic benefits of component sharing. Case studies from the automotive industry are used to present these results. The article is organized in the following manner. First, previous literature regarding product platforms, product families, and commonality measures is summarized to provide the reader with an understanding of the state-of-the-art regarding parts sharing in product platforms. Second, the process-based cost modeling methodology is summarized and commonality and cost saving metrics are introduced. Next, case studies are presented that exercise these models. Finally case study results and metric performance are discussed regarding their applicability for decision-making regarding product families. 1 The Shared case represents the projected cost of a variant including any cost savings that occur from component or process sharing within the product family. 2 The Standalone case represents the projected cost associated with a variant of a product family assuming that each variant was produced independently (i.e., without the benefits of component sharing). 6

7 Previous Work Several researchers have investigated the benefits (as well as the drawbacks) of product platforming and the use of product families. As mentioned previously, these benefits include reduced manufacturing cost and development time. Decreased manufacturing cost is due to the increased number of units over which fixed costs are spread for parts that are shared in the product family (Muffatto, 1999; Krishnan and Gupta, 2001). It has been shown that in the automobile industry, the sharing of underbodies can lead to a 50% reduction in capital investment (Muffatto, 1999). The reuse of subassemblies and components can reduce the cost of design as well as the time required to bring variant products to market (Muffatto, 1999; Gonzalez-Zugasti, Otto et al., 2000; Krishnan and Gupta, 2001). The reuse of ready-made solutions can also reduce technological risk (Clark and Fujimoto, 1989; Rosenthal and Tatikonda, 1992). From an operational standpoint, the use of shared parts can also lead to reduced holding costs and work in process (Vakharia, Parmenter et al., 1996). MacDuffie et al. also show that parts commonality increases labor productivity in automotive manufacturing (1996). Alcatel greatly reduced their product development cycle time and increased quality by moving towards product platforms and common usage of parts (McDermott and Stock, 1994). Proper design of a product family can have significant technological and economic advantages (Maier and Fadel, 2001). There can also be drawbacks to the use of a platform strategy. The requirement that parts be shared throughout the product family may constrain the design and may cause some difficulties in coordination (Clark and Fujimoto, 1989). Langerak and Hultink 7

8 show that while the reduction of parts and components (reuse from platforming) has a positive effect on development speed for fast followers, it has a negative effect for technological pioneers (2005). There is also the possibility of lost revenue due to lower perceived quality in products with shared parts (Yu, Gonzalez-Zugasti et al., 1999; Gonzalez-Zugasti, Otto et al., 2000; Krishnan and Gupta, 2001). Desai et al. propose that when determining which components to share for a product family both cost and revenue implications should be noted; even well hidden shared parts can lower perceived value in consumer products (2001). This is further evidenced by Kim and Chhajed who show that parts commonality caused by product platforming can call for an adjustment in pricing (2001). These negative aspects of part sharing for a product family must be compared with the benefits outlined above to determine the proper strategy for the product family. In some cases market forces dictate which product family strategy is preferable. Fisher et al. show that the number of unique brake rotors for automobiles decreases as the variation of production volumes across models increases; the increase in the number of models with production volumes that cannot justify unique brake rotors decreases with the overall number needed (1999). Ulrich and Ellison show that the number of unique components designed will increase if customer requirements are coupled or if size, mass, or variable costs need to be minimized; this is provided that the cost penalties for these strategies are not too great (1999). In all cases the trade-offs between unique and common parts should be assessed. To better understand product portfolio strategies and determine the trade-offs that need to be made between competing options, some method of measuring the degree of part commonality within a family of products is needed. In addition to being an important 8

9 metric in and of itself, this measure can also be used to determine the effects of commonality on other performance measures. Collier proposes a commonality index that relates the number of parent items to the number of distinct component parts. It is shown that total cost decreases substantially with higher degrees of commonality (1981). Guerrero uses Collier s commonality index and shows that high commonality product structures are less cost sensitive to production strategy given unknown demand (1985). Jiao and Tseng add production volume and cost of component parts to Collier s commonality index and suggest that high cost uncommon parts should be avoided (2000). Wacker and Treleven propose an ordinal measure of part commonality as well as suggesting different types of part commonality that can be measured (1986). These types include total commonality (an ordinal version of Collier s commonality index), within product commonality, between product commonality, and incremental commonality (similar to between product commonality, but measures the percentage of new uncommon components). Tsubone et al. describe part commonality as ratio of finished product varieties to the number of part item varieties (1994). A measure of process commonality is also proposed; this metric, defined as the process flexibility factor, is computed by determining the number of processes that a component can be processed by and summing over the entire set of components. These measures are then used to determine the effects of component part commonality on buffer size and workload imbalance. Thomas defines commonality as the number of unique components in a system relative to the total number of system components (1992). Excess functionality is then shown to increase as the number of unique parts is decreased (given that component requirements for the system do not change). As mentioned above, this excess 9

