African Journal of Business Management Vol. 7(1), pp. 39-55, 7 January, 2013
Available online at http://www.academicjournals.org/AJBM
DOI: 10.5897/AJBM12.611
ISSN 1993-8233 ©2013 Academic Journals
Full Length Research Paper
Product variety management: A synthesis of existing
research
Augusto da Cunha Reis1,2*, Luiz Felipe Scavarda1 and Beatriz Moreira Pancieri1,3
1
Industrial Engineering Department, PUC-Rio, Brazil.
Industrial Engineering Department,CEFET-RJ UnED Nova Iguaçu, Brazil.
3
Industrial Engineering Department, UNAMA, Pará, Brazil.
2
Accepted 3 September, 2012
This article presents a systematic review of the literature on product variety management (PVM). The
review examines publications between 2005 and 2010 found in the Elsevier electronic database. The
review emphasises various internal and external pressures that encourage companies to increase or
reduce the variety of products that they offer (that is, PVM input), different ways of dealing with these
different pressures (that is, structure and processing), and the expected results of good management
practices (that is, outputs). The framework also includes a fourth dimension that highlights the context
in which the studies are grounded. The results highlight the main themes in PVM research, identify the
key issues addressed in this research, and emphasise remaining gaps that deserve special attention in
future research.
Key words: Supply chain, business processes, information technology, mitigation strategies, metrics.
INTRODUCTION
Systematic literature reviews are a means of providing an
objective theoretical evaluation of a particular topic
(Hopayian, 2001). A systematic literature review
facilitates the identification, evaluation, and interpretation
of studies in a given area by examining existing concepts,
practices, and theories and ultimately summarising the
state of the reproducible research in a specific area
(Rowley and Slack, 2004; Seuring and Müller, 2008).
Thus, the use of literature reviews is necessary for those
seeking to better understand the issues associated with a
topic of research (Burgess et al., 2006) and to provide
direction for future studies that can address existing
knowledge gaps.
Several concepts and themes related to industrial
engineering have been analysed by means of systemic
reviews. These include supply chain management
(Croom et al., 2000; Burgess et al., 2006; Seuring and
Müller, 2008), customer
relationship management
*Corresponding author. E-mail: [email protected].
(Nagai et al., 2009), electronic commerce (Nagai et al.,
2002), and logistics (Marasco, 2008; Pokharel and
Mutha, 2009). However, despite the importance of
product variety management (PVM) and the large
number of published studies on this subject, the
academic literature lacks a systematic review of PVM
research.
Product variety is one of the traditional competitive
priorities in manufacturing, and thus, it is associated with
operational trade-offs (Hayes and Pisano, 1996; Mapes
et al., 1997; da Silveira and Slack, 2001). Today, the
increasing variety of products offered to customers has
emerged as a major trend (Scavarda et al., 2010;
Stäblein et al., 2011), and great academic interest has
developed in the effects and consequences of product
variety on production systems. Balakrishnan and
Chakravarty (2008), Vaagen and Wallace (2008), Murthy
et al. (2009), and Zhang and Huang (2010) reported that
product variety refers to variations in product attributes
and/or characteristics that allow for different product
configurations. Escobar-Saldívar et al. (2008) characterised product variety as the number of existing
40
Afr. J. Bus. Manage.
Figure 1. Audi`s product variety.
product lines and the number of products offered in each
line.
Winkler (2000) conducted a study of the dynamic range
of product variety at Audi (early 1980s to early 2000s).
During the early 1980s, this Vehicle Assembler offered
the market just two models (Audi 80 and Audi 100)
whereas in the early 2000s it offered more than 20
vehicle models to the market, divided into 5 segments, as
shown in Figure 1. Furthermore, there was an expansion
in the number of body types offered by the automaker. In
in early 1980s, it offered only a sedan body type, while in
early 2000s it offered many different types (for example,
sedan, hatchback, wagon and coupe).
Elmaraghy et al. (2009) highlighted the difficulty of
balancing the customer and company viewpoints on
variety, offering sufficient variety to the customer while
also considering the effect of that variety on production
systems. This problem is especially challenging for firms
because of the dearth of models and tools that they can
use to achieve an appropriate balance between the
positive and negative aspects of product variety.
According to the authors, this lack of models and tools
constitutes a significant gap in the literature. Elmaraghy
et al. (2009) noted that managing variety at all levels of
production and support is one of the most important
priorities for companies in the current dynamic environment. The management of product variety makes it
possible to offer customers a variety of products while
simultaneously maintaining high levels of quality,
responsiveness, and adaptation to change, thereby
generating profits.
In this context, the objective of this study is to conduct
a systematic review of the existing literature by reporting
findings from published papers on PVM, highlighting the
state of the art and identifying any gaps that could be
addressed in future research. This paper is subdivided as
follows: This paper was first introduced, after which the
framework used to guide the systematic review was
presented. This is followed by a description of the
research methodology used, after which the analysis and
results were presented. Finally, the study was concluded.
FRAMEWORK FOR A SYSTEMATIC REVIEW
A research framework indicates how researchers’ understanding of a particular theme has developed (Rowley
and Slack, 2004). Research frameworks are carefully
tailored to address the fundamental aspects of the theme
under study. In other words, at the end of the research
process, such conceptual frameworks must be useful for
other researchers interested in the same theme (Seuring
and Müller, 2008).
The key dimensions of PVM are shown in the framework depicted in Figure 2. The construction of this figure
was guided by a review of the existing frameworks used
in the literature review content analysis. The framework
draws particularly on Marasco (2008) and Pokharel and
Mutha (2009), adopting the categories for analysis
designated by those authors.
The proposed framework includes four dimensions:
context, inputs, structure and processing, and outputs.
Context includes the characteristics of the studies
contained in the literature review and the backdrop
against which they were developed, as outlined in
Burgess et al. (2006). This dimension includes the
characteristics highlighted in Rowley and Slack (2004),
such as the journal in which a study was published, the
year of publication, and the sectors on which the study
was focused (for example, the manufacturing or service
Reis et al.
41
CONTEXT
Journal name
Industry sector
(manufacturing or service)
Year of publication
INPUT
Theoretical and/or
empirical
Descreptive or prescreptive
STRUCTURE AND
PROCESSING
OUTPUT
Relationships and
participants
External pressures that
influence the number of
product variety
Business Process
Information Technology
Internal pressures that
influence the number of
product variety
Objectives aimed to be
achieved as a result of
efficient PVM
Mitigation Strategies
Metrics
Figure 2. The content analysis framework.
industry). In addition, this dimension includes the characteristics highlighted by Croom et al. (2000), whether a
study is theoretical or empirical and whether its
contribution is descriptive and/or prescriptive. Inputs in
this case are pressures that influence the increase or
decrease in the variety of products offered to customers,
whether internal or external to the company’s power to
control its stock. Structure and processing characteristics
are the means that organisations use to deal with these
pressures. These resources can be grouped into the
following categories: (i) relationships and participants,
which can be considered from both the intra-organisational perspective (when the focus is departments or
areas internal to departments; Shapiro, 1977; Bowersox
et al., 2000; Malhotra and Sharma, 2002) and the interorganisational viewpoint (when the focus is the various
members of a supply chain; Croom et al., 2000; Lambert
and Cooper, 2000); (ii) business processes (Davenport,
1990, Lambert and Cooper, 2000, Lambert, 2004); (iii)
information technology (Croom et al., 2000); (iv)
mitigation strategies (Pil and Holweg, 2004; Scavarda et
al., 2010); and (v) metrics (Gunasekaran et al., 2004;
Nudurupati et al., 2011). Finally, outputs are the
objectives that companies hope to achieve as a result of
efficient PVM (da Silveira, 1998).
