Mass customization design_choice_menu

XII ICIEOM - Fortaleza, CE, Brasil, October 9 - 11, 2006

Designing choice menus for mass customization

Flávio Sanson Fogliatto (DEPROT/UFRGS) ffogliatto@producao.ufrgs.br
Giovani J. C. da Silveira (University of Calgary/Canadá) giovani.dasilveira@haskayne.ucalgary.ca

Abstract
This paper proposes a method for designing choice menus for mass customization. The
method is based on the analysis of stated preferences on product or service attributes
obtained through panel studies. The method is presented, followed by a real world case
application in a natural gas distribution company. The application indicated the method was
able to elicit stated preferences on a broad range of attributes enabling the design of choice
menus for alternative customer segments, balancing the trade-off between flexibility and
value that is in the core of choice menu design.
Keywords: Mass customization, Choice models, Conjoint analysis, Stated preference.

1. Introduction
Mass customization (MC) has been defined as the ability to produce individually designed
products and services at near mass production cost (DAVIS, 1987). It is enabled by a series of
advanced technologies and practices including flexible manufacturing systems, computer
aided design, and lean manufacturing (Da SILVEIRA et al., 2001). Over the last decade, MC
has evolved from being a visionary idea to become a widespread strategy in manufacturing
and service industries. Research on MC has also progressed from an initial focus on the
manufacturing capabilities to produce variety at low cost to a broader emphasis on supply
chain coordination and customer involvement in the conception of MC products and services.
In this expanded view of MC, facilitating customer involvement in the process of
specification and design of a personalized product has become one major determinant of a
successful customization strategy (DURAY, 2002). Due to the limitation of traditional
techniques such as surveys and interviews to elicit individual customer preference in an
efficient and reliable manner, firms have been increasingly using choice menus (LIECHTY et
al., 2001). Choice menus consist of producer-user interfaces that enable customers to select
product attributes and features in a consistent and economical way (OLIVA, 2002).
Notwithstanding the increasing presence of choice menus in business and consumer industries
ranging from personal computers to financial services (SLYWOTZKY, 2000), few studies
have focused on problems associated to their design and configuration. In particular, the
design of choice menus must balance a trade-off between flexibility and value to customers,
as complexity aversion implies that the value of a menu often decreases with cardinality.
Despite the documented cases of companies that have been challenged by this particular
trade-off (WIND & RANGASWAMY, 2001), there has been limited research on methods to
specify the set of options to use in choice menus.
This paper proposes a method for choice menu design in a MC context. The method, based on
the analysis of customers’ stated preferences regarding product or service attributes, has some
important features. First, it is based on the use of well-known market research techniques,
such as focus groups, questionnaires and SP modeling, not demanding any special training
from analysts. Second, it uses cluster analysis and experimental design techniques to guide

ICIEOM 2006 ABEPRO 1


data collection from customers, leading to databases that are both cost-efficient and
representative. Third, it proposes the use of SP modeling using logistic regression, which is
both easy to interpret and available from common statistical packages.
2. Research background
This literature review is divided in two parts. Initially, we present the current relevant
research on choice menus. Then, we introduce basic aspects of stated preference modeling.
The ability of customers to co-design products or services based on individual preferences is
one of the most distinctive features of MC. Over recent years, firms have been increasingly
offering choice menus to allow customers to design solutions by selecting items that can best
fulfill their needs (LIECHTY et al., 2001). Choice menus, also called choiceboards or design
palettes, involve a broad range of customer-supplier interfaces, from simple menus offering
product options and features to intelligent aid, mostly web-based systems assisting in the
process of designing, comparing and cost-estimating mass customized orders.
From a research perspective, the major challenge with choice menus is the specification of a set
of options to balance flexibility and value to customers. On one hand, larger menus provide more
flexibility as they offer more options. On the other hand, complexity-aversion implies that the
value of a menu decreases with cardinality (SONSINO & MANDELBAUM, 2002). Problems
with flexibility-value trade-offs are compounded by the fact that both flexibility demands and
complexity-aversion levels will often vary across customer groups. Demand for flexibility
increases with increased uncertainty about future tastes; complexity aversion increases with the
desire to minimize the risk of making wrong decisions (STODDER, 1997). Thus, as pointed out
by DeShazo and Fermo (2002), designers must build choice sets that minimize the detrimental
effects of choice complexity to a customer category. In practice, information about customer
preferences must be used to tailor the design of the choice menu itself, customizing the set of
options presented to buyers and promoting sales (SLYWOTZKY, 2000).
The stated preference (SP) method is an applied conjoint paradigm that quantifies
respondents’ choices regarding hypothetical market situations (UNTERSCHULTZ et al.,
1997). Preferences are given to commodity alternatives decomposed into separable attributes,
each of which can be examined for their individual influence on choice. This approach is
derived from Lancaster’s (1966) theory of characteristics stating that utility is derived not
from goods themselves, but from the attributes or characteristics of goods.
The SP method is based on random utility theory (RUT) choice models. The theory is derived
from the observation that an individual can make different judgments from one occasion to
the next. Therefore, utility is expressed as a sum of observable and non-observable (random)
components (HENSHER et al., 1999):
U in = Vin + ε in , (1)
where U in is respondent n’s utility of choosing alternative or scenario i, Vin is the systematic,
observable component of utility and ε in is the random component. The utility Vin of
alternative i is a function of its attributes, which is often assumed to have linear form,
Vin = β 0 + β1 xin1 + β 2 xin 2 + ... + β k xink , (2)
where Vin is respondent n’s systematic utility of alternative i, xink are the attributes of
alternative i for respondent n and β 0 to β k are the coefficients to be estimated.



