Relating product portfolio complexity
and portfolio profitability: a case study
Thesis MSc. Double Degree of Operations Management
University of Groningen and Newcastle University Business School December 2013
Name: Erik Breeuwsma
Details: Folkingestraat 52c
9711 JZ Groningen +31 6 4639 8020
e.breeuwsma@student.rug.nl
Student number: 1790692 (RUG)
120528105 (NUBS)
Supervisor University of Groningen: prof.dr. D.P. (Dirk Pieter) van Donk Co-assessor Newcastle University Business School: prof. C. (Chris) Hicks
Supervisors Wavin Group: E. (Eelco) Spaans
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Preface
This report is the final result of my Master Thesis performed at Wavin, The Netherlands in the fulfilment of the requirements for the degree of Master of Science in Technology and Operations Management (University of Groningen) and Master of Science in Operations and Supply Chain Management (Newcastle University Business School).
Although writing a Master’s Thesis is an individual project I could not have done it without the help of the people involved and I would like to take this opportunity to thank certain people. First of all, I would like to thank Warse Klingenberg for his support during the first period of the Master Thesis. Unfortunately, due to personal circumstances he was not able to complete the whole trajectory and instead Dirk Pieter van Donk was assigned as my new supervisor of the University of Groningen. During different stages of my thesis Dirk Pieter van Donk supported and guided me through the research with insightful remarks and suggestions that all contributed to the final result I am able to present here. Furthermore, I would thank my co-assessor from the Newcastle University Business School, Chris Hicks, for his invaluable insights when I visited him in Newcastle last October.
Next, I would really like to thank my first supervisor at Wavin, Eelco Spaans. During the almost weekly meetings we had, he continuously challenged me with his insightful and critical questions regarding the portfolio complexity subject. In combination with his continuous drive for improvements, his profound business knowledge and enthusiasm in the supply chain field helped me to deliver this research. Second, I would like to thank my second supervisor at Wavin, Richard van Delden. During our meetings he always provided me with enthusiastic feedback on how to improve and extend my research to achieve the best possible outcome. Special thanks will go out to my colleague Johan Hoekstra, with whom I had almost daily discussions about the research topic and furthermore, he supported and helped me a lot in what I was doing. I would also thank my colleagues, Egbert Jan van der Veen and Amar Sadagic for their interest they placed in my research and the off topic conversations we regularly had.
Finally, I want to thank all my close friends and family for their support and expressing their interest throughout the whole trajectory. Without them, writing this thesis would have been a whole lot harder.
Erik Breeuwsma
Abstract
The research into product portfolio complexity (PPC) has mainly examined the theoretical relation of how complexity levels of a product portfolio affect the operational performances of an organisation. As a consequence, empirical evidence on the relation between PPC and their effect on operational performance is limited. Moreover, literature indicates that measurements that quantify PPC are limited and ill-suited to organisations. To contribute to this gap, this thesis aims to empirically assess the relation between PPC and total portfolio profitability (TPP). It uses an explorative single case study to measure both PPC and TPP with respectively, a product structure diagram and a cost and revenue analysis for a particular product portfolio based on a year of gathered historical data. The results show that the generalised complexity index (GCI) can be used to express the relational and combinatorial dimensions of PPC. Furthermore, this thesis presented a framework that: (1) illustrate the relation between PPC and TPP and (2) presents a first indication to facilitate management decisions on when to maintain or eliminate group of stock keeping units (SKUs) from the examined product portfolio. This framework can be seen as a starting point for further research to empirically investigate the relation between PPC and operational performances.
Keywords: Product portfolio complexity; Portfolio profitability; Product portfolio; Product
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Table of content
Preface... 2 Abstract ... 3 1. Introduction ... 5 2. Theoretical background ... 72.1 Product portfolio complexity ... 7
2.1.1 Product portfolio complexity drivers ... 7
2.1.2 Product portfolio complexity measurement ... 8
2.1.3 Benefits and costs of product portfolio complexity ... 10
2.2 Portfolio profitability ... 11
2.2.1 Cost and revenue analysis ... 11
2.2.2 Total portfolio profitability measurement ... 12
2.3 PPC – TPP framework ... 12
2.3.1 Best Candidate(s) to Eliminate ... 13
2.3.2 Second Best Candidate(s) to Eliminate ... 13
2.3.3 Best Candidate(s) to Maintain ... 14
2.3.4 Second Best Candidate(s) to Maintain ... 14
3. Case study methodology ... 15
3.1 Case selection ... 15
3.2 Research context ... 15
3.3 Data collection and analysis ... 16
3.3.1 Semi-structured interviews ... 16
3.3.2 Quantitative data ... 16
4. Results ... 18
4.1 Product portfolio complexity PVC-KG Portfolio ... 18
4.2 Total portfolio profitability PVC-KG Portfolio ... 21
4.3 Applicability PPC – TPP framework ... 24
4.3.1 Variety group of SKUs ... 24
4.3.2 Volume group of SKUs ... 25
5. Discussion ... 27
5.1 Driving factors PPC and TPP ... 27
5.2 Relating PPC and TPP ... 28
5.3 Facilitate management decisions ... 29
6. Conclusion ... 30
7. Appendices ... 32
Appendix I: Semi-structured interviews ... 32
Appendix II: Detailed information of volume and variety group of SKUs... 33
1. Introduction
Recently, literature has indicated that a growing interest has emerged into the concept of product portfolio complexity (PPC) and how PPC can be managed effectively. In this thesis, product portfolio is defined as: ‘the complete set of possible product configurations offered by a business unit at a given point in time’ (Closs et al., 2008, p.591). In addition, it is stated by Jacobs & Swink (2011, p.679) that: ‘a product portfolio can be deemed complex if it is made up of a multiplicity of diverse, interrelated elements’. Several authors agree that the management of a product portfolio can be a daunting and complex organisational task (Closs
et al., 2008; Jacobs & Swink, 2011) as countless decisions and trade-offs need to be made that
can affect many organisational departments (Wang & von Tunzelmann, 2000). However, if organisations are able to establish an effective portfolio management it can lead to significant profit improvements (Olavson & Fry, 2006; Hoole, 2006).