10 functionality and its cost must be compared to the benefits of sharing parts. Kota et al. develop a product line commonality index that incorporates the number of components that can be standardized, part geometry, part material, manufacturing process, and joining method (2000). They show that the Sony Walkman line of products performs better (according to this index) than competitors. The Sony Walkman is an oft-cited case of superior product family management (Sanderson and Uzumeri, 1995). Mikkola and Gassmann introduce an exponential equation to measure modularity, this is defined as the reuse of constituent components as well as their interfaces (2003). Several measures of part sharing have been proposed and shown to be valuable tools in assessing portfolio strategies. Nobelius and Sundgren suggest that such metrics can be used as targets in development efforts to increase the use of common parts (2002). While several metrics of part commonality have been proposed, most require complex parent-child relationships that might not be available at the early stages of development when decisions regarding product families can have the most impact. This work presents a set of metrics that can be used with a simple non-hierarchical bill of materials. In addition to being tractable, metrics should also be relevant; they should correlate to some desired quantity or outcome. While previous work has compared various commonality metrics to a desired goal (e.g. cost), no systematic comparison of these metrics to a consistent set of goals has been performed. Although commonality metrics can be related to many goals (e.g. recycling and reuse, inventory reduction, etc.), this work evaluates the relationship between commonality metrics and production and development costs. In this work, production and development costs are projected using process-based cost modeling (PBCM). This allows several metrics to be evaluated against a consistent set of data 10

11 representing the desired quantity (cost savings). In the following section, a set metrics for measuring part commonality is introduced. A methodology for projecting fabrication, assembly, and development costs for both Shared and Standalone cases is also developed. Methods As mentioned previously, it is important to have a metric to assess the performance of product families. But while determining the amount of part sharing in a given family is important, to be useful it should relate to some relevant and desirable goal. The goal used in this work is cost savings. To provide an objective measure against which these commonality metrics are compared, process-based cost models are used to project total product cost (including development, fabrication, and assembly). Specifically, commonality metrics will be compared to the difference between the Standalone and Shared costs of a product family. Process-based cost modeling (PBCM) is an early stage cost estimation tool that has been used to project manufacturing or assembly costs based on part and process characteristics. While PBCM approaches have previously been used to answer research questions related to manufacturing costs (Busch, 1987; Han, 1994; Jain, 1997; Kang, 1998; Kelkar, 2000), this method can be adapted to forecast engineering development costs and lead time as well (Johnson, 2004). Process-based cost models are constructed by working backward from cost -the model s objective- to physical parameters that can be controlled the model s inputs. The modeling of cost involves 1)correlating the effects of these physical parameters on the cost-determinant attributes of a process (e.g., cycle time, equipment performance requirements), 2) relating these processing attributes to manufacturing resource requirements (e.g., kg of material, 11

12 number of laborers, number of machines and/or tools), and 3) translating these requirements to a specific cost (Kirchain and Field, 2001). The relationship between physical parameters and process characteristics is determined by using physical relationships and/or through statistical analysis. 3 A detailed description of PBCM methods is beyond the scope of this article. Interested readers are referred to Busch (1987) or Kirchain and Field (2001) for a full description of the method. In the case of engineering development costs, the model s objective would be both the resource requirement of engineering effort and the subsequent cost. Because product family decisions inherently involve multiple products, explicit rules are required for allocating common cost elements. The Shared Cost of a constituent part or subassembly of a variant is the production volume weighted ratio of that variant s cost contribution to the total cost for that part or subassembly. In aggregate, the Shared Cost of variant j in platform w was calculated as follows in Eq. 1: j PV = + Eq. 1 w PV j j Shared Cost ExclusiveCost Total Shared Cost platform where {platform} is the set of all variants. Here Exclusive Cost is the sum of the costs of unique components, those that are not shared with other variants in the {platform}. For the product family the costs of all variants can be determined as if each variant were produced independently, without the benefits of sharing; this is defined as the variant s Standalone Cost. These costs can then be summed to determine the cost of the entire product family assuming no sharing; this results in the Standalone Cost of the product 3 In the case of the development model, this is limited to statistical analysis, since no physical relationships were identified to correlate between part and project characteristics and development effort. 12