3. Method
The method proposed in this paper is illustrated in Figure 1. There are three phases, each with
a number of operational steps and decision points detailed next. Although the method may be
applied in the design of product and service choice menus, we hereafter refer to products only.

Figure 1 – Phases and operational steps of the method proposed

The main objective in Phase 1 is to obtain a comprehensive list of product attributes relevant
to customers. Such attributes will be treated as candidate choice menu variables. We start this
phase by analyzing the product market in terms of customer diversity. When customers have
theoretically distinct customization demands it may be advisable to qualitatively cluster them,
using expert opinion, prior to data collection; otherwise, a representative random sample of
unclusterized product customers may be used in the focus group sessions.
Data collection using focus groups is typically accomplished in six steps: (i) focus group
planning, (ii) participants selection, (iii) definition of questions and session moderation
guidelines, (iv) definition of sessions logistics, (v) choice of moderator and (vi) data collection
and analysis. For details on each step, see Greenbaum (2000).
Information in Phase 1 may be organized in a table, with columns headings corresponding to a
description of the ad hoc clusters (in case they arise in the analysis), and row elements given by the
attributes elicited by individuals in focus groups. Ad hoc clusters are identified by Ci′ , i = 1 I ′ ,

and attributes by Aij , j = 1 J . It is expected that attributes in different clusters coincide.
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Objectives in Phase 2 are (i) to obtain importance weights wij to attributes Aij listed in Phase
1 and (ii) group customers using formal clustering techniques. To accomplish objective (i) we
propose a quantitative research using a questionnaire for data collection; to accomplish
objective (ii) a cluster analysis may be performed.
Using the proper sampling technique is fundamental in the research design. If the sample is
intended to be a smaller scale representation of a population of interest, the researcher should
know beforehand whether customers differ substantially regarding customization demands. In
case there is evidence to the existence of clusters of customers, their proportion in the
population should be estimated, to enable a probabilistic sampling of that population.
Otherwise, a non-probabilistic sampling strategy may be appropriate (for details on sampling
strategies and samples size determination see LEVY & LEMESHOW, 1999).
Questionnaires must be elaborated such that the objective of data collection is clear to



respondents upfront; for that matter, a title and introduction text must always be used. If
possible, the analysis should group similar customization attributes and provide a subtitle to
each group. As the number of attributes increases, grouping of items becomes crucial.
To gather importance weights wij from respondents there are two possible approaches. In
case j > 15 and attribute descriptions are complex, a good strategy is to ask respondents to
rank attributes within each group. An item ranked in the k-th position will be given an
importance weight 1 k (similar to situations where a ratio scale is used, importance of items
becomes directly comparable within a group). If the number of attributes is small, weights
may be elicited using an importance scale, such as the Likert 5-point scale. Weights from all
respondents are then added up to obtain the final importance weight for a given attribute.
Once importance weights are available to each attribute, cluster analysis is performed using wij
as classificatory variables. The objective is to formally identify clusters of customers with similar
demands in terms of product customization attributes and optimize data collection in Phase 3. If
ad-hoc clusters of customers were previously identified in Phase 1, formal cluster analysis will
allow validation of such clustering. We denote the new clusters by Ci , i = 1 I , and point out