RESEARCH METHODS
Li and Cavusgil (1995) classified existing literature reviews intended
to summarise the state of the art in specific areas by distinguishing
between reviews that employ the Delphi method, those that use
meta-analysis, and those that employ content analysis. The present
study used content analysis. According to the GAO (1996), content
analysis allows researchers to select, filter, and summarise large
volumes of data, thereby facilitating data analysis. Holsti (1969)
suggests that this technique facilitates objective and systematic
inference, making it possible to identify the relevant features of a
42
Afr. J. Bus. Manage.
particular subject, especially those isolated by multiple researchers.
Moreover, content analysis is a systematic technique that is
replicable by other researchers because it is based on explicit rules
(Weber, 1990).
The methodological approach adopted in this research was
based on the studies carried out by Rowley and Slack (2004) and
Kirca and Yaprak (2010). First, the criteria for the selection and
inclusion of the studies were defined. Then, based on the framework presented, the collected data were organised, after which the
results were analysed. This made it possible for the conclusions of
this paper to be presented.
The data from the review were gathered exclusively from
scientific journals. This limitation is justified because academics and
professionals generally use such journals to acquire knowledge and
disseminate new results. Thus, these journals represent the highest
level of research (Nord and Nord, 1995; Ngai and Wat, 2002, Ngai
et al., 2009).
As Rowley and Slack (2004) have indicated, online databases
are an important tool in the selection of articles from scientific
journals. Science Direct was the database used in this research.
This means that the present study is non-exhaustive because other
databases may contain additional relevant studies on the subject.
Furthermore, the only journals included were those that publish
articles in the following areas of study: “Business, Management and
Accounting”,
“Computer
Science”,
“Decision
Sciences”,
“Economics, Econometrics and Finance”, “Engineering”, and “Social
Sciences”.
To select articles, an advanced search was performed using
Boolean expressions ("AND" and "OR") that combined keywords to
best approximate specific terms, as advocated in Rowley and Slack
(2004). The research procedure included an initial filter created by
searching for the expression "product AND variety" in the article
abstracts, keywords, and titles. In spite of the high amount of
articles published on this subject, it was observed that only a small
part of the authors conceptualized product variety (Balakrishnan
and Chakravarty, 2008; Vaagen and Wallace, 2008; Murthy et al.,
2009; Zhang and Huang, 2010). However, the term “Product
Variety” is widely adopted and configures a good expression to
cover the vocabulary knowledge in this field. The “AND” Boolean
expression was used, as these terms do not always come together.
Other key words such variations, variants, product line were not
used in inclusion criteria as they normally come together within the
terms mentioned before. Even so, the expression “managing OR
management” was used as a second filter to retrieve some papers
that do not directly address in their abstracts, key words, and titles
the terms “product” and “variety”.
The research analysis also only included articles from 2005 to
2010. Nevertheless, this six-year scope is sufficient to cover the
relevant and current references, thereby making it possible to
analyse the current state of the art of PVM. The first phase yielded
285 articles for possible inclusion in the systematic review. To
ensure a focus on the topic of product variety, additional filtering
was performed through analyses of the article abstracts and then of
entire articles.
It is noteworthy that the three researchers involved in this study
read the abstracts. During this step, each researcher classified the
studies in binary form, assigning a value of zero (0) to articles
unrelated to PVM and a value of one (1) to related articles. The
next step was to compile the values assigned to the abstracts in a
Microsoft Excel® spreadsheet. Any instances of disagreement were
addressed by the three researchers so that they might reach a
consensus regarding the inclusion or exclusion of the article in
question. After this step had been completed, the number of articles
included in the study decreased to 73.
Next, the three researchers each read each of these 73 articles
in its entirety. Based on this review process, only 60 articles were
selected for the systematic literature review. The data from these 60
articles were organised in Microsoft Excel® spread sheets based on
the framework described previously. After selection, the units of
analysis were sorted. These units included words, sentences, and
paragraphs of the text, as recommended by Unerman (2000).
PRESENTATION AND ANALYSIS OF RESULTS
This section presents and analyses the results obtained
from the systematic review of PVM research, organised
according to the four dimensions presented in Figure 3.
Context
The 60 articles selected for review are listed and
numbered in Table 1. These numbers will be used to
refer to the studies throughout this section. The columns
group the studies according to publication year. The last
row of the table shows the total number of studies
published each year.
Figure 3 shows the distribution of studies by journal. It
is notable that PVM studies are published mainly in
journals that emphasise operations and manufacturing
management, although journals focused on finance/
economics and marketing are also represented in the
table. These results indicate the interdisciplinary nature of
the subject.
Table 2 presents combined data on the sector or
sectors under study in particular articles (manufacturing,
services, or both) and the types of studies presented
(theoretical, empirical, or both).
Many of the articles analysed (that is, 80.0% of articles)
are focused on the manufacturing sector in industries
such as the automobile, mobile phone, computer, textile,
paper, and coffee industries. Within the service sector,
the retail sector was the most common focus, with
studies addressing problems such as product variety
assortment, shelf allocation (Morales et al., 2005; Chen
and Lin, 2007; Hariga et al., 2007), and product recommendation systems for online retailers (Albadvi and
Shabazi, 2009). These results suggest that researchers
should further explore the nature of the service sector
given its increasing importance in industrial engineering.
The data indicate that 48.3% of articles are theoretical:
they seek general solutions that may be useful to other
companies in the same industry or sector or that may be
applicable to more than one type of industry or sector.
The remaining articles are purely empirical (38.3%) or
both empirical and theoretical (13.4%), proposing
methodologies and testing them empirically. Empirical
studies are predominantly focused on manufacturing.
This further emphasises the need for more empirical
studies on the service sector.
Sixty percent of the articles analysed are prescriptive,
and they mostly propose practices for adoption by
companies. Prescriptive and combined descriptiveprescriptive studies together account for 85% of the total
articles; only 15% of the studies are purely descriptive.
Reis et al.
43
10
1321
Tourism Management
Systems Engineering - Theory and Practice
Reliability Engineering and System Safety
Omega - The International Journal of Management Science
Journal of Retailing and Consumer Service
Journal of Retailing
Journal of Materials Processing Technology
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
Journal of International Money and Finance
Journal of International Economics
Journal of Economic Theory
Journal of Consumer Psycology
International Journal of Research in Marketing
Information Processing and Management
Computers & Industrial Engeneering
Advanced Engineering Informatics
Technovation
Robotics and Computer-Integrated Manufacturing
Journal of Operations Management
Journal of Manufacturing Systems
0
International Journal Production Economics
Expert Systems with Applications
European Journal of Operational Research
International Journal of Industrial Organization
Annals of the CIRP
CIRP Annals - Manufacturing Technology
Computer-Aided Design
Computers & Operations Research
Computers in Industry
Decision Support Systems
Journal of Manufacturing Systems
Journal of Operations Management
Robotics and Computer-Integrated Manufacturing
Technovation
Advanced Engineering Informatics
Computers & Industrial Engeneering
Information Processing and Management
International Journal of Research in Marketing
Journal of Consumer Psycology
Journal of Economic Theory
Journal of International Economics
Journal of International Money and Finance
Journal of Materials Processing Technology
Journal of Retailing
Journal of Retailing and Consumer Service
Omega - The International Journal of Management Science
Reliability Engineering and System Safety
Systems Engineering - Theory and Practice
Tourism Management
Decision Support Systems
1
Computers in Industry
2
Computers & Operations Research
3
Computer-Aided Design
4
CIRP Annals - Manufacturing Technology
5
Distribution of studies by publication journal
Annals of the CIRP
6
9
6
4
4
3
3
2
2
2
2
2
2
2
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
International Journal of Industrial Organization
7
European Journal of Operational Research
8
Expert Systems with Applications
9
International Journal Production Economics
Expert Systems with Applications
European Journal of Operational Research
International Journal of Industrial Organization
Annals of the CIRP
CIRP Annals - Manufacturing Technology
Computer-Aided Design
Computers & Operations Research
Computers in Industry
Decision Support Systems
Journal of Manufacturing Systems
Journal of Operations Management
Robotics and Computer-Integrated Manufacturing
Technovation
Advanced Engineering Informatics
Computers & Industrial Engeneering
Information Processing and Management
International Journal of Research in Marketing
Journal of Consumer Psycology
Journal of Economic Theory
Journal of International Economics
Journal of International Money and Finance
Journal of Materials Processing Technology
Journal of Retailing
Journal of Retailing and Consumer Service
Omega - The International Journal of Management Science
Reliability Engineering and System Safety
Systems Engineering - Theory and Practice
Tourism Management
International Journal Production Economics
Distribution of studies by publication journal
Figure 3. Distribution of studies by publication journal.