13 family. These definitions and calculations can be used for individual cost categories (i.e. development, fabrication, or assembly) or for the aggregate cost of all three categories. As mentioned previously, the economic value of a product family strategy arises from the assumed differential between its Standalone Costs and the Shared Costs. To normalize this differential and give a consistent output against which commonality metrics can be compared, the Cost Savings metric (S) was calculated as follows in Eq. 2: S = d m d m Χ ij i= 1 j= 1 i= 1 j= 1 d m i= 1 j= 1 Χ ij Δ ij Eq. 2 where Χ i,j is the cost of part i for variant j assuming that there is no sharing, the Standalone Cost; Δ i,j is the Shared Cost of a part for that variant. The Cost Savings metric (S) provides a relevant and objective goal against which commonality metrics can be assessed. The commonality metric (C), used in this work, is defined as the ratio between the number of parts shared and the number of parts that could be shared in the given product family; its basic form is referred to as Piece-based commonality (C Piece ). This ratio is calculated for each line item in the combined bills of materials for all variant products in the family being analyzed. These quantities are summed and then divided by the total number of line items. This metric is similar to the ordinal version of Collier s commonality metric presented by Wacker and Treleven (1986); however, this metric can be used with a simple non-hierarchical bill of materials. Specifically, the amount of sharing is calculated as follows in Eq. 3: 13

14 C Piece = d i= 1 m γ ij 1 m 1. Eq. 3 d j= 1 Where γ = 1 if variant j contains part i, and zero if it does not. Here m is the total number of product variants and d is the number of distinct items in the bill of materials. An example bill of materials for a product family is shown in Table 1. The sharing calculation for this product family is shown in Table 2. In this example, C Piece is equal to This same methodology can be used to determine the commonality of subassemblies in a product family by replacing the part line items with subassembly line items. Insert Table 1 Here Insert Table 2 Here It is widely believed that for a commonality metric to be relevant and useful, it should reflect the relative production volumes as well as some measure of cost and/or complexity of the parts being analyzed (Wacker and Treleven, 1986; Jiao and Tseng, 2000). Generically, the following is a sharing metric that includes such a measure: C φ = d i= 1 φi m j= 1 d i= 1 γ ij 1 m 1 φ i Eq. 4 where φ i is the measure of a part s relative importance, such as it s complexity, mass, piece cost, or an associated fixed investment (e.g. tooling). This measure will be referred 14

15 to as the φ-weighted commonality metric (C φ ). All other items are as previously defined. Incorporating the effect of differing production volumes for the variant products of the family, leads to a relationship of the form shown in Eq. 5: C PV / φ = m ( PV γ ) d ij ij i j= 1 φi i= 1 PVTot PVmin d i= 1 φ i min( PV ) Eq. 5 here PV is the production volume for variant j and min( PV ) i is the minimum variant production volume for part i (a unique line in the bill of materials). In cases where there is no sharing, the minimum production volume is that of the variant that requires the given part. The figures derived using Eq. 5 will be referred to subsequently as Production Volume / φ-weighted commonality measures (C PV/φ ). For the case where all parts are weighted equally (i.e., φ = 1 i ), Eq. 5 collapses to provide an analogous production i volume adjusted version of the Piece-based commonality metric that is referred to subsequently as the Production Volume-weighted commonality metric (C PV ). This is used as a penalty for parts that are not shared, even if high production volume variants use these parts. PV Tot is the sum of the production volumes for all variants in the product family. PV min is the production volume for the product family variant that has the minimum production volume. Table 3 shows production volumes for variants containing parts of different masses; a breakdown for the calculation of CPV / φ (a Production Volume/Mass-weighted commonality measure) is also shown. For this example CPV / φ is equal to

