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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
1Industrial Engineering Department, PUC-Rio, Brazil.
2Industrial Engineering Department,CEFET-RJ UnED Nova Iguaçu, Brazil.
3Industrial Engineering Department, UNAMA, Pará, Brazil.
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) charac-terised 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 environ-ment. 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’ under-standing 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 frame-work 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
Journal nameIndustry sector
(manufacturing or service)
Theoretical and/or
empirical
Year of publication Descreptive or prescreptive
INPUT STRUCTURE AND
PROCESSINGOUTPUT
Relationships and
participants
Business Process
Information Technology
Mitigation Strategies
Metrics
CONTEXT
External pressures that
influence the number of
product variety
Internal pressures that
influence the number of
product variety
Objectives aimed to be
achieved as a result of
efficient PVM
Figure 2. The content analysis framework.
industry). In addition, this dimension includes the charac-teristics 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-organi-sational perspective (when the focus is departments or areas internal to departments; Shapiro, 1977; Bowersox et al., 2000; Malhotra and Sharma, 2002) and the inter-organisational 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 frame-work 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 recom-mendation 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 descriptive-prescriptive studies together account for 85% of the total articles; only 15% of the studies are purely descriptive.
Reis et al. 43
Distribution of studies by publication journal
9 Distribution of studies by publication journal
6
4 International Journal Production Economics 1
International Journal of Industrial Organization 4 Expert Systems with Applications 1
Annals of the CIRP 3 European Journal of Operational Research 1
3 International Journal of Industrial Organization 1
2 Annals of the CIRP 1
2 CIRP Annals - Manufacturing Technology 1
2 Computer-Aided Design 1
2 Computers & Operations Research 1
Journal of Manufacturing Systems 2 Computers in Industry 1
Journal of Operations Management 2 Decision Support Systems 1
Robotics and Computer-Integrated Manufacturing 2 Journal of Manufacturing Systems 1
2 Journal of Operations Management 1
Advanced Engineering Informatics 1 Robotics and Computer-Integrated Manufacturing 1
1 Technovation 1
Information Processing and Management 1 Advanced Engineering Informatics 1
International Journal of Research in Marketing 1 Computers & Industrial Engeneering 1
Journal of Consumer Psycology 1 Information Processing and Management 1
Journal of Economic Theory 1 International Journal of Research in Marketing 1
Journal of International Economics 1 Journal of Consumer Psycology 1
Journal of International Money and Finance 1 Journal of Economic Theory 1
Journal of Materials Processing Technology 1 Journal of International Economics 1
Journal of Retailing 1 Journal of International Money and Finance 1
Journal of Retailing and Consumer Service 1 Journal of Materials Processing Technology 1
Omega - The International Journal of Management Science 1 1321 Journal of Retailing 1
Reliability Engineering and System Safety 1 Journal of Retailing and Consumer Service 1
Systems Engineering - Theory and Practice 1 Omega - The International Journal of Management Science 1
Tourism Management 1 Reliability Engineering and System Safety 1
Systems Engineering - Theory and Practice 1
Tourism Management 1
European Journal of Operational Research
CIRP Annals - Manufacturing Technology
Technovation
Decision Support Systems
Computer-Aided Design
Computers & Industrial Engeneering
International Journal Production Economics
Expert Systems with Applications
Computers & Operations Research
Computers in Industry
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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 commo-dities 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 Number References Total
2005 1 Allanson and Montagna, 2005 10
2 Chen and Wu, 2005
3 Hsiao and Liu, 2005
4 Jiao and Zhang, 2005
5 Kim et al., 2005
6 Kimura and Nielsen, 2005
7 Lee and Lee, 2005
8 Morales et al., 2005
9 Moshirian et al., 2005
10 Nepal et al., 2005
2006 11 Hashmi, 2006 6
12 Uffmann and Sihn, 2006
13 Nagarjuna et al., 2006
14 Fernandes and Carmo-Silva, 2006
15 Brabazon and MacCarthy, 2006
16 Sered and Reich, 2006
2007 17 Sholz-Reiter and Freitag, 2007 11
18 Bryan et al., 2007
19 Chen and Li, 2007
20 Wang and Che, 2007
21 Hariga et al., 2007
22 Jiao et al., 2007a
23 Jiao et al., 2007b
24 Meredith and Akinc, 2007
25 Erkal, 2007
26 Weng and Yang, 2007
27 Wu et al., 2007
2008 28 Aramand, 2008 12
29 Escobar-Saldívar et al., 2008
30 Hu et al., 2008
31 Tseng et al., 2008
32 Wang et al., 2008
33 Balakrishnan and Chakravarty, 2008
34 Innes, 2008
35 Chauhan et al., 2008
36 Sen, 2008
37 Morgan and Fathi, 2008
38 Spulber, 2008
39 Vaagen and Wallace, 2008
2009 40 Lambertini and Mantovani, 2009
9
41 Albadvi and Shahbazi, 2009
42 Cebeci, 2009
43 Murthy et al., 2009
Table 1. Contd.