Inputs
Inputs are factors that drive the adoption of PVM;
they include both external and internal pressures
on firms. Table 3 presents the main pressures
identified in the literature review and related
references together with the direction of the
impact pressure on PVM.
The main responsibility of companies is their
need to meet and satisfy the diverse needs of
customers by increasing the variety of products
that they offer, introducing new products (Kim et
al., 2005; Aramand, 2008), and adding new
features or functions to existing products (Chen
and Lin, 2007). Globalisation has contributed to
the proliferation of variety because geographically
dispersed demand increases the need to offer
products that are appropriate for different cultures
and meet the demands of a diverse customer
base. Example of this dispersion is offering
tourism packages with wide range of options for
the traveller (local transportation, lodging, etc...)
due to the easy access to different geographic
areas (Weng and Yang, 2007).
In addition to the customization needs by final
customers, pressures to increase the variety of
products can also be made by intermediary
customers. Retailers, for example, want greater
variety to prevent the transformation of the
products offered in its sales outlets in commodities subject to price competition (Johnson and
Kirchain, 2009). Another example is the industry
of high-technology products, characterized by
products with short life cycle due to technological
developments, such as software products and
services. According to Aramand (2008), this
industry needs to meet the demand for variety in
accordance with the changing requirements of
clients represented by other
44
Afr. J. Bus. Manage.
Table 1. Studies selected for analysis and their distribution from
2005 to 2010.
Year
2005
Number
1
2
3
4
5
6
7
8
9
10
References
Allanson and Montagna, 2005
Chen and Wu, 2005
Hsiao and Liu, 2005
Jiao and Zhang, 2005
Kim et al., 2005
Kimura and Nielsen, 2005
Lee and Lee, 2005
Morales et al., 2005
Moshirian et al., 2005
Nepal et al., 2005
Total
10
11
12
13
14
6
16
Hashmi, 2006
Uffmann and Sihn, 2006
Nagarjuna et al., 2006
Fernandes and Carmo-Silva,
2006
Brabazon and MacCarthy,
2006
Sered and Reich, 2006
2007
17
18
19
20
21
22
23
24
25
26
27
Sholz-Reiter and Freitag, 2007
Bryan et al., 2007
Chen and Li, 2007
Wang and Che, 2007
Hariga et al., 2007
Jiao et al., 2007a
Jiao et al., 2007b
Meredith and Akinc, 2007
Erkal, 2007
Weng and Yang, 2007
Wu et al., 2007
11
2008
28
29
30
31
32
33
Aramand, 2008
Escobar-Saldívar et al., 2008
Hu et al., 2008
Tseng et al., 2008
Wang et al., 2008
Balakrishnan and Chakravarty,
2008
Innes, 2008
Chauhan et al., 2008
Sen, 2008
Morgan and Fathi, 2008
Spulber, 2008
Vaagen and Wallace, 2008
12
Lambertini and Mantovani,
2009
Albadvi and Shahbazi, 2009
Cebeci, 2009
Murthy et al., 2009
9
2006
15
34
35
36
37
38
39
2009
40
41
42
43
Table 1. Contd.
2010
44
45
46
47
48
Elmaraghy et al., 2009
Brambilla, 2009
Matsubayashi et al., 2009
Johnson and Kirchain, 2009
Shiue, 2009
49
50
51
52
53
54
55
56
57
58
59
60
Côte et al., 2010
Rabinovich et al., 2010
Zhang and Huang, 2010
Lim et al., 2010a
Lim et al., 2010b
Kucuk and Maddux, 2010
Xu, 2010
Nazarian et al., 2010
Foubert and Gijsbrechts, 2010
Puligada et al., 2010
Lin et al., 2010
Faure and Natter, 2010
12
industries (telecommun-ications, electronics etc.), and
end users. These intermediate customers of the company
responsible for producing the variety can be first-tier
(direct downstream in the chain), but not necessarily the
ultimate customers in the supply chain, they can be
second-tier customers (customers of your client).
Increased competition may result in the implementation
of personalisation strategies and in product diversification
(Uffmann and Sihn, 2006; Bryan et al., 2007; Wang et al.,
2008; Elmaraghy et al., 2009) as necessary to achieve
market differentiation and attract more customers. These
efforts, in turn, can involve increasing product variety.
However, product quality can diminish as a result of such
increases in variety (Hashmi, 2006; Matsubayashi et al.,
2009), generating resistance against the latter.
Market requirements are dynamic, often shortening the
product lifecycle (Uffmann and Sihn, 2006; Aramand,
2008). This reduction in lifecycle may also be affected by
technological change, leading companies to develop new
products more rapidly (Bryan et al., 2007). A short
product lifecycle creates a greater range of new products
offered over time (Uffmann and Sihn, 2006).
The issue of environmental responsibility is an
increasing focus and is regarded as an external pressure
that reduces variety (Tseng et al., 2008). Environmental
responsibility is also discussed in Cebeci (2009) in which
the demand for environmentally friendly products is
shown to often be determined by legal and technical
regulations, which in turn affect the variety of products
offered on the market.
To counteract the negative consequences of the
proliferation of product variety, companies should make
sure that their offerings are not so extensive as to cause
Reis et al.
45
Table 2. Distribution of articles by sector and type of study.
Sector
Manufacturing
Theoretical
[1], [6], [11], [12], [13], [14], [15], [17], [18],
[25], [30], [32], [33], [34], [39], [40], [48],
[49], [51], [56], [57]
Type of study
Empirical
[3], [4], [5], [7], [10], [20], [22], [24],
[27], [29], [31], [36], [37], [42], [44],
[47], [50], [54], [58]
Both
[16], [23], [35],
[45], [52], [53],
[59], [60]
TOTAL
(nº / %)
48 / 80.0
Service
[2], [8], [43], [21], [26], [38], [46]
[9], [19], [41], [55]
-
11 / 18.3
Both
[28]
-
-
1 / 1.7
TOTAL (nº / %)
29 / 48.3
23 / 38.3
8 / 13.4
60 / 100
[1], Albadvi and Shahbazi , 2009; [2], Allanson and Montagna, 2005; [3], Aramand, 2008; [4], Balakrishnan and Chakravarty, 2008; [5], Bowersox et
al., 2000; [6], Brabazon and Maccarthy, 2006; [7], Brambilla, 2009; [8], Bryan et al., 2007; [9], Burgess et al., 2006; [10], Cebeci , 2009; [11],
Chauhan et al., 2008; [12], Chen and LIN, 2007; [13], Chen and Wu, 2005; [14], Côté et al., 2010; [15], Brabazon and MacCarthy, 2000; [16], Da
Silveira, 1998; [17], Da Silveira and Slack, 2001; [18], Davenport , 1990; [19], Elmaraghy et al., 2009; [20], Erkal, 2007; [21], Escobar-Saldívar et
al., 2008; [22], Faure and Natter, 2010; [23], Fernandes and Carmo-Silva, 2006; [24], Foubert and Gijsbrechts, 2010; [25], GAO , 1996; [26],
Gunasekaran et al., 2004; [27], Hariga et al.,2007; [28], Hashmi, 2006; [29], Hayes and Pisano, 1996; [30], Holsti, 1969; [31], Hopayian, 2001; [32],
Hsiao and Liu, 2005; [33], Hu et al., 2008; [34], Innes, 2008; [35], Jiao et al., 2007a; [36], Jiao et al., 2007b; [37], Jiao and Zhang, 2005; [38],
Johnson and Kirchain, 2009; [39], Kim et al., 2005; [40], Kimura and Nielsen, 2005; [41], Kirca and Yaprak, 2010; [42], Kucuk and Maddux, 2010;
[43], Lacity et al., 2009; [44], Lambert, 2004; [45], Lambertini and Mantovani , 2009; [46], Lee and Lee, 2005; [47] Li and Cavusgil, 1995; [48], Lim
et al., 2010a; [49], Lim et al., 2010b; [50], Lin et al., 2010; [51], Malhotra and Sharma, 2002; [52], Mapes et al., 1997; [53], Marasco, 2008; [54],
Matsubayashi et al., 2009; [55], Mendelson and Parlaktürk, 2008; [56], Meredith and Akinc, 2007; [57], Morales et al., 2005; [58], Morgan and Fathi,
2008; [59], Moshirian et al., 2005; [60], Murthy et al., 2009.
