An Investigation of the Most Important Factors for Sustainable Product Development Using Evidential Reasoning

NUMERICAL ALGEBRA, doi:10.3934/naco.2017027 CONTROL AND OPTIMIZATION Volume 7, Number 4, December 2017 pp. 435–455

AN INVESTIGATION OF THE MOST IMPORTANT FACTORS FOR SUSTAINABLE PRODUCT DEVELOPMENT USING EVIDENTIAL REASONING

Farzaneh Ahmadzadeh,∗ Kathrina Jederstrom,¨ Maria Plahn, Anna Olsson and Isabell Foyer School of Innovation, Design and Engineering M¨alardalenUniversity, Eskilstuna, Sweden

Abstract. Those working in product development need to consider sustainability, being careful not to compromise the future generations ability to satisfy its needs. Several strategies guide companies towards sustainability. This paper studies six of these strategies: eco-design, green design, cradle-to-cradle, design for environment, zero waste, and life cycle approaches. Based on a literature review and semi-structured interviews, it identiﬁes 22 factors of sustainability from the perspective of manufacturers. The purpose is to determine which are the most important and to use them as a foundation for a new design strategy. A survey based on the 22 factors was given to people working with product development; they graded each factor by importance. The re- sulting qualitative data were analyzed using evidential reasoning. The analysis found the factors minimize use of toxic substances, increase competitiveness, economic beneﬁts, reduce material usage, material selection, reduce emissions, and increase product functionality are more important and should serve as the foundation for a new approach to sustainable product development.

1. Introduction. It is increasingly important for industries to reduce their environmental impact. Initially, they were simply worried about the levels of emissions they produced but have now broadened their scope to include the concept of sustainability [24]. The ﬁrst international conference on sustainability, held in 1972, led to the establishment of the United Nations Environmental Program (UNEP). The term sustainable development was ﬁrst articulated in the 1987 Brundtland report as development which meets the needs of the present without compromising the ability of future generations to meet their own needs [47]. Sustainable development is applied to economic development, social equity and environmental protection but is most frequently associated with the latter. The term is now used worldwide, with new interpretations being generated [15]. One of the 17 goals created by the United Nations Sustainable Development Summit was to ensure sustainable consumption

2010 Mathematics Subject Classiﬁcation. Primary: 62C86, 62P12; Secondary: 90B50. Key words and phrases. Sustainable design, product development, evidential reasoning, sustainable product development strategy, Multi Criteria Decision Making. This paper was prepared at the occasion of The 12th International Conference on Industrial Engineering (ICIE 2016), Tehran, Iran, January 25-26, 2016, with its Associate Editors of Nu- merical Algebra, Control and Optimization (NACO) being Assoc. Prof. A. (Nima) Mirzazadeh, Kharazmi University, Tehran, Iran, and Prof. Gerhard-Wilhelm Weber, Middle East Technical University, Ankara, Turkey. ∗ Corresponding author.

435 436 AHMADZADEH, JEDERSTROM,¨ PLAHN, OLSSON AND FOYER and production patterns [42]. To do so, it suggested reducing resources and pollution along the whole lifecycle, something requiring cooperation in the whole supply chain, from producer to final consumer [43]. Sustainability is an increasingly important factor in product development. Even though the effort may increase the cost of developing products, more companies are integrating sustainability into their product development processes, largely in response to consumers preferences for eco-friendly products [23]. Myriad methods and strategies for developing sustainable products have been introduced, all consid- ering the distinct relationship between environment and product [44]. The question is how to weight and choose an approach and then apply it in an organization [11]. This paper looks at the possibility of developing a new approach to sustainable design. The goal is to offer a more holistic view of sustainable design, especially for companies working with product development. The proposed approach needs to be efficient, reliable and practical to implement. Through a literature review, it identi- fies and integrates the advantages of six design strategies: eco-design, green design, cradle-to-cradle, design for environment, zero waste, and life cycle approaches. To validate the findings in the literature and identify any new concerns and advantages, managers in Swedish companies working in product development were interviewed. Once all factors were identified, a survey was given to people involved in product development; they were asked to rate the importance of each. Because of the qualitative nature of the data, the Evidential Reasoning (ER) approach was used to rank the identified advantages of each approach. ER advocates a general, multi-level evaluation process for dealing with Multiple Criteria Decision Making (MCDM) problems. Because ER can handle qualitative and quantitative uncertain information of any kind, e.g., lacking data, missing data, incomplete information, it is preferable to fuzzy logic or any other approach to ranking. ER is based on Dempster-Shafer evidence theory [40], the evaluation analysis model [56], and decision theory. When the concepts of belief structure [56], [51], [53], and the belief decision matrix [50] are introduced to ER, it is possible to model various types of uncertainties in a unified format [49]. In the present case, for example, the most important factors in sustainability can be identified and a new sustainable design approach suggested. The paper is organized as follows. Section 2 comprises an introduction to sustainable product development. Section 3 outlines the ER approach, while Section 4 provides the results and a discussion. Section 5 offers a conclusion and explores further research directions.