16 Insert Table 3 Here Finally, before computing any of the above measures of platform commonality, it is necessary to establish a more formal definition for the value of γ, the variable describing a part s shared status. This is required because in real-world products, shared subassemblies and components do not have to be identical. Some components may share the majority of forming production steps, differing only because of limited finishing operations such as trimming or drilling of holes. For the purpose of this work, a component or assembly was considered shared if it used the same primary forming tooling and equipment as another part in the family. 4 The next section will use two automotive case studies to assess these metrics. Case Studies To assess the relevance of the presented metrics to the stated goal cost savings resulting from product platforming the following two case studies are presented. In both cases, two alternative product families are analyzed. Six commonality metrics are calculated for each of the product families. Process-based cost models are used to project the development, fabrication, and assembly costs (details of these process- based cost models can be found in Johnson (2004)) that are used to assess the congruence of the metric with the cost savings goal. Process-based cost models are also used to project the expected fixed investment and piece cost per component used in some weighted commonality metrics. Standalone Costs, Shared Costs, and Cost Savings are presented; Cost Savings 4 The purpose of this work is to assess the economic impact of product platforming strategies. Since secondary costs such as drilling and finishing are usually much less than major forming cost, components that share major forming tooling are considered common. In other cases where complexity or holding costs are being assessed, part differences such as finishes and hole patterns might become relevant. 16

17 are then compared with the various commonality metrics. Due to the highly proprietary nature of some data used in this work, all cost data is disguised through normalization to protect the industrial sponsor. All trends shown are consistent and proportional to those found using unnormalized data. This section is organized as follows. First metrics are calculated and costs projected for each of the two case studies. These metrics are them compared with calculated Cost Savings to determine the fidelity of the metrics to the stated goal. Case One: Alternative Instrument Panel (IP) Beam Designs The first case investigates two alternative instrument panel (IP) beam designs. The two designs include: 1) a tube-based steel design 5 and 2) a die-cast magnesium design which affords significant parts consolidation. The steel IP beam (denoted Steel IP) consisted of a tubular structure with over two-dozen brackets attached. The magnesium design comprised a primary die cast magnesium structure (denoted Mg IP) with two additional unique bracket pairs. Processing information for these parts was estimated using the process-based models. Notably, the major die cast part was projected to have a production rate approximately two to three times slower than that of the analogous steel components. In both cases, the three variants were modeled at various production volumes; these are shown in Table 4. Both cases also were designed so that variants one and two shared a larger portion of parts between themselves than either variant one or variant two shared with variant three. In the case of the magnesium IP beam, variants one and two shared the main die cast part, while variant three had a unique die cast part. 5 Most vehicles today use similar tube-based steel instrument panel beams. 17

18 In the case of the steel IP beam, variants one and two shared several brackets as well as the major structural elements; while variant three had several unique elements, it also shared many parts with the other two variants. Insert Table 4 Here Table 5 shows the various commonality metrics (calculated using the formulae developed in the preceding section) for the two IP beam cases. The Piece-based commonality metric (C Piece ) is based solely on the number of shared and exclusive parts (see Eq. 3), while all other presented metrics are weighted based on some possible measure of the component s relative importance. Specifically, in addition to the Piece-based commonality metric, Table 5 presents weighted metrics (i.e., φ = mass, piece cost, investment, or production volume) based on Eq. 4 as well as the simple Production Volume-weighted metric (C PV ) and the Production Volume / φ-weighted metric where φ= associated fabrication investment (C PV/φ=investment ) both of which are calculated using Eq Insert Table 5 Here Notably while both the Piece-based and Production Volume-weighted metrics are relatively similar (e.g., C Piece = 65% for the steel case; 50% for the magnesium case), the four weighted metrics, C φ=mass, C φ=piece cost, C φ=investment, and C PV/φ=investment, show significant resolution between the two designs. In fact, for the latter four metrics the Steel IP design displays values approximately two-times larger than those for the Mg IP. 6 Investment is used as the additional weighting factor because it was shown to be more highly correlated with savings than the other weighting factors when production volume is not considered (Johnson, 2004). 18