confusion in the customer decision-making process,
otherwise known as "mass confusion" (Jiao et al., 2007b).
In the presence of many options, a customer may take
too long to make purchasing decisions, may not be able
to determine the best alternative (Matsubayashi et al.,
2009), or may even make mistakes during the selection
process. This will result in a high rate of product returns
(Rabinovich et al., 2010).
Many studies highlight that significant increases in
product variety can compromise operational efficiency by
complicating manufacturing (Jiao et al., 2007b; Tseng et
al., 2008; Chauhan et al., 2008; Elmaraghy et al., 2009),
distribution (Jiao et al., 2007; Vaagen and Wallace,
2008), and supply processes in production systems and
in the entire supply chain (Hu et al., 2008; Sen, 2008).
Moreover, increased product variety can increase the
complexity of administrative management (EscobarSaldívar, 2008).
Increased product variety can also raise costs (Jiao et
al., 2007b). The main types of costs include investments
made to install production systems and/or increase their
efficiency (Wang and Che, 2007; Wu et al., 2007); the
costs associated with supplying a greater variety of
products in smaller quantities (Sen, 2008; Vaagen and
Wallace, 2008); manufacturing costs (Allanson and
Montagna, 2005; Hsiao and Liu, 2005; Jiao and Zhang,
2005; Nepal et al., 2005; Meredith and Akinc, 2007;
Balakrishnan and Chakravarty, 2008; Morgan and Fathi,
2008; Johnson and Kirchain, 2009); product-specific
costs (Hsiao and Liu, 2005; Nepal et al., 2005; Jiao et al.,
2007a); market brokerage costs (Wu et al., 2007; Sen,
2008); the cost of transport and distribution (Weng and
Yang, 2007; Allanson and Montagna, 2005; Sen, 2008);
set-up costs (Escobar-Saldívar et al., 2008); inventory
costs (Hariga et al., 2007; Escobar-Saldívar et al., 2008;
Sen, 2008); costs associated with product storage and
display (Tseng et al., 2008); and quality requirements and
maintenance costs (Wu et al., 2007). The major
challenge highlighted in the literature is the requirement
that firms to offer greater product variety at a lower cost.
As such, analyses of cost relative to variety should be
performed during the product development phase
(Johnson and Kirchain, 2009).
Industries characterised by a wide variety of products
must work with different production set-ups. The higher is
the set-up time; the lower is the production efficiency
(Escobar-Saldívar et al., 2008). Another important
consideration is the required stock level, which can cause
management problems and require additional warehouse
space (Hariga et al., 2007; Escobar-Saldívar et al., 2008)
when inventory is too high.
Issues such as limited capacity and resources are also
cited as encouraging a decrease in variety. Capacity
limitations may include inventory limitations (for example,
the available shelf space can restrict the range of
products provided by a supplier; Chen and Lin, 2007;
Hariga et al., 2007), limitations on the capacity of
warehouses to allocate products (Jiao et al., 2007b),
production and/or assembly limitations (Brabazon and
MacCarthy, 2006; Bryan et al., 2007; Escobar-Saldívar et
al., 2008; Sen, 2008), and limitations on labour force
capacity (for example, restrictions related to overtime and
subcontracting; Meredith and Akinc, 2007). Even the
resources available for production can create
46
Afr. J. Bus. Manage.
Table 3. Internal and external pressures that influence PVM.
Internal and external
pressures that influence PVM
Predominantly
Positive
Negative
Support for and/or
responsiveness to the diverse
needs of clients (customised)
Both
Total of
references
References
[3],
[4],
[5],
[6],
[7],
[11],
[12],
[14],
[15],
[16],
[17],
[18],
[19],
[20],
[21],
[22],
[23],
[24],
[26],
[28],
[30],
[31],
[33],
[35],
[38],
[39],
[41],
[44],
[47],
[48],
[50],
[51],
[53],
[54],
[55],
[56],
[58],
[59]
[3],
[46],
[6],
[12],
[4],
[7],
[15],
[4],
[20],
[23],
[4],
[47],
[11],
[13],
[5],
[8],
[18],
[13],
[25],
[29],
[6],
[50],
[12],
[18],
[12],
[19],
[19],
[23],
[31],
[39],
[10],
[53],
[22],
[20],
[18],
[23],
[21],
[26],
[36],
[50],
[11],
[56]
[23],
[28],
[25],
[41],
[23],
[44],
[44],
[54]
[16],
[20],
[21],
[22],
[23],
[24],
[26],
[27],
[29],
[33],
[36],
[37],
[39],
[27],
[31],
[28],
[46],
[24],
[45],
[48]
[29],
[32],
[32],
[50],
[29],
[50]
[30],
[36],
[33],
[54],
[36],
[31],
[39],
[39],
[58],
[44]
[35],
[48]
[42],
[59]
[36],
[53],
[46],
[39],
[54]
[54]
[44],
[48],
[50],
[53],
[57]
X
X
X
X
X
[1],
[45],
[4],
[10],
[3],
[4],
[4],
[1],
[6],
[21],
X
[22],
[25],
[33],
[50],
[58]
5
X
X
[11],
[11],
[12],
[29],
[12],
[31],
[28],
[44],
[46],
[42],
[38]
[56]
[54]
[44]
X
[10],
[6],
[2],
[22],
5
5
4
4
X
[11],
[36],
[42]
X
Miscellaneous costs
X
Operational complexity
Product lifecycle
Differentiation from competitors
Customer choice process
Capacity limitations
Economies of scale
Resource limitations
Stock levels
Management of the number of
components that comprise the
finished product
Customer quality needs
Environmental responsibility
Evolution of technology
Time and/or number of setups
Compliance with technical and
legal regulations
X
X
X
X
38
25
18
13
13
11
10
8
7
6
3
[1], Albadvi and Shahbazi , 2009; [2], Allanson and Montagna, 2005; [3], Aramand, 2008; [4], Balakrishnan and Chakravarty, 2008; [5], Bowersox et al., 2000; [6], Brabazon and Maccarthy, 2006; [7],
Brambilla, 2009; [8], Bryan et al., 2007; [9], Burgess et al., 2006; [10], Cebeci , 2009; [11], Chauhan et al., 2008; [12], Chen and LIN, 2007; [13], Chen and Wu, 2005; [14], Côté et al., 2010; [15],
Brabazon and MacCarthy, 2000; [16], Da Silveira, 1998; [17], Da Silveira and Slack, 2001; [18], Davenport , 1990; [19], Elmaraghy et al., 2009; [20], Erkal, 2007; [21], Escobar-Saldívar et al., 2008;
[22],Faure and Natter, 2010; [23], Fernandes and Carmo-Silva, 2006; [24], Foubert and Gijsbrechts, 2010; [25], GAO , 1996; [26], Gunasekaran et al., 2004; [27], Hariga et al.,2007; [28], Hashmi,
2006; [29], Hayes and Pisano, 1996; [30], Holsti, 1969; [31], Hopayian, 2001; [32], Hsiao and Liu, 2005; [33], Hu et al., 2008; [34], Innes, 2008; [35], Jiao et al., 2007a; [36], Jiao et al., 2007b; [37], Jiao
and Zhang, 2005; [38], Johnson and Kirchain, 2009; [39], Kim et al., 2005; [40], Kimura and Nielsen, 2005; [41], Kirca and Yaprak, 2010; [42], Kucuk and Maddux, 2010; [43], Lacity et al., 2009; [44],
Lambert, 2004; [45], Lambertini and Mantovani , 2009; [46], Lee and Lee, 2005; [47] Li and Cavusgil, 1995; [48], Lim et al., 2010a; [49], Lim et al., 2010b; [50], Lin et al., 2010; [51], Malhotra and
Sharma, 2002; [52], Mapes et al., 1997; [53], Marasco, 2008; [54], Matsubayashi et al., 2009; [55], Mendelson and Parlaktürk, 2008; [56], Meredith and Akinc, 2007; [57], Morales et al., 2005; [58],
Morgan and Fathi, 2008; [59], Moshirian et al., 2005; [60], Murthy et al., 2009.