2. Sustainable product development. Companies have many different tools, methods, and strategies to use if they wish to establish a more sustainable method of product development. The difficulty is selecting one strategy: they all have different objectives, and each has its own advantages and disadvantages. Section 2.1 lists some sustainable design strategies; Section 2.2 gives their advantages and disadvantages; Section 2.3 describes the interview process used in this study and explains how the survey was developed.

2.1. Sustainable design strategies.

2.1.1. Eco-design. Eco-design strives to minimize the negative environmental impact of all product parameters [7], [46], [36]. This can be done by avoiding toxic substances and reducing resource and energy use throughout the product life cycle. It can also be done by prolonging product life, for example, by repairing and FACTORS OF SUSTAINABLE PRODUCT DEVELOPMENT 437 recycling, or providing the possibility of upgrading the product. Alternatively, the strategy may focus on minimizing the weight and the number of joints in a product [32]. Eco-design can increase a products functionality and improve its economic and environmental performance [36]. Several advantages result from eco-design, including increased competitiveness, lower costs, and better company image [24]. When looking more closely at the economic factors of eco-design, Plouffe et al. [36] find sales volume and revenue increase, while variable costs decrease. However, they also find fixed costs for eco-design products are higher, and the economic benefits of eco-design are mainly short term. 2.1.2. Green design. Green design, or sustainable design as it is also called, aims to reduce the negative impact on the environment by minimizing waste, reducing the use of non-renewable resources [6], [19], [34], and optimizing operational practices [5], [17], [19]. Another way of reducing the negative environmental impact is to minimize the hazardous inputs and outputs from energy and materials and use renewable materials and energy. Green design is a process, with products designed to have an afterlife [3], [34]. Green design should incorporate ecological and social business strategies for sustainability [10]. To work with green design, companies need to invest in specific analytical and organizational tools, systems and software [5], [17]. The tools for green design include checklists and guidelines. One identified disadvantage to green design is that it contains too many suggestions that have not been practically tested [6]. 2.1.3. Cradle-to-cradle. The cradle-to-cradle concept divides materials into two categories: biological and technical. The biological category contains biodegradable materials; the technical category contains materials that can be reused or recycled without losing their properties. The life-cycle of the material flow in cradle-to-cradle is circular, with material reused to reduce negative effects on the environment [9]. As the method mimics the metabolism of a body, the materials are considered food and nutrients for new products [8]. Products and materials meeting cradle-to- cradle standards comply with certain principles and criteria, including elimination of the concept of waste, use of renewable energy, carbon management, water stew- ardship, and social fairness [33]. Because of the material types, the availability of raw materials is ensured. In addition, the reprocessing of materials may lead to increased economic activity and more job opportunities [9]. Even so, there can be over-confidence in the approach; people may believe the method has the answers to everything, and this can harm its implementation [4]. 2.1.4. Design for environment. Design for environment (DfE) can be used to evaluate progress towards cradle-to-cradle products [39]. The DfE tool is intended to prevent built-up waste caused by the product and its production [16]. Products can be evaluated by scoring them in three categories: material chemistry, disassembly, and recycling [39]. Many different tools and techniques can be used when working with DfE. The plethora of choices causes designers to struggle when choosing tools to work with [45]. 2.1.5. Zero waste. Zero waste is a natural cycle defined by Zero Waste International Alliance (ZWIA) in which waste is eliminated by designing it to become a new resource [55] and increasing recyclability by leaving nothing for disposal [18]. Zero waste can be achieved by redesigning products and their packaging, thus putting the responsibility for waste on the producer [31]. By implementing zero waste, 438 AHMADZADEH, JEDERSTROM,¨ PLAHN, OLSSON AND FOYER environmental factors such as air and water are greatly improved, and there is much less pollution [55], [30]. The zero waste strategy can result in economic benefits by decreasing costs of waste disposal [18], [32], [12] and by increasing earnings by selling material that can be recycled [14]. To adopt a zero waste product development strategy, most companies must transform their current systems to a refined zero waste system [54]. The implementation of a zero waste strategy may result in increased short-term costs; some businesses may consider this incurs too much risk [14]. 2.1.6. Life-cycle approaches. Life-cycle approaches are divided into life-cycle analysis and life-cycle design. Life-cycle analysis (LCA) evaluates the impact of a product on the environment throughout its entire life-cycle [38], [57]. The tool analyzes such factors as raw materials, usage, disposal, recycling, reuse, and end of life [29]. The evaluation is often used for benchmarking studies, trade-off decisions, and hot-spot analysis and evaluation. Life-cycle analysis from older projects can also be used as a source of knowledge [4]. Unfortunately, the potential of LCA is not fully developed; companies more commonly use a retrospective view and/or take a marketing perspective [28], [38]. Another weakness is that it cannot evaluate the balance between material and energy consumption, especially when the life cycle has loops in terms of remanufacturing, reuse, and recycling [41]. Life-cycle design is a holistic concept where products are developed for the public social and environmental needs without compromising the clients requirements [4], [38], [57]. The use of products is increasing worldwide; when existing products are compared to new and environmentally improved ones, most can be seen as waste [25]. The concept also decreases the cost of maintenance services [41]. 2.2. Advantages and disadvantages of sustainable design strategies. The literature review shows there are several advantages and disadvantages of these various strategies, from both an environmental and a business perspective. Most strategies have more advantages than disadvantages, however. Table1 lists the various design strategies, along with their advantages and disadvantages. As Table1 shows, eco-design has the most advantages as well as disadvantages together with green design, zero waste and life-cycle approaches. When we look at the advantages, we see patterns emerging, more clearly shown in Table2, where the advantages are grouped under factors. By finding the most common patterns, we may be able to determine which factors are most important to sustainable design. Note that there may be more advantages than disadvantages because most articles focus on the benefits of the strategies. However, some strategies do not have a clear line to follow, involve too many tools for measurements, and it may be difficult to implement them. 2.3. Interview and survey. To support the literature review and find factors other than those mentioned in the literature, we conducted semi-structured interviews with managers from three Swedish companies working with product development; see appendixA. The first company has 44000 employees worldwide. The managers said the most important concerns in their product development are selecting sustainable materials and reducing energy use both in the manufacturing process and later when the product is in use. The company works with sustainability and follows ISO14001 standards. ISO14001 is an international standard for environmental management systems [21]. FACTORS OF SUSTAINABLE PRODUCT DEVELOPMENT 439