19 The large difference between the values of the Production Volume weighted metric and the Production Volume/ Investment metric weighed by investment for the magnesium case arises due to the fact that while the steel IP beams all share a large number of parts, the main die cast structure for the magnesium IP beams is only shared between the high production volume variant (75,000 per year) and the low production variant (25,000 per year). This low production volume sharing, when coupled with the weighting of investment, dramatically penalizes the magnesium IP beam product family for this metric. Total costs including development, fabrication, and assembly were modeled using the methods described above. The cost breakdowns for the magnesium IP beams are shown in Figure 1. In all cases, fabrication costs represent a large portion of the total costs. For the Standalone Mg variants fabrication cost are 74% of total costs; this is due to the high cost of the magnesium material in the design (the magnesium material accounts for nearly two-thirds of the fabrication cost). The Standalone Cost for variant 2 is much higher due to its low production volume; its Shared Cost is 47% less than the Standalone Cost. The vast majority of cost savings in the magnesium case result from reduced fabrication (43% of cost savings) and assembly (48% of cost savings) costs due to part sharing. The cost breakdowns for the steel IP beam cases are shown in Figure 2. However, in this case, costs are much more evenly distributed among the various cost categories (48% for fabrication, 29% for development, and the balance for assembly). Again, the cost of the second variant in the standalone situation is much higher due to reduced production volume; in this case its Shared Cost is 52% less than the Standalone Cost. For the steel IP 19

20 beam cases, the reduction in development cost due to part sharing accounts for the largest portion (over 40%) of the cost savings. This is due to the large number of total parts that needed to be developed. At low production volumes, this represented a significant cost, but when the parts were shared among the variants, this cost dropped significantly. Insert Figure 1 Here Insert Figure 2 Here The overall Cost Savings (S) as a result of parts sharing in the two cases are shown in Figure 3; these savings were calculated using Eq. 2 for both fixed and total costs. The total Cost Savings for the steel IP beam case are over 15% higher than those of the magnesium case. The differential between fixed and total savings for the two cases is also large; this is due to the much higher portion of material costs in the case of the magnesium IP beam. The portion of fixed costs is much higher in the case of the steel IP beam. Johnson presents a more detailed breakdown of the costs for the two IP beam designs (2004). Insert Figure 3 Here Case Two: Alternative Body-In-White (BIW) Architectures The second case analyzes two alternative body-in-white (BIW) architectures. The first was a traditional stamped steel unibody. The second was a structure consisting mostly of tubes and cast nodes. In both cases, three variants were modeled. In the case of the traditional stamped steel unibody, the variants consisted of an entry-level luxury sedan, a crossover (the emergent class of vehicles combining features of sport utility vehicles and passenger cars) vehicle, and a luxury sedan. In the case of the tubular vehicle, the 20

21 variants were a sedan, a crossover vehicle, and a convertible. In both cases the sedans were modeled at a 75,000 unit annual production, the crossovers were modeled at a 50,000 units annually, while the convertible and luxury sedan were modeled at 25,000 units annually. A summary of these production volumes is shown in Table 6. These production volumes were believed to be both representative and reasonable considering current market conditions. As for the cases above, the cost analysis comprehended the development, fabrication, and assembly of the body-in-white. Insert Table 6 Here The values of the various commonality metrics for the two product families are shown in Table 7. There is a much more marked difference for the assorted metrics in the case of the tubular design, than for those of the steel unibody. While the tubular design had high levels of sharing as measured by the Piece-based, Mass-weighted, and Production Volume-weighted commonality metrics, the design did not perform as well according to the other weighted metrics. The Piece Cost-weighted commonality metric is almost half that of the Piece-based and Mass-weighted metrics. This difference emerges due to the type of parts that the tubular architecture shares. While the tubular design shares a large number of parts and even a large portion of the mass of the product family, these parts do not account for a large portion of the product family s cost. The contrast with the Piecebased weighted metric is even more pronounced for the Investment-weighted metric; the parts shared by the tubular architecture account for a small portion of the product family s total investment. In contrast, all of the commonality metrics for the steel unibody case are relatively uniform, with the two simple commonality metrics (Piece and Production Volume) are only slightly higher than the weighted metrics. In the case of the 21