limitations, for instance, if natural resources
(Tseng et al., 2008) or other types of raw
materials (Erkal, 2007) are not sufficiently
available.
The last pressure identified is the production of
Different (especially smaller) lot sizes due to
product proliferation, which can negatively affect
economies of scale (Elmaraghy et al., 2009).
Overall, the pressures identified as increasing or
decreasing product variety emphasises the
importance of PVM in promoting a balance
between the positive and negative factors at play.
Subsequently, the PVM structures and processes
are explained.
PVM structures and processes
The results in this category are presented and
Reis et al.
47
Table 4. Intra- and inter-organisational perspectives in PVM.
Intra-organisational
Horizontal integration
Vertical integration
Inter-organisational
Supply chain integration
[3],
[4],
[2],
[4],
[23],
[4],
[5],
[36],
[17],
[12],
[42],
[20],
[21],
[49]
[25],
[23],
[24],
References
[44],
[53]
Total
9
5
[27],
References
[28],
[30],
[33],
Total
15
[36],
[38],
[39],
[42],
[51],
[59]
[2], Allanson and Montagna, 2005; [3], Aramand, 2008; [4], Balakrishnan and Chakravarty, 2008; [5], Bowersox et al., 2000; [12], Chen and LIN, 2007;
[17], Da Silveira and Slack, 2001; [20], Erkal, 2007; [21], Escobar-Saldívar et al., 2008; [23], Fernandes and Carmo-Silva, 2006; [24], Foubert and
Gijsbrechts, 2010; [25], GAO, 1996; [27], Hariga et al.,2007; [28], Hashmi, 2006; [30], Holsti, 1969; [33], Hu et al., 2008; [36], Jiao et al., 2007b; [38],
Johnson and Kirchain, 2009; [39], Kim et al., 2005; [42], Kucuk and Maddux, 2010; [44], Lambert, 2004; [49], Lim et al., 2010b; [51], Malhotra and
Sharma, 2002; [53], Marasco, 2008; [59], Moshirian et al., 2005.
analysed using the following categories: relationships
and participants, business processes, information
technology (IT), mitigation strategies, and metrics.
Relationships and participants
Table 4 summarises the results related to this item and
related studies. It is evident that many researchers
emphasised both intra- and inter-organisational perspectives. The results highlight the need for companies to
internally coordinate their supply and demand capacity in
seeking product variety; in this way, they can avoid
creating conflicts between departments. There should be
strong intra-organisational relationships between the
departments involved in the marketing and design of
products (Hsiao and Liu, 2005) and between those
involved in the marketing, production and/or engineering
processes (Jiao and Zhang, 2005; Meredith and Akinc,
2007).
Those operational activities that are necessary for
product variety should be consistent with the strategic
objectives of the firm (Côte et al., 2010). Thus, the
involvement of top-level management is a necessity (Jiao
and Zhang, 2005; Cebeci, 2009). To ensure inter-organisational coordination, the work of individual companies
should be synchronised with that of other important
members of the supply chain (Wu et al., 2007; Hu et al.,
2008; Sen, 2008). Wang and Che (2007), Aramand
(2008), and Cebeci (2009) highlight the need for
communication channels between buyers and sellers
throughout the supply chain, which will strengthen the
relationship between the supply chain links and make it
possible to control and manage both the suppliers
themselves and all externally produced items. Moreover,
Jiao and Zhang (2005) and Wu et al. (2007) add that
aligning the information exchanged between companies
and their suppliers requires an understanding of the
needs of the supply chain endpoint (that is, the
consumer) and of the limitations of the whole chain, (that
is, the functional requirements that must be fulfilled to
manufacture a variety of products). In particular, Chen
and Wu (2005) state that companies need to develop key
links with distributors and customers.
The links, both upstream and downstream the supply
chain, can be managed in the medium and long term as
partnerships between affiliates or between companies
(Sen, 2008; Brambilla, 2009), which allows knowledge
access about specific processes production towards
achieving greater flexibility in the offer of the range of
products requested. Other types of relations that aid the
PVM may be merges and acquisitions (Uffmann, 2006).
These closer types of relationships allow companies to
control the flow of materials and especially the necessary
information to each participant. Thus, this systematic
review indicates that modern companies must now refine
their processes at the supply chain level. These efforts
require the integration of participants internal to
companies (that is, intra-organisational integration) and of
external participants (that is, inter-organisational
integration).
Business processes
PVM can involve business processes. Table 5 presents
the business processes identified in the review, and
grouped following the study of Lambert (2004). Most
studies addressing the theme of PVM business
processes analyse them with a particular emphasis on
the manufacturing flow management. For example, Hu et
al. (2008) address the need to consider the impact of
adding variants when planning the assembly sequence in
a multi-stage system, where complexity spreads from one
workstation to another. Fernandes and Carmo-Silva
(2006) describes a system for controlling production and
the flow of materials to improve performance and reduce
delivery time, whereas Jiao et al. (2007a) propose a
system for identifying similarities between materials,
resources, and processes. It is suggested that firms can
gain competitive advantage by exploiting these
similarities and thereby increasing PVM effectiveness.
Chen and Lin (2007) highlight the use of point-of-sale
(POS) transactions to collect data on consumers and
48
Afr. J. Bus. Manage.
Table 5. Business Processes under PVM.