Method Advantages Disadvantages Eco Design Increased competitiveness [13] Increased fixed Decreased variable costs [32],[36] costs [36] Less use of toxic materials [32] Only short term Increased product functionality [36],[46] economic benefits Improved economic performance [36] [36] Increased revenue [13] Increased sales volumes [13] Less energy usage [32] Prolonged product life [32],[36],[46] Improved company image [13] Reduced material use [7],[24],[32],[36],[46] Green Optimized operational practices [5],[17],[19] Requires investment design Reduced use of non-renewable resources in new operating [3],[19],[34] tools [5] Waste minimized [6],[19],[34] Too many unclear Increased use of renewable materials [3],[34] suggestions [6] Increased use of renewable energy [3],[19],[34] Social business strategies incorporated [10] Cradle-to- Waste eliminated [8],[9],[33] Might be cradle Products are biodegradable [9] overconfident [4] Eternal recyclability [9] Increased economic activity [9] Increased job opportunities [9] Certification available [33] Design for Waste is reduced [16],[45] Too many tools and environment Improved material chemistry [39] techniques [45] Improved design for disassembly [16],[39], [45] Increased recyclability [39],[45] Zero Waste Pollution is prevented [30],[55] Requires Waste eliminated [18],[31],[55] transformation of Reduced toxicity [30],[55] current systems [54] Increased recyclability [18] Increased short- Increased reuse of materials [55] term costs [14] Decreased costs of waste disposal [12],[18],[31] Increased revenue by selling used materials [14] Life-Cycle Reduced long term environmental impact Often used in approaches of the product [29],[38] retrospect [28],[38] Decreased costs for service [41] Cannot be used Increased environmental impact awareness properly for reused, [57] recycled and re- Holistic approach [4],[38],[57] manufactured products [41] Table 1. Advantages and disadvantages of sustainable design strategies 440 AHMADZADEH, JEDERSTROM,¨ PLAHN, OLSSON AND FOYER

Factors Design Strategy Reduce energy usage Eco-design Reduce material usage Eco-design, Life-cycle approaches Reduce use of non-renewable resources Green design Reduce waste Design for Environment Eliminate waste Cradle-to-cradle, zero waste Eliminate emission Zero waste Minimize use of toxic substances Eco-design, zero waste Minimize waste Green design Recycle materials/components Cradle-to-cradle, design for environment, zero waste, life-cycle approaches, eco-design Reuse materials/components Zero waste, life-cycle approaches, eco-design, cradle-to-cradle Increase product functionality Eco-design Increase product lifespan Eco-design Increase use of renewable energy Green design, cradle-to-cradle Increase use of renewable materials Green design, life-cycle approaches, cradle-to-cradle Increase use of biodegradable materials Cradle-to-cradle Closed loop material flow Cradle-to-cradle Holistic approach Life-cycle approaches, cradle-to-cradle Sustainable social standards Green design, cradle-to-cradle Economic benefits Eco-design, cradle-to-cradle, zero waste Increase competitiveness Eco-design Table 2. Factors identified in sustainable design and the corresponding strategies

The second company has 43000 employees worldwide and divides work on sustainability into two categories, product content and emissions. The eﬀorts of the company to reduce harmful substances in its products are based on industry standards, with a further goal of reducing emissions from products using the environmental product declaration (EPD). They also work with sustainability by following ISO14001 standards. By reducing materials, the managers said, costs are reduced. In addition, sustainability is used to increase competitiveness. The third company is based in Sweden and has about 50 employees. The company focuses its environmental work on minimizing the use of toxic substances and com- plying with existing rules and regulations. It works with sustainability by following ISO14001, as well as EU-directives for the environment [21]. Managers mentioned the eﬀects of pressure from their customers to develop sustainable products and use sustainable materials. According to our interviewees, important concerns in developing a sustainable strategy are: • Sustainable material selection • Reduced energy usage • Reduced emissions • Minimized use of toxic substances • Increased competitiveness FACTORS OF SUSTAINABLE PRODUCT DEVELOPMENT 441