Although this sound promising, several case studies have indicated that organisations struggle with the management of PPC as the effects on operational performances are not yet fully understood (Calinescu et al., 1998; Closs et al., 2008; Jacobs & Swink, 2011; Fernhaber & Patel, 2012; Manuj & Sahin, 2011). For instance, Kekre & Srinivasan (1990) stated that when organisations widen their product portfolio, more customers can be reached which might result in an increase in sales. However, other researchers argue that by doing so, additional complexity is added to a product portfolio resulting in difficulties to manage operational performances such as unit fill rate, cycle time, productivity, and set up time (Bozarth et al., 2009; Fernhaber & Patel, 2012; Jacobs, 2013; Wang & von Tunzelmann, 2000; Ward et al., 2010). As a result, Sievänen et al. (2004) indicated that at some point, costs associated with the added complexity outweigh the profitability levels of a product portfolio but that the effect raises questions.
This is acknowledged by Fisher & Ittner (1999) who stated that there is a lack of understanding on how complexity affects costs. The same is indicated by Berman (2011), Fernhaber & Patel (2012), and Ramdas (2009) who agreed that further research is needed to investigate the nonlinear relationship between complexity and costs. Moreover, Closs et al. (2008) and Jacobs & Swink (2011) indicated that empirical tests of their propositions represent an important research opportunity. In their papers, the propositions address the impact PPC could have on the profitability levels for a business unit, a product portfolio, or a product. Furthermore, when the relationship between PPC and portfolio profitability is explored and investigated, quantification of PPC (Isik, 2010; Jacobs, 2013; Perona & Miragliotta, 2004) and portfolio profitability is important (Sievänen et al., 2004; Wilson & Perumal, 2009). However, current literature addresses that a measurement that quantifies both PPC and portfolio profitability is limited and ill-suited to organisations in order to support management decisions on when to maintain or delete stock keeping units (SKUs) in their product portfolio (Brun & Pero, 2012; Ramdas, 2009; Schleich et al., 2003; Scavarda et al., 2010; Stäblein et al., 2011; Wan et al., 2012).
6 decisions in deciding when to eliminate or maintain stock keeping units (SKUs) in a product portfolio based on both PPC and portfolio profitability measurements. As a result, this thesis seeks to address the following research questions:
RQ1: What factors drive PPC and portfolio profitability?
RQ2: What is the relation between PPC and portfolio profitability?
RQ3: How can the developed framework facilitate management decisions regarding PPC and portfolio profitability?
This research uses both literature and case study data to identify the relation between PPC and portfolio profitability and to construct a framework that can facility management decisions regarding the concept of PPC. A case study design is chosen because: (1) the theory of PPC is described by Closs et al. (2008, p.592) as ‘immature, overly simplistic, and in need of richness’ and (2) Jacobs & Swink (2011) indicated that a (single) case study could be a good method to develop measurements that link PPC in operational performances. As a result, a case company is selected in the building material industry. The analysis of this thesis is conducted based on their historical data of the year 2012 representing 118 SKUs for the selected product portfolio.
2. Theoretical background
In this section, the theoretical foundation of the study is described. In the first part, external and internal drivers for PPC, a measurement instrument to measure PPC, and the benefits and costs of PPC are discussed. In the second part, a cost and revenue analysis and a measurement instrument to measure portfolio profitability are discussed. Finally, based on the available theory a framework is proposed that can quantify the relation between PPC and portfolio profitability.
2.1 Product portfolio complexity
2.1.1 Product portfolio complexity drivers
According to the literature there are several drivers, both external as internal, that affect complexity within a product portfolio. In this thesis, PPC is defined as: ‘a design state manifested by the multiplicity, diversity, and functional interrelatedness of products within the portfolio’ (Jacobs & Swink, 2011, p.679). The internal drivers are presented here as multiplicity, diversity, and interrelatedness and are described in more detail later on. The external drivers are based on the environmental factors that affect the organisation (Closs et
al., 2008). Among others these include: market diversity, industry standards, regulations, and
8 portfolio (Jacobs & Swink, 2011; Jacobs, 2013). Note that the interrelatedness depends on its commonality as presented in the bill of materials (Bernstein et al., 2011). In this paper, the commonality of components is expressed in a product structure diagram. This can be explained because Jacobs (2013) indicate that: (1) a product structure diagram helps to understand the complexity within a product portfolio and (2) reveals not only the interrelatedness but also shows the multiplicity and diversity levels.
As the aim of this thesis is to identify how PPC drivers affect the profitability levels of a product portfolio, its only focus is on the internal drivers as they can be directly adapted by organisations. Discussion of the external drivers should not be forgotten but it is meaningless to incorporate them into the decision framework as they are hard to control and predict. Based on the discussion of the internal drivers of PPC it can be assumed that an increase or decrease in one or more of the three PPC drivers result in an increase or decrease of PPC. Therefore, in order to investigate the relation between the three internal drivers of PPC and portfolio profitability it is agreed that quantification of multiplicity, diversity, and interrelatedness is needed (Jacobs, 2013). In the next section, a measurement to quantify these complexities is introduced.
2.1.2 Product portfolio complexity measurement
Until recently, an easy to use measuring instrument to measure PPC was not available. For instance, Ward et al. (2010) described the so called Revenue Coverage Optimization (RCO) tool that was developed by a group of operation researchers for Hewlett-Packard. The advantage of the RCO tool is its proven ability to assist management in managing product variety after the introduction phase of a SKU. However, because the RCO tool is highly company specific it is hard to implement this tool at other companies. Another quantitative tool, developed by Martin & Ishii (1997), proposes a set of indices to measure the cost of providing variety and exists of the commonality index (CI), the differentiation index (DI), and the setup index (SI). Here, CI measures the number of unique parts, DI measures the differentiation points and the place where value is added, and SI measures product switchover costs. Although multiplicity and interrelatedness could be measured more or less with respectively the CI and the DI, measurement of diversity is lacking. However, Jacobs (2013) continued the work of Martin & Ishii (1997) and developed the Generalized Complexity Index (GCI). The GCI gives an indication of the level of complexity within a product portfolio based on the three internal drivers multiplicity, diversity, and interrelatedness (see equation 4). The use and applicability of the GCI is discussed next.