that I ′ may differ from I , and that the final clustering of customers should arise from careful
analysis of the groupings in Ci′ and Ci , since they were generated using different information.
The main objective in Phase 3 is to obtain preference models relating stated preference (SP) and product
attributes. Design of experiments (DOE) in conjunction with SP modeling is used for that purpose. DOE
guarantees a data collection both efficient and economically feasible. The SP method allows data
modeling where the influence of individual attributes as well as their interactions on customer preference
may be assessed. Once SP models are at hand choice menus may be defined for the product, both in
terms of attributes to be customized as in terms of choice levels within each attribute.
We stress the importance of building models where the significance of interactions between
attributes is assessed. SP data are usually summarized using main effect models, which demand a
low cost data collection. However, customization attributes of a product may not affect
independently customer preference and therefore the significance of interaction terms should be
verified. Clustering of customers in Phase 2 allows us to use more complex, yet still economically
feasible data collection designs enabling SP modeling of both main effects and interactions.
The SP method is used here to compare a control (or reference) scenario against several
alternative scenarios. The control scenario expresses the standardized version of the product
under analysis. In the alternative scenarios, product attributes are made flexible to create
different customized products. To make an attribute flexible implies in setting it to a given
level. Therefore, an SP customization model will indicate not only the relative importance of
attributes but also the market benefits from offering each attribute at different levels.
As previously mentioned, data collection for SP modeling must be planned separately within
clusters of customers. Recall that customers in a given cluster value similarly the same
customization attributes. Therefore, creating SP scenarios specifically for each cluster enables
to include only attributes that are highly valued by them, reducing the number of attributes
and consequently the number of alternative scenarios in the SP study.
We recommend using factorial designs to organize the SP data collection process. In a factorial
design applied to collect SP data from a given cluster of customers, attributes are varied within
pre-defined levels to generate alternative scenarios such that the total number of scenarios



analyzed will be given by N = ∏ j =1 k j , where k j denotes the number of levels of attribute j.
J

Clearly, a large number of attributes and levels will lead to a large data collection, which is usually
undesirable. Therefore, we should restrict the SP study to include attributes with large values of wij
(excluding attributes not included in the study from the choice menu). Similarly, the number of
levels of an attribute should be greater than two only if there is strong evidence of a non-linear
relationship between attribute levels and the response (i.e. the stated preference). Fractioning and/or
blocking the experimental data collection matrix are usually necessary in SP studies and are likely to
be needed when applying the method we propose. If clustering of customers in Phase 2 leads to
clearly defined clusters, blocking the data collection within clusters is the recommended course of
action; otherwise, fractioning should be preferred. Once the experimental matrix is defined, data
collection may take place following the guidelines of SP studies such as Bateman et al. (2002).
Data modeling should lead to preference models for each cluster, following the guidelines in
section 2. Eq. (2) gives the respondent’s preference to a given scenario, which is the usual
outcome of an SP study. Here, respondents are customers grouped in clusters and it is
assumed that they share the same model. Using such models, it is possible to arrive to choice
menus for the product under study. This is done upon inspection of their regression
coefficients and preference values, as follows. Coefficients that are significant at (1 – p)% are
ranked in importance according to their p-values (typical choices are p < 0.05 or p < 0.1); they
indicate the attributes to be included in the choice menu. Attributes that appear significant
exclusively in interaction terms should also be considered for inclusion in the menu. The
number of levels of an attribute in a menu should be defined considering the attribute’s rank
position and the practical relevance of offering a large number of levels to customers.
Menus may also have their attributes and levels defined based on preference threshold values
chosen by the analyst, performing simulations with the preference models. For example, it
may be possible that only certain levels of an attribute yield predicted preference values above
the threshold; these are thus the levels to be included in the choice menu. It may also happen
that even excluding an attribute from the model one is able to determine combinations of
levels for the remaining attributes that yield predicted preference values above the threshold.
If parsimony is sought, such attributes could be also excluded from the choice menu.
4. Case application
The method proposed in this paper was applied in a case study where the main objective was
to determine product, service and technological attributes valued by potential natural gas
(NG) customers of a Brazilian distribution company, as well as their proper customization
levels. Although available in several regions in central Brazil, only recently the NG
distribution network reached the south of the country, where the present study took place. A
wide variety of industrial, commercial, automotive and residential NG applications are
possible, and customer needs regarding the commodity tend to vary according to end use.
Following the steps proposed in section 3 we were able to identify clusters of NG customers
in the geographical region of interest and create choice menus for them, as detailed next.
We start describing the steps in Phase 1. To obtain the list of NG attributes from focus groups
in the case study, ad-hoc clustering of customers was mandatory for two main reasons. First,
there are a large number of uses for NG reported in the literature (for example, as a vehicular
fuel in cars and buses as well as in the air-conditioning of hospitals). Although the product is
essentially the same, some of its attributes are more or less valued according to the final use.
Therefore, treating all potential NG customers as members of the same population would lead



