# Comparison of Criticality of Configuration Choices for Market Price and Product Cost

Peter Nielsen, Thomas Ditlev Brunoe

► To cite this version:

Peter Nielsen, Thomas Ditlev Brunoe. Comparison of Criticality of Configuration Choices for Market Price and Product Cost. 19th Advances in Production Management Systems (APMS), Sep 2012, Rhodes, Greece. pp.262-269, 10.1007/978-3-642-40352-1_33. hal-01472251

HAL Id: hal-01472251

https://inria.hal.science/hal-01472251

Submitted on 20 Feb 2017

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
Comparison of criticality of configuration choices for
market price and product cost

Peter Nielsen and Thomas Ditlev Brunoe

Aalborg University, Department of Mechanical and Manufacturing Engineering
{peter,tdp}@m-tech.aau.dk

**Abstract.** The paper presents a quantitative method for determining the critical-
ity of components to costs and sales price in mass customization environments.
The method is based information of historically sold configurations and uses
backward elimination to arrive at a reduced linear model. The variables includ-
ed in this model are then the most significant describing variation of (materi-
al/salary/total) costs, sales price or profit margin. The method is tested on data
from a large manufacturer of mass customized products.

**Keywords:** Mass customization, backward elimination, linear model.

# 1 Introduction

In companies offering customized products, manufacturing costs and sales prices will depend on a particular configuration of a product and those companies often experience that it is not obvious which product properties drive cost and which properties allow for a high sales price. This leads to the fact that it is nontrivial to identify which products are profitable and which are not. In any company it is crucial to continuously evaluate the profitability of the product range, however in companies with a significant product variety such as mass customization or engineer to order companies, this is a challenging task. This evaluation and the resulting development of a product portfolio in mass customizing companies is referred to as solution space development, which is one of three fundamental organizational capabilities which differentiate successful mass customizers from the non successful [11].

In mass customization where it is uncommon to sell and produce more than a few
identical products but rather sell high numbers of individually customized products, it
makes little sense to evaluate the profitability of a single product. Instead the solution
space must be evaluated as a whole. Evaluating the profitability of the solution space
can be approached in several different ways. Fundamentally, a qualitative or quantita-
tive approach can be chosen. However, due to the vast complexity of a mass customi-
zation solution space in terms of the number of product features and modules, usually
leaving a practically infinite solution space, a qualitative approach seems unfeasible
indicating that a quantitative approach should be pursued.
A number of manufacturing processes dependent methods have been developed for cost estimation, implying that the particular method of estimating cost can only be applied to certain processes e.g. casting or welding [4], [5], [12], [13], [13], [17]. Cost estimation methods dependent on the specific product type also exist. In particular much research has been presented within the area of estimating cost during product development for both the finished product and the development process [2], [8], [9], [16], [19]. Kingsman & de Souza [7] introduced a general framework for cost estimation and pricing decisions, but no practical methods for estimating cost are presented. Other studies focus primarily on describing mathematically using synthetic models how customized products can be priced, however primarily compared to similar non-customized products [1], [14].

One of the deficiencies of the approaches found in literature is that most are not specific to mass customization and do thus not take into account a large solution space but focus rather on a single product. Furthermore most are synthetic, meaning that a cost and pricing model must be developed in order to evaluate the product profitability which is complicated by a high variety. Finally a number of the approaches described in literature are product specific rather than generic.

The research objective of this paper is to create an analytic method which can assist companies in evaluating the profitability of a product family based on historical configuration data. By basing the method on historical configuration data, the combinations of modules and features actually sold are evaluated in contrast to a synthetic approach. The research questions of this research presented in this paper are:

1. Which method should be applied to identify which configuration variables are Critical to cost, sales prices and profit margin based on historical configurations?

2. How can the output be analyzed and utilized to identify relations between configuration variables, cost and sales price and profit margin?

3. How may the criticality of configuration variables with respect to cost and sales price be interpreted and utilized to develop the solution space to produce more profitable products?

## 2 Methodology

The aim is in a simple quantitative manner to establish which variables are critical for various costs (typically material and salary costs) and which variables are critical for sales price and high profit margin. The aim is then to identify where there is a gap between these models. I.e. which variables are critical for cost aspects, but not critical for price and profitability in the form of net margin (sales price – variable costs).