differentiation points can be taken from the product structure diagram to quantify the interrelatedness (I), see equation 3. Note that a product structure diagram with fewer differentiation points in the product portfolio compared to the total possible differentiation points in the product portfolio is considered to be less complex (Jacobs, 2013). The multiplicity (V) of the product portfolio can also be deduced using the product structure diagram as it presents the total products variants available, see equation 1. All equations for the internal drivers of PPC are presented here:
(1) Multiplicity = V = # of variants
(2) Diversity = D = where U = unique product configurations (or components) in the product portfolio
T = total product configurations (or components) possible in the product portfolio
(3) Interrelatedness = I = where A = differentiation points made in the product portfolio
M = total differentiation points possible within the product
portfolio
Before the three internal drivers are combined into the GCI, it is important to note that each internal driver for PPC affects the total outcome of GCI. Additionally, it is stated by Jacobs and Swink (2011, p.681) that: ‘multiplicity is a prerequisite for both interrelatedness and diversity, yet is not perfectly correlated with either driver’. Therefore, when discussions take place on how PPC affects, in this case, portfolio profitability, it is important to identify the effects of each complexity driver individually and not solely rely on the GCI outcome. The three measurements can eventually be combined into the GCI, see equation (4):
(4) PPC (measured by) = GCI
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2.1.3 Benefits and costs of product portfolio complexity
Offering a complex product portfolio can have both benefits and costs for the organisation. According to Wan et al. (2012) for instance, simply increasing product variety within a product portfolio is not automatically associated with higher sales, nor guarantees long run profits. In fact, it can even worsen competitiveness (Ramdas, 2009; Trentin et al., 2013). Note that product variety can be expressed with the internal drivers for PPC. Moreover, as product variety increases, changes in supply chain activities can result in economies of scale being reduced (Schleich et al., 2003). This negatively affects the product cost or the selling price. Furthermore, several researchers have proved that an inverted U-shape relationship exists between PPC and operational performance (Fernhaber & Patel, 2012; Sievänen et al., 2004; Wan et al., 2012). This finding is interesting as it claims that at some point, costs associated with PPC outweigh the benefits of a broad product variety. In addition, Brun & Pero (2012) presented a literature review that illustrates the effect of product variety on profit (see Table 2.1). The point of view and their associated effect on profitability correspond with the debates presented in literature and moreover acknowledge that no consensus is reached about how drivers for PPC affect portfolio profitability. It can therefore be argued that more research needs to done into the cost and benefits of PPC.
As a first step, Jacobs & Swink (2011) related increasing levels of multiplicity and diversity to additional portfolio costs. They based their suggestions on the Transaction Cost Economic theory and indicated that an increase in multiplicity and/or diversity result in higher number of transactions. Higher numbers of transactions cause among others higher administration and information technology costs as more transactions need to be added and processed (Hitt et al., 1997). Moreover, it is indicated by Conner (1991) that the costs of controlling the organisation increase as well. However, almost all researchers discussing PPC indicate that the unavailability of a cost and revenue analysis resulted in a lack of understanding how PPC affect the portfolio profitability (e.g. Closs et al., 2008; Fernhaber & Patel, 2012). Therefore, in the next part a cost and revenue analysis is presented that: (1) presents the product profitability per SKU, (2) presents the total portfolio profitability, and (3) can be used to express the relation between PPC and TPP.
2.2 Portfolio profitability
Several authors indicate that the costs of providing product variety are not known (Martin & Ishii, 1997; Zhang & Tseng, 2007). Olavson & Fry (2006) and Ward et al. (2010), agree with this by stating that measuring the cost of product variety and their benefits are sometimes more challenging than the management of product variety itself. The cost of variety includes all fixed and variable costs to introduce and maintain a SKU over its product lifecycle. Therefore, before both product and (total) portfolio profitability can be investigated it is claimed by several authors that a cost and revenue analysis needs to be performed first (Closs
et al., 2008; Olavson & Fry, 2006; Sievänen et al., 2004). Product profitability is described as
the relation between a SKU’s selling price and its costs (Sievänen et al., 2004). Total portfolio profitability (TPP) is the sum of all product profitability within a portfolio.
2.2.1 Cost and revenue analysis
The difficult part of the cost and revenue analysis is that many cost drivers for different departments should be classified together and allocated to the SKUs involved (Olavson & Fry, 2006). Cost drivers are described as any factor, usually volume or a variety measure that causes costs to be incurred (Edmonds et al., 2009). Moreover, they have a cause-effect relationship with costs (Schniederjans & Garvin, 1997). Furthermore, it is important that both direct and indirect costs are examined for each department as they help to provide better and more accurate information (Homburg, 2001). Therefore, as a first step all the direct and indirect costs of each department that contributes to the SKUs of the product portfolio should be identified. In line with the accounting theory, each department with its associated costs can be described as a cost centre (Park & Simpson, 2008). A cost centre is defined as a: ‘organisational unit that incurs costs but does not generate revenue’ (Edmonds et al., 2009, p.401). As a next step, the cost objects within the cost centres should be identified. Cost objects involve objects for which costs need to be computed (Atkinson et al., 2007). These cost objects can involve among others labour costs, material costs, maintenance cost, energy costs, and depreciation. Finally, the cost drivers per cost object should be identified. Examples of commonly used cost drivers include the number sales order lines, material hours and labour hours (Edmonds et al., 2009). It can be suggested, based on Olavson & Fry (2006) that it helps to distinguish and group the indicated costs (after the cost and revenue analysis) between variety driven costs and volume driven costs because it provides another layer of information describing the effects of how PPC affects TPP. Variety driven costs are the costs that are required to introduce, maintain, and support a specific SKU during its product life cycle. These costs are in general classified as fixed costs. On the other hand, volume driven costs are denoted as variable costs because the unit costs per SKU varies when insufficient volume increases or decreases to reach operational efficiencies (Olavson & Fry, 2006; Ward
et al., 2010). In general, the indication of the revenue per SKU and per product portfolio is
more easily to compute as less specific information is needed. Basically, the units sold for a specific period should be known and the market selling price per unit should be known to determine the revenue.
12 cost and revenue analysis the product profitability and the TPP can be expressed. For the purpose and clarity of this study, the TPP measurement is illustrated in the next part.
2.2.2 Total portfolio profitability measurement
According to Sievänen et al. (2004), as for all profits, TPP is affected by both portfolio costs and portfolio revenues. Therefore, it is proposed to measure TPP in terms of total portfolio costs (TPC) and total portfolio revenue (TPR), see equation (5). TPR is expressed as the total revenue that the product portfolio generates when actual units sold are multiplied with the agreed sales price including bonuses and discounts. TPC includes all the variable costs and the fixed costs that are made for the product portfolio. Furthermore, all (in)direct costs at unit, batch, product-sustaining, order, and facility level are included (Sievänen et al., 2004). (5) where: TPR = actual units sold * agreed sales price
TPC = variable costs + fixed costs
It can be argued that based on the theoretical section of this thesis a complete and representable analysis can be performed that expresses the relation between PPC and TPP when the right methodology is used. As measurement instruments for PPC and TPP, respectively indicated by, a product structure diagram that reveals the complexity of a product portfolio, and a cost and revenue analysis that reveals the cost and revenue of providing product variety are proposed. Note that the ‘right’ methodology depends on the case selection, data collection, and data analysis as discussed in the next section. However, before the methodology is discussed the PPC – TPP framework, which is proposed to identify and visualise the relation between PPC and TPP is explained in more detail.