44 Elmaraghy et al., 2009
45 Brambilla, 2009
46 Matsubayashi et al., 2009
47 Johnson and Kirchain, 2009
48 Shiue, 2009
2010 49 Côte et al., 2010 12
50 Rabinovich et al., 2010
51 Zhang and Huang, 2010
52 Lim et al., 2010a
53 Lim et al., 2010b
54 Kucuk and Maddux, 2010
55 Xu, 2010
56 Nazarian et al., 2010
57 Foubert and Gijsbrechts, 2010
58 Puligada et al., 2010
59 Lin et al., 2010
60 Faure and Natter, 2010
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 Type of study TOTAL
(nº / %) Theoretical Empirical Both
Manufacturing [1], [6], [11], [12], [13], [14], [15], [17], [18], [25], [30], [32], [33], [34], [39], [40], [48], [49], [51], [56], [57]
[3], [4], [5], [7], [10], [20], [22], [24], [27], [29], [31], [36], [37], [42], [44], [47], [50], [54], [58]
[16], [23], [35], [45], [52], [53], [59], [60]
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 (Escobar-Saldívar, 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 Both References
Total of
references Positive Negative
Support for and/or responsiveness to the diverse needs of clients (customised)
X [3], [4], [5], [6], [7], [11], [12], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [26],
38 [28], [30], [31], [33], [35], [38], [39], [41], [44], [47], [48], [50], [51], [53], [54], [55], [56], [58], [59]
Miscellaneous costs
X
[1], [3], [4], [6], [10], [11], [16], [20], [21], [22], [23], [24], [26], [27], [29], [33], [36], [37], [39], 25
[45], [46], [47], [50], [53], [56]
Operational complexity
X
[4], [6], [11], [12], [22], [23], [27], [29], [30], [31], [35], [36], [39], [44], [48], [50], [53], [57]
18
Product lifecycle
X
[10], [12], [13], [18], [20], [28], [31], [32], [36], [39], [48] [53], [54]
13
Differentiation from competitors X
[3], [4], [5], [12], [18], [25], [28], [32], [33], [39], [42], [46], [54]
13
Customer choice process
X
[4], [7], [8], [19], [23], [41], [46], [50], [54], [58], [59]
11
Capacity limitations
X
[4], [15], [18], [19], [21], [23], [24], [29], [36], [44]
10
Economies of scale
X
[1], [4], [13], [23], [26], [44], [45], [50]
8
Resource limitations
X
[6], [20], [25], [31], [36], [44], [48]
7
Stock levels
X
[21], [23], [29], [39], [50], [54]
6
Management of the number of components that comprise the finished product
X
[22], [25], [33], [50], [58]
5
Customer quality needs
X
[10], [11], [12], [46], [54]
5
Environmental responsibility
X
[6], [11], [31], [42], [44]
5
Evolution of technology X
[2], [12], [28], [38]
4
Time and/or number of setups
X
[22], [29], [44], [56]
4
Compliance with technical and
legal regulations X
[11], [36], [42]
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 References Total
Horizontal integration [3], [4], [5], [12], [21], [23], [24], [44], [53] 9
Vertical integration [4], [23], [36], [42], [49] 5
Inter-organisational References Total
Supply chain integration [2], [4], [17], [20], [25], [27], [28], [30], [33], [36], [38], [39], [42], [51], [59] 15
[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 pers-pectives. 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-organi-sational 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 References Total
Manufacturing flow management [11], [12], [14], [22], [30], [32], [44], [48], [56] 9
Demand management [15], [19], [35], [36], [57] 5
Product development and commercialization [12], [47], [49], [52], [53] 5
Customer service management [15], [54], [58], [59] 4
Order fulfillment [15], [37], [49] 3
Procurement [20], [51] 2
Customer relationship management [59] 1
Returns [50] 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 References Total
E-Commerce [15], [19], [28], [41] 4
Other softwares [4], [13], [22], [29] 4
Manufacturing Technologies [16], [17], [36], 3