The paper utilizes the method for determining the criticality of various parameters criticality to cost presented in Brunø and Nielsen [3], developed for Engineer-to-Order cost estimation, and adapts this to a method for comparing the cost aspects and the profit margin per sold product. Due to the complexity of the problem and the number of variables (large number of components and resources involved) considered multi-
ple linear regression must be used [18]. However, since some manner of relations can
be expected between the independent variables, all insignificant variables cannot just
be removed in one step. To overcome this issue backward elimination is used since in
product configuration it seems better to risk dropping variables that explain a given
behaviour (in this case of cost or profit margin) than having an overly complex model
with several variables explaining the same behaviour [3]. The latter being a potential
consequence of using e.g. forward selection.

The principle proposed in this paper of applying backward elimination to the problem
is to estimate a (simple) linear model for historical configuration data and correspond-
ing costs or profit margins. This creates a linear model that can explain costs or profit
margin for new configurations [10]. The way backward elimination simplifies the
linear model is by iteratively removing variables from a large set of data describing a
number of historical product configurations and fitting a model to the reduced set of
data. The set of variables describing the configurations can contain different types of
variables as they are typically related to processes used in the manufactur-
ing/assembly process and Bill of material contents. Since the method reduces the
number of variables considerably it is unnecessary to qualitatively select the variables
before the method is applied, but merely provide a gross list of variables describing
each historical product and a corresponding incurred cost, sales price or profit margin.
The result of applying this method (see [3] for further details) is a net list of critical
variables fitted to a given dependent variable. In the context of this research the de-
pendent variables will as a minimum be a relevant cost per piece (e.g. registered sal-
ary or material cost) sales price and profit margin (preferably a contribution margin
excluding the variable costs). An overview of the methodology can be seen in Fig. 1.

<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Overview of methodology partly based on [3].</figcaption></figure>
### 3 Case study application

The proposed method has been tested on data from a manufacturing company. The case concerns a medium sized company in Denmark producing technical products for domestic water installations which are configured within a predefined solution space. The products share a common structure. The company produces products configured with in a given set of fixed options. The company registers material and salary costs as well as net margin for all sold products. Furthermore a full route and Bill of material is available for all sold configurations.

The initial model contains 194 variables. This is through use of the method presented in Brunø and Nielsen [3] reduced to 22 variables. These results match results achieved by the authors when applying the method to a number of other cases. To simplify the case application of the method the 10 most significant variables for salary, material and profit margin respectively have then subsequently been identified using the method from Brunø and Nielsen (forthcoming). The results are illustrated in Table 1 below.

<table><caption>**Table 1.** Overview of the 10 most significant variables for material cost, salary costs, sales price and profit margin. The variables highlighted with **bold** occur more than once. All dependent variables are calculated per piece.</caption><thead><tr><th>Rank of significance</th><th>Salary costs</th><th>Material costs</th><th>Sales Price</th><th>Profit margin</th></tr></thead><tbody><tr><td>1</td><td><strong>Number of pieces</strong></td><td><strong>Var6</strong></td><td>Var13</td><td><strong>Var15</strong></td></tr><tr><td>2</td><td><strong>Var1</strong></td><td><strong>Var1</strong></td><td><strong>Var10</strong></td><td>Var17</td></tr><tr><td>3</td><td>Var2</td><td><strong>Var10</strong></td><td><strong>Var7</strong></td><td><strong>Var12</strong></td></tr><tr><td>4</td><td><strong>Var3</strong></td><td><strong>Var5</strong></td><td><strong>Var5</strong></td><td><strong>Var1</strong></td></tr><tr><td>5</td><td><strong>Var4</strong></td><td><strong>Var4</strong></td><td><strong>Var4</strong></td><td><strong>Var8</strong></td></tr><tr><td>6</td><td><strong>Var5</strong></td><td><strong>Var3</strong></td><td>Var14</td><td><strong>Var5</strong></td></tr><tr><td>7</td><td><strong>Var6</strong></td><td><strong>Var9</strong></td><td><strong>Var15</strong></td><td><strong>Var4</strong></td></tr><tr><td>8</td><td><strong>Var7</strong></td><td>Var11</td><td><strong>Var16</strong></td><td><strong>Var16</strong></td></tr><tr><td>9</td><td><strong>Var8</strong></td><td><strong>Var7</strong></td><td><strong>Number of pieces</strong></td><td>Var18</td></tr><tr><td>10</td><td><strong>Var9</strong></td><td><strong>Var12</strong></td><td><strong>Var9</strong></td><td>Var19</td></tr></tbody></table>