2.3 PPC – TPP framework
As a result of the theoretical section, a framework is developed that provides a better understanding of the relation between PPC and TPP. In this framework, the trade-off between the costs and benefits within a product portfolio and their corresponding increase or decrease in complexity is visualised. Moreover, it is expected that the outcomes can be quantified in the PPC – TPP framework based on equations 4 and 5. The expected relations are presented in Figure 2.1 and are divided into four quadrants. It is important to note that the presented configurations with the PPC – TPP framework should be seen as a first step towards facilitating further management decisions.
Figure 2.1: PPC – TPP framework
Furthermore, due to the scope of this thesis the relation between PPC and TPP is investigated within a product portfolio when groups of SKUs are eliminated with same characteristics (volume or variety – see methodology). However, it is believed that the framework can be used as well to investigate the relation between PPC and TPP when single SKUs are eliminated within the product portfolio or even to indicate the relation between PPC and TPP for multiple product portfolios. In the next part, the four quadrants are described in more detail.
2.3.1 Best Candidate(s) to Eliminate
In the PPC – TPP framework, groups of SKUs are nominated for elimination when by doing so TPP increases and GCI decreases, as presented in the right-bottom quadrant. This can be explained as an increase in TPP result in better profitability levels of the product portfolio affecting variable costs, fixed costs, and revenues. And a decrease in GCI results in lower multiplicity, diversity, and/or interrelatedness levels of the product portfolio (Jacobs, 2013). As it is expected that both outcomes are beneficial for the current product portfolio, it can be suggested to eliminate the groups of SKUs that are located in this quadrant.
2.3.2 Second Best Candidate(s) to Eliminate
Based on Closs et al. (2008) it can be suggested that higher profitability levels for a business unit or a product portfolio have more added value to the organisation than lower levels of PPC. This is because it directly affects the operational performances of the organisation. Therefore, an increased TPP outweighs the benefits of a decreased GCI. Therefore, the groups of SKUs that increases TPP but decreases GCI are presented as the second best candidates to eliminate in the right-top quadrant. Note that it may occur that trade-off decisions between TPP and GCI must be made in this quadrant when for instance TPP increases are marginal compared to a tremendous increase in GCI.
TPP
GCI
Best Candidate(s) to Maintain:
- Decreases TPP if eliminated
- Increases GCI if eliminated
Best Candidate(s) to Eliminate:
- Increases TPP if eliminated
- Decreases GCI if eliminated
Second Best Candidate(s) to Eliminate:
- Increases TPP if eliminated - Increases GCI if eliminated
Second Best Candidate(s) to Maintain:
- Decreases TPP if eliminated - Decreases GCI if eliminated
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2.3.3 Best Candidate(s) to Maintain
An opposite result from the right-bottom quadrant is presented in the left-top quadrant. In this quadrant, groups of SKUs that should otherwise be eliminated from the current product portfolio are actually indicated as beneficial for the current product portfolio and should therefore be maintained. This is indicated by a decrease in profitability levels of the product portfolio and an increase in complexity for the product portfolio when they are eliminated.
2.3.4 Second Best Candidate(s) to Maintain
Finally, the second best candidates to maintain in the portfolio are expressed in the left-bottom quadrant. Although they represent a decrease in TPP, which can be seen as unbeneficial for the organisation (e.g. Sievänen et al., 2004), they decrease the levels of complexity within the current product portfolio, which is beneficial for the organisation (Jacobs, 2013).
In the next section, the methodology is presented and based on this methodology and the theoretical background for this study, the applicability of the PPC – TPP framework is tested in the result section.
3. Case study methodology
This research uses a single case study as an exploratory perspective is needed that creates the opportunity for in-depth data observations to investigate the relation between PPC and portfolio profitability (Jacobs & Swink, 2011). Furthermore, case research can increase the rigor of the analyses, provides richer insights by observing data in practice and in more detail (Weick, 2007), and can be used to find the underlying mechanisms between variables (Eisenhardt & Graebner, 2007). The case selection is discussed first, followed by a description of the research context. In the last part, the data collection and analysis is presented.
3.1 Case selection
The case company is selected based on the theoretical boundaries that characterise the research to answer the research questions and fulfil the two folded aim. The boundary involved is the availability of a product portfolio that can be expressed in terms of multiplicity, diversity, and interrelatedness to address the total complexity of the product portfolio. Secondly, complete and detailed cost and revenue data need to be available per SKU to express TPP. Moreover, a small portfolio size is desirable that still represents the relation between PPC and TPP to establish in-depth data observations. Furthermore, an industry needs to be chosen that manufactures physical and assembled products in order to construct a product structure diagram with varying product configurations. Based on these boundaries the case company Wavin was selected. Wavin manufactures and assembles plastic fittings and pipes for the building material industry. Wavin was chosen because of the nature of the produced products, its availability of complete cost and revenue data per SKU and the fact that they are currently active in managing their portfolio complexity. Moreover, it offers a wide set of product portfolios that enable the researcher to choose the ‘best possible’ product portfolio to investigate. Based on the same considerations that were used to select the case company, the PVC-KG portfolio was selected from the 223 available product portfolios to be the focus of this research. It must be mentioned that the availability of complete cost and revenue data was decisive in comparison with other product portfolios when the PVC-KG portfolio was selected. In order to avoid inconsistency in PPC and TPP, the research focuses only on fittings and leaves pipes outside the scope of this research. As fittings contain much more variety than pipes due to their varying geometric shapes it is expected that better insights into the PPC concept can be gained. Therefore, the PVC-KG fitting portfolio is chosen as the unit of analysis. It consists of 118 unique SKUs, sold 5.9 million units over the year 2012 with a loss of -13.5%. Due to confidential policies of Wavin, the financial company specific data is shown in this thesis as percentages.
3.2 Research context
16 installers. Their complete range of products consists of more than 80.000 active SKUs divided over four business units with in total 27 categories consisting of 223 product portfolios. The company reported revenue of over EUR 1.2 billion for 2012.