Enterprise Resource Planning - ERP [17], [42] 2
Eletronic Data Interchange - EDI [36] 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)
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 computer-integrated 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 (Escobar-Saldívar et al., 2008; Sen, 2008) and sales (Escobar-Saldívar 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).
Reis et al. 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 asso-ciated 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 References Total
Use of common components
[3], [4], [5], [10], [16], [17], [18], [22], [25], [27], [28], [30], [31], [32], [33], [36], [43], [44], [47], [49], [51], [52], [53], [56] 24
Mass customisation [4], [5], [7], [20], [22], [24], [28], [30], [31], [44], [51], [52], [53], [58] 14
Product families [3], [5], [6], [18], [22], [23], [44], [47], [49], [52], [53] 11
Flexible manufacturing [13], [14], [17], [27], [29], [32], [34], [56] 8
Production strategies [14], [15], [24], [35], [49] 5
Use of common processes
[22], [29], [33], [49] 4
Postponement [15], [24], [44] 3
Option bundling [26], [57] 2
Lean Manufacturing [14], [29] 2
Cellular manufacturing [17] 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 decrea-ses in product quality due to increased variety, sug-gesting 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 References Total
Order fulfillment [15], [12] 2
Production quality failures [12], [24] 2
Production cycle time [12], [23] 2
Set-up time [12], [18] 2
Production costs [10], [27] 2
Rate of reuse [18] 1
Cycle time for consumer requests [15] 1
Set-up cost [18] 1
Average rate and net contribution [24] 1
Product return rate [50] 1
Number of components and products [27] 1
Number of products and/or platform variants [5] 1
Number of models or brands offered by the company [5] 1
Customer satisfaction regarding product variety [58] 1
Relationship between the configuration recommended and the configuration sold [59] 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), pur-chasing (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 main-tenance 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 inte-grates the main findings related to this topic and highlights the current state of the art. PVM is an inter-disciplinary 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 References Total
Increased profitability [3], [5], [18], [19], [21], [24], [26], [27], [29], [33], [35], [39], [44] 13
Cost reduction [2], [7], [10], [11], [16], [33], [35], [37], [40], [42], [49], [56] [56] 13
Increased market share [4], [5], [10], [26], [27], [33], [39], [42], [45], [48] 10
Analyses of the optimal level of variety to be offered [4], [23], [39], [42], [43], [50] 6
Improved customer service [8], [35], [48], [49] 4
Reductions in time-to-market required for product introduction [16], [40], [45] 3
Increased revenue [4], [33], [57] 3
Improved brand image [42], [45] [57] 3
Reduced production system complexity [30], [37] 2
Maintenance of customer loyalty [42] 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 func-tional areas including R&D, purchasing, pro-duction, 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 com-mon parts across different product categories and production processes and through the use of flexible systems, lean manufacturing, or post-poned production. The main objective is to offer a wide variety of products to the customer while keeping the production of these products mana-geable from the company’s perspective. Metrics
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 mea-surement 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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