• Economic benefits Most correspond directly to the factors identified in the literature, with the addition of two new ones: sustainable material selection and reduce emissions. Next, a survey was designed based on the 20 factors collected from the literature review and the two new factors; see appendixB. The survey was distributed, along with instructions, to people working in product development. Respondents were asked to rate the importance of each factor in sustainable product development based on five grades: H = {H1 = unimportant, H2 = not very important, H3 = quite important, H4 = important, H5 = very important}. They were asked to answer the questions by assigning a degree of belief, from 0 to 100 percent in different grades and for different answers. If they were unsure of the importance of a factor, they could respond dont know. Because of time constraints, the surveys were answered by only 10 respondents with an average of eight years of experience in product development, but it is advisable to have more respondents.

3. Methodology. MCDM methods aid decision-making in situations where there are multiple, often conflicting, criteria like multiple quality and quantity attributes. Many MCDM methods have been developed, such as Multiple Attribute Utility Theory (MAUT) [22], and Analytical Hierarchy Process (AHP) [27], [35]. Most are suitable for solving small scale MCDM problems without uncertainty. In uncertain situations, the Fuzzy Multi-Criteria Decision Making (FMCDM) [20], [26] approach provides an ideal option; it has been tested by a number of researchers to rank alternatives in a variety of situations. However, the fuzzy approach is used only when uncertainty is predominant. In other words, when a particular parameter is quantifiable with fair degree of accuracy, or there are a missing or incomplete data, this approach need not be used. Most real-life decisions use a mixture of qualitative and quantitative attributes with varying degrees of uncertainty, increasing the need for the development of scientific methods and tools that are rational, reliable, repeatable, and transparent. ER is one of the latest developments in the MCDM literature and appears to be the best fit to handle uncertain information [48]. It can model multiple attribute decision problems, using both quantitative and qualitative attributes. The approach is flexible and can process objective or subjective information. It can also handle uncertain, incomplete, or missing information [1], [2]. Given this, we selected ER to interpret the data and to rank the factors.

3.1. Basic evaluation framework. The ER algorithm can be used on MCDM problems; with this algorithm, a complex general property which is usually diﬃcult to assess directly can be broken down and operationalized by using well-deﬁned, measurable concepts that together constitute the general property. The result of such a breakdown is a multiple attribute framework taking the shape of a tree (hierarchy) structure, with assessable basic attributes at the lowest level. The assessment of these basic attributes can be aggregated to an assessment of the upper level of the tree (Figure1). Upon assessment of the basic attributes, however, there is always a certain level of uncertainty. Dempster-Shafer mathematics are designed to aggregate the uncertainties in the basic attributes to a total uncertainty of the total assessment [37].

3.2. ER approach based on Dempster-Shafer theory. Steps for the overall assessment of the complex general property are suggested by Yang [52] based on 442 AHMADZADEH, JEDERSTROM,¨ PLAHN, OLSSON AND FOYER

Dempster-Shafer theory [40] and summarized by Pontus et al. [37]. The steps are given below and shown in Figure1. As the ﬁgure shows, the application of Dempster-Shafer theory to one sub tree (dashed rectangular) and reasoning can be generalized to an entire tree consisting of several sub trees.

Figure 1. Generic framework to assess general property

Step 1. Define a set of L basic attributes including all factors influencing the assessment of the upper level attribute as follows: E = {ε1, ε2 . . . εL}. Now estimate the relative weights of the attributes, where ω1 is the relative weight for basic attribute εi and is normalized so that Σ ωi = 1 and 0 ≤ ωi ≤ 1. Define N distinctive evaluation grades Hn, n = 1,...,N as a complete set of standards to assess each option on all attributes. For example, H = {H1 = worst, H2 = poor,... , HN−1 =good, HN =excellent}. For each attribute εi and evaluation grade Hn a degree of belief βn is assigned. The degree of belief denotes the sources level of confidence when assessing the level of fulfillment of a certain property.

Step 2. Let mni be a basic probability mass, representing the degree to which th the i basic attribute εi supports a hypothesis that the general attribute is th assessed to the n evaluation grade Hn. Then, mni is calculated as follows:

mni = ωi βn,1 (1)

Let mHi be the remaining probability mass unassigned to each basic attribute, εi so mHi is calculated as follows:

N N X X mHi = 1 − mn,1 = 1 − ωi βn,i (2) n=1 n=1

Decompose mHi intom ¯ Hi andm ˜ Hi as follows:

N X m¯ Hi = 1 − ωi andm ˜ Hi ωi (1 − β(n,i)) (3) n=1

mHi =m ¯ Hi +m ˜ Hi (4) FACTORS OF SUSTAINABLE PRODUCT DEVELOPMENT 443

Step 3. The assessments of the basic attributes constituting the general property are aggregated to form a single assessment of the general property. The probability masses assigned to the various assessment grades, as well as the probability mass left unassigned, are denoted by mn,I(L),m ¯ H,I(L),m ˜ H,I(L) and mH,I(L). Let I(1) = 1. This gives: mn,I(1) = mn,1(n {1, 2,...,N}),m ¯ H,I(1) =m ¯ H,1,m ˜ H,I(1) =m ˜ H,1 and mH,I(1) = mH,I(1) The combined probability masses can be generated by ag- gregating all the basic probability assignments using the following recursive ER algorithms:

{Hn} : mn,I(i+1) = KI(i+1) mn,I(i) × mn,i+1 + mH,I(i) × mn,i+1 + mn,I(i) × mH,i+1 (5) (n {1, 2,...,N})

In equation5, we continue to let i = 1. The term mn,1, mn,2 measures the degree of attributes ε1 and ε2 supporting the general attribute y to be assessed to Hn; the term mn,1, mH,2 measures the degree of only ε1 supporting y to be assessed to Hn; the term mH,1, mn,2 measures the degree of only ε2 supporting y to be assessed to Hn.

{H} : mH,I(i) =m ¯ H,I(i) +m ˜ H,I(i) (6)

m˜ H,I(i+1) = KI(i+1) m˜ H,I(i) × m˜ H,i+1 +m ¯ H,I(i) × m˜ H,i+1 +m ˜ H,I(i) × m¯ H,i+1 (7)

m¯ H,I(i+1) = KI(i+1) m¯ H,I(i) × m¯ H,i+1 (8)

N N h X X i−1 KI,(i+1) = 1 − mt,I(1) × mj,i+1 (i {1, 2, . . . , L, −1}) (9) t=1 j=1,j6=t

In equation7, the term ˜mH,1,m ˜ H,2 measures the degree to which y cannot be assessed to any individual grades due to the incomplete assessments for both ε1 and ε2. The termm ¯ H,1,m ˜ H,2 measures the degree to which y cannot be assessed due to incomplete assessments for ε2 only. The termm ˜ H,1,m ¯ H,2 measures the degree to which y cannot be assessed due to incomplete assessments for ε1 only. The term m¯ H,,m ¯ H,2 in equation8 measures the degree to which y has not yet been assessed to individual grades due to the relative importance of ε1 and ε2 after ε1 and ε2 have been aggregated. KI(2), as calculated by equation9, is used to normalize mn,I(2) and mH,I(2) so that:

N X mn,I(2) + mH,I(2) = 1 (10) n=1 Step 4. Let βn denote the combined degree of belief that the higher level property assessed to the grade Hn is generated by combining the assessments for all the associated basic attributes εi. So βn is calculated by:

mn,I(L) {Hn} : βn = (n {1, 2,...,N}) (11) 1 − m¯ H,I(L)

m˜ H,I(L) {H} : βn = (12) 1 − m¯ H,I(L) 444 AHMADZADEH, JEDERSTROM,¨ PLAHN, OLSSON AND FOYER

Steps 1-4 can now be employed for the other sub-trees to obtain the combined degree of belief in the higher level of the hierarchy model. Step 5. In this step, the utilities of the respective assessment grades H(1 . . . n) are estimated via utility functions (u(Hn)). A range of methods and techniques can be utilized for this purpose. In this paper, we assume the utilities of the respective assessment grades can be appreciated in a linear fashion. Therefore, the top level score of the hierarchy model can be obtained by Σβnu(Hn), n = 1,...,N (cf. Figure 2). Figure2 gives a visual representation of the ﬁve steps in the form of a ﬂowchart.

Figure 2. Visual representation of ER steps FACTORS OF SUSTAINABLE PRODUCT DEVELOPMENT 445

3.2.1. Illustrative example. In Figure1, the application of ER approach described through an example in which a complex property is broken down into two sub trees, but we will focus on only the left sub tree (dashed rectangular). Step 1. It is assumed ω1 = 0.35, ω2 = 0.65, H = {H1 = poor,H2 = average,H3 = good}. Attribute11, ε1, is assessed by an observer as the following: a poor degree of belief, β1,1, is 40%; average is β2,1 or 50%; and good is β3,1 or 0%. This can be represented by the following distribution: Observer assessment for ε1 = {(poor, β1,1 = 40%), (average, β2,1 = 50%), (good, β3,1 = 0%)}. In the same way, Attribute12, ε2, can be presented by the following distribution: Observer assessment for ε2 = {(poor, β1,2 = 10%), (average, β2,2 = 75%), (good, β3,2 = 15%)}. Step 2. The probability mass m1,1 for ε1 for diﬀerent grade values, such as poor (H1), with the values of the weight ω1 = 0.35 and the degree of belief β1,1 assigned in step 1 is calculated by applying equation1 as: m1,1 = ω1β1,1 = 0.35×0.40 = 0.14. The probability mass of ε1 being average (H2) m2,1 is m2,1 = ω1β2,1 = 0.35×0.50 = 0.175. The probability mass of ε1 being good (H3) m3,1 is m3,1 = ω1β3,1 = 0.35×0 = 0 In a similar way, we calculate the probability mass of ε2 using the values of the weight ω2 = 0.65 and the assigned degree of belief in step 2: m1,2 = ω2β1,2 = 0.65 × 0.10 = 0.065. The probability mass of ε2 being average (H2) m2,2 is m2,2 = ω2β2,2 = 0.65 × 0.75 = 0.4875. The probability mass of ε2 being good (H3) m3, 2 is m3,2 = ω2β3,2 = 0.65 × 0.15 = 0.0975. The remaining probability mass of ε1, ε2 is obtained by applying equations2,3 and4 as shown below. Results are given in Table 3. m¯ H,i = 1 − ωi, m¯ 1,1 = 1 − ω1 = 1 − 0.35 = 0.65, m¯ 1,2 = 1 − ω2 = 1 − 0.65 = 0.35, PN m˜ H,i = ωi(1 − n=1 βn,i), PN m˜ 1,1 = ω1(1 − n=1 βn,1 = 0.35(1 − 0.4 − 0.5 − 0) = 0.035, PN m˜ 1,2 = ω2(1 − n=1 βn,2 = 0.65(1 − 0.1 − 0.75 − 0.15) = 0, mH,i =m ¯ H,i +m ˜ H,i, mH,1 =m ¯ H,1 +m ˜ H,1 = 0.65 + 0.035 = 0.685.