The PVC-KG portfolio is part of the business unit foul water. Foul water systems connect the dirty water streams from houses to the sewers by means of pipes, which are connected by fittings and are made of polyvinylchloride (PVC). The 118 PVC-KG SKUs are primarily made at the manufacturing operating company (MOC) located in Hardenberg (HB), the Netherlands and are transported almost immediately to the selling operating company (SOC) located in Twist (TW), Germany from where the first-tier customers and intercompany sales are supplied.
3.3 Data collection and analysis
Yin (1998) argues that researchers should be continuously judged on the quality of their case study design to ensure reliability and validity. To ensure reliability, all gathered data were stored in a central case study database and categorised according to the different drivers of PPC and the variables TPR and TPC for TPP. Validity of the case study design was achieved by scheduled meetings once every two weeks with the supply chain optimisation (SCO) manager and the supply chain and operational excellence (SCOPEX) coordinator to discuss the findings and verify the research and analysis steps taken. Moreover, one of the advantages of using case study research is that it utilizes multiple sources of evidence to construct reliability, validity, and triangulation (Karlsson, 2009). The data sources used in this thesis are: (1) semi-structured interviews and (2) quantitative data.
3.3.1 Semi-structured interviews
Semi-structured interviews were held to understand the different perspectives on the causes and effects of PPC and TPP for the case company. Moreover, it provided information on the indirect costs and the potential cost drivers as this information was incomplete. Therefore, multiple interviews were conducted from multiple management levels within different departments on the headquarters (HQ), MOC-HB, and SOC-TW. In total, six interviews were held with the SCOPEX department (HQ), finance & controlling department (MOC-HB), the supply chain planning and inventory control department (MOC-HB), and the foul water product development department (SOC-TW). The duration of interviews ranged from 40 minutes to 90 minutes and they were taken between August and October of 2013. The interviews held at the HQ and MOC-HB was taken in person whereas the interview held with the SOC-TW was taken by telephone. To ensure reliability, the interview results were discussed with all the interviewees and different perspectives were discussed and adjusted where necessary. Specific information regarding the semi-structured interviews and the interview questions can be found in Appendix I.
3.3.2 Quantitative data
Information on the product structure of the 118 PVC-KG SKUs that indicate the internal complexity drivers were gathered from the available bill of materials. Based on these data, the (primary) product configurations and product characteristics were used as an indication to construct a product structure diagram for the PVC-KG portfolio. Changes in multiplicity, diversity, and interrelatedness were analysed based on this product structure diagram.
In order to perform a cost and revenue analysis for the PVC-KG portfolio, one year of historical data for the year 2012 was collected directly from the central business warehouse system for both the MOC-HB and the SOC-TW. The collected PVC-KG data consists of the cost centres, the associated cost objects, and the revenues. Additional data for the year 2012 was collected directly from the Enterprise Resource Planning (ERP) system and included information on: sales order lines, machine hours, labour hours, produced units, sold units, produced volume and truckload & mileage. These additional collected data formed the basis for the identification of the applied cost drivers as explained in the cost and revenue analysis. The chosen cost drivers combined with the distinction between variety driven or volume driven costs were enriched with inter-rater reliability of both the manager and the coordinator of the SCOPEX department and the trainees of the finance and controlling department. In addition, confidentiality policies, marketing strategies, and management slides were gathered to strengthen internal validity.
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4. Results
In this section, the results of the research are presented. First, the PPC of the PVC-KG portfolio is expressed with the product structure diagram (see Figure 4.1) and the GCI. In the second part, the TPP of the PVC-KG portfolio is presented based on the results of the cost and revenue analysis including the determined cost centres, cost objects, and the cost drivers. In the last part, the applicability of the PPC – TPP framework is tested based on the variety and volume group of SKUs.
4.1 Product portfolio complexity PVC-KG Portfolio
Based on the gathered product information and the conducted interviews, five classes of product characteristics and 33 product configurations are presented that represent the 118 PVC-KG SKUs, see Table 4.1. Note that the PPC results neglect the characteristics of the MOC-Hardenberg (HB) (the production characteristics) and the SOC-Twist (TW) (the market & sales characteristics) but focus solely on the product characteristics of the PVC-KG SKUs due to the GCI formulae. Here, the product characteristic ‘Shape’ represents the eight product configurations that functions as the first product configuration for a PVC-KG SKU. They are identified as primary product configurations. For instance, the SKU ‘PVC-KG Cap DN200
BR’ consists of three product characteristics. These are shape, DN_1 (the diameter), and
colour. Here, the cap is the primary product configuration and 200 and BR correspond respectively to, DN_1 and colour. For this particular SKU, no product characteristic DN_2 (only represented by the product configuration reducer and branch) or angle (only represented by the product configuration branch and bend) is determined.
Based on the product characteristics and the product configurations, the product structure diagram is constructed and illustrated in Figure 4.1. Note that the profitability per SKU is aligned to the product structure diagram to indicate TPP in the bottom row as presented in Figure 4.1. The product structure diagram illustrates the changes in multiplicity, diversity, and the interrelatedness when groups of SKUs are eliminated. These changes are expressed with the GCI and are calculated for the PVC-KG portfolio based on equations (1) – (4).
Product Characteristic ( =5) Product Configuration ( = 33)
Colour Brown Grey
DN_1 (mm) 100 110 125 150 160 200 250 300 400
Angle (o) NO 15 30 45 67 87
DN_2 (mm) NO 100 125 150 160 200 250 300
Shape Cap Plug Adaptor Coupler Access Reducer Branch Bend
20
Multiplicity (V) is expressed by the number of product variants and is 118. Based on the multiplicity, the diversity (D) for the PVC-KG portfolio is calculated with equation (2) and is 0.944. Here, (U) consists of the unique product configurations and is 33 (see Table 4.1 and Figure 4.1) and (T) is calculated by multiplying the total product variants (V) with the number of available product characteristics and is 590.
D = 0.944 = where U = 33
T = 590 = 5 * 118
Finally, interrelatedness (I) is calculated with equation (3) and is 0.029. (M) is calculated by multiplying the number of product configurations per product characteristic with each other and is 6912. (A) can be extracted from the product structure diagram by counting all the connections made and is 202.