22 steel unibody design, the value and portion of fixed costs for shared parts are not considerably different from those of the rest of the product family. Insert Table 7 Here As with the previous cases, the costs were modeled using the methods detailed above. The cost breakdowns for the steel unibody designs are shown in Figure 4. In all variants, fabrication costs account for the largest portion of total costs. For Standalone Costs in the unibody design, fabrication costs accounted for 53% of total costs, assembly costs were 37% of the total with development costs accounting for the balance. Reduced fabrication and assembly costs account for the vast majority of cost savings resulting from part sharing. The reduction in fabrication costs due to part sharing accounted for 55% of total cost savings; the reduction in assembly costs from sharing was 29%. The cost breakdown for the tubular design case is shown in Figure 5. In this case, fabrication costs are again the largest portion of total costs, accounting for 54% of total costs; assembly cost account for 35% of total costs, with development costs making up the balance. However, in this case, development and fabrication cost reductions account for all of the cost savings resulting from parts sharing; these cost savings are split almost equally between these two cost categories. The lack of cost savings from sharing of assembly processes is due to the large amount of labor required for the assembly of the tubular design bodies-in-white. This greatly reduces the opportunity for cost savings that arise from the sharing of assembly processes. Insert Figure 4 Here 22

23 Insert Figure 5 Here The overall cost savings as a result of parts sharing for the unibody and tubular cases are shown in Figure 6. The total Cost Savings (S) for the unibody design are more than double those of the tubular design (13.2% for the steel unibody vs. 6.4% for the tubular design); this is in spite of the tubular design s much higher Piece-based commonality metric value. Again, the differential between fixed and total savings for the two cases is also large. The ratio of variable costs to total costs is 39% for the tubular design while it is only 27% for the unibody design. This large portion of variable costs reduces the ability of the tubular design to leverage part sharing among members of the product family for cost reduction. Insert Figure 6 Here Comparison of Commonality Metrics with Cost Savings As mentioned previously, the value of product family commonality metrics is in their ability to indicate congruence with a desired outcome. In the case of this work, that desired outcome is cost savings. The various part commonality metrics were regressed against the resulting Cost Savings for each of the four product families. The aim was to determine how well each of the commonality metrics correlated with Cost Savings. The adjusted R 2 results are shown in Table 8. It should be noted that these regressions contained a limited number of data points (4 for each regression). The adjusted R 2 statistic was used to account for the minimal degrees of freedom in the regression analysis. 23

24 Insert Table 8 Both the Piece-based and Production Volume-weighted metrics performed poorly in their relationships to cost savings (according to the adjusted R 2 measure). The Mass- and Piece Cost-weighted metrics performed marginally better. Of the metrics considered in this paper, both the Investment-weighted and the metric combining production volume and fabrication investment performed quite well with adjusted R 2 values of 0.84 and 0.87, respectively. Plotting the commonality metrics vs. the Cost Savings with a line of best fit produced the following observations. In the case of the Piece-based commonality metric, the Cost Savings were less than expected for the tubular body-in-white design (data point below the line of best fit) and more than expected for the steel unibody and the steel IP beam cases (data points above the line of best fit). With the exception of the magnesium IP beam case, all data points were significantly remote from the line of best fit. Similar observations were made in the case of the Production Volume-weighted metric. For the Mass and Piece Cost-weighted metrics, the tubular design again was remote from and below the line of best fit; all three other data points were a moderate distance above the line of best fit. Only for the two Investment-weighted metrics were all four data points comparable distances from the line of best fit. These two metrics were better correlated to the desired goal cost savings, but this was especially true for the Production Volume/Investment-weighted metric. 24

25 Discussion and Future Work One way of combating the effects of the increased product variety and decreasing product lifetimes has been the introduction of product platforms. For these platforms to be effective, they must be organized in a way that achieves the goals of the firm. A number of authors have suggested that effective design performance measures should promote platform designs that support those goals. This work has examined the fidelity of several possible early-stage commonality metrics with the goal of reducing the overall (manufacturing, assembly, and development) costs of the product. Specifically, through the use of case studies, several metrics were assessed to determine their congruence with Cost Savings associated with specific platforming strategies. To obtain consistent costs, the method of process-based cost modeling was used to project costs for family of products both when produced with potential part sharing (Shared Cost) and in a hypothetical context where all parts are produced independently (Standalone Cost). The first case study presented two alternative designs, one steel and one magnesium, for instrument panel beams produced at various production volumes. Both the steel and magnesium designs were found to perform well and similarly according to the simple Piece-based and Production Volume-weighted commonality metrics (i.e., metrics greater than 50%). In contrast, all of the other weighted metrics (mass, piece cost, and fabrication investment) provided distinct resolution between the two product families with the magnesium IP beam family leading to values on average half that of the steel IP family. This was due to the type of parts that the magnesium IP beam family shared. While all three IP beams shared the brackets attached to the main part, only two variants shared the 25