Business process
Manufacturing flow management
Demand management
Product development and commercialization
Customer service management
Order fulfillment
Procurement
Customer relationship management
Returns
[11],
[15],
[12],
[15],
[15],
[20],
[59]
[50]
[12],
[19],
[47],
[54],
[37],
[51]
[14],
[35],
[49],
[58],
[49]
References
[22], [30], [32],
[36], [57]
[52], [53]
[59]
[44],
[48],
[56]
Total
9
5
5
4
3
2
1
1
[11], Chauhan et al., 2008; [12], Chen and LIN, 2007; [14], Côté et al., 2010; [15], Brabazon and MacCarthy, 2000; [19], Elmaraghy
et al., 2009; [20], Erkal, 2007; [22], Faure and Natter, 2010; [30], Holsti, 1969; [32], Hsiao and Liu, 2005; [35], Jiao et al., 2007a;
[36], Jiao et al., 2007b; [37], Jiao and Zhang, 2005; [44], Lambert, 2004; [47], Li and Cavusgil, 1995; [48], Lim et al., 2010a; [49],
Lim et al., 2010b; [50], Lin et al., 2010; [51], Malhotra and Sharma, 2002; [52], Mapes et al., 1997; [54], Matsubayashi et al., 2009;
[56], Meredith and Akinc, 2007; [57], Morales et al., 2005; [58], Morgan and Fathi, 2008; [59], Moshirian et al., 2005.
Table 6. Information Technology under PVM.
Information Technology
E-Commerce
Other softwares
Manufacturing Technologies
Enterprise Resource Planning - ERP
Eletronic Data Interchange - EDI
References
[15], [19], [28],
[4], [13], [22],
[16], [17], [36],
[17], [42]
[36]
[41]
[29]
Total
4
4
3
2
1
[4], Balakrishnan and Chakravarty, 2008; [15], Brabazon and MacCarthy, 2000; [16],
Da Silveira, 1998; [17], Da Silveira and Slack, 2001; [36], Jiao et al., 2007b; [28],
Hashmi, 2006; [42], Kucuk and Maddux, 2010; [41], Kirca and Yaprak, 2010; [22],
Faure and Natter, 2010; [19], Elmaraghy et al., 2009; [13], Chen and Wu, 2005; [29],
Hayes and Pisano, 1996;
thus develop demand management that can reduce
supply uncertainty while facilitating product sorting and
shelf allocation. Sen (2008) states that most large
retailers use demand management to analyse and
address customer demand for product variety.
Uffmann and Sihn (2006) show that in the process of
developing new products, firms must consider failure
rates. Côte et al. (2010) highlight the importance of
returning to previous projects to develop new product
varieties. Brabazon and MacCarthy (2006) address
customer service management in the automotive industry
by monitoring demand through information systems in
which customers can access real-time information on
product variety. To effectively comply with customer
requests, dealers can share information with automakers
about the products available so that customers can
purchase cars with their desired configuration of features.
Kucuk and Maddux (2010) describe this process in
electronic retailing.
Wang and Che (2007) highlight the importance of
supplier selection in addressing the management of
supplier relationships (procurement). Lin et al. (2010)
address customer relationship management in electronic
retailing, highlighting the importance of one-to-one
marketing. Rabinovich et al. (2010) analyse returns in
Internet retail, suggesting that companies experience a
large number of returned products resulting from poor
choices by end customers, which in turn are theorised to
be due to excess product variety.
Information technology
The use of IT in business can be analysed from different
perspectives. Table 6 summarizes the IT covered in the
systematic review of the literature on PVM. From the
standpoint of internal organisation, IT has a key role in
ensuring the fluidity of and control over operations. Sen
(2008), Côte et al. (2010) and Lim et al. (2010a, b)
address
product
development,
suggesting
that
technologies such as computer-aided design (CAD) can
accelerate product development and that store data can
also be used to support future modifications. This is very
important in environments that require wide variety and
short product lifecycles (Sen, 2008; Lim et al., 2010a). In
these types of environments, Nagarjuna et al. (2006)
Reis et al.
suggest that the material handling systems (MHSs) can
be used to facilitate material flow.
Component variety may also be better managed using
support systems for manufacturing, such as computerintegrated manufacturing (CIM) (Sered and Reich, 2006;
Scholz-Reiter and Freitag, 2007) and computer-aided
manufacturing (CAM) (Sen, 2008). Also focusing on
production, Jiao et al. (2007a) emphasise the use of data
mining and text mining to analyse the historical evolution
of product and process variations. It is suggested that this
information can be used to create processing platforms
for efficiently managing the variety and production of
customised products. Other areas of firms that require
information technology include purchasing (EscobarSaldívar et al., 2008; Sen, 2008) and sales (EscobarSaldívar et al., 2008). More generally, Scholz-Reiter and
Freitag (2007) and Cebeci (2009) suggest that enterprise
resource planning (ERP) can assist in PVM by integrating
information across all company areas and departments.
Within the supply chain, Chen and Lin (2007) and Lin et
al. (2010) propose that companies implement systems for
collecting information about client preferences and use
data mining to analyse buying behaviour in electronic
retail markets, thereby facilitating PVM. Albadvi and
Shahbazi (2009), Rabinovich et al. (2010), and Lin et al.
(2010) suggest that information systems can also assist
clients in searching for and selecting their desired
merchandise, particularly when they are faced with an
enormous variety of products. Such systems have
already been implemented in large electronic retail
networks such as Amazon.com and web banking.
Virtual-build-to-order (VBTO) systems merge these two
perspectives by aligning client demand with available
products by, for example, by aligning customer demand
regarding car colours and options with the cars that are in
dealer lots, in transit, or currently being produced by the
carmaker (Brabazon and MacCarthy, 2006).
Scholz-Reiter and Freitag (2007) highlight the use of
radio frequency identification device (RFID) technology
and Sen (2008) electronic data interchange (EDI) to
assist in the handling of a wide variety of products along
the supply chain.
Mitigation strategies
Mitigation strategies are used to alleviate the negative
effects of increased product variety. Table 7 lists the
strategies mentioned in the literature. The mitigation
strategy most often cited is the adoption of common
components in the production process. The use of
common components to make a variety of products
facilitates cost reduction (Balakrishnan and Chakravarty,
2008; Johnson and Kirchain, 2009). For instance,
common platforms can be developed for different
products, as has commonly occurred in the automotive
industry (Erkal, 2007).
49
Common platforms allow companies to reduce their
investments in research and development and introduce
new products more quickly (Sered and Reich, 2006).
Another particular case is that of modularisation, which
increases the agility of the manufacturing process (Nepal
et al., 2005) and allows for increases in product variety
through the sharing of modules across different product
lines. A third similar strategy is that of organising
products with similar features and attributes into families
(Bryan et al., 2007; Jiao et al., 2007a, b; Elmaraghy et al.,
2009; Johnson and Kirchain, 2009), which reduces the
complexity associated with producing a variety of
products. Elmaraghy et al. (2009), Zhang and Huang
(2010), and Lim et al. (2010) suggest that long-term
planning for product families be focused on enabling both
the sharing of components and platforms and
modularisation, as this will facilitate PVM. Offering
products that can be grouped into packages facilitates
the management of a large variety of products. Working
in this vein, Weng and Yang (2007) examine the case of
tour packages.
The mass customization is another mitigation strategy
that can increase the product variety with low impact on
costs (Jiao et al., 2007a). Lee and Lee (2005) exemplify
this strategy in the computer industry where by offering
standard models, the client can customize products by
adding other attributes with a relatively low cost.
Jiao et al. (2007a) and Balakrishnan and Chakravarty
(2008) see the use of common processes as a mitigation
strategy that can help firms to avoid experiencing
dramatic increases in production costs as a result of their
variety offering level and/or introducing new and different
products. Côte et al. (2010) indicate the need to draw
from previous projects in developing new product
varieties.
The mitigation strategies outlined in these studies also
address production processes. The related strategies
include lean production (Fernandes and Carmo-Silva,
2006; Escobar-Saldívar et al., 2008) and the use of
cellular manufacturing systems (Scholz-Reiter and
Freitag, 2007). The use of postponement is also associated with PVM. In postponement, part of product
production is transferred downstream in the supply chain
to a point closer to the end customer to allow the
company to adapt more easily to the particular needs of
its clients (Meredith and Akinc, 2007; Elmaraghy et al.,
2009).