I = 0.029 = where A = 202
M = 6912 = 2 * 9 *6 * 8 * 8
Based on the multiplicity, diversity, and the interrelatedness, the GCI of the PVC-KG portfolio can be calculated with equation (4) and is 3.23:
GCI = 3.23
So far, the number 3.23 has no additional value for the organisation as it only represents the current complexity levels of the product portfolio based on the three internal complexity drivers. However, when changes are made within the PVC-KG portfolio the GCI of 3.23 functions as a benchmark to demonstrate an increase (GCI > 3.23) or decrease (GCI < 3.23) in complexity for the product portfolio. Moreover, the closer to zero the GCI is, the less complex the product portfolio is (Jacobs, 2013).
4.2 Total portfolio profitability PVC-KG Portfolio
22 Interview findings indicated that no agreements on the cost drivers for these particular cost centres were established. Based on these findings and on knowledge and time constraints of the researcher it is chosen to use the cost driver number of sales order lines to allocate the cost objects for the particular cost centres. Therefore, it must be indicated that the results presented, based on the cost driver number of sales order lines, are limited and biased. As a next step, the cost objects were assigned and allocated to the right cost drivers as presented in the third column of Table 4.2. Before the costs were calculated, the fixed costs (FC) and the variable costs (VC) per cost centre are identified. This is done to differentiate the costs between volume driven (representing variable costs) and variety driven (representing fixed costs) (Olavson & Fry, 2006). This distinction between VC and FC enables the researcher to further elaborate on the changes in TPP and is expressed in the fourth column of Table 4.2. It indicates that 82.3% of the total costs represent VC and 31.2% represent FC. Note that the costs are presented as a percentage of TPR. From Table 4.2 it can be seen that no production costs are made for TW. This can be explained as the PVC-KG SKUs are primarily manufactured in HB. Moreover, low storage & dispatch costs and supporting costs are found for HB in comparison with TW. This can be explained as the manufactured PVC-KG SKUs are almost immediately transported to TW. Furthermore, inventory costs are based on 10% weighted average cost of capital (WACC) on the end of month inventory value and therefore no cost driver is applied.
As the costs for the PVC-KG portfolio are described above, the revenue of the PVC-KG portfolio is described next. The revenue was calculated based on the gathered data for the actual units sold and the agreed sales price including bonuses and discounts per PVC-KG SKU, see equation (5). The total portfolio revenue (TPR) can then be presented when the revenue per KG SKU are summed up and is 100.0%. Eventually, the TPP for the PVC-KG portfolio can be calculated with equation (5) and results in a loss of 13.5%. Therefore, changes in TPP are presented as beneficial for the organisation when TPP > -13.5% and unbeneficial when TPP < -13.5%. It can be agreed that organisation want to achieve as high as possible TPPs.
Based on the results of the cost analysis, a deeper understanding of the current TPP for the PVC-KG portfolio is illustrated in Figure 4.2. The graph presents the cumulative TPR and TPP, calculated from the cost and revenue analysis. From this graph it can be seen that the cumulative profitability of the 118 PVC-KG SKUs is negative and ends in -100%. The highest cumulative profit margin of 30.38% is achieved with 42 SKUs representing 25% of TPR. After 92 SKUs, the cumulative profitability margin is 0% representing 49% of the TPR. This means that the last 26 SKUs result in a total loss for the PVC-KG portfolio of 13.5% representing a TPR of 51% (!). The results indicate that the product’s profitability varied greatly for the PVC-KG portfolio.
Cost centre Cost objects Cost driver(s) FC/VC Cost (% of TPR)
HB_DIRECT MATERIAL Production consumption, bought-in, and packaging
Production quantity (units)
VC 58.0%
HB_DIRECTPRODUCTION_FIXED Depreciation and other Production volume (kg) FC 10.6%
HB_DIRECTPRODUCTION_VARIABLE Labour, maintenance, energy, and packaging
Labour time (hr) Machine time (hr)
VC 12.1%
HB_INDIRECTPRODUCTION Labour, maintenance, energy, and other
Machine time (hr) Production volume (kg)
VC 4.6%
HB_STORAGE&DISPATCH_DIRECT Labour, maintenance, energy, packaging, and other
# sales order lines VC 0.4%
HB_STORAGE&DISPATCH_INDIRECT Labour, maintenance, energy, packaging, and other
# sales order lines FC 0.3%
HB_SUPPORTING Labour, maintenance, energy, and other
# sales order lines # SKUs
FC 1.2%
HB-TW_FREIGHT Freight Truckload & mileage VC 1.4%
TW_STORAGE&DISPATCH_DIRECT Labour, maintenance, energy, packaging, and other
# sales order lines VC 1.5%
TW_STORAGE&DISPATCH_INDIRECT Labour, maintenance, energy, packaging, and other
# sales order lines FC 6.8%
TW_SUPPORTING Labour, maintenance, energy, and other
# sales order lines # SKUs
FC 10.1%
TW_FREIGHT Freight Truckload & mileage VC 4.3%
TW_HB_INVENTORY COST Inventory surcharge - FC 2.2%
TOTAL COST PVC-KG (TPC) 113.5%
TOTAL REVENUE PVC-KG (TPR) 100.0%
TOTAL PROFIT/LOSS PVC-KG (TPP) -13.5%
Table 4.2: Cost and revenue analysis PVC-KG portfolio (2012)
-100,00% -80,00% -60,00% -40,00% -20,00% 0,00% 20,00% 40,00% 60,00% 80,00% 100,00% 1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97 10 3 10 9 11 5 % # of SKUs TPP (%) TPR (%)
24 4.3 Applicability PPC – TPP framework
4.3.1 Variety group of SKUs
The first results are presented based on changes made in the product variety for the PVC-KG portfolio when one of the primary product configurations (in this case indicated by the product characteristic ‘shape’) is eliminated from the product portfolio (Jacobs, 2013). The primary product configurations involved are (see Table 4.1): the cap, plug, adaptor, coupler, access, reducer, branch, and bend. As can be seen in Figure 4.1, multiple SKUs exist for each product configuration that affects PPC and TPP. The effect on PPC and TPP are indicated with the PPC – TPP framework when each product configuration is eliminated, see Figure 4.3. The base value that represents the current PVC-KG portfolio for GCI is 3.23 and for TPP -13.5% as presented in respectively section 4.1 and 4.2.