Evalutation Grade Weight Belief H1,H2,H3 ωi β1,i β2,i β3,i βH ε1 0.35 0.4 0.5 0 0.1 ε2 0.65 0.1 0.75 0.15 0 Probability Mass m1,i m2,i m3,i mH,i m¯ H,i m˜ H,i 0.14 0.175 0 0.685 0.65 0.035 0.065 0.4875 0.0975 0.35 0.35 0 Table 3. Assigned weights, belief degrees and calculated probability masses

Step 3. We use the recursive ER algorithm to aggregate the probability masses of the basic attributes ε1 and ε2 to the intermediate property Statement 1. Putting 446 AHMADZADEH, JEDERSTROM,¨ PLAHN, OLSSON AND FOYER the values from Table3 into equations5,6,7,8 and9 above, gives us an assessment of the Statement 1 property for the grade H1 = poor: P3 P3 KI(2) = [1 − t=1 j=1,j6=t mt,I(i) × mj,i+1] − 1 = 1.12402, mn,I(i+1) = KI(i+1)[mn,I(i) × mn,i+1 + mH,I(i) × mn,i+1 + mn,I(i) × mH,i+1] n = 1, . . . , N, i = 1. Assume I(1) = 1, m1,I(2) = KI(2)[m1,1 × m1,2 + mH,1 × m1,2 + m1,1 × mH,2] = 0.1154, m˜ H,I(2) = KI(2)[m ˜ H,1 × m˜ H,2 +m ¯ H,1 × m˜ H,2 +m ˜ H,1 × m¯ H,2] = 0.0138, m¯ H,I(2) = KI(2)[m ¯ H,1 × m¯ H,2] = 0.2557, mH,1 =m ¯ H,1 +m ˜ H,1 = 0.2557 + 0.0138 = 0.2695. In the same way, assessments of the Statement1 property for the grade H2 = average, H3 = good are equal to 0.5401 and 0.0751 respectively. Step 4. By using equations 11, 12 and with the values calculated in step 3, we get the combined degrees of belief for the property Statement1 for the diﬀerent grade values. These can be expressed as: m1,I(L) 0.1154 β1,1 = = = 0.155, 1−mH,I(L) 1−0.2557 m2,I(L) 0.5401 β2,1 = = = 0.7257, 1−mH,I(L) 1−0.2557 m3,I(L) 0.0751 β3,1 = = = 0.1009, 1−mH,I(L) 1−0.2557 mH,I(L) 0.0138 βH = = = 0.0185, 1−mH,I(L) 1−0.2557 Steps 1-4 can be employed for the other sub-trees using Statement 2 at the higher levels to obtain the combined degrees of belief. In this case, Statement 1 and Statement 2 will be recursively aggregated to form the combined degree of belief for the general property at the top level of the hierarchy model. By applying the linear utility function given in step 5 in section 3.2, the score of the top level can be obtained.

4. Results and discussion. Section 4.1 analyzes the collected data using the relevant software; Section 4.2 gives the results of the sensitivity analysis and evaluates to what extent the factors with high uncertainty aﬀect the outcome.