The importance of flexible manufacturing systems is
widely discussed in the context of PVM. For instance,
Fernandes and Carmo-Silva (2006) cite the importance of
quick response manufacturing (QRM) as a competitive
strategy for companies that work on a make-to-order
(MTO) or engineering-to-order (ETO) basis, indicating
that such systems enable firms to produce a wide variety
of products and meet variable demand. The selection of a
production strategy suitable to the level of product variety
offered is also discussed in the literature. Meredith and
50
Afr. J. Bus. Manage.
Table 7. Product variety mitigation strategies.
Mitigation strategies
Use of common
components
Mass customisation
Product families
Flexible manufacturing
Production strategies
Use of common
processes
Postponement
Option bundling
Lean Manufacturing
Cellular manufacturing
References
[3], [4], [5], [10], [16], [17], [18], [22], [25], [27], [28], [30], [31], [32], [33], [36], [43], [44], [47], [49], [51], [52], [53], [56]
[4],
[5],
[7],
[20], [22], [24], [28],
[3], [5], [6], [18], [22], [23], [44], [47], [49], [52], [53]
[13], [14], [17], [27], [29], [32], [34],
[14], [15], [24], [35],
[49]
[22], [29], [33],
[49]
[15],
[26],
[14],
[17]
[24],
[57]
[29]
[44]
[30],
[56]
[31],
[44],
[51],
[52],
[53],
[58]
Total
24
14
11
8
5
4
3
2
2
1
* Caso especial de uso de componentes comuns. [1], Albadvi and Shahbazi , 2009; [2], Allanson and Montagna, 2005; [3], Aramand, 2008; [4],
Balakrishnan and Chakravarty, 2008; [5], Bowersox et al., 2000; [6], Brabazon and Maccarthy, 2006; [7], Brambilla, 2009; [8], Bryan et al., 2007;
[9], Burgess et al., 2006; [10], Cebeci , 2009; [11], Chauhan et al., 2008; [12], Chen and LIN, 2007; [13], Chen and Wu, 2005; [14], Côté et al.,
2010; [15], Brabazon and MacCarthy, 2000; [16], Da Silveira, 1998; [17], Da Silveira and Slack, 2001; [18], Davenport , 1990; [19], Elmaraghy et
al., 2009; [20], Erkal, 2007; [21], Escobar-Saldívar et al., 2008; [22], Faure and Natter, 2010; [23], Fernandes and Carmo-Silva, 2006; [24], Foubert
and Gijsbrechts, 2010; [25], GAO , 1996; [26], Gunasekaran et al., 2004; [27], Hariga et al.,2007; [28], Hashmi, 2006; [29], Hayes and Pisano,
1996; [30], Holsti, 1969; [31], Hopayian, 2001; [32], Hsiao and Liu, 2005; [33], Hu et al., 2008; [34], Innes, 2008; [35], Jiao et al., 2007a; [36], Jiao
et al., 2007b; [37], Jiao and Zhang, 2005; [38], Johnson and Kirchain, 2009; [39], Kim et al., 2005; [40], Kimura and Nielsen, 2005; [41], Kirca and
Yaprak, 2010; [42], Kucuk and Maddux, 2010; [43], Lacity et al., 2009; [44], Lambert, 2004; [45], Lambertini and Mantovani , 2009; [46], Lee and
Lee, 2005; [47] Li and Cavusgil, 1995; [48], Lim et al., 2010a; [49], Lim et al., 2010b; [50], Lin et al., 2010; [51], Malhotra and Sharma, 2002; [52],
Mapes et al., 1997; [53], Marasco, 2008; [54], Matsubayashi et al., 2009; [55], Mendelson and Parlaktürk, 2008; [56], Meredith and Akinc, 2007;
[57], Morales et al., 2005; [58], Morgan and Fathi, 2008; [59], Moshirian et al., 2005; [60], Murthy et al., 2009.
Akinc (2007) and Chauhan et al. (2008) highlight the
MTO and assembly-to-order (ATO) strategies. Côte et al.
(2010) underscore the advantages of ETO as compared
to MTO and ATO when a firm offers an undefined
number of variations (that is, open product variety).
Whereas Brabazon and MacCarthy (2006) analyse VBTO
in the automotive sector, Meredith and Akinc (2007)
addresses make-to-forecast (MTF) systems, which
combine the make-to-stock (MTS) and MTO strategies to
deliver customised products quickly without increasing
costs. Mass customisation is another strategy highlighted
in the literature (Lee and Lee, 2005; Jiao et al., 2007).
Metrics
Table 8 presents the metrics mentioned in studies that
assess the PVM processes adopted by companies.
Brabazon and MacCarthy (2006) and Meredith and Akinc
(2007) suggest using a metric to monitor order fulfilment
and evaluate the production system with respect to the
number of product varieties offered. Different papers
mention metrics that can be used to evaluate the
efficiency of production processes, whether analysing the
variety of items produced or the customer service offered.
Toward this end, metrics for different temporal intervals
are suggested. These include production cycle time (Jiao
et al., 2007b; Uffmann and Sihn, 2006) and set-up time
(Uffmann and Sihn, 2006; Bryan et al., 2007). Compe-
titiveness can be achieved by lowering these indices and
thus increasing productivity.
Related financial considerations include production
costs (Nepal et al., 2005; Wu et al., 2007), set-up costs
(Bryan et al., 2007), net product contribution (Meredith
and Akinc, 2007), and product returns (Rabinovich et al.,
2010). Wu et al. (2007) and Bryan et al. (2007) refer to
metrics that measure the complexity of the product
variety offered by a company, including the number of
components and products. These metrics also indicate
the rate of reuse for system elements used for product
reconfiguration relative to that of the overall system of
elements.
Kim et al. (2005) and Lim et al. (2010a) emphasise the
relationship between product variety and product family
commonalities. Kim et al. (2005) propose that the number
of models and brands that share a common platform be
used as an indication of company strategy regarding
product variety.
Meredith and Akinc (2007) and Uffmann and Sihn
(2006) emphasise concerns regarding possible decreases in product quality due to increased variety, suggesting that firms determine what percentage of units
produced have not met quality requirements guidelines.
Puligadda et al. (2010) use end customer satisfaction
regarding production variety to assess PVM. Lin et al.
(2010) present a metric that can be used to evaluate
variety recommendation systems employed in electronic
retail, determining the type of relationship between the
Reis et al.
51
Table 8. Metrics used to assess PVM.
Metrics
Order fulfillment
Production quality failures
Production cycle time
Set-up time
Production costs
Rate of reuse
Cycle time for consumer requests
Set-up cost
Average rate and net contribution
Product return rate
Number of components and products
Number of products and/or platform variants
Number of models or brands offered by the company
Customer satisfaction regarding product variety
Relationship between the configuration recommended and the configuration sold
References
[15], [12]
[12], [24]
[12], [23]
[12], [18]
[10], [27]
[18]
[15]
[18]
[24]
[50]
[27]
[5]
[5]
[58]
[59]
Total
2
2
2
2
2
1
1
1
1
1
1
1
1
1
1
[5], Kim et al., 2005; [10], Nepal et al., 2005; [12], Uffmann and Sihn, 2006; [15], Brabazon and MacCarthy, 2006;
[18],Bryan et al., 2007; [23],Jiao et al., 2007b; [24],Meredith and Akinc, 2007; [27], Wu et al., 2007; [50], Rabinovich et
al.,2010; [58], Puligada et al., 2010; [59],Lin et al., 2010.
configuration recommended by the system and that sold
to the customer.
Although metrics were highlighted in many papers as
being very important, none of the papers worked directly
with metrics aimed at measuring the PVM itself. The
identified metrics have different scopes looking into
specific and particular aspects of PVM. As for the
description of PVM, measurement issues were highly
dispersed and metrics varied widely among authors with
no common classification. Future research on this topic is
suggested.