Figure 4.3: PPC – TPP Framework based on variety group of SKUs
From Figure 4.3, it can be seen that for three out of the eight product configurations the GCI decreases and that for five out of the eight product configurations TPP increases. From the three product configurations that result in a decrease of GCI, the adapter decreases the TPP as well when it is eliminated from the portfolio. As a result, Figure 4.3 indicates that the best candidates to eliminate are the coupler and the branch configurations as they increases TPP and decreases GCI when they are eliminated from the portfolio. On the other hand, the access and cap configurations show that they decrease TPP and increase GCI when they are eliminated and therefore are important to maintain in the portfolio. At last, the plug, reducer and the bend configurations are indicated as the second best configurations to be eliminated from the PVC-KG portfolio. Findings on changes in FC and VC are expressed in Appendix II, Table 7.2 when variety driven group of SKUs are eliminated from the PVC-KG portfolio. Unfortunately, no explicit distinction can be found in these costs. Furthermore, the highest increase on TPP is expressed in this quadrant when the bend configurations would be eliminated. The findings expressed in the second best candidates to eliminate quadrant acknowledge that potential positive effect on operational performance can occur when PPC
increases. According to Jacobs & Swink (2011) this finding is not explicitly addressed in prior literature and therefore it is discussed in more detail in the discussion section.
4.3.2 Volume group of SKUs
The second result that helps to investigate the relation between PCC and TPP and at the same time test the applicability of the PPC – TPP framework is identified when SKUs are eliminated from the PVC-KG portfolio based on their volumes. This is based on statements by Olavson & Fry (2006) and Roberts (1999) who suggests that a strategy to manage product variety is to cut low-volume products. However, Byrne (2007) suggests that the main improvements in a product portfolio can be achieved when mid- or high- volume products are eliminated, as they have higher impacts on revenue and total costs. Therefore, groups of SKUs are formed when low-volume (<=10.000 kg), mid-volume (10.000 – 50.000 kg), or high-volume (>=50.000 kg) products are eliminated from the PVC-KG portfolio. These findings are presented in Figure 4.4. Again, the base value of the PVC-KG current portfolio is represented by a GCI of 3.23 and a TPP of -13.5%.
Figure 4.4: PPC – TPP Framework based on volume group of SKUs
From Figure 4.4, it can be seen that when low-volume SKUs are eliminated from the product portfolio the decreases in GCI are high in comparison with the increases on TPP, which improve slightly. When mid-volume SKUs are eliminated from the product portfolio, small increases in TPP and small decreases in GCI can be achieved. However, this does not apply for the mid-volume 20000 – 30000 SKU group, which decreases TPP. For the high-volume groups of SKUs, improvements of the TPP are expressed with declining decreases of the GCI. Another finding that can be presented is the effect on FC and VC when volume group of SKUs are eliminated. When low-volume and mid-volume group of SKUs are eliminated from the PVC-KG portfolio it can be seen in Appendix II, Table 7.3 that they impact FC more than
5. Discussion
The main idea of this thesis is that the level of complexity within a product portfolio influences the total profitability a product portfolio generates. Specifically, this relation is expressed in more detail with the three internal complexity drivers that are identified to drive PPC (e.g. Jacobs & Swink, 2011) and with the cost drivers that are identified to drive TPP (Homburg, 2001). Therefore, it is argued in this thesis that a product structure diagram needs to be constructed and a cost and revenue analysis needs to be performed to indicate this relation. While it can be argued that the results of these two methods can be illustrated with the developed PPC – TPP framework to facilitate management decisions (see Figure 2.1), it does not fully confirm how PPC influence the relation with TPP. Moreover, in contrast with the expectations, the findings do not show a pattern that a decrease in PPC leads to an increase in TPP because it is indicated that an increase in PPC can lead to an increased TPP as well. In the remainder of this section, the expected and unexpected results are critically discussed.
5.1 Driving factors PPC and TPP
Research findings show that the driving factors for PPC and TPP can be indicated with respectively, the GCI and the assigned cost drivers. The driving factors for PPC were expressed in this thesis with multiplicity, diversity, and interrelatedness. It is important to note that each complexity driver can affect TPP. Until now, only subjective data were used to express the driving complexity factors within a product portfolio to provide insights into their effect on operational performances (e.g. Jacobs & Swink, 2011). However, this research has shown that by making use of the product structure diagram in combination with the GCI as proposed by Jacobs (2013), the relationship of the three complexity drivers for a product portfolio can be empirically assessed. Therefore, in line with Jacobs (2013), it can be acknowledged that opportunities are provided to perform empirical investigations into PPC and their effect on operational performances. However, a limitation of the GCI measurement that must be addressed is the fact that is does not incorporate external complexity drivers as identified by Closs et al. (2008). It is therefore advised that the GCI measurement should be adapted to incorporate external complexity drivers as pricing, industry standards and market characteristics. Further research should investigate this.
28 volume driven costs. It is therefore recommended that further studies use an activity based costing approach to obtain more reliable results.
5.2 Relating PPC and TPP
The analysis as depicted in Figure 4.3 shows that the mutations in PPC are not inherent with equivalent changes in TPP and mutations made for TPP do not, by definition, correspond with increased or decreased GCIs. Although there seems to be a relation between PPC and TPP, their interaction is not indicated. It was suspected that when GCI increases it leads to decreased TPPs, as more complexity is ‘added’ to the product portfolio (Bozarth et al., 2009; Meeker, 2009; Wan et al., 2012). However, from Figure 4.3 it can be seen that only the product configurations access and cap represent this effect as the other product configurations that express an increased GCI show increased TPP (plug, reducer, and bend). The effect of an increased GCI can be explained as for roughly the same levels of multiplicity, the diversity and interrelatedness levels differ (see Appendix II, Table 7.2). This relation is acknowledged by Jacobs & Swink (2011, p.681) who state that: ‘multiplicity is a prerequisite for both interrelatedness and diversity, yet it is not perfectly correlated with either dimension’. However, the relation between GCI and TPP of the product portfolio, and therefore the TPR and TPC as well, remains unclear. It is suggested that a distinction between the complexity of the product and the complexity of the resources that are needed to process the products need to be identified (Jacobs & Swink, 2011). Furthermore, it can be argued that not enough data points were gathered and analysed as a single explorative case study design was chosen that investigates only one single product portfolio with the same product characteristics. Therefore, to better understand the relation between PPC and TPP it is strongly advised to investigate multiple product portfolios based on multiple case studies.