4.1. Data analysis. Table4 shows the mean value for each grade ( H = {H1 = unimportant, H2 = not very important, H3 = quite important, H4 = important, H5 = very important}.) and factor based on the survey results. The mean value is calculated by adding up the respondents degree of belief in each grade. A Windows- based Intelligent Decision System (IDS) is applied to implement the ER approach. IDS is a general-purpose multiple criteria decision analysis tool; it provides graphical interfaces to build a decision model and can be assessed on a hierarchy of criteria. The factors of sustainability considered here are not arranged by hierarchy; rather, all are assumed to be top-level criteria. The mean values for each factor were entered into the IDS. The results are shown in Figure3 and Table5. The results from the IDS shows all factors are important, with average score of more than 53%, but the most important ones, i.e., those with a score of over 65% (the mean value of all factors), are the following: minimize use of toxic substances (82%), increase competitiveness (76%), economic beneﬁts (75%), reduce material usage (74%), sustainable material selection (72%), reduce emissions (69%), and increase product functionality (69%). In Table4, the column marked unassigned indicates the percentage of degree of belief that was not given; a high unassigned percentage may indicate uncertainty in the answers. FACTORS OF SUSTAINABLE PRODUCT DEVELOPMENT 447

Evaluation grade (%) Factors H1 H2 H3 H4 H5 Unassigned Reduce energy usage 5 15 27 24 10 19 Reduce material usage 1 5 22 31 37 4 Reduce use of non- 1 21 21 18 23 16 renewable resources Reduce waste 1 4 28 41 10 16 Reduce emissions 1 4 18 38 21 18 Eliminate waste 11 14 30 23 13 9 Eliminate emissions 10 5 24 31 8 22 Minimize use of toxic 0 0 8 26 50 16 substances Minimize waste 3 3 30 37 5 22 Recycling components/ 0 17 29 26 18 10 materials Reusing components/ 11 17 12 34 19 7 materials Increase product function- 0 2 29 27 26 16 ality Increase product lifespan 3 19 36 26 14 2 Increase use of renewable 0 8 20 40 10 22 materials Increase use of renewable 2 8 20 29 19 22 energy Increase use of 1 13 36 30 5 15 biodegradable materials Sustainable material 0 9 15 47 25 4 selection Circular material flow 0 7 28 11 5 49 Holistic view 4 6 9 28 16 37 Sustainable social 4 3 21 26 20 26 standards Economic benefits 0 1 26 22 40 11 Increased competitiveness 0 1 27 31 38 3 Table 4. Factors identified in sustainable design and the corresponding strategies

When we look at the unassigned percentage for each factor, a few things stand out; notably, the factor circular material flow has 49% unassigned, holistic view has 37% unassigned, and sustainable social standards has 26% unassigned. This indicates respondents are uncertain of their answers or unsure of the definition of the factor or how it is connected to sustainable product development. Several factors have a high unassigned percentage at around 20%; this may affect the reliability of the answers; when the information is complete, the results are more reliable. Table2 and Table6 shows that most of the important factors are covered by the Eco-design strategy, apart from the two mentioned during the interviews. Clearly, Eco-design is the dominant environmental strategy 448 AHMADZADEH, JEDERSTROM,¨ PLAHN, OLSSON AND FOYER

Figure 3. Diagram showing the importance of factors for sustainable product development

Factors Ranking score (%) Rank Minimize use of toxic substances 82 1 Increase competitiveness 76 2 Economic beneﬁts 75 3 Reduce material usage 74 4 Sustainable material selection 72 5 Reduce emissions 69 6 Increase product functionality 69 7 Reduce waste 64 8 Increase use of renewable energy 64 9 Sustainable social standards 64 10 Increase use of renewable materials 63 11 Holistic view 62 12 Recycling components/materials 61 13 Reduce use of non-renewable resources 60 14 Minimize waste 59 15 Reusing components/materials 58 16 Increase use of biodegradable materials 58 17 Increase product lifespan 57 18 Eliminate emissions 56 19 Reduce energy usage 55 20 Circular material ﬂow 54 21 Eliminate waste 53 22 Table 5. Important design factors, relevant score and rank FACTORS OF SUSTAINABLE PRODUCT DEVELOPMENT 449

Most important identiﬁed factors Design strategy (%) Minimize use of toxics substances (82%) Eco-design and Zero waste Increased competitiveness (76%) Eco-design Economic beneﬁts (75%) Eco-design, Cradle-to-cradle and Zero waste Reduce material usage (74%) Eco-design and life-cycle strategies Material selection (72%) ··· Reduce emissions (69%) ··· Increase product functionality (69%) Eco-design Table 6. Important design factors and corresponding design strategy

4.2. Sensitivity analysis. IDS software can be used to visually conduct a sensitivity analysis to indicate how much the various factors influence the results. The weight of each factor is set to a value of 1 in the default settings of the software; by decreasing the weight of one or more factors, the effect on the average score of the complete analysis on different factors (advantages) can be determined. When the weight of the seven most important factors is decreased, one by one, to a value of 0, the average score for the analysis decreases from 65 to 64%. When the weight of all of the most important factors is decreased at the same time, the average score of the analysis decreases to 60%. In Table4, several factors have a high unassigned percentage; as noted above, this may indicate uncertainty in the answers. The factors with the highest unassigned percentages are circular material flow, holistic view, sustainable social standards, e- liminate emissions, minimize waste, increase use of renewable materials and increase use of renewable energy. To evaluate how much the factors with high uncertainty affect the outcome of the analysis, the weight of those factors is reduced, one by one, to a value of 0; in this case, the average for the complete analysis stays the same. When we decrease the weight of those factors together, the average score of the analysis is increased to 66%. The factors with a high unassigned percentage affect the average score very little, suggesting the results are reliable. In sum, the factors scoring the highest and found to be the most important have the highest impact on the average score of all the factors.