From Table 1 it is easy to conclude that there is a relative large overlap between the variables that cause a high material and salary cost (6 out of 10 are the same) although the significance of the variables varies between the two cost structures. It is also easy to conclude that only 5 out of 10 variables that are significant for profit margin are critical for material or salary costs or both. It is interesting also to note that the two most critical variables for profit margin (i.e. the two features most critical to establish the profit margin) are not even on the list of the ten most significant variables for material or salary costs. It is also interesting to note that 6 out of 10 variables that are
critical for the sales price (i.e. the price a customer is willing to pay for a piece) are in fact also critical for determining the costs. However, in the same sense it is also very important to note that the variables that are critical for the sales price are only critical for the profit margin 4 out of 10 times. This could indicate that the company is unable to transfer the features that are critical for sales price in to features that are critical for the profit margin.

# 4 Discussion

The following discussion is based on a single case application. This of course somewhat limits the ability to generalize the results. An analysis identifying variables critical to cost and sales price can be utilized for a number of different purposes, however the most obvious opportunities are development of the solution space. Within this area the method could more specifically be applied in cost reduction projects to reduce the cost of expensive features, in adaption of pricing schemes for different features and for identifying non profitable features which should likely be removed from the solution space. However, the method has a main limitation, namely the requirement for a high volume of structured historical data including configuration variables, cost and sales price. As a result, this method cannot be applied for new products which have not yet been sold. Previous research indicates that the model fit increases the less variation is found in the product structure for different configurations. Finally, the method cannot react to changes in cost and pricing structure before a sufficient number of configurations are produced. Fig. 2 illustrates different scenarios for results regarding a specific variable.

<figure><img src="image_3.png" /><figcaption>Fig. 2. Scenarios for variable criticality</figcaption></figure>
In scenario 1 the variable is critical to the sales price but not the cost, indicating that this particular feature increases the sales price without adding extra cost to the product meaning that this feature can be exploited by e.g. increasing the sales effort for this feature. In scenario 2 the variable is critical to both sales price and cost indicating that the feature is probably necessary since it drives the sales price, however as it is also significant to the cost, efforts for reducing manufacturing costs should be focused here. In scenario 3, the variable is non critical to both sales price and cost, which indicates that these features are not influencing the profitability of the variety in the solution space. However, cost reduction efforts are still relevant in this scenario, but could presumably be addressed as cost reduction of standard product as little variation is found for these variables. In scenario 4, the variable is critical to cost but not to sales price, indicating that attention is required, since this feature basically being priced unacceptably low or the implementation of the feature is too expensive. This suggest that the feature should either be removed, priced higher or be significantly cost reduced to avoid decline in profit margins. The conclusions and suggested actions for the 4 scenarios are to be perceived as indications only. Although for example a variable in scenario 4 may not seem profitable, externalities may imply that it cannot be removed from the solution space or priced differently and thus a qualitative assessment should be performed for each variable. One simple way to apply the information generated and displayed in table 1 is to plot the variables in the matrix illustrated in figure 2. A display of this type comparing salary costs to sales price is presented in figure 3 below.

<figure><img src="image_4.png" /><figcaption>Fig. 3. Scenarios for variable criticality comparing salary costs and sales price</figcaption></figure>

As seen from figure 3, there are no variables in the third quadrant. In reality all features not found to be significant by the method and displayed in table 1, are in the quadrant. From the example we can see that we can place 14 variables in the matrix out of a total of 22 variables to begin with. This matches the concept of value engineering seen in e.g. [6], where experiences on value engineering from the automotive
industry are presented. The remaining variables then belong to third quadrant. The
critical variables from this example then become the five in quadrant 4, i.e. Var1,
Var2, Var3, Var6 and Var8. These five variables are insignificant for the sales price,
but significant for the salary costs. When redesigning the product (family) it would
then be relevant to first focus on these five and try to move them (at first) to quadrant
3 or even 1 or 2 if at all possible. The second step would then be to focus on the vari-
ables (features) found in quadrant 2 and investigate if they can somehow be moved to
quadrant 1. However, there will always be some variables that are critical for cost
variation, so better that they are placed in quadrant 2 than in quadrant 4. It is notewor-
thy that the variables seem to be equally distributed between the quadrants.