to a list of attributes excessively broad and non-representative. Second, data collection tends
to be time consuming if applied to a large number of focus groups. Therefore, we must
deviate from the ideal case where a list of customization attributes is obtained from each type
of NG customer, and collect data from groups of customers that use the commodity similarly.
The first clustering of NG customers was obtained following a three-step approach. We first
identified economic sectors with a reported history of NG use in the literature. We then verified
both the presence and the economical representation of such sectors in the geographical region
of interest. Finally, ad-hoc clustering of sectors was obtained based on expert opinion from
members of the NG distributor technical staff. A total of 39 relevant economic sectors, all
potential users of NG, were identified in the region. Their typical NG applications were listed
and served as basis to clustering. As partially shown in Table 2 (first and second columns),
eleven clusters of customers (identified as economic sectors) were formed. Focus groups were
limited to ten participants. In a given focus group, the number of cluster sector representatives
was determined based on their economical relevance and NG usage potential.

Economic sectors Ad-hoc clustering ( Ci′ - Phase 1) Formal clustering ( Ci - Phase 2)
Supermarkets 1 5
Meat markets 1 1
Open malls 2 1
Hotels 2 5
Hospitals 2 5
Food manufacturers 3 2
Rubber processors
¢
3 ¢
5 ¢

Tanneries 11 5
Table 1 – Sample of ad-hoc and formal clusters in the case study

Information gathering from the 11 focus groups was completed in approximately 4 months.
Sessions varied in length from 60 to 90 minutes and were moderated by one individual and two
assistants. Invited members from sectors in each cluster voluntarily participated in the meetings.
They were requested to list important attributes related to the product, the distribution network (and
services provided by the distributor) and the technological aspects of using NG (in particular those
related to equipment conversion and its maintenance). At the end of each section, participants were
requested to rank attributes in importance within each category. Although respondents were aware
that attributes listed should be preferably customizable, there were exceptions (for example, supply
longevity). Table 2 displays a list of the highest ranked attributes in each category over all groups.

Product Network Equipment
Price Time to supply in large scale Safety
Operative performance Network capillarity Compliance to legislation
Emission of pollutants Contractual conditions Specific technical solutions
Storage space Supply regularity Maintainability
Reliable measurement system Supply diversity Technical literature availability
NG adoption projects provided by Standardized valves and Technical training prior to
distributor connectors equipment use
Supply longevity Network maintenance Convertibility
Cost to install NG tubulation Multi-fuel capacity (NG / LPG)
Network pressure Operational cost
Table 2 – Some NG attributes as elicited by consumers in focus groups

′
Ranking of attributes varied substantially among clusters. For example, customers in C1



ranked price, supply regularity and operative performance as most important attributes, while
′
customers in C11 elected time to supply in large scale, price and supply longevity. That
exemplifies the importance of pre-clustering customers prior to focus group data collection.
Phase 2 starts with quantitative research to obtain importance weights wij to attributes in Table 2. A
questionnaire was prepared for that, with 18 attributes (six in each category) to be ranked in
importance by respondents. We used a questionnaire where items are ranked in importance.
Questionnaires were customized for clusters of clients presenting the attributes most valued by them
in Phase 1, but applied separately to customers in the 39 economic sectors previously identified.
The average sample size per economic sector was 16 questionnaires, estimated using proper sample
size estimation techniques. This average value was adjusted to include the following information
regarding each sector: (i) relative percentage of electricity consumption, (ii) relative number of
participants in sector (in %) and (iii) NG adoption potential (given as a probability). The smallest
and largest adjusted samples sizes were 5 and 50, respectively. A total of 450 questionnaires were
personally applied by a team of administrators. As previously mentioned, questionnaire respondents
were asked to rank attributes in importance regarding their customization requirements. To convert
ranks into scores, we used the reciprocal of the attribute rank position as explained in section 3.
A formal cluster analysis was carried out using the 18 attributes as clustering variables.
Responses from a same economic sector were added and normalized to generate a (39×18)
data matrix used in the analysis. Cluster analysis was carried out in two steps. First, the proper
number of clusters to be used was identified using a hierarchical approach. Once the ideal
number of clusters was identified, sectors to integrate each cluster were determined using a k-
means partitioning algorithm. The final assignment of sectors to clusters is partially presented
in the last column of Table 2. As expected, clustering in Phase 2 yielded different results from
clustering in Phase 1. We entered Phase 3 of the method using the Ci clusters.
We now describe the steps in Phase 3. Twelve experiments were planned to collect SP data
from clusters. Each experiment was comprised of six attributes, corresponding to the two
most important in each category, explored at different levels. Price, an attribute included in all
clusters, was explored at four levels; all remaining attributes were investigated in two levels.
Thus, the total number of scenario alternatives investigated in each experiment was 128
(25×41). Further, it was decided that 16 scenarios would be presented to each respondent, to
avoid fatigue. Therefore, experiments were divided into 8 blocks with 16 scenarios each.
Attributes were coded A to F and attribute levels were coded -1, -0.3, +0.3, +1.
Each of the eight blocks was presented as a questionnaire to respondents and replicated ten
times. As discussed earlier, in a customization SP study alternatives may be compared to a
control scenario where the product is presented in a standardized format; the alternatives
therefore make the product flexible regarding attributes of interest. However, in our study,
NG was not yet available to customers. Therefore, the control scenario was established to be
the alternative energy sources, e.g. PLG, oil, wood, etc. (and their corresponding
characteristics) used at that moment by customers from each cluster. Alternative scenarios
presented levels of NG attributes in each cluster in comparison to that control scenario.
Due to the large number of clusters in this case study, we restrict ourselves to present results
from cluster 5 that grouped a large number of industrial and commercial sectors. The
preference loglinear model for cluster 5 is:



ln ( y (1 − y )) = −1.488 + 0.241X 1 (.000) + 0.176 X 2 (.001) + 0.344 X 3 (.000) + 0.267 X 4 (.000) +
(3)
0.214 X 5 (.000) + 0.770 X 6 (.000) + 0.170 X 1 X 2 (.002) + 0.300 X 3 X 6 (.000) + 0.232 X 4 X 6 (.002)
where y denotes the response (i.e. preference of a given scenario for each cluster) and X i (i = 1,…, 6) are the
attributes investigated (detailed in Table 3). P-values are given within parentheses after their corresponding
model term. Transformation in response y led to a linear model, with coefficients determined using the
statistical package SPSS. The model fit given by its coefficient of determination was R 2 = 0.704 .
Coefficients in eq. (3) were directly comparable in view of the attributes level coding. As expected,
Price appeared as the most important attribute, followed by Emission of pollutants and Convertibility.
Three interactions were significant: Supply regularity × Cost to install NG tubulation, Emission of
pollutants × Price and Convertibility × Price. Since all interaction coefficients were positive, they
were easy to interpret. Consider interaction X 1 X 2 , for example, and suppose a menu where all
attributes are set at their most favorable levels, including X 1 and X 2 . Customer preference in that case
would be 77.3%; ignoring the interaction the preference value decreases to 74.2%. It is also
noteworthy that some interactions had larger coefficients than individual attributes. Ignoring such
interactions, as in classical SP data analysis, would lead to a less representative model.
Attribute Description Levels
X1 = Supply regularity Degree to which customers may be exposed to (-1) Irregularity possible
interruptions in NG supply (+1) Irregularity not possible
X2 = Cost to install NG Cost incurred by customers to extend tubulation (-1) High; (+1) Low
tubulation from distributor’s network to point of use
X3 = Emission of NG emission of pollutants into environment in (-1) Pollutes more
pollutants comparison to energy sources to be replaced (+1) Pollutes less
X4 = Convertibility Degree of equipment convertibility to NG (-1) Low; (+1) High
X5 = Operational safety Level of equipment operational safety using NG in (-1) Safety decreases
comparison to energy sources to be replaced (+1) Safety increases
X6 = Price Indicates expenses with GN in comparison to the (-1) 10% larger; (-0.3) About
energy sources it is intended to replace the same; (+0.3) 10% smaller
(+1) 10% to 30% smaller
5. Conclusions
This paper proposed a method to design choice menus in a MC context. The method
incorporates stated preferences to define which attributes of a product or service should be
offered at different levels to be selected by individual customers. The method aims mainly at
designing choice menus with an appropriate number of options to balance the flexibility-
complexity trade-off indicated by Stodder (1997), among others. This appears to be one of the
first methods to support the design of choice menus for MC.
Due to its originality and stated objectives, this study has limitations, most of which can be
addressed by further studies. First, the method did not incorporate approaches to update choice
menus through combining revealed preferences with the stated preferences assessed in the panel
study. Second, the idea of incorporating interactive terms to assess the moderated effect of one
attribute on the regression estimate of another attribute may have broad implications for
modularity and bundled choice, but these implications were not sufficiently explored in this paper.
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