5. Conclusion. The results suggest the need to develop a new sustainable design strategy, as no single existing strategy meets all the most important needs. The study considers a wide range of sustainable design strategies proposed in the literature and selects six common strategies focusing on the environment and business: eco-design, green design, cradle-to-cradle, design for environment, zero waste, and life-cycle approaches. Although the literature mentions both advantages and disadvantages for each strategy, we focus on advantages, mainly because the aim is to lay the groundwork for a new design strategy with advantages from a business perspective. Data evaluation is simplified by using such qualitative terms as unimportant, not very important, quite important, important, and very important. This might have induced some overly simplified answers for very complex questions on sustainability, but it makes the results much more comprehensible. The data from the IDS rank all factors as important, but seven stand out: minimize use of toxic substances, increase competitiveness, economic benefits, reduce 450 AHMADZADEH, JEDERSTROM,¨ PLAHN, OLSSON AND FOYER material usage, sustainable material selection, reduce emissions and increase product functionality. Most are covered by the eco-design strategy; however, that strategy also contains certain factors that are not considered important, while two of the more important factors are not found in any strategy, only in the interviews. It is possible that these two factors are more fundamental to sustainable product development and manufacturing in real life and, thus, have not been considered in design strategies in the literature. When developing a new approach to sustainability, it is important to consider possible disadvantages and ensure they are not incorporated into it. In this sense, eco-design can serve as an inspiration, but more work is needed to develop new guidelines, tools and methods. In addition, as a new method is developed, it would be advisable to test and evaluate it continuously in collaboration with product development companies. Two of the most important factors are economic benefits and increase competitiveness; these are proof that companies need a strategy with advantages for both business and the environment. The results may be questioned because of the uncertainty of some factors, but the sensitivity analysis shows the factors with a high unassigned percentage of uncertainty affect the average scores very little, indicating that the results are reliable. However, the research was limited by its timeframe and we consulted only respondents working with product development. In the process of developing a tool for sustainable product design, it would be advantageous to collect more surveys and interviews from companies and compare them with the results from this study. Arguably, this study has found the most important factors, but having more respondents complete the survey and possibly clarifying some factors in the survey might help. The results lay the foundation for a new tool combining advantages that are at- tractive from a business perspective, but for future research, it would be advisable to consider different approaches. If the evaluation base is in pure mathematics, i.e. a purely quantitative approach, Preference Function Modeling (PFM) is preferable. If it is a mathematically rigorous synthesis of judgements, i.e. a qualitative approach, researchers could use Direct-Interactive Structured-Criteria Utility Scoring (DISCUS MCDM).

Acknowledgments. The research work is part of the initiative for Excellence in Production Research (XPRES), a cooperative eﬀort between M?lardalen University, the Royal Institute of Technology, and Swerea. XPRES is one of two governmen- tally funded Swedish strategic initiatives for research excellence within Production Engineering. The authors would like to thank Prof. Dong-Ling Xu for his advice, comments and review which helped to improve the quality of the article. Finally, their access to an open source IDS work package made a valuable contribution to embedded computing. FACTORS OF SUSTAINABLE PRODUCT DEVELOPMENT 451

Appendix A. Interview question. Identiﬁcation of important factors in developing a sustainable strategy 1. What factors regarding environmental aspects and sustainability do you estimate to be of greater importance to your company? 2. Does the company work actively with environmental issues? 3. Does the company work with any speciﬁc method to handle your environmental impact? 4. What advantages or disadvantages do you perceive working with that method? 5. Will it be all right if we send you a survey to follow up on these questions?

Appendix B. Survey. We are doing a survey about how companies doing product development work with environmental issues and sustainability. We have re- searched various strategies for enhancing the environmental impact and selected some key factors within these strategies. With this survey, we would like to get an understanding of which factors are the most important in sustainable product development. For each factor, you have 100 percent to divide between five alternatives based on how confident you are in the answer. The percentages are divided on the following scale: unimportant, not so important, quite important, important and very important. The higher the percentage, the more confident you are in your choice. You may leave an option without a percentage, but the percentage of each factor may not exceed 100 percent. You may also indicate dont know. See example below.

How important are Un- Not very Quite Important Very the following important important important important factors for sustainable design? Reduce energy 20 30 40 usage Reduce material Don’t 40 30 20 usage know

All respondents will remain anonymous. Thank you for your participation! How many years’ experience do you have in product development? ...... 452 AHMADZADEH, JEDERSTROM,¨ PLAHN, OLSSON AND FOYER

How important are Un- Not very Quite Important Very the following important important important important factors for sustainable design? Reduce energy usage Reduce material usage Reduce use of non- renewable resources Reduce waste Reduce emissions Eliminate waste Eliminate emission Minimize use of toxic substances Minimize waste Recycle materials/ component Reuse material/ components Increase product functionality Increase product lifespan Increase use of renewable energy Increase use of renewable materials Increase use of biodegradable materials Material selection Closed loop material ﬂow Holistic Approach Social standards Economic beneﬁts Increased competitiveness

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