It is important to keep in mind that the approach presented in this paper only address-
es the variation in sales price and cost and does thus not address the base cost, i.e. the
intercept of the linear model. This cost and sales price is however expected to be de-
fined by a common product platform, i.e. product properties and components which
are part of all configured products and can thus be addressed as a non customized
product.

5 Conclusion

It can be concluded that the method can be used to identify which variables are criti-
cal for the costs, sales price and profit margin. It is also noteworthy that in this partic-
ular case there is a significant difference between which variables are critical for sales
price and costs. It is also possible to conclude that the variables associated with a
given feature can be categorized using the developed matrix and that this can serve as
a first quantitative step in a redesign process.

Future work will focus on two aspects. First, to further refine the quantitative method for identifying critical components / features. Second, to investigate and implement the method in several redesign processes in practice.

References

1. Alptekinoglu, A., & Corbett, C. J.: Mass Customization Vs. Mass Production: Variety and Price Competition. Manufacturing Service Oper.Management, **10** (2008) 204–217

2. Ben-Arieh, D., & Qian, L.: Activity-Based Cost Management for Design and Develop-
ment Stage. Int J Prod Econ, **83** (2003) 169-183

3. Brunoe, T. D., & Nielsen, P.: A Case of Cost Estimation in an Engineer-to-order Compa-
ny Moving Towards Mass Customisation. International Journal of Mass Customisation,
**4** (2012) 239-254

4. García-Crespo, Á, Ruiz-Mezcua, B., López-Cuadrado, J. L. et al.: A Review of Conven-
tional and Knowledge Based Systems for Machining Price Quotation. J. Intell. Manuf.,
1-19

5. H'mida, F., Martin, P., Vernadat, F.: Cost Estimation in Mechanical Production: The Cost Entity Approach Applied to Integrated Product Engineering. Int J Prod Econ, **103** (2006) 17-35
6. Ibusuki, U., & Kaminski, P. C.: Product Development Process with Focus on Value Engineering and Target-Costing: A Case Study in an Automotive Company. Int J Prod Econ, **105** (2007) 459-474

7. Kingsman, B. G., & De Souza, A. A.: A Knowledge-Based Decision Support System for Cost Estimation and Pricing Decisions in Versatile Manufacturing Companies. Int J Prod Econ, **53** (1997) 119-139

8. Layer, A.: Recent and Future Trends in Cost Estimation. Int. J. Comput. Integr. Manuf., **15** (2002) 499-510

9. Niazi, A., Dai, J. S., Balabani, S. et al.: Product Cost Estimation: Technique Classification and Methodology Review. Journal of manufacturing science and engineering, **128** (2006) 563

10. Nielsen, P., & Petersen, T. D.: Criticality of Components and Specific Configurations in an Engineer-to-Order Environment. Proceedings of the 14th International Annual Eu-rOMA Conference, (2007)

11. Salvador, F., de Holan, M., Piller, F.: Cracking the Code of Mass Customization. MIT Sloan Management Review, **50** (2009) 70-79

12. Shehab, E., & Abdalla, H.: An Intelligent Knowledge-Based System for Product Cost Modelling. The International Journal of Advanced Manufacturing Technology, **19** (2002) 49-65

13. Shtub, A., & Versano, R.: Estimating the Cost of Steel Pipe Bending, a Comparison between Neural Networks and Regression Analysis. Int J Prod Econ, **62** (1999) 201-207

14. Syam, N. B., & Kumar, N.: On Customized Goods, Standard Goods, and Competition. Marketing Science, (2006) 525-537

15. Tornberg, K., Jamsen, M., Paranko, J.: Activity-Based Costing and Process Modeling for Cost-Conscious Product Design: A Case Study in a Manufacturing Company. Int J Prod Econ, **79** (2002) 75-82

16. Verlinden, B., Duflou, J., Collin, P. et al.: Cost Estimation for Sheet Metal Parts using Multiple Regression and Artificial Neural Networks: A Case Study. Int J Prod Econ, **111** (2008) 484-492

17. Walpole, R. E., Myers, R. H., Myers, S. L. et al.: Probability and statistics for engineers and scientists. Macmillan New York (1989)

18. Weustink, I., ten Brinke, E., Streppel, A. et al.: A Generic Framework for Cost Estimation and Cost Control in Product Design. Journal of Materials Processing Tech., **103** (2000) 141-148