Figure 4.4 shows that when, in general, group of SKUs based on volume characteristics should be eliminated from the product portfolio this leads to an increase in TPP and a decrease in GCI. Specifically, a high decrease in the complexity levels for low-volume group SKUs are expressed with low increases of TPP. The high decreases in GCI can be explained as low-volume SKU groups have a significant effect on the multiplicity and diversity levels with fairly constant levels of interrelatedness (see Appendix II, Table 7.3). The low increases of TPP can be explained as in general the low-volume SKU groups consist of low revenue and costs SKUs that when eliminated from the product portfolio slightly affects TPP. Therefore, these findings show that a strategy to effectively manage product variety is to eliminate low-volume SKU groups, confirming earlier work of Olavson & Fry (2007) and Roberts (1999). Moreover, because the high-volume SKUs mainly represent the few high revenue SKUs within a product portfolio, their placement in the PPC – TPP framework can be explained. These results correspond with the proposed strategy of Byrne (2007) that when high-revenue SKUs are eliminated, high profitability improvements are achieved.
were eliminated affecting the differentiation points made in the product portfolio (A) and the total differentiation points possible within the product portfolio (M). However, when volume group of SKUs are eliminated they mainly affect the diversity and multiplicity levels as the depth of the product portfolio is affected.
5.3 Facilitate management decisions
It can be argued that the product structure diagram and the cost and revenue analysis can be incorporated into existing analysis of the organisation to facilitate management decisions on the PPC concept. The results expressed with the PPC – TPP framework can be used to provide insights into the operational and financial consequences of eliminating a certain group of SKUs. Therefore, it presents a way to define a set of criteria on how variation in a product portfolio can be managed based on PPC and TPP outcomes (Olavson & Fry, 2006). It is believed that these insights can help to support and enhance the quality of management decisions regarding which further research steps should be performed per group of SKU. However, it is important to understand that the results that are presented with the decision framework are not leading; they only function as guidelines to facilitate management decisions.
30
6. Conclusion
This study is an attempt to better understand the relation between product portfolio complexity (PPC) and total portfolio profitability (TPP). Most research has examined the theoretical relation between complexity levels of a product portfolio and the operational performances of an organisation (e.g. Closs et al., 2008; Jacobs & Swink, 2011). This thesis is distinct and builds on this previous research as it empirically assesses the relation between PPC and TPP by illustrating and incorporating measurements that have not previously been used to examine the relation between PPC and TPP.
First, it is found that multiplicity, diversity, and interrelatedness are the internal complexity drivers that drive PPC. Furthermore, it is shown that the interaction between the three internal complexity drivers can be expressed with the product structure diagram and quantified with the generalized complexity index (GCI) as proposed by Jacobs (2013). Second, it is observed that the determination of cost drivers influences the outcomes of TPP and is therefore identified to drive TPP. A prerequisite to express TPP is to perform a cost and revenue analysis of the examined product portfolio to better understand how the identified cost drivers influence product portfolio costs. Third, it is demonstrated that a framework expressing PPC and TPP can be constructed so that it facilitates management decisions on when to maintain or eliminate a group of SKUs from the examined product portfolio. Unfortunately, no clear answer was found that explicitly identified the relation between PPC and TPP. It is believed that multiple internal and external factors relating to the examined product portfolio significantly impacted the outcomes of both the PPC and TPP.
This thesis contributes to the existing theory in several ways: (1) it presents a way to empirically test the relation between PPC and TPP, (2) it contributes to the PPC concept as the findings show that complexity measurements can be used that measure the relational and combinatorial dimensions of complexity (Closs et al., 2008), and (3) it shows that these complexity measurements can be aligned with portfolio profitability outcomes. Although this research has made several contributions to the existing literature, a number of implications for practitioners were found as well. First, the PPC – TPP framework has proven that it can function as a first indication to facilitate further management decisions regarding product portfolio decisions. Moreover, research findings indicate that increased complexity is not by definition wrong and can actually lead to increased profitability levels. Finally, based on the research findings it can be argued that eliminating low-volume and low-revenue group of SKUs have the highest impact on reducing complexity in the product portfolio but with marginal increases in profit.
32
7. Appendices
Appendix I: Semi-structured interviews
Interviewees:
Location Department Function Duration (min)
Date
HQ SCOPEX Coordinator SCOPEX 90 28-08-2013
Executive Director SCOPEX 45 10-09-2013
SCO Manager 80 06-09-2013
MOC-HB Supply Chain Planning and Inventory Control
Project Manager 55 29-08-2013
MOC-HB Finance and Controlling Trainee 70 02-09-2013
SOC-TW Foul Water Product Development
Product Manager 40 02-10-2013
Table 7.1: Detailed information interviewees
Questions:
1. How can you describe the product portfolio management for the PVC-KG portfolio? a. Do you consider the current product portfolio as sufficient?
b. Which characteristics of the product portfolio affect complexity the most? i. Product characteristics?
ii. Characteristics of the MOC-HB? iii. Market and Sales characteristics? c. Which characteristic is the most important? d. How do you deal with conflicting characteristics?
2. How can you describe the cost management for the PVC-KG portfolio? a. Do you consider the available cost and revenue data as sufficient? b. How do you allocate the direct and indirect costs to each SKU? c. Which cost element and/or object is the most important?
Appendix II: Detailed information of volume and variety group of SKUs Variety group of SKUs represented by the primary product configurations of the PVC-KG portfolio
GCI V D U T I A M TPP TPR TPC FC VC Current 3.256 118 0.944 33 590 0.029 202 6912 0.00% 0.00% 0.00% 0.00% 0.00% NO Access 3.606 116 0.945 32 580 0.033 199 6048 0.80% -1.61% -1.32% -0.90% -1.50% NO Cap 3.405 112 0.943 32 560 0.032 195 6048 0.50% -2.16% -1.85% -2.72% -1.47% NO Reducer 3.819 112 0.945 31 560 0.036 191 5292 -7.08% -3.40% -3.84% -4.56% -3.54% NO Plug 3.614 109 0.943 31 545 0.035 189 5376 -3.13% -5.81% -5.50% -7.31% -4.73% NO Adapter 3.209 108 0.941 32 540 0.032 191 6048 8.84% -4.96% -3.31% -5.17% -2.53% NO Coupler 3.113 106 0.940 32 530 0.031 189 6048 -6.43% -10.29% -9.83% -8.37% -10.45% NO Bend 3.625 83 0.930 29 415 0.047 142 3024 -41.08% -44.00% -43.65% -38.74% -45.73% NO Branch 2.886 80 0.928 29 400 0.039 147 3780 -7.24% -27.76% -25.32% -20.14% -27.50%
Table 7.2: Detailed information variety characteristics (2012)
Volume group of SKUs represented by low-volume, mid-volume and high-volume SKUs of the PVC-KG portfolio
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