Outputs
Outputs are the results sought by companies adopting
PVM. Table 9 presents the main outputs identified in the
literature. Increased profitability is the output related to
PVM that is most discussed in these studies. This
increase can be achieved by highlighting those products
offered in retail outlets that have higher profit margins
(Chen and Lin, 2007). According to Vaagen and Wallace
(2008), increases in profitability should occur if a firm
determines the optimal level of variety for each market
(which is itself another output that is widely cited in the
literature). Chauhan et al. (2008) point out that improving
customer service is also a major objective of PVM. Weng
and Yang (2007), Balakrishnan and Chakravarty (2008),
and Vaagen and Wallace (2008) indicate that increases
in both profitability and market share are the main goals
of PVM. Nepal et al. (2005) emphasise the importance of
offering a wide variety of products to increase market
share, and Brambilla (2009) emphasises that to increase
market share, the time needed to introduce new products
to the market should be decreased. According to Cebeci
(2009) and Brambilla (2009), PVM has improved the
brand value of firms and increased their market share by
providing them access to new markets.
Cost reduction is another output associated with PVM.
The main costs to be reduced are those associated with
product development projects (Côte et al., 2010), purchasing (Balakrishnan and Chakravarty, 2008; Cebeci,
2009); inventory (Chauhan et al., 2008), and production
(Nepal et al., 2005; Morgan and Fathi, 2008). In addition
to focusing on cost reduction, Balakrishnan and
Chakravarty (2008) cite increases in revenue and
profitability as PVM outputs.
Finally, Hu et al. (2008) and Morgan and Fathi (2008)
cite the minimisation of production complexity as goals of
PVM. Such a reduction in complexity should influence
both assembly lines (Hu et al., 2008) and production
systems (Morgan and Fathi, 2008). Another output
identified in the review is the improvement or maintenance of customer loyalty (Cebeci, 2009).
Conclusions
This paper presents a systematic literature review of
PVM research published from 2006 to 2010, being the
first paper to do so.
The review is based on a content analysis that integrates the main findings related to this topic and
highlights the current state of the art. PVM is an interdisciplinary topic of interest not only for researchers in the
areas of operations and manufacturing management but
52
Afr. J. Bus. Manage.
Table 9. Results sought by companies adopting PVM.
Output
Increased profitability
Cost reduction
Increased market share
Analyses of the optimal level of variety to be offered
Improved customer service
Reductions in time-to-market required for product introduction
Increased revenue
Improved brand image
Reduced production system complexity
Maintenance of customer loyalty
[3],
[2],
[4],
[4],
[8],
[16],
[4],
[42],
[30],
[42]
[5],
[7],
[5],
[23],
[35],
[40],
[33],
[45]
[37]
[18],
[10],
[10],
[39],
[48],
[45]
[57]
[57]
[19],
[11],
[26],
[42],
[49]
[21],
[16],
[27],
[43],
References
[24], [26], [27],
[33], [35], [37],
[33], [39], [42],
[50]
[29],
[40],
[45],
[33],
[42],
[48]
[35],
[49],
[39],
[56]
[44]
[56]
Total
13
13
10
6
4
3
3
3
2
1
[2], Allanson and Montagna, 2005; [3], Aramand, 2008; [4], Balakrishnan and Chakravarty, 2008; [5], Kim et al., 2005; [7], Brambilla, 2009; [8], Bryan et al., 2007; [10], Nepal et al.,
2005; [11], Chauhan et al., 2008; [16], Da Silveira, 1998; [18], Davenport , 1990; [19], Elmaraghy et al., 2009; [21], Escobar-Saldívar et al., 2008; [23], Jiao et al., 2007b;
[24],Meredith and Akinc, 2007; [26], Gunasekaran et al., 2004; [27], Wu et al., 2007; [29], Hayes and Pisano, 1996; [30], Holsti, 1969; [33], Hu et al., 2008; [35], Jiao et al., 2007a;
[37], Jiao and Zhang, 2005; [39], Kim et al., 2005; [40], Kimura and Nielsen, 2005; [42], Kucuk and Maddux, 2010; [43], Lacity et al., 2009; [44], Lambert, 2004; [45], Lambertini and
Mantovani , 2009; [48], Lim et al., 2010a; [49], Lim et al., 2010b; [50], Rabinovich et al.,2010; [56], Meredith and Akinc, 2007;[57], Morales et al., 2005;
also for scholars working on finance, economics,
and marketing. The interdisciplinary nature of this
topic is reflected in the large number of related
studies published in journals of different areas.
The results indicate the increased focus of
research on manufacturing companies; there are
fewer studies of service companies. Therefore,
future studies might address the issue of variety in
the service sector. It would also be helpful for
future studies to draw more heavily on the
practical experiences of companies, as this
research strategy would enrich the debates on
PVM and increase the impact of empirical PVM
research PVM should be used to balance the
positive and negative effects of increasing product
variety. Based on the results of this study, it
appears that cost is the most important factor
causing decreases in product variety, whereas the
greatest positive influence on product variety is
customer needs.
The studies examined also emphasise the roles
of various actors, both within and outside
companies, in implementing PVM. These studies
highlight the need for internal integration (that is,
intra-organisational integration). This type of
integration can occur horizontally across functional areas including R&D, purchasing, production, distribution, and marketing; it can also
occur vertically across hierarchical levels. In
addition, the external integration of a firm with its
suppliers and customers in the supply chain (that
is, inter-organisational integration) has also been
identified as a pertinent issue. The topic of
integrated business processes is discussed in
some studies. A major concern raised in most of
these studies is the management of production
flow. Other business processes, such as those
related to marketing, are also discussed in the
literature. This corroborates the interdisciplinary
nature of PVM. The management and operation of
these business processes given a wide variety of
inputs and existing products is made possible
using IT. IT also helps firms to integrate and share
information throughout their companies and
throughout the entire supply chain, facilitating
relation-ships within and between organisations.
Strategies for mitigating the negative effects of
product variety are also discussed in these
studies. The most prominent strategy involves the
use of common components, including common
platforms and modules. In general, mitigation
strategies are targeted toward the product, for
example, by ensuring that products include common parts across different product categories and
production processes and through the use of
flexible systems, lean manufacturing, or postponed production. The main objective is to offer a
wide variety of products to the customer while
keeping the production of these products manageable from the company’s perspective. Metrics
Reis et al.
for evaluating PVM are still not widely discussed in the
literature; there are few studies that mention such
metrics. Moreover, the metrics discussed vary, and there
is no consensus regarding how they can be adapted for
use in different organisations. Thus, future research
should focus on indicators and performance measurement systems for environments with high product
variety.
Finally, the main objectives of companies that adopt
PVM may include improved financial results (e.g.,
increased profitability, cost reduction, or revenue
increases) and market-related improvements (such as
increased market share, improved service level, and
better brand value).
Although this article is not exhaustive, the 60 selected
studies constitute a significant and representative portion
on the scientific research carried out on PVM. Thus, this
analysis provides a reliable view of the state of the art of
PVM research. Because it is impossible to cover every
available study on any given subject, this research has its
limitations. As this study involved the exclusive use of
one electronic database, relevant studies may have been
omitted if they are only indexed in other databases.
Additionally, the use of Boolean expressions in the
selection process may have caused the researchers to
omit studies that address this theme using other words or
terms. The six-year period examined also constitutes a
limitation because important related concepts could have
been disseminated in other years. Thus, future research
should address these limitations.
ACKNOWLEDGEMENTS
The authors would like to thank the Brazilian research
agencies CNPq (projects numbers: 590030/2010-8) and
CAPES (BRAGECRIM 010/09) for their support as well
as the two anonymous reviewers for their indispensable
input that improved the paper significantly.
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