26 main IP structure (one of which was the lowest production volume variant). This is in contrast to the steel IP beam family that shared a wider array of parts. Similarly, the platform Cost Savings as a result of parts sharing were projected to be much lower for the magnesium case than for the steel IP beam case. The low percentage of development cost (in the magnesium beam case), which accounted for a large portion of the cost savings in the steel IP beam case, limited the opportunity for cost savings in this category. In addition to sharing parts that cost and weighed less, the magnesium IP beam case also had a much higher percentage of variable costs (69% for the magnesium IP beams vs. 43% for the steel IP beams). This reduced the opportunity for cost savings resulting from part sharing. In the end, while the simple metrics treated all sharing as being equal, the weighted metrics penalized the magnesium case in a manner that reflected the impact on cost, providing much better insight into the design goal. In the second case study, two alternative body-in-white architectures, one tubular-based and one unibody, were compared. The tubular design was shown to perform much better than the unibody according to all the presented commonality metrics. The performance differential was much lower for the metrics that included fabrication investment as a weighting factor. While the tubular design shared a large number of parts, most of these had high variable costs that reduced the opportunity for costs savings resulting from part sharing. Overall the tubular design had a much higher percentage of variable costs than that of the steel unibody design (39% for the tubular design vs. 27% for the steel unibody). Nevertheless, when only fixed costs are considered, the tubular architecture has much higher cost savings than that of the steel unibody architecture. Not surprisingly, this relative trend correlates well with several of the commonality metrics. However, the 26

27 relatively high variable costs associated with the tubular design lead to overall cost savings less than half of the unibody design. As such, in the end, none of the commonality metrics provide a proxy proportional to the relative cost savings of these two alternative architectures. However, of those investigated, the Investment-weighted measures provide by far the greatest fidelity. Finally, the various commonality metrics were compared to costs savings that were produced as a result of part sharing. Both the Piece and the Production Volume-weighted metric performed poorly. The Mass and Piece Cost-weighted metrics performed marginally better. The Investment- weighted, and the Production Volume/Investmentweighted metrics performed very well (adjusted R 2 values over 0.8). These metrics were shown to correlate better with cost savings for various types of product families. The unique characteristics of the tubular steel body-in-white design (overall high variable costs and extensive sharing of high variable cost parts) made it a challenging case. Nevertheless, for both cases, the Investment- weighted, and the Production Volume/Investment-weighted metrics showed the best correlation to cost saving. While this data represents a limited sample size, it agrees with previous work that compared variant pairs, as well as three variant product families, and did not take production volume into account (Johnson, 2004). When developing product families two main considerations should be taken into account: which types of parts are being shared and what type of product is being produced. Certain combinations of manufacturing processes and materials are known to have a much higher percentage of variable costs and thus reduce the cost saving effects of part sharing (e.g. magnesium die casting). The metrics presented in this work allow for the 27

28 alternative product family concepts to be assessed based on their parts sharing. In particular, given access either to process-based cost models of fabrication or to accounting data of sufficiently similar parts, various product family strategies can be assessed with significantly improved insight using the metrics presented herein. It is also possible to enhance the development of variant products derived from those already in the market or under development. Ranking parts according to fabrication investment would allow designers to readily identify those components which would be the most valuable to share; this should allow for a more targeted and effective component sharing strategy. As mentioned previously, this work contains a limited number of cases and deals mainly with metal forming processes. Future work should expand these methods to include a larger number of variants at a wider range of production volumes as well as alternative manufacturing processes. There is no reason to limit the goal of the product family to reducing costs, these methods could also be used to limit the different types of materials used. This could allow for the reclamation of certain types of materials to become economically feasible and thus increase recycling and reuse. The cost differential between a recycling enhanced and a baseline case could be used along with the costs/benefits of recycling to determine a suitable strategy. 28

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